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EXPERIMENT 6: Perceptual-motor skills and sensitivity to TTC as a function of preferred time-headway in car-following

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EXPERIMENT 6: Perceptual-motor skills and sensitivity to TTC as a function of preferred time-headway in car-following

This is chapter 9 from the thesis “From adaptive control to adaptive traffic behaviour” about traffic psychology and behavioural adaptation of drivers, by Wim van Winsum. The thesis is from 1996. It describes a number of behavioural experiments into car driving that were performed in a research driving simulator.

Other chapters of this thesis can be found here:

 

Based on the results of previous experiments it was tested whether the sensitivity of the braking response to time-to-collision information differs as a function of preferred time-headway in car-following. In an experiment performed in a simulator time-to-collision was manipulated by  varying the level of deceleration of the lead vehicle with a pre-selected group of short and long followers. In addition, it was tested whether choice of time-headway is related to more general differences in perceptual-motor skills. It was found that short followers perform better at both lateral- and longitudinal tracking tasks and that the braking response of short followers is more sensitive to differences in time-to-collision. The results support the hypothesis that preferred time-headway is at least to some extent an adaptation to individual differences in operational braking performance and perceptual-motor skills.

 

9.1 Introduction

 

The hierarchical control structure of car driving behaviour  has been presented as a framework for driver modeling (Michon, 1985; Ranney, 1994)­ in which driving is regarded as concurrent activity on strategic, tactical and operati­o­nal levels. The tactical level includes, for example, choice of speed on straight roads and in curves and choice of headway in car-follo­wing. Steering and braking are on the operational level. Adapta­tion may be understood as a compensation  of behaviour  on the strategic and tactical levels of the driving task for individual diffe­rences in skills at the operational level. Thus, the process of adaptation connects the different levels of the driving task. It has been used as an explanati­on for the relatively safe driving records of functionally impaired drivers (Brouwer et al., 1988).

Adaptive processes involving interaction between different control levels has been demonstrated for the case of transient changes in operational level performance, through environmental manipulations (reducing sight distance) or changes of internal state (marijuana). Tenkink (1988) demonstrated that a reduction of sight distance affects operational performance of the lane-keeping task and results in a speed reduction to compen­sate for these effects. He found that under self-paced conditi­ons where drivers were free to choose their speed, a reduction of sight distance resulted in the choice of a lower speed while the standard deviation of lateral position was not affected. Under non self-paced conditions, however, reduc­tions of sight dis­tance resulted in a higher standard deviation of lateral position. Also, variations in internal state-related factors have been shown to result in adaptations of behaviour on the tactical level. For instance, in a study of Casswell (1977) drivers under marijuana appea­red to compen­sate for what they perceived as adverse effects on driving ability by driving more slowly. Marijuana affects operational car driving performance while it also results in increased time headway (THW) during car-following and in choosing a lower speed (Smiley et al., 1981; Smiley et al., 1985; Smiley et al., 1986). Time-on-task has been shown to increase time-headway during car-follo­wing, accompanied by verbal reports of performance decrements, drowsiness and exhaustion (Fuller, 1981). These fin­dings suggest that the driver compensates for effects of various factors on operational performance by adapting behaviour on the tactical level.

Recently, Van Winsum and colleagues found some evidence that supports the adaptation theory on the individual level. In a study on speed choice in curves as a function of curve radius a clear relation was established between steering competence and speed choice in curves: drivers with larger steering errors during straight road driving, indicating poorer steering competence, choose lower speeds in curves (Van Winsum and Godthelp, 1996). Also, in a number of experiments it was investigated whether drivers who follow at a larger THW can be characte­rized by poorer braking performance compared to drivers who follow at a smaller THW. In Van Winsum and Heino (1996) and Van Winsum and Brouwer (1996) preferred THW during car-following proved to be consistent within the driver. This means that drivers can be characterized as consistent short or long followers and, thus, that individual differences in choice of THW are consis­tent. Van Winsum and Heino (1996) found that the initiation and control of braking are both affected by time-to-collision (TTC) at the moment the lead vehicle starts to brake. This strongly suggests that TTC information is used for judging the moment to start braking and during the control of braking. Drivers with a smaller preferred THW were better able to program the intensity of braking to required levels, depending on TTC, and tuned the control of braking better to the development of criticality in time during the braking process. Short followers appeared to be more sensitive to TTC information and may differ from long followers in programming and execution of the braking response as a function of TTC information.

Van Winsum and Brouwer (1996) analyzed the braking response in terms of three sequential phases in the braking process. The first phase covers the interval between the moment the lead vehicle starts to brake and the moment the driver releases the accelerator pedal. This is measured by the reaction time (RT). The second phase consists of the open-loop ballistic motor response and is measured as the interval between the moment the accelerator pedal is released and the moment the brake pedal is touched and referred to as Brake Initiation Movement Time (BIMT). The third phase is a closed-loop motor response during which visual feedback is used to control the braking response. The duration of the open-loop phase was strongly determined by TTC at the moment the accelerator pedal was released. It was found that drivers who prefer a smaller THW during car-following (short followers) exhibited a faster open-loop motor response that was not caused by a smaller TTC at detection time.

The results of both experiments support the hypothesis that the motor response of short followers is more sensitive to TTC information compared to drivers who prefer a larger THW (long followers). This hypothesis is tested explicitly in the present experiment.

The results of Van Winsum and Brouwer (1996) have indicated that the duration of the open-loop phase is strongly affected by the TTC at the moment the driver detects the deceleration of the lead vehicle. Manipulation of this TTC then is expected to affect the duration of the open-loop response, but more so for short followers compared to long followers. TTC at the moment of detection has been operationalized as TTC at the moment the accelerator pedal is released (tacc) by Van Winsum and Brouwer. TTCtacc is affected by the level of deceleration of the lead vehicle. If the lead vehicle decelerates stronger, the TTC at the moment the driver initiates the motor response will be smaller if all drivers are subjected to an equally small initial time-headway to the lead vehicle.

In summary, in the present experiment the level of deceleration of the lead vehicle is manipulated and this manipulation is expected to affect TTCtacc. Since TTCtacc affects the duration of the open-loop phase of the motor response (BIMT), an effect of level of deceleration of the lead vehicle on BIMT is expected. The main hypothesis is that short followers differ from long followers in the sensitivity of the motor response to differences in TTC. From this it is predicted that there is a significant interaction between following group (short vs. long) and level of deceleration of the lead vehicle on BIMT.

In addition to this, the aim of the present study is to acquire more insight in the basic skills underlying these performance differences. Differences in sensitivity of the motor response to visual information may be the result of a more general skill involved in the transformation of dynamic visual information into an appropriate motor response. Tracking tasks require the subject to continuously use visual feedback to control a motor response and as such these tasks differ from braking for a lead vehicle where discrete responses are required. In the present experiment a longitudinal and a lateral tracking task are used to test whether short followers differ from long followers in basic skills related to the transformation of visual input to a motor response. The experiment was performed in the TRC driving simulator.

 

9.2 Method

 

Subjects. Eighteen subjects participated in the experiment. The subjects were selected from the TRC database by the following procedure. First a preselection was made on the basis of age and driving experience. Only subjects between 25 and 40 years of age with a minimum driving experience of 10000 km that were known not to be susceptible to simulator sickness were preselected from the database, resulting in 150 cases. These were send a photo-preference test that measures preferred THW. This test consisted of 6 numbered photographs with scenes of a lead vehicle at different distances in front of the car on a highway. The pre-selected subjects were required to choose the number of the photograph that best matched the preferred time-headway while driving with a speed of 110 km/h on a highway. This test procedure has been shown to result in a reliable estimation of preferred time-headway during car-following on the road (Heino et al., 1992). Table 1 shows distance and time-headways, as well as the number of subjects that participated in the experiment for each photo number.

 

 

Table 1. Relation between photo number and headway on the photo

preference test and number of subjects.

 

Photo number      DHW        THW         number of subjects

 

1                            6                0.20          0

2                            11              0.36          0

3                            25              0.81          5

4                            33              1.08          5

5                            45              1.47          3

6                            65              2.13          5

 

DHW=distance headway in meters, THW=time headway in seconds.

Subjects with a preferred headway of less than or equal to 4 were categorized as short followers (10 subjects) while a score of larger than or equal to 5 resulted in assignment to the group of long followers (8 subjects). These groups are referred to as ‘THWpref groups’.

The average age was 31 years. The subjects had held a driving license for 11 years on average and the average annual kilometrage was 26000. Sixteen subjects were male and two were female. Table 2 gives the results of analyses of variance for age and driving experience as a function of THWpref groups. The short followers who participated in this experiment had more driving experience, expressed as annual kilometrage, compared to the long followers.

 

Table 2. Age and driving experience: effects of THWpref group, df between brackets.

 

dependent                      F (17,1)    short                  long

 

age                                  1.86          29.50        32.50

years licensed               0.40          10.55        12.06

annual kilometrage       5.48*        38000       11625

 

 

*=p<0.05; **=p<0.01.

 

Apparatus. The experiment was performed in the driving simulator of the Traffic Research Centre (TRC). This fixed-based simulator consists of two inte­grated subsys­tems. The first subsystem is a conventional simulator composed of a car (a BMW 518) with a steering wheel, clutch, gear, accele­ra­tor, brake and indica­tors connected to a Silicon Graphics Skywriter 340VGXT compu­ter. A car model converts driver con­trol actions into a displacement in space. On a projection screen, placed in front, to the left and to the right of the subject, an image of the outside world from the perspective of the driver with a horizontal angle of 150 degrees is projected by three graphi­cal videopro­jectors, controlled by the graphics software of the simulator. Images are presented with a rate of 15 to 20 frames per second, resulting in a sug­gestion of smooth move­ment. The visual objects are buil­dings, roads, traffic signs, traffic lights and other vehicles. The sound of the engine, wind and tires is presented by means of a digital soundsam­pler recei­ving input from the simulator computer.

The second subsystem consists of a dynamic traffic simu­la­tion with interacting artificially intelligent cars. For experimental purpo­ses different traffic situa­tions can be simulated. The simula­tor is described in more detail elsewhere (Van Wolffe­laar & Van Winsum, 1992 and Van Winsum & Van Wolf­felaar, 1993).

 

Tasks and procedure. The circuit was made of two-lane roads with a lane-width of 3 m. and alternating left- and right turning curved road sections (radii 1000 m.). All roads had delineation with broken center lines and closed edge lines.

 

Lateral tracking task. After a practice run in the simulator for about 8 minutes, the subject was instructed to drive with a fixed speed of 100 km/h on a winding road while steering as accurately as possible. Steering performance was measured on 2 left- and 2 right-turning curves. The speed (in km/h) was shown in front of the subject in the same place as during the longitudinal tracking task.

 

Longitudinal tracking task. During this task a lead vehicle pulled up to 100 km/h. Then it alternated its speed continuously between 100 and 80 km/h, while it decelerated and accelerated smoothly with a frequency of 0.07 Hz. In front of the subject a text with the speed of the simulator car was shown. If the bumper to bumper distance was precisely 5.7 m., the text fell on the line between the rearlights of the lead vehicle. The subject was required to maintain the text precisely on that line by following the speed of the lead vehicle as accurately as possible. In order to do this, the subject was allowed to only use the accelerator pedal and to drive in third gear. After a practice period, behaviour was measured on the same 2 left and 2 right turning curves as during the lateral tracking task. During the longitudinal tracking task steering performance was measured as well since this constitutes a more difficult (double task) lateral tracking task.

 

Braking task. After this, braking behaviour was measured by the following procedure. The subject was instructed to drive with a constant speed of 100 km/h, to stay in the right lane and to avoid a collision with a lead vehicle in case it braked. While driving, the subject was overtaken by another vehicle every 5 seconds on average. The lead vehicle merged in front of the lead vehicle and started to drive at a fixed THW of 0.8 seconds. After a stable THW was reached it braked from 100 to 60 km/h. After a while the lead vehicle pulled up to 120 km/h, while the subject pulled up to 100 km/h. The another vehicle merged in front of the simulator car and the cycle repeated itself. Braking occurred twice per minute on average. The lead vehicle applied either a deceleration of 3 or 6 m/s² in random order. The driver was subjected to a total of 30 braking trials, with 15 trials for each level of deceleration. The task took 15 minutes to complete.

 


Data collection and analysis. Lateral tracking performance was measured with the steering error, dse (see Van Winsum and Godthelp, 1996), computed on-line and sampled with a frequency of 10 Hz, together with lateral position. Steering error was computed as the difference between the actual steering-wheel angle and the required steering-wheel angle (ds – dsr), whereas required steering-wheel angle was computed as dsr=GL(1+Ku²)/Rr (see Godthelp, 1986). In this Rr represent the road radius in meters, G the steer-to-wheel ratio, L the wheel base, K a vehicle related stability factor and u the longitudinal speed in m/s. From dse the following measures were derived :

– standard deviation of dse, SDdse

– the average of all steering error maxima, MAXdse

– the average duration of the period where steering error was larger than zero, Tdse

 

A larger MAXdse means a larger steering error, while a smaller Tdse indicates more frequent steering corrections, see figure 1. In addition to this the standard deviation of the lateral position (SDlatpos) was analyzed. Only those samples were analyzed where the subject had traversed more than 100 meters from the start of a curved segment until the subject was 100 meters to the next curved segment. This procedure ensured that only closed-loop steering in the curve was analyzed. Lateral tracking performance was measured during the lateral tracking task and the longitudinal tracking task. This constitutes the within-subjects manipulation TASK. The dependent variables were analyzed with repeated measures analysis of variance with TASK as a within-subjects factor and THWpref group as a between-subjects factor.

During the longitudinal tracking task the speed of the lead vehicle and the simulator car and the bumper to bumper distance between the two vehicles were sampled with a frequency of 10 Hz. The subject was instructed to keep the distance to the lead vehicle constant. The standard deviation of distance headway, SDDHW, then measures the quality of longitudinal tracking performance. In order to keep the distance headway as constant as possible, the subject had to vary the speed in the same manner as the lead vehicle. This was analyzed with a coherence analysis of the two speed signals (see Brookhuis and De Waard, 1994, for an explanation of the method). From this analysis three measures express the quality of tracking performance: coherence, phase shift and modulus. The coherence is a measure of the accuracy of the subject’s speed adaptations. The phase shift measures the delay of the subjects’ speed variations with respect to the speed variation of the lead vehicle. The delay can be computed from the phase shift via a simple transformation. The modulus is a gain factor that expresses the extent to which the subject overreacts to decelerations and accelerations of the lead vehicle.

During the braking task the following variables were analyzed. At t0 the lead vehicle started to brake. The moment the accelerator pedal position was less then 4% after t0 was registered as tacc, and RT was computed as tacc-t0. The moment after tacc at which the brake pedal force was more than 3 Nm, was registered as tbr (the moment the brake pedal was touched). BIMT (Brake Initiation Movement Time, or the open-loop ballistic response) was computed as tbr-tacc. The maximum brake force was detected on-line and the moment this was reached was registered as tmaxbr. BCMT (Brake Control Movement Time, or the closed-loop braking response) was computed as tmaxbr-tbr. The maximum brake force excerted, MAXBRFO, was stored as well. During the closed-loop phase a number of decelerations typically occur in the brake pedal signal. These decelera­tions reflect movement velocity correc­tions of the right foot. The number of decelerati­ons in the brake pedal signal (NRCOR) was analyzed as well. The time-history of braking can be seen in figure 2.

The dependent variables were analyzed with repeated measures analysis of variance with THWpref group as a between-subjects factor and deceleration of the lead vehicle as a within-subjects factors.

Figure 1. Time history of steering errors during curve negotiation.

 

Figure 2. Time history of braking and dependent variables.

 

 

9.3 Results

 

Lateral tracking performance. Table 3 lists the results of analysis of variance and table 4 gives the average values.

 

Table 3. Lateral tracking performance: effects of TASK and THWpref group, df between brackets.

 

dependent             effect                          F (16,1)   

 

SDdse                      THWpref                         6.44*

TASK                            89.22**

THWprefxTASK              6.66*

SDlatpos                           THWpref                          1.19

TASK                              0.14

THWprefxTASK              6.61*

MAXdse                 THWpref                          6.85*

TASK                            80.01**

THWprefxTASK              3.84

Tdse                         THWpref                          2.62

TASK                          167.88**

THWprefxTASK             0.02

 

*=p<0.05; **=p<0.01.

 

 

Table 4. Averages of lateral tracking performance measures by TASK and THWpref group.

 

dependent             lateral task                  longitudinal task

                              short                  long           short                  long

 

SDdse                      1.468        1.986        2.919        4.528

SDlatpos                           0.157        0.162        0.143        0.172

MAXdse                 1.969        2.848        4.117        6.201

Tdse                         2.749        2.437        1.319        0.978

 

SDdse and MAXdse in degrees, SDlatpos in meters and Tdse in seconds.

 

Standard deviation of steering errors was significantly affected by TASK. During the longitudinal tracking task, which is a double task situation, SDdse was larger compared to the simple lateral tracking task. This suggests that the double task situation deteriorated lateral tracking performance. The effect of THWpref group on SDdse was significant as well. This means that short followers steered more accurately compared to long followers. Performance in the double task situation deteriorated for both groups, but much stronger for the long followers, see figure 3. Lateral tracking performance during the more difficult longitudinal tracking task was characterized by larger steering errors (effect of TASK on MAXdse) and more frequent steering corrections (effect of TASK on Tdse). Close followers made smaller steering errors compared to long followers, but the interaction between THWpref and TASK was only marginally significant (p<0.068). The results indicate that the effects of THWpref group on the standard deviation of the steering errors was mainly caused by the fact that close followers committed smaller steering errors. The effects of TASK on MAXdse were counterbalanced by faster steering corrections. This is supported by the large negative correlation between MAXdse and Tdse (R=-0.88, p<0.01, in the longitudinal tracking task). This possibly prevented a significant TASK effect on SDlatpos, although the interaction between THWpref group and TASK on SDlatpos was significant.

Figure 3. Standard deviation of steering errors as a function of THWpref group and TASK.

 

The results indicate that long followers steer less accurately. Steering behaviour deteriorates when the task becomes more demanding, but it deteriorates stronger for long followers compared to short followers. Short followers then differ from long followers in lateral tracking performance.

Longitudinal tracking performance. Table 5 gives the results from the analyses of variance on the dependent variables.

 

Table 5. Longitudinal tracking performance: effects of THWpref group,

df between brackets and averages.

 

dependent             F (16,1)    short                  long

 

SDDHW                           9.79**      1.049        1.489

Coherence            5.69*        0.998        0.991

Delay                    4.22*        0.458        0.625

Modulus (gain)    5.63*        1.134        1.182

 

*=p<0.05; **=p<0.01.

 

Short followers performed significantly better on the longitudinal tracking task on all dependent variables. For both groups the coherence was extremely high, indicating that the task was performed quite well by both groups. Close followers maintained a more constant distance to the lead vehicle (effect on SDDHW), controlled their speed more in accordance with the speed of the lead vehicle (effect on coherence), responded faster to speed variations of the lead vehicle (effect on delay) and overreacted less strongly (effect on modulus) compared to long followers.

 

Table 6. Correlations between lateral and longitudinal tracking performance measures.

 

SDDHW      Coherence         Modulus   Delay

 

Lateral

task:

SDdse                   0.40*       -0.70**             -0.17                  0.39*

MAXdse               0.43*       -0.76**             -0.18                  0.39*

Tdse                      -0.28                   0.21                 0.10                  -0.34

longitudinal

task:

SDdse                   0.44*       -0.62**             0.24                  0.26

MAXdse               0.38                  -0.53*                0.25                  0.20

Tdse                      -0.44*       0.49*                -0.21                  -0.31

 

*=p<0.05; **=p<0.01.

 

 

Table 6 shows the correlations between the performance measures of the lateral and longitudinal tracking tasks. It can be seen that the correlations of SDdse and MAXdse with especially coherence are substantial. This suggests that, to some extent, the quality of performance on both lateral and longitudinal tracking depend on the same basic skills.

 

Braking performance. As expected, there was a significant main effect of level of deceleration of the lead vehicle (DEC) on TTCtacc (F(16,1)=293.24, p<0.0001). The effect of THWpref group on TTCtacc was not significant (F(16,1)=0.75, p<0.40), and neither was the interaction between THWpref group and DEC on TTCtacc (F(16,1)=0.88, p<0.363). This indicates that the manipulation of the deceleration of the lead vehicle was successful in affecting TTCtacc, and that differences between short and long followers cannot be attributed to differences in TTCtacc.

Table 7 lists the effects of THWpref group and deceleration of the lead vehicle on braking performance measures and table 8 lists the average values.

 

Table 7. Braking task: effects of THWpref group and deceleration (DEC), df between brackets.

dependent             effect                 F (16,1)  

 

RT                         THWpref               0.19

DEC                    2.67

THWprefxDEC     0.66

BIMT                    THWpref               7.16*

DEC                   20.58**

THWprefxDEC     8.33**

BCMT                   THWpref               2.12

DEC                     0.23

THWprefxDEC     0.13

MAXBRFO          THWpref               0.00

DEC                   72.79**

THWprefxDEC     0.04

NRCOR                THWpref               0.24

DEC                   18.38**

THWprefxDEC     0.14

 

*=p<0.05; **=p<0.01.

 

There were no significant effects of deceleration of the lead vehicle and THWpref group on RT. A larger deceleration of the lead vehicle resulted in a faster open-loop motor response (BIMT), as expected. Also, in support of the hypothesis, the interaction between THWpref group and level of deceleration of the lead vehicle on BIMT was statistically significant, see figure 4. The deceleration of the lead vehicle did not affect the duration of the closed-loop phase (BCMT). A larger deceleration of the lead vehicle resulted in a higher maximum brake pressure and fewer movement corrections during the closed-loop response. There were no statistically significant effects of THWpref group on the closed-loop response related variables.

 

Table 8. Averages of braking performance measures by deceleration (DEC) and THWpref group.

 

dependent    DEC = 3 m/s²             DEC = 6 m/s²

                     short                  long           short                  long      

 

RT                  0.600        0.598          0.589        0.546

BIMT             0.981        0.580          0.598        0.495

BCMT            1.276        1.114          1.210        1.104

MAXBRFO 50.185      51.982      156.945    153.677

NRCOR         3.280        3.094          2.443        2.392

 

RT, BIMT and BCMT in seconds, MAXBRFO in Nm.

Figure 4. Duration of the open-loop motor response (BIMT) as a function of deceleration of the lead vehicle (DEC) and THWpref group.


9.4 Discussion and conclusions

 

The theoretical perspective of the present study is that choice of time-headway during car-following is an adaptation to skills involved in operational braking performance. Drivers with poorer operational performance adapt their behaviour on the tactical level accordingly as a form of self-regulation. In the introduction evidence was presented that adaptation of behaviour on the tactical level occurs for transient degradations in operational performance. Here it is suggested that this phenomenon is more general and also occurs on the level of individual differences in tactical behaviour. In Van Winsum and Heino (1996) and Van Winsum and Brouwer (1996) it was demonstrated that there are consistent individual differences in choice of THW during car-following. The main hypothesis of the present study was that drivers who prefer a smaller time-headway during car-following differ from driver who prefer to follow at a larger time-headway in the sensitivity of the motor response of braking to differences in time-to-collision (TTC). This hypothesis was inferred from the results of two previous experiments (Van Winsum and Heino, 1996; Van Winsum and Brouwer, 1996). TTC was manipulated by the level of deceleration of the lead vehicle. The results indicate that the open-loop response of short followers is more sensitive to differences in TTC compared to long followers. Long followers generate a similar motor response irrespective of the task demands or visual input characteristics, while close followers adjust the open-loop motor response to what they perceived visually.

The assumed causal chain in this reasoning is that individual differences in some basic psycho-motor skill affect the quality of the braking response. It is assumed that drivers are aware of this and adapt the choice of time-headway during car-following accordingly. In this way drivers protect themselves against poorer operational performance. However, it may be argued that short followers have had more practice in braking resulting in increased operational performance because of learning effects. To rule out this explanation it was examined whether short followers differ from long followers in psycho-motor performance in tasks unrelated to braking. It is demonstrated that short followers perform better on lateral tracking tasks as well as on a continuous longitudinal tracking task. In addition to this, performance on both types of tracking tasks correlates significantly. The results indicate that:

 

1) Short followers differ from long followers in perceptual-motor skills related to the transformation of visual information to a motor response,

2) these differences in skill are not acquired as a function of differences in following behaviour,

3) these differences in skill affect the quality of braking performance in the sense that short followers tune the braking response better to the requirements of the situation, giving them a higher sense of control,

4) resulting in the choice of a larger time-headway for drivers with poorer operational performance and a smaller time-headway for drivers with better operational performance.

 

The short followers in the present experiment were more experienced drivers compared to the long followers in the sense that annual kilometrage was higher. This factor may have contributed to the higher skill level and better operational performance of short followers. Van Winsum and Godthelp (1996) found a relation between annual kilometrage and steering performance. On the other hand, it is somewhat hard to image how the skills involved in the longitudinal tracking task could improve as a function of driving experience.

The results suggest that adaptation of behaviour on the tactical level, such as the choice of speed in curves and straight road sections and choice of headway during car-following, to operational performance may be a general phenomenon that applies to both transient and situational determined changes in operational performance as well as to individual differences in operational performance.

 


EXPERIMENT 5: The effects of deceleration on braking reactions as a function of preferred time-headway

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EXPERIMENT 5: The effects of deceleration on braking reactions as a function of preferred time-headway

 

This is chapter 8 from the thesis “From adaptive control to adaptive traffic behaviour” about traffic psychology and behavioural adaptation of drivers, by Wim van Winsum. The thesis is from 1996. It describes a number of behavioural experiments into car driving that were performed in a research driving simulator.

Other chapters of this thesis can be found here:

 

Abstract 

The manoeuvre of braking for a decelerating lead vehicle was separated into three sequential processes that were manipulated independently. The initial time-headway to the lead vehicle at the moment it started to decelerate affected reaction time. Primary deceleration of the lead vehicle manipulated the duration of the open-loop phase. From the moment the driver touched the brake pedal, the deceleration of the lead vehicle was changed. This secondary deceleration was assumed to affect the closed-loop phase of braking. The hypothesis was that drivers who prefer a small time-headway during car-following (short followers) differ from drivers who prefer to follow at a large time-headway (long followers) in both the open- and closed-loop phases. In that case an interaction is expected between following group (short vs. long follo­wers) and primary deceleration on the duration of the open-loop phase and between following group and secondary deceleration on the duration of the closed-loop phase, the maximum brake force exerted and the number of movement corrections. In general terms, these predictions could not be confirmed. The lack of confirmation of the hypothesis is explained in terms of task characteristics that resulted in startle reactions and vigilance effects.

 

8.1 Introduction

 

A number of studies of car driving have shown that behaviour on the tactical level such as speed choice and choice of time-headway in car-following is sensitive to a variety of factors that affect operational perfor­mance. For example, marijuana affects operational car driving performance while it also results in driving with a lower speed and in choosing a larger time-headway (THW) during car-following (Smiley, et al., 1981; Smiley et al., 1985; Smiley et al., 1987). Time-on-task results in the choice of a larger time-headway during car-follo­wing, accompanied by verbal reports of performance decrements, drowsiness and exhaustion (Fuller, 1981). Brookhuis et al. (1991) reported an increase in THW when using a car telephone while driving. These fin­dings suggest that behaviour on the tactical level is used by the driver to compensate for effects on operational performance. For the case of car-following this means that any factor that may lead to performance decrements in braking for lead vehicles may result in a compensatory increase of time-headway. From the same perspective it was studied whether individual differences in preferred THW are related to individual differences in braking performance in a number of experiments (Van Winsum, 1996; Van Winsum and Heino, 1996; Van Winsum and Brouwer, 1996). In Van Winsum and Heino (1996) and Van Winsum and Brouwer (1996) preferred THW proved to be consistent within the driver. This means that drivers are consistently short or long followers and, thus, that individual differences in THW are consis­tent.

In Van Winsum (1996) it was found that, under the instruction to brake as fast as possible as soon as a deceleration of the lead vehicle was detected, reaction time and motor-response times are not different between short and long followers. Also, no evidence was found for differences in the informa­tion processing stages of stimulus encoding of a braking lead vehicle and motor adjustment. However, when the driver did not know the deceleration of the lead vehicle in advance, short followers generated a faster motor response, i.e. they moved the foot faster from the accelerator pedal to the maximum brake position when the lead vehicle decelerated. This suggests that short followers differ from drivers with a larger preferred THW in the transformation of visual feedback to a required motor response.

In Van Winsum and Heino (1996) it was found that the initiation and control of braking are both affected by time-to-collision (TTC) at the moment the lead vehicle starts to brake. This suggests that TTC information is used for judging the moment to start braking and in the control of braking. No evidence was found for differences between short followers and long followers in the ability to accurately perceive TTC. However, short followers were better able to program the intensity of braking to required levels and tuned the control of braking better to the development of criticality in time during the braking process. It was concluded that short followers may differ from long followers in programming and execution of the braking response as a function of TTC information.

Van Winsum and Brouwer (1996) analyzed the braking response in terms of three sequential phases in the braking process. The first phase consists of the interval between the moment the lead vehicle starts braking and the moment the driver releases the accelerator pedal. This is measured by the reaction time (RT). The second phase consists of the open-loop ballistic motor response. It is measured as the interval between the moment the accelerator pedal is released and the moment the brake pedal is touched and referred to as Brake Initiation Movement Time (BIMT). The third phase is a closed-loop motor response during which visual feedback is used to control the braking response while braking. The duration of the open-loop phase was strongly determined by the TTC at the moment the accelerator pedal was released, while the duration of the closed-loop phase was strongly determined by the number of movement corrections in the brake pedal signal. It was found that short followers exhibited a faster open-loop motor response which was not caused by a smaller TTC at detection time. Also, short followers generated a faster closed-loop response which was caused by fewer movement corrections. These results again supported the hypothesis that short followers differ from long followers in the efficiency of programming and execution of the braking response.

According to Van Winsum and Brouwer (1996), the duration of the three phases is affected by different factors. The RT interval is assumed to be affected by the THW at the moment the lead vehicle starts braking. This means that RT is expected to be faster if the momentary time-headway at the moment the lead vehicle starts to decelerate is smaller. The open-loop interval (BIMT) is assumed to be affected by the primary deceleration of the lead vehicle. If the lead vehicle decelerates more strongly, TTC at detection time is smaller resulting in a faster open-loop response. The closed-loop interval (BCMT) is affected by the number of movement correcti­ons. If the deceleration of the lead vehicle changes after the subject’s braking response has started, the speed and intensity of the braking response has to be changed, based on visual feedback. This means that a change in the level of deceleration after the braking response has started (secondary deceleration) is assumed to result in more movement corrections and thus affects the duration of the closed-loop phase. If short followers differ from long followers in both the open- and closed loop phases, then an interaction is expected between following group (short vs. long follo­wers) and primary deceleration on BIMT and between following group and secondary deceleration on BCMT, the maximum brake force exerted and the number of movement corrections. These hypotheses are tested in the present experiment.

 

8.2 Method

 

Subjects. Twenty-two subjects participated in the experiment. The subjects were selected from the TRC database by the following procedure. First a preselection was made on the basis of age and driving experience. Only subjects between 25 and 40 years of age with a minimum driving experience of 10000 km that were known not to be susceptible to simulator sickness were preselected from the database, resulting in 150 persons. These were send a small photo-preference test that measures preferred THW. This test consisted of 6 numbered photographs with scenes of a lead vehicle on a highway at different distances in front of the car. The preselected subjects were required to fill out on a form the number of the photograph that best matched the THW chosen by the subject while driving with a speed of 110 km/h on a highway and to return the form if they were intere­sted in participating in the experiment. This test procedure has been shown to result in a reliable estimate of preferred time-headway during car-following on the road (Heino et al., 1992). From the returned forms, 11 subjects with a small preferred THW and 11 subjects with a larger preferred THW were invited for participation in the experiment. Subjects with a preferred photo number of less than or equal to 3 were assigned to the group of ‘short followers’. Subjects who preferred photo number 5 or 6 were assigned to the group of ‘long followers’, see table 1. These two groups are referred to as ‘THWpref groups’.

 

Table 1. Relation between photo number and headway on the photo preference test and number of subjects. DHW=distance-headway in meters, THW=time-headway in seconds.

 

 

Photo number      DHW        THW         number of subjects

 

1                            6                0.20          0

2                            11              0.36          2

3                            25              0.81          9

4                            33              1.08          0

5                            45              1.47          4

6                            65              2.13          7

 


Apparatus. The experiment was performed in the driving simulator of the Traffic Research Centre (TRC). This fixed-based simulator consists of two inte­grated subsys­tems. The first subsystem is a conventional simulator composed of a car (a BMW 518) with a steering wheel, clutch, gear, accele­ra­tor, brake and indica­tors connected to a Silicon Graphics Skywriter 340VGXT compu­ter. A car model converts driver con­trol actions into a displacement in space. On a projection screen, placed in front, to the left and to the right of the subject, an image of the outside world from the perspective of the driver with a horizontal angle of 150 degrees is projected by three graphi­cal videopro­jectors, controlled by the graphics software of the simulator. Images are presented with a rate of 15 to 20 frames per second, resulting in a sug­gestion of smooth move­ment. The visual objects are buil­dings, roads, traffic signs, traffic lights and other vehicles. The sound of the engine, wind and tires is presented by means of a digital soundsam­pler recei­ving input from the simulator computer.

The second subsystem consists of a dynamic traffic simu­la­tion with interacting artificially intelligent cars. For experimental purpo­ses different traffic situa­tions can be simulated. The simula­tor is described in more detail elsewhere (Van Wolffe­laar & Van Winsum, 1992 and Van Winsum & Van Wolf­felaar, 1993).

 

Procedure. The circuit was made of two-lane roads with a lane-width of 3 m., and straight road sections alternated with left-turning curved road sections. All roads had delineation with broken center lines and closed edge lines. Before the experiment started subjects practiced braking several times by approaching a traffic light that turned on red after a certain time-to-intersection was exceeded. This required the subjects to come to a full stop. After this, preferred time-headway was measured by the following procedure. The subject was instructed to drive with a fixed speed of 100 km/h, while continuously being overtaken by other cars. One of these cars merged in front of the subject and adopted a time-headway of 2 s. The subject was asked to rate on a scale from 1 to 10 how well the present THW resembled the THW normally adopted by the subject in similar situations on the road. If THW was too small it was increased with 0.5 s. If THW was too large it was decreased by 0.5 s. After this the subject was again asked for a rating. This continued until a definite peak was found in the subject’s rated THW, i.e. until the preferred THW was found. After this the braking trials started. The subject was instructed to drive with a constant speed of 100 km/h, to stay in the right lane and to avoid a collision with a lead vehicle in case it braked. While driving, the subject was overtaken by another vehicle every 5 seconds on average. The lead vehicle merged in front of the lead vehicle and started to drive at a fixed THW of either 0.8 or 1.2 seconds (THW condition). After a stable THW was reached it either pulled up again or it braked from 100 to 60 km/h. Braking occurred on average once in every 5 minutes. The lead vehicle applied either a deceleration of 3 or 6 m/s² (initial deceleration). After the subject touched the brake in response to the lead vehicle, the deceleration of the lead vehicle changed either to 3 or 6 m/s² (secondary deceleration), resulting in the following deceleration patterns for both THW conditions (0.8 vs. 1.2 s.): 3 to 3 m/s², 3 to 6 m/s², 6 to 3 m/s² and 6 to 6 m/s². Thus, the driver was subjected to a total of 8 braking trails, that were counterbalan­ced.

 

Data collection and analysis. During the braking trials the following data were sampled with a frequency of 50 Hz.: speed of the simulator car and the lead vehicle in m/s, accelera­tor pedal position, brake pedal position and force excerted in Nm, accele­ration in m/s², time-to-collision (TTC) and bumper to bumper distance from the lead vehicle. At t0 the lead vehicle started to brake.

 

Figure 1. Time history of braking and dependent variables.

The moment the accelerator pedal position was less then 4% after t0 was registered as tacc, and RT was computed as tacc-t0. The moment after tacc at which the brake pedal force was more than 3 Nm, was registered as tbr (the moment the brake pedal was touched). BIMT (Brake Initiation Movement Time, or the open-loop ballistic response) was computed as tbr-tacc. The maximum brake force was detected on-line and the moment this was reached was registered as tmaxbr. BCMT (Brake Control Movement Time, or the closed-loop braking response) was computed as tmaxbr-tbr. The maximum brake force excerted, MAXBRFO, was stored as well. During the closed-loop phase a number of decelerations typically occur in the brake pedal signal. These decelera­tions reflect movement velocity correc­tions. The number of decelerati­ons in the brake pedal signal (NRCOR) was analyzed, together with the maximum deceleration (DECmax) of the simulator car, the minimum TTC (TTCmin) and the minimum distance to the lead vehicle (DISmin) during the braking maneuver. In previous studies, the TTC on the moment the driver initiates braking, that is, TTC on tacc (TTCtacc) has proven to be an important variable controlling subsequent phases in the braking process. For this reason TTCtacc was analyzed as well. The time-history of braking can be seen in figure 1.

The dependent variables were analyzed with repeated measures analysis of variance with THW, initial deceleration and secondary deceleration as within-subjects factors, and THW-group as a between-subjects factor.

 

8.3 Results

Characteristics of THWpref groups. Table 2 presents the results of analysis of variance and the averages of THW as measured in the simulator, age, number of years licensed and kilometrage per year as a function of THWpref groups.

Table 2. Statistical effects and averages of THW as measured in the simulator, age, number of years licensed and kilometrage per year as a function of THWpref groups.

Dependent variable                F(22,1)     short                  long

 

THW simulator                      34.24**      1.5             2.9

Age                                            0.03        31.6          31.3

Years licensed                         0.65        12.8          11.3

Annual kilometrage               5.30*       28084       13227

 

** = p<0.01; * = p< 0.05.

 

There was a strongly significant difference between THWpref groups on the preferred THW as measured in the simulator. This supports the validity of the simulator for measuring car-following behaviour. There were no signifi­cant differences in age or number of years licensed to drive a car between short and long followers, but short followers drove significantly more kilometers per year.


Effects of manipulations. Table 3 lists the main effects of the manipulations on the dependent variables. The averages of RT, BIMT, BCMT, MAXBRFO and NRCOR as a function of the manipulations are shown in table 4. RT was significantly affected by the factor THW (0.8 vs. 1.2 s): a smaller THW at which the lead vehicle started to brake resulted in a smaller RT. BIMT was both affected by THW and by initial decele­ration: a smaller THW and a larger initial deceleration both resulted in a smaller BIMT. These effects match the significant effects of THW and initial deceleration on TTCtacc.

 

Table 3. Main effects of manipulations on dependent variables. ** = p<0.01; * = p< 0.05,

dec-1 represents primary deceleration and dec-2 secondary deceleration.

 

Dependent                     Independent       F(21,2)

 

RT                                  THW                  8.44 **

dec-1                 0.57

dec-2                 3.63

BIMT                             THW                  20.06 **

dec-1                 17.68 **

dec-2                 0.19

BCMT                            THW                  0.11

dec-1                 19.23 **

dec-2                 0.73

MAXBRFO                   THW                  26.83 **

dec-1                 41.68 **

dec-2                 71.64 **

NRCOR                         THW                  0.04

dec-1                 7.65 *

dec-2                 7.65 *

TTCmin                            THW                  14.89 **

dec-1                 77.23 **

dec-2                 35.13 **

DECmax                          THW                  17.43 **

dec-1                 65.31 **

dec-2                 57.20 **

DISmin                            THW                  81.40 **

dec-1                 80.23 **

dec-2                 9.02 **

TTCtacc                           THW                  15.69 **

dec-1                 160.42 **

dec-2                 0.86

 

Thus, if criticality, measured by TTCtacc, is higher the open-loop ballistic motor response is faster. The duration of the closed-loop phase, BCMT, was only significantly affected by initial deceleration, but not by THW or secondary decelera­tion: a larger initial deceleration resulted in a smaller BCMT. The maximum force, MAXBRFO, excerted on the brake pedal was signifi­cantly affected by all independent factors. Thus, a higher secondary deceleration, after the brake pedal was touched, resulted in a higher maximum brake force instead of a faster BCMT. The number of decelerations in the brake pedal signal (NRCOR) was both affected by initial and secondary deceleration. A larger initial and secondary deceleration both resulted in fewer decelerations in the brake pedal signal.

 

Table 4.  Averages as a function of the manipulated factors time-headway on which lead vehicle starts to decelerate, initial deceleration and secondary deceleration. RT, BIMT and BCMT in seconds, MAXBRFO in Nm. dec-1 represents primary deceleration and dec-2 secondary deceleration.

 

THW                                             0.8                                                                   1.2                           

dec_1                          3                                  6                                  3                                  6             

dec_2                 3                6                3                6                3                6                3               6      

 

RT                      0.73          0.79          0.74          0.88          0.91          1.05          0.91          0.83

BIMT                 0.63          0.73          0.49          0.50          0.98          0.89          0.66          0.58

BMCT                1.64          1.50          1.13          1.16          1.43          1.69          1.09          1.30

MAXBRFO       51.68        113.22      103.91      218.22      48.26        87.21        80.24        98.21

NRCOR             3.68          2.77          3.09          2.32          3.73          3.05          2.73          2.55

 

 

This indicates that this variable basically measures the necessity to move the pedal straight to the maximum without hesitation. Finally, a smaller initial THW resulted in a smaller minimum TTC, a larger deceleration and a smaller minimum distance to the lead vehicle. Similar effects were found for a larger initial deceleration by the lead vehicle and a larger secondary deceleration by the lead vehicle.

 

Effects of THWpref group. Table 5 lists the effects of THWpref group and interactions between THWpref group and the independent factors on the dependent variables.

 

Table 5. Main effects of THWpref and interactions on depen­dent variables. ** = p<0.01;

* = p< 0.05, dec-1 represents primary decele­ration and dec-2 secondary deceleration.

 

Dependent            Independent                F(21,2)

 

RT                         THWpref                     0.10

THWprefxTHW           0.08

THWprefxdec-1          0.60

THWprefxdec-2          0.25

BIMT                    THWpref                     0.36

THWprefxTHW           0.09

THWprefxdec-1          0.11

THWprefxdec-2          0.01

BCMT                   THWpref                     2.04

THWprefxTHW           0.13

THWprefxdec-1          0.30

THWprefxdec-2          0.67

MAXBRFO          THWpref                     0.76

THWprefxTHW           1.15

THWprefxdec-1          0.02

THWprefxdec-2          0.10

NRCOR                THWpref                     4.47 *

THWprefxTHW           0.45

THWprefxdec-1          8.18 **

THWprefxdec-2          4.03 *

 

 

There were no significant main effects of THWpref group on RT, BIMT, BCMT and MAXBR­FO. Also none of the interactions of THWpref with the independent factors reached significan­ce on any of these dependent variables. This means that these results do not support the hypotheses mentioned in the introduction. However, NRCOR, the number of movement corrections during the closed-loop phase, was significantly affected by THWpref group and revealed significant interactions of THWpref group with initial and secondary deceleration, see figure 2. The effects on NRCOR were as follows: only for the group of short followers was NRCOR affected by dec-1 (F(10,1)=20.65, p<0.001) and by dec-2 (F(10,1)­=12.08, <0.006). For the group of long followers, the effects of both dec-1 and dec-2 on NRCOR were not significant (F(10,1)=0.05, p<0.822 for dec-1 and F(10,1)=0.46, p<0.512 for dec-2).

 

These effects strongly indicate that long followers moved their foot directly to the maximum brake position, irrespecti­ve of the development of criticality in time, while short followers were more sensitive to the manipulations of initial and secondary deceleration on this measure.

Figure 2. NRCOR as a function of THWpref group, initial deceleration (dec-1)

and secondary deceleration (dec-2).

 

 

8.4 Discussion and conclusions

 

Based on a number of previous experiments it was tested whether short followers differ from long followers in both the open-loop and closed-loop phases of the braking process. This was tested by manipulating these phases. The open-loop phase was manipulated with two levels of initial deceleration of the lead vehicle. After the brake was touched by the subject, the deceleration of the lead vehicle changed. This secondary deceleration manipulated the closed-loop phase of the braking response. The hypotheses were:

1) there is an interaction between following group and primary deceleration on the duration of the open-loop phase (BIMT), defined as the interval between the moment the foot is released from the accelerator pedal and the moment the foot touches the brake pedal. This would support the idea that short followers differ from long followers in the open-loop phase.

2) there is an interaction between following group and secondary decelera­tion on the maxim­um brake force excerted, the number of movement corrections during the closed-loop phase and the duration of the closed-loop phase (BCMT), defined as the interval between the moment the foot touches the brake pedal and the moment the maximum brake force is excerted. This would support the idea that short followers differ from long followers in the closed-loop phase.

In general, these hypotheses were not supported. There was no signifi­cant interaction between following group and any of the independent factors, initial THW, primary and secondary deceleration, on RT, BIMT, BCMT and the maximum brake force. However, the interaction between following group and initial deceleration on the number of movement corrections was significant as was the interaction between following group and secondary deceleration on this variable. The number of movement corrections (NRCOR) during the closed-loop phase were conceived as an expression of uncertainty induced by a change in deceleration after the braking response was initia­ted. Although NRCOR was affected by secondary deceleration, it was also affected by primary deceleration. The pattern of effects suggests that NRCOR expresses the necessity to move the pedal straight to the maximum without hesitation. The results showed that only the group of short followers was sensitive to the effects of initial and secondary decelera­tion on NRCOR, while the long followers moved their foot to the maximum with the same number of movement corrections independent of primary and secondary deceleration. In a previous study it was found that NRCOR strongly determines the duration of the closed-loop phase. From this perspective, it would be expected that NRCOR and BCMT are affected by following group and the independent factors in a similar way. However, as was already apparent, there were no significant effects of following group on BCMT. Closer inspection of the data revealed that only in the trials where the secondary deceleration was high, the correlations between NRCOR and BCMT were significant, see table 6.

 

Table 6. Correlation between NRCOR and BCMT, depending on THWpref

group and secondary deceleration (dec-2).

 

dec-2

  3              6         

short                      0.30          0.68 **

long                       0.30                   0.59 **

 

 

This suggests that a causal relation between NRCOR and BCMT only exists if criticality is high enough.

 

The lack of support for the hypotheses may have been caused by specific task related factors. The subjects generally described the task as boring, mainly because of the long task duration and the low event-rate. There were only 8 braking trials over a duration of 45 minutes. This may have resulted in a vigilance task with two separate effects. On the one hand, the braking trials may have generated startle reactions, resulting in fast responses irrespective of the manipulations. On the other hand some responses may have been slow because of state-related factors. This would have resulted in a high variance in the data that was not caused by the manipulations of THW, initial and secondary deceleration. Figure 4 illustrates the distribution of RT as a function of the THW manipulation. It can be seen that the distributions are skewed on the right side, especially for the THW=1.2 condition, sugge­sting low-vigilance effects, although there are two distinctive peaks in the distributions.

 

Figure 4. Distributions of RT as a function of the THW manipulation.

 

Figure 5 and 6 show the distributions of BIMT as a function of initial deceleration for THW=0.8 and THW=1.2 respectively. These figures show that the distributions of BIMT are skewed on the right side and that the effects of initial deceleration are mainly caused by ‘outliers’ on the right side of the distributi­on. Especially for the THW=0.8 condition, the primary peak of the low deceleration condition occurs before the primary peak of the high deceleration condition, which obviously is not in the expected direction and opposite to the effects of the analyses of variance, which are based on the means. The distributions of BIMT in the THW=0.8 condition are visualized separately for the short followers and long followers in figure 7.

 

It can be seen that for the short followers there are two distinctive peaks as a function of initial deceleration in the expected direction, while for the long followers the primary peaks overlap. Moreover, it can be seen, that the primary peak in the BIMT of long followers occurs before the peaks of the short followers. This suggests that BIMT of long followers has suffered more from startle reactions resulting in BIMTs that were fast and not tuned to differences in initial deceleration, while the BIMTs of short followers were more sensitive to initial deceleration.

Thus, the distributions of the data and task-induced startle responses and low-vigilance effects may have given results that failed to support the hypotheses. This will be tested in the next experiment, with multiple measurements per manipulation, a higher event-rate and shorter task duration in order to prevent undesirable state-related effects.

Figure 5. Distribution of BIMT as a function of initial deceleration for the THW=0.8 conditi­on.

Figure 6. Distribution of BIMT as a function of initial deceleration for the THW=1.2 conditi­on.

Figure 7. Distribution of BIMT as a function of initial deceleration for the

THW=0.8 conditi­on, for short and long followers.


EXPERIMENT 4: Time-headway in car-following and operational performance during unexpected braking

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EXPERIMENT 4: Time-headway in car-following and operational performance during unexpected braking

This is chapter 7 from the thesis “From adaptive control to adaptive traffic behaviour” about traffic psychology and behavioural adaptation of drivers, by Wim van Winsum. The thesis is from 1996. It describes a number of behavioural experiments into car driving that were performed in a research driving simulator.

Other chapters of this thesis can be found here:

 

The relation between choice of time-headway during car-follo­wing and the quality of braking skills was studied in a driving simulator. The theoreti­cal perspective was that individual differen­ces in behaviour on the tactical level may be related to skills on the operational level of the driving task via a process of adaptation. In a sample of 16 young and middle-aged experienced drivers independent asses­sments were made of preferred time-headway and braking skill. Starting from modern theories of visual-motor learning, braking skill was analyzed in terms of a reaction time component, an open-loop visual-motor component, and a closed-loop visual-motor component involving the precise adjustment of braking (timing and force) to the situation. The efficiency of the visual-motor component of braking was a strong and significant predictor of time-headway in such a way that more efficient braking indicated a shorter preferred time-headway. This result appears to support the adaptation theory on an individual level.

 

7.1 Introduction

For many years it has been realized that individuals who have very good driving skills in the sense of great fluency and agility in performing the basic driving tasks of visual orientation and vehicular control, are not necessarily safe drivers (Williams and O’Neill, 1974; Evans, 1991). Traf­fic safety de­pends on what the driver will do in a given situa­tion and not on the maximum level of performance (Nääta­nen and Summala, 197­6) or, as Evans (1991) puts it, what is cruci­al is not how the driver can drive but how the driver does drive. The failure of driver skill models in explaining accident involvement has been attribu­ted to various adaptive mechanisms. For example, drivers with poor skills might compensate by driving slower, or, the other way around, very skilled drivers might tend to drive very fast. Because traditional skill models do not incorporate such compensatory mechanisms they are not suitable for assessing and understanding individual differences in the safety of traffic behaviour.

A solution to this problem may be the application of the hierarchical framework discussed by Michon (1985) to driving behaviour (Ranney, 1994). In this framework driving is viewed as a hierarchically organized set of tasks on the strategic, tactical and operational level. On the strategic level trip planning and the selection of trip goals and route occur. The tactical level includes, for in­stance, choice of speed on straight roads and in curves and choice of headway in car-follo­wing. Steering and control­ling the timing and intensity of braking are activities on the operational level. Traditionally the study of driving skill was aimed as the efficiency of performance on the operational level. However, in this article the importance of the interrelation between behaviour on the operational and the tactical level is stressed.  In this framework the adaptation problem may be understood as a compensation on the tactical and strategic levels of the driving task for individual differences in skills on the operational level (Brouwer et al., 1988). This adaptation theory has been used as an explanati­on for the relatively safe driving records of functionally impaired drivers. The link with driver safety now becomes clear. Driver safety may be defined in terms of the relationship between operational level skills and choices and preferences on the tactical level.

Recently, Van Winsum and Heino (1996) found some evidence with regard to the relationship between individual differences in operational level skills and tactical behaviour which appears to fit an adaptation theory on the individual level. In a study on time-headway in car-following, they found evidence for a relationship between braking skill and choice of time-headway. Since time-headway (THW), defined as the time interval between two vehicles in car-following, represents the time availa­ble to reach the same level of deceleration as the lead vehi­cle in case it brakes, they studied whether choice of THW is related to time-critical skills underlying braking performance. THW was constant over the range of speeds studied. Drivers were con­sis­tent in their choice of THW, evidencing systematic individual diffe­rences in choice of THW during car-following. The results sugge­sted differen­ces in skills related to the motor control of braking as a function of preferred time-headway. What was lacking in this study was a specific model of the braking skills so that it was difficult to pinpoint in which respect drivers with short THW differed from those with long THW. In the present study a model for the decomposition of perceptual-motor processes in braking is proposed and individual differen­ces in the effi­ciency of such processes are related to the choice of time-headway in a free field situation.

Braking for a decelerating lead vehicle requires substan­tial perceptu­al-motor skills because of the dynamic task environ­ment. Lee and his co-workers have shown in a number of publications that a perceptu­al varia­ble, named tau, which is the inver­se of the expansion of the retinal image, is used in action. This variable directly specifies time-to-contact in dynamic situations. Thus, perception is assumed to guide action and this relation between tau and action has been established in a number of different tasks such as long jum­pers running up to a take-off board (Lee et al., 1982) and jum­ping up to hit a falling ball (Lee et al., 1983). Also, Bootsma and Van Wieringen (1988) found that time-to-contact plays an important role in the guidance of actions of an experienced table tennis player. In car driving and braking the equivalent of time-to-contact is time-to-collision (TTC). Lee (1976) suggested that TTC information is used by the driver in the initia­tion and control of braking. Van Winsum and Heino found that the initiation and control of braking for a decele­rating lead vehicle was very sensitive to TTC information. The timing of the initiation of the braking response was equally sensitive to TTC for short follo­wers and long follo­wers. However, short followers were more efficient in the control of braking, braked harder and adju­sted the inten­sity of braking better to the critica­li­ty (as measured by TTC) at the moment the lead vehicle started to decelerate, compared to long followers. So, it appeared that the difference between the short and long followers was in the execution of the motor response.

­   Substantial individu­al differen­ces in the ability to accu­rate­ly estimate TTC have been reported in the literature (for instance see Schiff and Detwiler, 1979). A general finding is that TTC is unde­res­timated with a constant proporti­on. TTC esti­mation is more accurate for smaller TTC’s, see for in­stance, McLeod and Ross (1983), Cavallo et. al (1986), Hoffm­ann and Mortimer (1994). Given this evidence, the re­sults of Van Winsum and Heino could have been af­fected by the fact that the TTC at the moment the lead vehicle started to brake (t0) was smaller for short follo­wers alt­hough the distan­ce at t0 was the same for all subjects. This was caused by a higher speed on average for short follo­wers on t0. Theoreti­cally, short followers were thus in a positi­on to estima­te TTC more accurately. In additi­on, since TTC was smaller for short follo­wers they may have been forced to brake harder and more accu­ra­tely. In order to control for effects of diffe­rential criti­cality and accu­racy of TTC estimation, all dri­vers will be subjec­ted to the same high level of criticali­ty in the present study.

In dynamic situations such as braking for a decele­rating lead vehicle, following the initial reaction of releasing the accelerator, the motor response is assumed to consist of two phases, i.e. an open-loop and a closed-loop phase. We attempt to separately assess these three processes by analyzing the braking response in terms of Reaction time (RT), the Brake Initiation Movement Time (BIMT) and the Brake Control Movement Time (BCMT) (see Figure 1). Starting from the adaptation theory we expect that the quality of these processes is related to preferred time-headway: specifically we hypothesize that preferred time-headway (behaviour on the tactical level) can be predicted from the BIMT and the BCMT (performance on the operational level). To be able to assess the reliability of preferred THW as an indicator of a stable individual characteristic, it is measured at four different speeds. It is expected that the results of Van Winsum and Heino concer­ning the con­sistency of THW and the constancy of THW over speed will be replica­ted.

Reaction time (RT) represents the interval between stimulus presentation and movement initiation. Several information processing stages including response selection and response preparation, together called motor program­ming, occur within this interval. Motor programming time, as a part of RT, is assumed to be related to temporal complexity and organization of the movement to be executed, but not with physical task dimensions such as distance (Kerr, 1978). This suggests that the time associated with parameter setting for a generalized motor program does not vary for different parameter values. Thus, TTC is not expected to affect RT because TTC is assumed to determine the speed parameter value for the generalized motor program.

The Brake Initiation Movement Time (BIMT) is used to operationally define the open-loop phase under the control of the genera­lized motor program for braking of which the speed parameter is set by TTC informa­tion. During this phase the influence of feedback is absent. Because of the time charac­teristics of the braking response the open-loop phase is defined here as the interval between the moment the driver withdraws the foot from the accele­rator pedal and the moment the brake pedal is touched. The duration of this phase is then assumed to be depen­dent on TTC at the moment the driver de­tects the deceleration of the lead vehicle or at the moment the driver decides to brake.

Error detection and error cor­rection are assumed to take place during the closed-loop phase, operationally defined here as the Brake Control Movement Time (BCMT). This is the inter­val between the moment the brake pedal is touched and the moment the brake maximum is reached. Since the environmen­tal goal of the movement is to avoid a collision and to keep sufficient dis­tance to the lead vehicle, TTC infor­mation is possibly used during this feedback process. Accor­ding to Hayes and Marteni­uk (1976) movement control complexity can be viewed as the informa­tion load imposed on the performer by the necessity to detect and correct movement errors. For more skilled operators movement time decreases because of a decrease in the number of movement corrections (Keele, 1968). During the closed-loop phase of the braking response, movement time is then expected to be related to the number of movement correcti­ons.

 

7.2 Method

 

Subjects. Sixteen (8 male, 8 female) subjects participated in the experi­ment. The average age was 33.6 years (sd. 6.1, range 22-47). They had held a driving license on average for 11.6 years (range 2-27). The average annual kilometrage was ap­proximately 10083 kilometers (range 1500-30000).

Apparatus. The experiment was performed in the Traffic Research Centre (TRC) fixed-based driving simulator. It consists of a car (BMW 518) with a steering wheel, clutch, gear, accele­ra­tor, brake and indica­tors connected to a Silicon Graphics Skywriter 340VGXT compu­ter. A car model converts driver control actions into a displacement in space. On a 2 x 2.5 meter projection s­creen, placed in front of the car mockup, an image of the outside world with a horizon­tal angle of 50 degrees is projec­ted by a graphical videopro­jector, controlled by the 3D-grap­hics software. Images are presented with a rate of 15 to 20 frames per second, resulting in a sug­gestion of smooth move­ment. The visual objects are buil­dings, roads, traffic signs, traffic lights and artificially intelligent traffic. The sound of the engine, wind and tires is presented by means of a digital soundsampler recei­ving input from the simulator computer. The simula­tor is described in more detail elsewhere (Van Wolffe­laar & Van Winsum, 1992 and Van Winsum & Van Wolf­felaar, 1993).

Procedure. A circuit of two-lane roads (lane-width 3 meters) with broken center lines and continuous edge lines was used. Since the subjects had participated in another simulator experiment not involving car-follo­wing prior to the present experiment, they were already sufficiently practi­ced. First, preferred time-headway was measured as a function of different speeds. Subjects were instructed not to overtake other vehicles, to respect the speed limit of 80 km/h and to follow other vehicles at a safe distance. While driving, the sub­jects approached vehicles that were parked on the right shoulder. At a distance of 200 meter these vehicles accelerated to a fixed cruising speed and merged in front of the simulator car. There were four of these trials that differed in the cruising speed of the lead vehi­cles. The order of speeds was 60, 40, 70 and 50 km/h for all subjects (speed condition). In every trial, time-headway was measured during 5 minutes.

After this, a vehicle, driving with a speed of 60 km/h, was approached. Prompted by the experi­menter the subjects were asked to rate the danger of the present headway on a scale from 1 to 5. Then they were requested to drive a bit closer and again asked to give a rating. This continued until a time-headway of 0.6 seconds was reached. At that moment the lead vehi­cle suddenly decelerated unexpectedly from 60 km/h to 30 km/h with a decelera­tion of 6 m/s². This constitutes the braking condition. The aim of this procedure was to ascertain a fixed time headway at the moment the lead vehicle started to brake for all subjects.

Data registration and analysis. Speed, brake and accele­rator pedal signal (percenta­ge pres­sed), distance-headway, time-headway and time-to-collision were sampled with a fre­quency of 10 Hz. Average THW was computed, for the four trials in the speed condition, from the moment the simula­tor car reached the same velocity as the lead vehicle until the lead vehicle left the road. THW’s were averaged over the four speed trials to compute preferred time-headway (THWpref). The effect of speed on THW was tested with multiva­riate analysis of variance with repeated measurements.

Figure 1 shows the time-history of braking together with a number of dependent variables. In the braking condition, t0 represents the moment a THW of 0.6 was reached. On t0 the lead vehicle started to brake. The moment the acce­lerator pedal was 5% less than the position on t0 represents tacc. Reaction time (RT) was calculated as the interval between t0 and tacc. The moment the brake pedal was pressed more than 5% is indicated as tbr. The interval between tacc and tbr represents the open-loop phase of the movement and is referred to as brake initiation movement time (BIMT). The moment the maximum brake position was reached is indicated by tmaxbr. The duration of the closed-loop phase, brake con­trol movement time (BCMT), was computed as the inter­val between tbr and tmaxbr. Move­ment time (MT) was compu­ted as the sum of BIMT and BCMT. TTC on tacc is referred to as TTCacc.

During the closed-loop phase the brake pedal is pressed. A typical time-history of this is presented in figure 2. It shows the percentage at which the brake pedal is pressed toge­ther with the velocity of pressing the brake pedal and accelera­tion of brake pedal signal as a function of time. The number of decelerations in this signal reflect the number of movement (speed) corrections. The effect of driver reactions to braking by the lead vehicle on THWpref were tested with multiple regression analysis.

 

Figure 1. Time-history of the braking maneuver. Vlead represents speed of lead vehicle in m/s, accel represents accelerator pedal position.

 

 

Figure 2. Brake pedal signal, velocity and acceleration of bra­king as a function of time during the closed-loop phase.

 

7.3 Results

Before testing the main hypothesis a preliminary assessment is made of the reliability of THWpref.

Reliability of preferred THW. THW was not significantly affected by the speed of the lead vehicle (F(3,15)=1.20, p>=0.352), where­as distance headway significantly increased with speed (F(3,15)=­20.20, p<0.00­01). This means that THW was constant over speed. The test’s reliability index (Cron­bach’s alpha) for the four measurements of THW over different speeds was 0.89, while the standardized alpha was 0.90. This was taken as eviden­ce that all THW’s are an expression of a subjects’ general preferred THW. THWpref was computed as the average THW over the four speed trials.

The correlations of THWpref with annual kilometrage and number of years licensed were not statistically significant (R=0.13 resp. -0.02). This means that preferred time-headway was not related to driving experience. Also, none of the braking-related variables correlated significantly with driving experience.

 

The relationship between preferred THW and braking skill. It is first tested whether the duration of the open-loop phase is determined by TTC after the RT interval and whether the duration of the closed-loop phase is affected by the number of movement corrections as predicted by the braking model presented in the introduction. The regression coefficients (Beta weights) are presented in table 1.

 

Table 1. Effects of regression analyses of TTCacc and movement corrections on

the duration of the open-loop (BIMT) and closed-loop (BCMT) phases.

 

Dependent   Independent       R(=Beta)  F      

 

BIMT           TTCacc                0.81          27.11 **

BCMT          nr corr               0.83          30.09 **

 

nr corr = number of movement corrections

** = p < 0.01;

 

From inspection of table 1 it appears that the duration of the open-loop phase was strongly determined by TTC at the moment the accelerator was released. The duration of the closed-loop phase was strongly determined by the number of movement corrections. This confirms the model of braking discussed in the introduction.

It was then tested whether operational braking performance affected choice of time-headway. For this the regressions of RT, BIMT and BCMT on THWpref were analyzed, controlling for the effects of TTCacc and number of movement corrections. In this way the direct effects the independent variables on THWpref could be established. Table 2 lists the effects of RT, BIMT and BCMT on THWpref. This table should be read as follows. The first column lists the dependent variable. The second column lists the indepen­dent variables in order of inclusion in the regressi­on equati­on. R represents multiple correlation after addition of the independent variable, and F represents the accompany­ing F statistic for the whole regression equation. Beta and T represent the Beta weight and t value when all dependent variables are included in the equation.

 

Table 2. Effects of multiple regression analyses of brake-related times on THWpref.

 

Dep              Indep         R                F               Beta         T           

 

THWpref       RT             0.31         1.50          -0.31                  -1.23N.S.

 

THWpref       BIMT        0.53         5.37          1.12                  3.25**

TTCacc       0.68         5.64          -0.74                  -2.13*

 

THWpref       BCMT       0.49         4.39          0.49                  2.09*

nr corr      additional contribution too small

for inclusion

THWpref       MT            0.67          11.24         0.67                  3.35**

 

* = p < 0.05; ** = p < 0.01; N.S. = not significant

 

Figure 3. Path diagram with partial regression coefficients.

*=p< 0.05, **=p< 0.01, ns=not significant.

 

There was no significant effect of RT on THWpref. The effect of BIMT on THWpref, with TTCacc controlled for, was statistically significant. This means that subjects with a faster open-loop motor reaction preferred a smaller THW. This was not simply caused by a smaller TTCacc for subjects with a smaller THWpref, since there also was a significant direct effect of TTCacc on THWpref indicating that drivers with a larger TTCacc tended to have a smaller preferred time-headway. The effect of BCMT on THWpref also was statistically significant. There was no significant direct effect of number of movement corrections on preferred time-headway. This means that the faster closed-loop motor response of drivers with a smaller preferred time-headway was caused by a smaller number of movement corrections. The effects of total movement time (MT) on THWpref also was statistically significant. The path diagram of dependent variables on preferred time-headway is presented in figure 3.

 

7.4 Discussion and conclusions

The hypothesis that preferred time-headway is consistent within the driver and constant over different speeds during steady-state car-following was confirmed for the range of speeds (40, 50, 60, 70 km/h) examined in the present experi­ment. This replicates the results of Van Winsum and Heino (1996).

Reaction time, i.e. the difference between the moment the lead vehicle started braking and the moment the accelerator was released, was not related to preferred time-headway. This confirms the results of Van Winsum and Heino that short follo­wers do not differ from long followers in perceptual mechanisms related to time-to-collision (TTC) detecti­on. The open-loop phase of the motor response was very sensitive to TTC, and especially to TTC at the moment the foot was released from the accelerator pedal. This suggests that as soon as the driver detects the deceleration of the lead vehicle, the speed parameter of the generalized motor program is set as a function of TTC. Drivers who moved their foot faster to the brake pedal had a smaller preferred time-headway. The direct effect of TTC at the moment the accelerator was released on preferred time-headway indicates that the effect of the duration of the open-loop phase on preferred time-headway was not caused by a smaller TTC for short followers. This suggests that drivers with a smaller preferred time-headway program the movement speed of the foot to a higher level than drivers with a longer preferred time-headway. This suggests differences in the transfor­mation of perceptual information into the adjustment of the speed parame­ter.

The duration of the closed-loop phase of the motor response was strongly related to the number of movement corrections. This confirms the expectations, discussed in the introduction, of separate influences on the duration of the open-loop and the closed-loop phases. Subjects who moved their foot faster to the maximum during the closed-loop phase and who exhibited fewer movement corrections had a smaller preferred time-headway. This suggests a higher skill level in these subjects. Subjects with a larger preferred time-headway appear to be more uncertain about the requi­red braking response.

An important result in the present experi­ment was the strong effect of total movement time on preferred time-headway. This strengthens the conclusion that short and long followers differ in both the open- and closed-loop phases of movement. Short follo­wers may be more sensitive to the task requirements in emergency braking situations. Short and long followers differ in the efficiency of the control of braking. This was also found in Van Winsum and Heino, but there results could have been affected by differences in absolute levels of TTC between drivers with different preferred time-headway.

Together, the results suggest that individual differences in choice of time-headway are related to individual difference in braking performance. This supports the hypothesis that drivers adapt their tactical level behaviour to their opera­tional skill level. However, the mechanism could also be the other way around: Short followers may have had more opportunities to acquire emergency braking skills, simply because they had to brake hard more often. It can be argued that this would be particularly the case in relatively inexperienced drivers. A very experien­ced driver, even when being a long follower, will probably have experienced a substantial number of emergency stops anyway. Also experience in other situations may count, e.g. for stationary objects such as traffic lights. Braking for stationary objects also requires a tuning of the braking response to perceptual information. In the present study, none of the braking related variables were affected by driving experience. This does not support the idea that short followers have learned to brake more efficient­ly because they have been exposed to critical encoun­ters more often. However, this is not enough evidence to rule out the alternative hypothesis and this issue will have to return in future research.


EXPERIMENT 3: Choice of time-headway in car-following and the role of time-to-collision information in braking

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EXPERIMENT 3: Choice of time-headway in car-following and the role of time-to-collision information in braking

This is chapter 6 from the thesis “From adaptive control to adaptive traffic behaviour” about traffic psychology and behavioural adaptation of drivers, by Wim van Winsum. The thesis is from 1996. It describes a number of behavioural experiments into car driving that were performed in a research driving simulator.

Other chapters of this thesis can be found here:

 

Time-headway (THW) during car-follo­wing and braking response were stu­died in a driving simulator from the per­specti­ve that behaviour on the tactical level (e.g. choice of THW) may be linked to ope­rational compe­tence of vehicle con­trol (e.g. bra­king) via a process of adapta­tion. Time-headway was con­sis­tent within dri­vers and con­stant over a range of speeds. Since time-headway repre­sents the time avai­lable to the driver to reach the same level of dece­lerati­on as the lead vehi­cle in case it brakes, it was studied whether choice of time-headway was related to skills underly­ing bra­king perfor­mance. The initiation and control of bra­king were both affec­ted by time-to-collision (TTC) at the moment the lead vehi­cle started to brake. This stro­ngly suppor­ted the idea that time-to-collisi­on informa­tion is used for jud­ging the moment to start bra­king and in the control of bra­king. No evidence was found that short followers differ from long follo­wers in the abili­ty to accura­tely per­ceive TTC. There was howe­ver eviden­ce that short follo­wers are better able to program the intensity of braking to required levels. Also, short followers tuned the control of braking better to the development of critica­lity in time during the braking process. It was conclu­ded that short follo­wers may dif­fer from long followers in programming and execu­tion of the braking respon­se.

 

6.1 Introduction

Close car-following has been associated with traf­fic accident invol­vement. Rear-end collisions accounted for about 24% of all acci­dents involving two or more vehi­cles in the U.S.A in 1990 (McGehee et al., 1992). These accidents are usually attribu­ted to maintaining insuffi­ciently long head­ways and/or to inatten­tive driving resulting in respon­ding too late to a decelerati­on of a vehicle in front. In the literature, headway is expres­sed either as distance headway (DHW) or as time head­way (THW) (Fuller, 1981). DHW is the bumper to bumper dis­tan­ce between the lead vehicle and the following vehi­cle­. THW is the time interval between two vehicles in car-follo­wing, calcula­ted as DHW divided by the speed (in m/s) of the follo­wing vehicle. When the follo­wing and the lead vehi­cle drive at the same speed (steady-state following), THW repre­sents the time available to the driver of the follo­wing vehi­cle to reach the same level of deceleration as the lead vehi­cle in case it brakes. This avai­la­ble time is independent of speed. A faster braking response is then requi­red with a smaller THW. Also, the con­trol of braking is more critical in that case. In this article, the THW during steady-state car-follo­wing will be referred to as THWpref (preferred time head­way).

Evans and Wasie­lewski (1982) found that drivers with a larger THWpref had a history of fewer traffic violati­ons and traffic acci­dents. However, the same authors also argued that acci­dent invol­vement did not have a reliable relation with THWpref by itself (Evans and Wasielew­ski, 1983). Especially younger drivers employed smaller THW­’s, as did drivers of newer cars and of vehicles with medium mass.

Several factors have been identified that influence choice of THW. Choice of THW has been associated with personality factors by some authors. Sensation seeking as a personality trait is assumed to be rela­ted to risky behaviour (Zuc­ker­man, 1979). For example, Zuckerman and Neeb (1980) found a positive corre­lation between the sensation seeking score and reported dri­ving speed, whereas Heino et al. (1992), using a realistic car-fol­lo­wing task, reported a smal­ler THWpref for sensation see­kers than for sensation avoi­ders. Ota (1994) studied car-following behaviour in relation to personality trai­ts. He suggested social maladjustment as an important factor in choice of THW, alt­hough correlations between THW and persona­lity test scores were not significant.

O­ther authors have stressed the importance of task-related factors with regard to THWpref. Fuller (1981) stu­died THW of truck drivers in convoy situations. During the late shift, cove­ring a large period of driving in the dark, THWpref was signi­ficantly larger than during daytime dri­ving. This was explai­ned as an effect of visual conditions. Brookhuis et al. (1991) reported an increase in THW when using a car telep­hone while dri­ving, which can be regarded as an additi­onal task compe­ting for attention. This sug­gests the driver is aware of effects of task demands on the ability to detect a dece­lera­tion of a lead vehicle and adapts THW according­ly.

Choice of THW also has been associated with temporary state-related factors. Fuller (1984) reported a time-on-task effect on THW for older truck drivers in the late shift. After seven hours of driving, THW increased quite strongly, accompa­nied by verbal reports of performance decre­ments, drowsiness and exhaustion. In an expe­riment reported by Smiley et al. (1981) in an inter­active dri­ving simulator, mariju­ana resulted in increased headways during car-follo­wing. Smiley et al. (1986) studied the effect of marijua­na on sever­al car-driving tasks on the road. Again marijuana significant­ly increased headway in a car-following task. In another simula­tor study, Smiley et al. (1985) found that marijuana increased headway while alcohol decrea­sed headway. These results strongly sug­gest effects of temporary states such as fatigue or states induced by marijua­na and alcohol on THWpref; fatig­ue and marijuana increase THWpref which may be a reflecti­on of an adaptation of THW to adverse effects on the brake reaction, whereas alcohol decreases THWpref, possi­bly because drivers overestimate their braking competen­ce under alcohol.

The effects of task-related factors and trans­ient states refer to intra-individual diffe­rences. The re­sults stron­gly suggest a process of adaptation of THW to changes in operati­onal level competen­ce which is influ­enced by task-related and state-related fac­tors. From the same perspec­tive, inter-indi­vi­dual differences in following behaviour, may be related to inter-individual diffe­rences in opera­tional level competence, such that THWpref is adapted to limitati­ons in braking-related competence. These limitations in braking compe­tence may then be determined by specific skills required for opti­mal braking performance. For this to be the case, THWpref must be consis­tent within the individual driver, while it differs between drivers as a function of operational skill. Since THWpref represents the ultimate reaction time in case of a deceleration by the lead ­ve­hicle, THWpref must be invariant over spe­ed. However, in spite of years of research into car-following it is still not clear whe­ther this time headway constancy holds over speed and whether it is consis­tent within drivers.

Fuller (1986) reana­lyzed the results of previous car-follo­wing experiments and found nega­tive correla­tions between speed and THW. Following distan­ce increased with speed but not enough to maintain THW at a constant le­vel. However, the conditions resulting in different speeds varied widely. High speeds were associated with rural open-road conditions with low traffic density and the absence of juncti­ons, pedes­trians and other hazards. Low speeds, on the other hand were associa­ted with oppo­site condi­tions. Conditions that resulted in lower spee­ds, and an accompanying larger THWpref, were characte­rized by multiple tasks compe­ting for atten­tion, possibly resulting in performance decrements in braking. Ota (1994) studied THW while drivers were required to drive with a speed of 50, 60 or 80 km/h and follow under dif­ferent instructions such as ‘follow at a comfortable dis­tance’ and ‘follow at a minimum safe distance’. No effects of speed on THW were found while instruction signi­ficantly affec­ted choice of THW. This sug­gests that THWpref is constant over different speeds.

In the present study, an important hypothesis is that THWpref is constant over speed and consistent within the driver. In order to test consistency of THWpref and con­stancy over speed, it is required that, besides speed, all other factors that might affect braking performance are con­stant.

Accor­ding to Lee (1976) dri­vers are able to control braking based on time-to-collision (TTC) information from the optic flow field (visual angle divided by the angular veloci­ty). This would enable the driver to judge the mo­ment to start braking and to control the braking pro­cess. The initia­tion of braking includes the timing of releasing the accelera­tor pedal after a deceleration of the lead vehicle has been detected as well as the interval between release of the acce­lerator pedal and the moment the foot touches the brake pedal. The control of braking includes braking intensity and the interval between the moment the brake is touched and the moment the maximum brake pressure is reached. Brake reaction time (BRT) is usual­ly measured as the interval between the onset of the stimulus, such as the brake ­lights of the lead ve­hi­cle, and the moment the brake is tou­ched. Therefore, BRT is an important measure for the initiati­on of braking. BRT to anticipated events is faster than to unexpec­ted events (Johan­sson and Rumar, 1971) and the DHW at the moment the lead vehicle brakes has a strong effect on BRT (Brook­huis and De Waard, 1994; McKnight and Shi­nar, 1992;, Sivak et al., 1981). An important skill that has been associa­ted with the initi­ation of bra­king rela­tes to the perception of time-to-collisi­on (TTC). TTC is defined as the time requi­red for two vehicles to colli­de if they continue at their present speed and on the same path (see Van der Horst, 1990). TTC is computed as DHW/Vr, where Vr is the relative velocity or speed diffe­rence which must be larger than zero. While the ability to accurately perceive TTC is often mentio­ned as an important factor for judging the moment to start bra­king, studies that related TTC to actual braking are scar­ce. However, Van der Horst (1990) reported evidence that both the decision to start bra­king and the control of braking are based on TTC information available from the optic flow field. If TTC is an important factor in the initiation of braking, a relati­on is expected be­tween the TTC at the moment the lead vehicles starts to brake (TTCt0) and BRT. Since TTCt0 is an index for criti­cality, it is expected that BRT is faster if criticality is higher, i.e. when TTCt0 is smaller. A con­sistent finding in the literature is an underes­timation of TTC, espe­cially at higher TTC’s. Schiff and Detwi­ler (1979) found substantial individual differences in the ability to give accurate judgme­nts of TTC and an avera­ge unde­restima­tion of TTC of 39%. McLeod and Ross (1983) found that men gave higher and more accurate judgments than women. They reported an unde­restimation of TTC of 42%. Cavallo et al. (1986) found that experienced drivers produced better estima­tes of TTC than inexperienced drivers. They reported a general unde­restimation of 35%. Hoffmann and Morti­mer (1994) found that both estima­ted TTC and standard deviati­on of esti­mated TTC were linear­ly related to actual TTC. They reported an unde­restima­tion of TTC of 20% on average, while other studies typi­cal­ly report an unde­restima­tion of around 40%. This better perfor­mance in TTC estimati­on was attributed by Hoffmann and Mortimer to the fact that in their expe­ri­ment both vehicles were in moti­on, while other experi­ments typically measured estimated TTC to a static ob­ject. The studies on TTC estimati­on give substantive eviden­ce for underesti­mation of TTC and for individual differences in the ability to accurately esti­mate TTC. Differences in ability to accurately estima­te TTC are assumed to be expressed in the initiation of braking. BRT of drivers with better TTC estimation skills is assumed to covary more with TTCt0 than BRT of less skil­led drivers. This is because better skilled dri­vers are more sensitive to varia­tions in TTCt0. A hypo­thesis in the present study is that THWpref is related to sensi­tivi­ty of the initiation of braking to TTC informa­tion. Drivers who are more sensi­tive to TTC are then better able to judge the moment to start bra­king, while dri­vers who are less sensitive to TTC informa­tion run a higher risk of starting to brake too late. This might result in a larger safety margin and thus a higher THWpref for these dri­vers.

Drivers may not only differ in the initiation of the bra­king response but also in the control of braking. Van der Horst (1990) studied the control of bra­king by the maximum deceleration reached by the driver (DEC­max), the mini­mum TTC reached during braking (TTCmi­n), and the time differen­ce be­tween the moment of TTCmin (tTTCmin) and the moment of DECmax (tDECmax). TTCmin des­cribes how immi­nent a colli­sion has been during the braking process. According to Van der Horst, tDEC­max gives an indi­cation of the moment the driver knows a collision will be avoided. During the time before tTTCmin is reached, TTC is still decreasing resulting in increasing criticality. If tDECmax occurs some time before tTTCmin, critica­lity is still incre­asing at the moment the driver already relaxes the dece­lerati­on. If tDECmax occurs some time after tTTCmin is reached the driver keeps increasing the deceleration when it is no longer necessary. A close relation in time between tDECmax and tTTCmin then suggests a more efficient control of braking, where the control of braking is better tuned to the deve­lopment of criticality in time. In the present expe­riment it will be examined whe­ther THWpref is related to braking control as indicated by these measures. In addi­tion to this, the maximum percentage brake pressed (M­AXB­R), and the interval between touching the brake­pedal and the moment the brake pedal is pressed to the maximum value are measu­red. Fur­thermo­re, it will be exami­ned whether the intensi­ty of the braking reacti­on, measu­red by MAXBR, is more sensi­tive to TTC at the moment the lead vehicle starts to brake for short follo­wers compa­red to long follo­wers. A higher sensiti­vity of the in­ten­sity of braking to TTCt0 suggests that the braking response is more adapted to critica­lity at the moment the driver de­tects the braking of the lead vehicle.

In summary, the following hypotheses will be tested in the present experiment.

1) Preferred time-headway is constant over different speeds.

2) Preferred time-headway is consistent within indivi­dual drivers, but differs between drivers.

3) The initiation of braking, measured by BRT, is more stron­gly related to TTC at the moment the lead vehicle starts to brake for short followers compared to long followers. This is assumed to be related to diffe­rences in the ability to percei­ve TTC information.

4) Preferred time-headway is related to the intensity of braking and quality of braking control. The intensity of braking is measured by MAXBR while the quality of bra­king control is measured by the sensitivity of the bra­king intensi­ty to criticality (as measured by TTC) and by the time diffe­rence between tTTCmin and tDECmax.

 

6.2 Method

 

Apparatus. The driving simulator of the Traffic Research Centre (TRC) was used for the present experi­ment. This fixed-based simu­lator consists of two inte­grated subsys­tems. The first subsystem is a conventional simulator composed of a car (a BMW 518) with a steering wheel, clutch, gear, accelera­tor, brake and indica­tors connected to a Silicon Graphics Skywriter 340VGXT compu­ter. A car model converts driver control actions into a displacement is space. On a 2 x 2.5 meter projections­creen, placed in front of the car mockup, an image of the outside world with a horizon­tal angle of 50 degrees is projec­ted by a graphical videopro­jector, controlled by the graphics software. Images are presented with a rate of 15 to 20 frames per second, resulting in a sug­gestion of smooth move­ment. The visual objects are buil­dings, roads, traffic signs, traffic lights and other vehicles. The sound of the engine, wind and tires is presented by means of a digital soundsampler recei­ving input from the simulator computer.

The second subsystem consists of a dynamic traffic simula­tion with interacting artificially intelligent cars. For experimental purpo­ses different traffic situa­tions can be simulated. The simula­tor is described in more detail elsewhere (Van Wolffe­laar & Van Winsum, 1992 and Van Winsum & Van Wolf­felaar, 1993). De Waard et al. (1994) reported a significant correlation (r=0.67) be­tween THW measured in this simulator and ratings of preferred headway on a photo-preference test. In this test subjects rated preferred headway from a series of photographs with a view of a lead vehicle through the wind­screen on a motorway. This supports the validity of this simulator for measuring car-following behaviour. Also, TTC has been reported to be directly available from the optic flow field without requiring speed and distance estimation. Since visual angle and angular velocity are identi­cal in the simula­tor and in real world driving, this simulator was assumed to be a valid instrument for estimation of TTC.

 


Procedure. The circuit was made of two-lane roads with a lane-width of 3 meters. All roads had delineation with broken center lines and closed edge lines. Side­roads connected with an angle of 45 degrees to the main road, allowing other vehi­cle to merge in front of the simulator car and leave the main road. The length of the circuit was 7600 meter.

Before the experiment started, subjects completed a ques­tionnaire on driving experience and age. After this, subjects were instructed to drive as if they had to reach their desti­nation as soon as possible, without overta­king other vehicles, to drive safely and to res­pect the speed limit of 80 km/h. The experimen­t started after a ten minutes practice drive.

The experiment consisted of two parts, separated by a 15 minutes break. During the first part choice of head­way was measu­red as a func­tion of speed. Lead vehicles drove with a con­stant veloci­ty of either 40, 50, 60 or 70 km/h. These diffe­rent speeds are referred to as ‘sp­eed conditions’. Sub­jects were required to drive around the cir­cuit twice. The first drive around the circuit was used to familiarize sub­jects with other traf­fic. Vehi­cles merged in front of the simulator car, control­ling their speed such that when the simula­tor car was 50 meter from the inter­secti­on, the lead vehicle was 100 meters in front of the simulator car.

During the second part of the experiment braking behaviour was measured. Vehicles merged in front of the simulator car in the same way as des­cri­bed above. Lead vehicles drove with a constant speed of either 60 or 50 km/h, resulting in two ‘braking conditions’. As soon as the lead vehicle was 50 meter in front of the simulator car (t0), it dece­lera­ted with -2 m/s², with its brake­lights on, to a speed 20 km/h below the initial cruise speed.  As soon as the simulator car reached this speed (40 of 30 km/h) the lead vehicle pulled up again. The two braking conditions (50 vs 60 km/h) were used to study wit­hin-subjects differences in braking as a func­tion of TTCt0.

 

Data registration and analysis. Speed of the simu­lator car (V) and lead vehicle (Vlead) in m/s, distance head­way (DHW) in meters, acceleration in m/s² and brake pedal signal expres­sed as per­cen­tage pressed were sam­pled with a frequency of 10 Hz. THW was calcula­ted as DHW/V. TTC was calcu­lated as DHW/Vr, with Vr being the relative speed (V-Vlead). Average THW was computed from the moment the simu­lator car and the lead vehi­cle drove with the same speed until the lead ve­hicle left the main road. THWpref was computed as the average THW over the four speed conditi­ons.

In the second part of the experiment t0 represents the moment a DHW of 50 me­ters was reached. On t0 the lead vehicle started to brake. TTCt0 repre­sents the TTC on t0. BRT was compu­ted as tbr – t0, where tbr refers to the moment the brake ­pe­dal was pres­sed more than 5%. TTCbr repre­sents TTC on tbr. On tmaxbr the maximum brake pressu­re, MAXBR, was reached. TTCmaxbr represents TTC on tmaxbr. Brake control move­ment time, (BCMT) was calcu­la­ted as tmaxbr-tbr. The moment the maxi­mum dece­lerati­on, DECmax, was reached is indica­ted as tDECmax. The moment the minimum TTC, TTCmin, was reached is indicated as tTTCmin. The abso­lute time difference between the moment of maximum dece­le­ration and the moment of minimum TTC was computed as ABS(tDEC­max-tTTCmin) and is referred to as tdif. Figure 1 shows a time history of bra­king, together with a number of depen­dent variables.

Analy­sis of covariance was applied to test differen­ces in sensiti­vity to TTC as a function of THWpref. For this, diffe­rences between the two braking conditions were studied to test whe­ther braking-related variables covaried with TTC. The diffe­ren­ce in TTCt0 between bra­king condition 60 (lead vehicle braked from 60 to 40 km/h) and braking condition 50 (lead vehicle braked from 50 to 30 km/h) is expressed as dTTCt0. The diffe­rences in MAXBR and BRT between these two conditions are ex­pressed as dMAXBR and dBRT. The regression coefficient of dBRT and dMAXBR on dTTCt0 is an indicator for the sensitivity of BRT and MAXBR to TTCt0. Higher sensitivity is expres­sed as a steeper slope (larger coefficient of regressi­on). Analysis of covariance was used to test differences in slope as a function of THWpref.

 

 

Figure 1. Time-history of braking and dependent varia­bles.

 

Effects of THWpref and braking conditions on dependent variables were tested with repeated measu­rements multi­variate analysis of variance (MANO­VA) with braking con­dition as a within-subjects factor.

Subjects. Fifty-four male subjects participated in the experi­ment. The average age was 29 years (sd. 8.12, range 19-48) with 65% of the subjects being younger than 30 years of ages. They had held a driving license for 9 years on average (range 1-29).

 

6.3 Results

 

Stability of THWpref. THW was not significantly affected by speed of the lead vehi­cle (F(135,3)= 1.27, p>=0.2­5), see figure 2. This sup­ported the hypothesis that THW is constant over speed.

 

Figure 2. THW as a function of speed.

A high correlation between THW’s in the four speed con­di­tions suggests consistent following behaviour. THW’s in all speed conditi­ons were signifi­cantly correlated (p < 0.001), as shown in table 1. Additio­nal evidence for consis­tency in following behaviour was obtained by con­sidering each THW as an “item” in a (4-item) “following behaviour” test (Hendrickx, 1991). The test’s reliability index (Cron­bach’s alpha) was found to be as high as 0.91. This was taken as evidence that all THW’s were an expression of a subjects’ general THWpref.

 

 

Table 1. Correlation matrix for THW’s in the four speed condi­tions

 

THW50       THW60    THW70

 

THW50     0.69**

THW60     0.76**        0.63**

THW70     0.67**        0.69**       0.60**

 

(** indicates p < 0.001).

THWxx : THW = time headway, xx = speed (km/h) of lead vehicle

 

These results supported the hypothesis that THW is con­sistent within drivers, but differs between drivers. For further analysis, the average THW over the four speed conditions was computed as THWpref. Based on the frequency distribution of THWpref, three groups of equal size were created. These groups are referred to as ‘THWpref groups’. These groups served as a between-subjects factor in subsequent analyses. Four subjects were not included because they failed to reach a stable THW in the 70 km/h condition. Table 2 shows number of subjects, average THW and stand­ard deviation of THW for the THWpref groups.

 

Table 2. Size, mean THW and sd of THW for THWpref groups

 

THWpref group      N               mean THW(s)   sd of THW

 

short                      17              0.67                   0.19

medium                 16              1.08                   0.09

long                       17              1.52                   0.27

 

Braking responses. Two additional sub­jects failed to display a clear brake res­pon­se in one of the two braking conditions. Therefo­re, the total number of sub­jects in the analyses was 48.

Figure 3 shows the time history of TTC for the three THWpref groups in both braking conditions. Four data­points are dis­played. The first point represents TTCt0, the second TTCbr, the third TTCmin and the fourth TTCmaxbr. The time interval between TTCt0 and TTCbr repre­sents BRT, while the time inter­val between TTCbr and TTCmaxbr repre­sents brake control movement time (BCMT).

 

The initiation of braking. Table 3 gives the MANOVA effects of THWpref group and braking condition on variables related to the initiation of braking.

TTCt0 and TTCbr were significantly smaller, while the relative speed (Vr) at t0 and tbr was signi­ficantly larger for subjects with a smaller THWpref. At t0 long followers already had lowered their speed to a greater extent than short followers. BRT was not significantly diffe­rent for short followers compared to long followers.

Table 3. Effects of THWpref group and braking condition on variables related to the

initiation of braking (F-sta­tis­tics)

 

Effect

Dependent               THWpref group           Braking con.               interac­tion

 

TTCt0                          8.57**                      0.16                           0.52

TTCbr                        18.05**                      0.59                           1.14

Vrt0                           15.83**                      6.79**                       1.90

Vrbr                           24.72**                      8.07**                       1.26

BRT                            0.62                          20.57**                      1.01

 

THWpref group effect : df = 45,2;

Braking condition  effect : df = 45,1;

Interaction effect: df= 45,2

** = p < 0.01

 

 

Figure 3. Time history of TTC as a function of THWpref group­s for braking

condition 50 (left) and braking con­dition 60 (right).

 

Braking condition had a significant effect on BRT. BRT was faster in the conditi­on where the lead vehicle decele­rated from 50 to 30 km/h. This was accompa­nied by a larger relative velocity on t0 and tbr in this condi­ti­on. None of the interac­tions were signi­ficant.

Table 4 presents the correlations of BRT with TTCt0 and TTCbr.

 

Table 4. Correlation of BRT with TTC in braking conditi­on 50 and 60

 

Condition 50     Condition 60

 

TTCt0            0.66**               0.61**

TTCbr            0.01                   -0.21

 

** = p < 0.01

 

The correlations of BRT with TTCt0 were highly signifi­cant. The correlations of BRT with TTCbr were not signi­ficant. Thus, BRT decreased as TTCt0 decreased for both braking condi­tions. This was taken as evidence that the initiation of braking, indica­ted by BRT, was sensitive to TTC information as an index for criticality. The significant effect of THWpref group on TTCt0 and the absen­ce of a significant effect of THWpref on BRT suggests the TTC criterion for initiating the braking response is lower for short followers.

One of the hypotheses was that the initiation of braking was more sensitive to TTC for short followers compared to long follo­wers. Sensiti­vity was expressed as the extent to which BRT covaries with TTCt0. Analysis of covariance revealed that dBRT was depen­dent on dTTCt0 (F(42,1) = 14.77, p<0.001). This means that, within Ss, a smaller TTCt0 resulted in a faster BRT. Since dBRT was compu­ted as the difference between BRT’s in the two braking conditions, the effect of braking condition on BRT is partly explained by within-subjects differences in TTCt0. Thus, the initiation of the braking response was very sensi­ti­ve to between-subjects as well as wit­hin-subjects variations of TTC at t0. The slope of the regres­sion of dBRT on dTTCt0 repre­sents the sensi­tivity of BRT for TTCt0. The magnitu­de of the slope as well as the correlation coeffi­cients are shown in table 5 for the three THWpref groups. Although the correla­tion and regres­sion coeffi­cients suggest a stronger relation be­tween dBRT and dTTCt0 for short followers, this was not con­firmed by analysis of covariance because the inter­action with THWpref groups was not signifi­cant (F(42,2­)=1.­62, p=0.210). Thus, the hypothesis that short follo­wers are more sensitive to TTC information in the initi­ation of the braking response was not confirmed.

 

 

Table 5. Correlation and sensitivity of BRT to TTCt0

 

THWpref group               R               coefficient of regression

 

short                               0.72**      0.19

medium                          0.63**      0.12

long                                0.51*        0.06

 

** = p < 0.01; * = p < 0.05

 

The control of braking. Table 6 shows the effects of THWpref group and braking condi­tion on variables rela­ted to the control of braking.

 

Table 6. Effects of THWpref group and braking condition on variables related to

the control of braking (F-statis­tics)

 

Effect

Dependent               THWpref group           Braking con.               interac­tion

 

TTCmin                      18.78**                      0.30                            1.23

TTCmaxbr                   16.13**                      0.01                            0.51

BCMT                        0.86                          2.01                            2.19

MAXBR                    6.24**                      7.12**                        0.33

DECmax                     4.54*                         2.49                            0.02

tdif                              3.88*                         0.75                            0.09

 

THWpref group effect      : df = 45,2

Braking con. effect          : df = 45,1

interaction effect             : df = 45,2

** = p < 0.01; * = p < 0.05

 

The minimum TTC during braking (TTCmin) was signi­ficantly smaller for short followers, as was the TTC at the mo­ment the brake was pressed to the maximum (TTCmaxbr). Short followers generated a more intense brake reaction than long followers : MAXBR was signifi­cantly larger for short followers. Also DECmax was larger for short follo­wers. This sup­ported the hypothesis that short followers differ from long followers in the inten­sity of the bra­king response. BCMT, the time wit­hin which the brake maximum was reached, was not affec­ted by THWpref groups.

The absolute time difference between tDECmax and tTTCmin, tdif, was seen as an indicator for the effi­ciency of bra­king con­trol. There was a significant effect of THWpref group on this measure. Tdif was smaller for short follo­wers compared to long follo­wers, see figure 4. This supported the hypothesis that short follo­wers differ from long followers in the quality of braking control.

 

In order to test the sensitivity of the intensity of braking to criticality, an analysis of covariance was performed on dMAXBR (diffe­rences in MAXBR between the two braking conditi­ons) as a function of dTTCt0 (diffe­rences in TTCt0 between the two braking conditi­ons), with THWpref group as a between-sub­jects factor. A smaller TTCt0 generally resulted in a larger MAXBR (F(42,1)=22.37, p=0.000). This means that the intensity of the braking reacti­on strongly depended on TTCt0. The inter­acti­on with THWpref group was significant as well (F(42,2) = 4.63, p=0.015). In table 7 it can be seen that MAXBR decreases more as a functi­on of TTCt0 for short followers compared to long followers. The differen­ces in slope indicate that the intensity of the bra­king res­ponse is more sen­si­tive to TTCt0 for drivers with a smaller THWpref, alt­hough the correlations between dMAXBR and dTTCt0 are comparable for the three groups.

This again supported the hypothesis that short follo­wers differ from long followers in the quality of braking control.

 

Figure 4. Difference between tTTCmin and tDECmax as a functi­on of THWpref group­s

and braking condi­tion.

 

 

Table 7. Correlation and sensitivity of MAXBR to TTCt0

 

THWpref group      R                       coefficient of regression

 

short                      -0.69**             -6.52

medium                 -0.57*                -3.13

long                       -0.58*                -1.13

 

** = p < 0.01; * = p < 0.05

 

 

6.4 Discussion and conclusions

 

The hypothesis that THWpref is consistent within the driver and the hypothesis of constancy of THWpref over speed during steady-state car-following were confir­med for the range of speeds examined in the present expe­ri­ment. The brake reaction of drivers was analyzed in order to investigate whe­ther diffe­ren­ces in THWpref during steady-state car-following are related to differences in braking performance and under­lying skills. Since THW during steady-state following represents the time avai­la­ble to the driver to give an appropriate braking res­pon­se in case the lead vehicle decelerates, THW may be the result of an adaptation of the driver to indi­vidual differences in braking competence. Braking perfor­mance was assumed to be related to the ability to percei­ve time-to-collision (TTC) and the ability to generate an efficient braking response, depen­ding on the criticality of the situation. The initiation of braking, as measured by brake-reaction time (BRT) was stron­gly related to TTC at the moment the lead vehicle started to brake (TTCt­0 ) and thus to criticality. This strong relation was apparent between subjects as well as within subjects. This conforms with the sugge­stion in the literature that TTC infor­mation is used by the driver to judge the moment to start braking. Howe­ver, dri­vers with a smaller THWpref during steady-state follo­wing start to brake at a lower TTC, i.e. when the criticality is higher. This suggests a different TTC criteri­on for the initiation of braking, depending on preferred time-head­way. Although the initi­a­tion of bra­king was very sensi­tive to TTC information, there were no diffe­rences between short followers and long followers in sensitivi­ty of BRT to TTCt0. Thus, the hypothesis that diffe­rences in THWpref during steady-state following are related to the ability to accurate­ly per­ceive TTC was not confirmed since a differential ability related to TTC perception was assumed to be expressed in BRT.

The minimum TTC during braking was smaller for short follo­wers. This indicates that a collision was more imminent for short follo­wers than for long followers. There were howe­ver differences in the control of bra­king. Firstly, short follo­wers pressed the brake pedal to a higher maximum, resul­ting in a larger deceleration. Secondly, for short followers the inten­sity of the bra­king response was more strongly depen­dent on the criti­cality at the moment the lead vehi­cle started to brake. This suggests that the intensity of braking is at least partly programmed before response execution and confirms the suggestion in the literature that TTC information is used in the control of braking. Short followers are then better able to ­program this response to the appropriate level, depending on criticality. However, at the moment the lead vehi­cle starts to decele­rate, the driver does not know how strong it will decelera­te and for how long. Therefore, visual feedback during the braking maneuver is important for conti­nuously adapting the braking res­ponse to the required level. The programmed braking intensity may then have to be adjusted to another level depending on the development of criticality in time. The moment of maximum deceleration (tDECmax) was assumed to indicate when the driver knows a collisi­on will be avoi­ded. A closer corres­pondence in time with the moment of minimal TTC (tTTCmin) sug­gests a better ability to adjust the control of braking to requirements. In this res­pect, the third difference was found between short en long follo­wers. For short followers the absolute difference between tDECmax and tTTCmin was smaller, indi­cating a more efficient bra­king control where the timing and intensity of bra­king is better tuned to the development of criti­cality in time during the braking process.

These results suggest differences in skills related to the res­ponse programming and response execution of braking between short and long followers. On the other hand, the absolute levels of TTCt0 were different between THWpref groups. An alter­native explanation may then be that short followers had to generate more efficient braking responses that were better tuned to criticality because criticality was higher for them to begin with. In other words, they may have been forced to perform better. Also, since TTC during the braking process was lower for short followers, and, as discussed in the introduc­tion, the estimation of TTC is more accurate for smaller TTC’s, the differences between short and long followers in braking con­trol may have been caused by a more accurate esti­ma­tion of TTC by short followers. In both cases, however, the sensi­tivity of BRT to TTCt0 is expected to be higher too for short follo­wers. Since this was not the case, the evidence presented suggests diffe­rences in skills related to the pro­gramming of the intensity of braking and the con­trol of bra­king between short and long followers.


EXPERIMENT 2: Preferred time-headway in car-following and operational skills in expected braking reactions

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EXPERIMENT 2: Preferred time-headway in car-following and operational skills in expected braking reactions

This is chapter 5 of the thesis from 1996 by van Winsum, “From Adaptive Control to Adaptive Driver Behaviour”. It concerns a number of behavioural studies into driver adaptation that have been performed in a research driving simulator.

Other chapters of this thesis can be found here:

 

In a simulator experiment the relation between preferred time-headway in steady-state car-following and operational competence in braking reactions was studied. The hypothe­sis that drivers with smaller preferred time-headways are able to react faster or generate a faster motor response per se was not confirmed. Also, no evidence was found for differences in perceptual processes related to the detection of braking by the lead vehicle be­tween short followers and drivers with a larger preferred time-headway. The results suggest that short followers generate a faster motor response when there is some uncertainty con­cerning the level and duration of decele­ration of the lead vehicle in case it brakes. The results suggest that short followers differ from long followers in the ability to transform visual feedback to a required motor response. However, the presence of brake lights is required for the relation between operational performance and choice of time-headway to hold, possibly because a change in feedback requirements, i.e. the absence of brake lights, is more detrimental for skilled performers.

 

5.1 Introduction

Choice of time-headway (THW) in car-following has been associated with task-related factors and with factors related to temporary state in a number of studies. The results of these studies may be explained in terms of an adaptati­on of choice of THW to perceived per­formance decrements in operati­onal skills related to braking. The impor­tance of task-related factors appears from the studies of Fuller (1981) and Brook­huis et al. (1991). Fuller (1981) stu­died THW of truck drivers. During the late shift, consis­ting mainly of driving in the dark, time-headway was signi­ficantly larger than during day time dri­ving. Fuller explai­ned this as an effect of visual conditi­ons. Brookhuis et al. (1991) reported an increase in THW when using a car telep­hone while dri­ving.  The effects on THW may be explained as a result of awareness of  the effects of task demands on the ability to detect a dece­lera­tion of a lead vehicle resulting in an adaptation of THW to compensate for this. A number of other studies have shown that choice of time-headway is sensitive to temporary states. Fuller (1984) reported a time-on-task effect on THW for older truck drivers in the late shift. After seven hours of driving, THW increa­sed quite strongly, accompa­nied by verbal reports of performance decre­ments, drowsi­ness and exhausti­on. In an expe­riment reported by Smiley et al. (1981) in an inter­active dri­ving simula­tor, mariju­ana resulted in increased headway during car-follo­wing. Smiley et al. (1986) again found that marijuana significant­ly increased headway in a car-follo­wing task. Smiley et al. (1985) reported that marijuana increa­sed headway while alcohol decrea­sed headway. These results strongly sug­gest effects of temporary states such as fatigue or states induced by marijua­na and alcohol on preferred THW; fatig­ue and marijuana increase preferred THW, which may be a reflecti­on of an adaptati­on of THW to perceived adverse effects on the braking response, whereas alcohol decreases preferred THW, possi­bly because drivers overestimate their braking compe­ten­ce under alcohol.

The effects of task-related factors and trans­ient states refer to intra-individual diffe­rences. The re­sults suggest a process of adaptati­on of THW to changes in operati­onal competen­ce which is influ­enced by task-related and state-related fac­tors. From the same perspec­tive, inter-indi­vi­dual differences in following behaviour, may be related to inter-individual diffe­rences in operational level competence, such that preferred THW is adapted to limitati­ons in braking-related competence. These limita­tions in braking competence may be determined by specific skills required for opti­mal braking performance. In that case drivers may adapt time-headway to their braking skills such that the time available to reach the same level of deceleration as the lead vehicle in case it brakes mat­ches the time needed by the driver to reach this level of deceleration. The former is equivalent to the momentary time-head­way. The latter may be related to braking related skills of the driver. Extrapolated to the more general case, behaviour on the tactical level is assumed to be adapted to opera­tional skills. The same reaso­ning was applied to speed choice in curves by Van Winsum and Godthelp (1996). They found a strong relation between choice of speed in curves and steering per­formance on straight roads, such that drivers adapt the speed in curves to their steering competence. An important research question then focuses on finding the relevant skills that discriminate drivers with different preferred time-headways.

In the normal case of braking for a decelerating lead vehicle, the driver adjusts the timing and intensity of the bra­king response to the criticality at the moment of detection of a deceleration of the lead vehicle and the deve­lopment of criticality in time. In this, TTC informati­on is assumed to plays an important role (e.g. Van der Horst, 1990; Cavallo et al., 1986; Cavallo and Laurent, 1988; Lee, 1976), although it is not clear how TTC information affects the braking response. However, when the driver is instructed to brake as fast as possible as soon as a deceleration of the lead vehicle is detected, the timing and intensity of braking are expected to depend on the limits of per­ceptual and motor skills instead of TTC information.

The dominant view in studies of braking has been that perceptual limitations, instead of response mecha­nisms, are responsible for rear-end collisions. In the literature braking skill is generally studied as the ability to brake as fast as possible instead of the ability to tune the timing and inten­sity of braking to the dynamic requirements of the situa­ti­on. This is somewhat surprising given the ecological desirabili­ty to brake with a velocity and intensity that matches the requi­re­ments of the situation. In the literature, brake reaction time (BRT), or alternatively, perception-response time is used as an index for braking per­formance. This is defined as the interval between the onset of the stimulus, usually the brake lights of the lead vehicle, and the moment the foot touches the brake. BRT differs from reaction time (RT) as it is nor­mally applied in experi­mental psychology. RT for a dece­lera­ting lead vehicle is measured as the interval between the moment the lead vehicle starts to decele­rate and the moment the foot is retracted from the accelerator pedal. Although BRT includes reaction time, it covers the time to move the foot from the accelerator to the brake pedal as well. A reduction of BRT has been proposed as a means to reduce the number of rear-end colli­sions. Experi­ments that were aimed at finding factors that decrea­se BRT have been carried out for years (see for example McKnight and Shinar, 1992). For this purpose, center high-mounted stop lamps (CHM­SL) have become standard equipment in passenger cars in the United States, although the evidence for actual reduc­tions in BRT by these lamps is limited (McKnight and Shinar, 1992, Sivak et al., 1981). There is however some evidence that CHMSL reduces the number of rear-end accidents (see for instance Rausch et al., 1982). Thus, the scientific answer to the assu­med perceptu­al limitations in braking has been to decrease the detection time by technical means. Other factors have been found that affect BRT as well. Johans­son and Rumar (1971) found that BRT to anticipated events is faster than for unex­pected events. Olson and Sivak (1986) reported an average BRT to expected stimuli of about 0.7 s. while it was about 1.1 s. to unexpec­ted stimu­li. The expectancy effect was also reported by Sivak (1987). The nature of the stimulus affects BRT as well. In car-following situations BRT is faster compa­red to other situati­ons such as the detection of a stati­onary police car (Sivak, 1987). Fur­thermore, distance headway has a substantial effect on BRT (see for instance Brookhuis and De Waard, 1994, McKni­ght and Shinar, 1992 and Sivak et al., 1981).

 

From an adaptation perspective, perceptual skills related to the detection of a deceleration of the lead vehicle may be a determining factor for choice of time-headway. In that case a relation is expected between preferred THW and reac­tion time. The reaction time interval consists of a series of information-processing stages. The additive factor method, introduced by Sternberg (1969), assumes that these processing stages are serial and that the duration of these stages are independent. It is a method for studying the locus of effect of differences in RT. Several task variables are known to affect RT via effects on specific information-processing sta­ges. According to the additive factor method, if two task variables interact in their effect on RT a common processing stage is involved. Additive effects of two task variables on RT are indicative of separate effects on different processing sta­ges. In this chapter, the additive factors method is used to determine whether differences in RT as a function of preferred THW are caused by differences in the input side or the output side of the information-processing chain. Figure 1 shows the successive information-processing stages that are assumed to determine RT.

Stimulus degradation is known to affect the stimulus enco­ding stage on the input or percep­tion side of information-processing (San­ders, 1990, Frowein, 1981). In braking for a decele­rating lead vehicle, the absence of brake lights (BL) may be regar­ded as a severe form of stimulus degradation. Alter­natively, differences in RT may have a locus of effect on the output or response preparation side of the information-processing chain. Time uncertainty, manipulated by means of presentation of a warning signal (WS) in advance of stimulus presentati­on is known to affect the output or motor side of the information-processing chain. San­ders (198­0a) and Frowein (1981) reported additive effects of time uncert­ainty and stimulus degrada­tion. This indica­tes that diffe­rent information-processing stages are affected by signal quality and time uncertainty. San­ders (1980b) repor­ted an interac­tion between time uncertainty and instruc­ted muscle tension on RT. This sugge­st that the factor WS affects  the motor-adjust­ment  stage.

Figure 1. Information-processing stages during the reaction time interval

as discussed by Frowein (1981)

 

Also, Spijkers (1989) reported an interac­tion between time uncertainty and response specificity sugge­sting an effect of time uncertainty, or WS, on motor adjustment. Motor adjustment represents the stage where the state of motor readiness is modulated by straining the muscles.

The additive factor method has not only been applied to the study of information-processing stages, it has also been used to study individual differences related to, for example, dementia (Jolles, 1985) and hyperactivity in children (Spijkers and Curfs, 1986). This is important since the present study uses the additive factor method to explore information-processing factors underlying individual differences in behaviour.

In summary, if short followers differ in RT from drivers who follow with a larger THW, the reasons for differences in RT may be located on the input and/or output side of the information-processing chain. It can then be tested whether short followers differ from drivers with a larger preferred THW in the stimulus encoding stage with the BL manipula­tion. If drivers with a larger preferred THW are less efficient or slower in stimulus encoding, stimulus degradation is predicted to result in a relative­ly larger effect on RT for these drivers. Thus, differences in stimulus encoding as a function of preferred THW expresses itself as an interaction between preferred THW and the BL manipulation on RT. This would mean that differences in RT as a function of preferred THW are caused by faster detection by short followers of a deceleration of the lead vehicle. Alternatively, an interaction between preferred THW and the WS manipulation on RT such that RT of short followers is less affected by the WS manipula­tion than the RT of drivers with larger preferred THW, would suggest that short followers reach the state of required motor readiness faster. In that case, differences in RT are related to response mechanisms instead of perceptual mechanisms.

 

Choice of time-headway may also be related to the speed at which the driver is able to move the foot. In that case choice of time-headway may be an adaptation to individual differences in motor speed­. However, the additive factor method has never been successfully applied to the motor phases of respon­se execution. This means that there is not sufficient reason to apply this method to the examination of motor execution during the braking response. Also, there are no theore­tical predictions for the effects of WS and BL on the duration of the motor phases that follow the RT interval when the subjects are required to brake as fast as possible.

In summary, the following questions are examined in the present expe­riment :

1) Is preferred time-headway related to differences in reaction speed to a deceleration of the lead vehicle, and if so, are the diffe­rences located on the perceptual or the response side of the information-processing chain.

  • Is preferred time-headway related to skills involved in motor executi­on.

 

The experiment was performed in an interactive simulator. This allows full control over the behaviour of the lead vehicle and accurate on-line measurement of time-related varia­bles.

 

5.2 Method

 

Apparatus. The experiment was performed in the driving simulator of the Traffic Research Centre (TRC). This fixed-based simulator consists of two inte­grated subsys­tems. The first subsystem is a conventional simulator composed of a car (a BMW 518) with a steering wheel, clutch, gear, accele­ra­tor, brake and indica­tors connected to a Silicon Graphics Skywriter 340VGXT compu­ter. A car model converts driver con­trol actions into a displacement in space. On a projection screen, placed in front, to the left and to the right of the subject, an image of the outside world from the perspective of the driver with a horizontal angle of 150 degrees is projected by three graphi­cal videopro­jectors, controlled by the graphics software of the simulator. Images are presented with a rate of 15 to 20 frames per second, resulting in a sug­gestion of smooth move­ment. The visual objects are buil­dings, roads, traffic signs, traffic lights and other vehicles. The sound of the engine, wind and tires is presented by means of a digital soundsampler recei­ving input from the simulator computer.

The second subsystem consists of a dynamic traffic simu­la­tion with interacting artificially intelligent cars. For experimental purpo­ses different traffic situa­tions can be simulated. The simula­tor is described in more detail elsewhere (Van Wolffe­laar & Van Winsum, 1992 and Van Winsum & Van Wolf­felaar, 1993).

 

Procedure. The experiment was preceded by another one in which the same subjects had been dri­ving in the simulator for about one hour. Instructions were delivered in wri­ting. Pre­ferred time-headway was measured as fol­lows. Subjects were in­structed to drive 80 km/h where possible and to follow the lead vehicle at the distance they would choose in real traffic. A lead vehicle in front of the simulator car controlled its speed such that a THW of 1 second was maintained. After a while the lead vehicle started to maintain a constant speed of 80 km/h and the subject was required to choose the preferred THW. As soon as the preferred THW was reached the subject pressed a button. Time-head­way, calculated as distance headway divided by the speed of the simulator car in m/s, at the moment the button was pressed was used as a measure for pre­ferred time-headway (THWpref).

After this, braking performance was measured. Four trials were executed suc­cessi­vely. A trial consisted of braking with the instruction to brake as fast as possible follo­wed by braking with the instruction to brake normally. Only the results of braking responses with the instruction to brake as fast as possible are reported here. Subjects were requested to drive with a con­stant speed of 80 km/h and not to exceed the lane boundaries. Speed (in km/h) was continu­ously pro­jec­ted on the screen in front, allowing subjects to monitor the behaviour of the lead vehicle. The lead vehicle maintained a constant time-headway of 1 second. After a while, i.e. about 1 minute, it braked  to a full stop with a deceleration of 6 m/s². In two trials, a warning signal (WS) was presented 1 second before the lead vehicle braked, while in the other two trials no WS was presented. A WS consisted of three stars projected on the screen during 1 second. Subjects were told a WS indica­ted that the lead vehicle might brake after 1 s. They were reque­sted not to release the right foot from the accelerator until they were sure that the lead vehicle actually braked. The lead vehicle only braked when the accele­rator position was not more than 5% less than 1 second before. This means that bra­king of the lead vehicle never occurred while the S was relea­sing the foot from the accelera­tor pedal. In two trials the lead vehicle carried brake lights during braking, while in the other two trials the brake lights were switched off. This constitu­tes the BL manipula­tion. The trials were administered in four different orders (see table 1). Subjects were randomly assigned to one of these orders with the re­striction that the same number of subjects were represen­ted in each order of trials.

 

Table 1. Order of trials. ! means not

 

Order

                  1                2                      3                4       

 

A             WS- BL    WS-!BL         !WS- BL   !WS-!BL

B                   WS-!BL   WS- BL          !WS-!BL   !WS- BL

C                   !WS- BL   !WS-!BL         WS- BL   WS-!BL

D            !WS-!BL   !WS- BL         WS-!BL   WS- BL

 

 

Data collection and analysis. Speed, distance-headway, time-headway, acce­lerator- and brake position were sampled with a frequency of 10 Hz. Reacti­ons to braking of the lead vehicle were stored in an event file. These events were monitored with a frequency of 50 Hz. The follo­wing events were stored:

– 1) time of presentation of WS

– 2) time of braking of lead vehicle (t0)

– 3) time at which accelerator position was decreased >= 5% since 2 (tacc)

– 4) time at which brake pedal position was >= 5% (tbr)

– 5) time at which a brake maximum was reached (tmaxbr)

– 6) value of brake maximum (MAXBR)

Reaction time (RT) was calculated as 3-2. Movement time (MT) was calculated as 5-3. MT was recoded as a mis­sing value when there was more than one brake peak in a trial. The occur­rence of more than one brake peak indicates that the subject braked, retrea­ted the foot, and pushed the brake again. This indicates that the instruction to brake as fast as possi­ble was not followed and it occurred in two sub­jects.

The effects of WS and BL on RT and MT were tested with an analysis of variance repeated measure­ment design. Preferred time-headway was treated as a between-subjects factor.

 

Subjects. 78 subjects participated in the experiment, 38 were male and 40 were female. 40 subjects were younger than 25 years of age, and 38 were older, but not older than 40. The average number of years the subjects were licensed to drive a car was 7.38 (sd. 4.87), total kilometrage was 88600 km (sd. 134355) on average, while the average annual kilometrage was 11786 (sd. 14794).

 

5.3 Results

 

Three groups (THWpref groups) of equal size were created from the distribu­tion of preferred time-headway. The group ‘short’ followers includes the subjects with smallest preferred time-headway, the group ‘medi­um’ follo­wers contains subjects in the middle range of preferred time-headway, while the group with hig­hest preferred time-headway are the ‘long’ follo­wers. The average time-headways of these groups can be seen in table 2.

 

Table 2. Average time-headway

 

group            THW         n   

 

short                   1.58           26

medium        2.13           26

long              3.16           26

 

The ef­fects of THWpref groups on RT and MT are listed in table 3.

 

Table 3. Effects of THWpref groups on RT and MT, df between brackets.

 

variable        F                            p        

 

RT                0.25 (75,2)           0.790

MT               0.75 (72,2)           0.477

 

 

Short followers did not exhibit a faster RT than dri­vers with a larger preferred time-headway. Also the duration of the movement phase of braking (MT) was not significantly affected by THWpref groups..

The effects of WS and BL on RT are shown in figure 2. There was a significant main effect of WS on RT (F(79,1)=4­5.91, p<0.001). The effect of BL on RT was statistically significant as well (F(79,1)= 290­.41, p<0.001). The inter­action was not signifi­cant (F(79,1)=2.18, p=0.144). WS and BL had additive effects on RT in the expected direction.

The effects of WS and BL on MT are presented in figure 3. WS had a signifi­cant main effect on MT (F(76,1)=12.50, p<0.00­1). The effect of BL was not significant (F(76,1)=0.21, p<0.6­46). The inter­ac­tion was not signi­ficant (F(76,1)=­0.49, p<0.487).

The interactions with THWpref group are listed in table 4.

 

Table 4. Interactions of THWpref group with WS and BL.

 

variable        effect                           F                             p         

 

RT                THWprefxWS               0.00 (75,2)           1.000

THWprefxBL                0.02 (75,2)           0.985

THWprefxWSxBL        0.35 (75,2)           0.708

MT               THWprefxWS               1.64 (72,2)           0.200

THWprefxBL                4.31 (72,2)           0.017

THWprefxWSxBL        0.63 (72,2)           0.537

 

 

The interactions of WS and BL with THWpref groups on RT were not signifi­cant. Thus, no evidence was found for differences between short followers and drivers with a larger preferred THW in the stimulus encoding and motor-adjustment stages. The interaction between THWpref and BL on MT was significant. This interaction was analy­zed in more de­tail. MT of the two extreme THWpref groups (short and long followers) were compa­red for the BL and non-BL trials separately. MT during BL trials was significantly faster for short follo­wers compared to long followers (F(49,1)=4.17, p<0.05). During non-BL trials MT was not significantly different for short and long followers however (F(49,1)=0.72, p=0.401), see figure 4. This means that only in trials in which the brake lights were switched on short followers moved their foot faster to the maximum level compared to long followers. Post-hoc analyses revealed that the THWpref x BL interaction on MT was mainly caused by an effect of preferred THW on MT for the first braking trials in which the lead vehicle carried brake lights. The results of regression analyses with MT as a dependent variable and prefer­red THW as an indepen­dent variable are listed in table 5, for BL and non-BL trials separately. It can be seen that only for first trials in which the brake lights on the lead vehicle were switched on MT was a function of preferred THW, such that drivers with a smaller preferred THW moved their foot faster from the accelerator pedal to the brake maximum.

 

Figure 2. RT as a function of WS and BL.

Figure 3. MT as a function of WS and BL.

 

Table 5. Effects of regression analyses of THWpref on MT for trial orders 1, 2, 3 and 4

and for BL and non-BL trials separately (df between brackets).

 

            Order         Beta          F                

BL trials

1                0.50         12.67 (38,1) **

2                0.02         0.02 (34,1)

3                0.29         3.59 (40,1)

4                -0.29        3.08 (34,1)

non-BL trials

1                -0.18        1.06 (33,1)

2                0.18         1.41 (40,1)

3                -0.17        0.96 (34,1)

4                -0.11        0.49 (40,1)

 

** = p < 0.01; * = p < 0.05

Figure 4. Average MT for short and long followers, for BL and non-BL trials.

 

It was tested whether this had caused the THWpref x BL interaction to become significant. The THWpref x BL interaction was examined for the last two trials (3 and 4) only. This interaction was not significant (F(75,2)=2.18, p=0.120), while the THWpref x BL interaction was significant for the first two trials (1 and 2) only (F(72,2)=4.52, p<0.05).

 

5.4 Discussion and conclusions

 

The experiment was performed in an interactive driving simula­tor. Drivers were subjected to a number of scenarios in which the lead vehicle braked sharply from 80 km/h until it came to a full stop. The lead vehicle started to brake at a time-headway of 1 second. Subjects were instructed to brake as fast as possi­ble as soon as the deceleration of the lead vehicle was detected. Subjects knew in advance that the lead vehicle would brake. Presentation of a warning signal (on/off) and application of brake lights on the lead vehicle (on/off) were admi­nistered in a within-subjects de­sign, resul­ting in four bra­king conditions.

The theoretical perspective of the present study was that drivers adapt time-headway to their braking skills in such a way that the time available to reach the same level of deceleration as the lead vehicle in case it brakes matches the time needed by the driver to reach this level of deceleration. Individual differences in choice of time-headway are then expected to be related to individual differences in braking skills. Braking for a lead vehicle requires a number of skills varying from perceptual skills needed for a fast detection of decelerations of the lead vehicle to perceptual-motor skills involved in tuning the motor response to visual input. This study was aimed at finding the relevant skills related to choice of time-headway during car-following.

In the literature on braking perceptual mechanisms, such as the estima­tion of time-to-collision and the detection of deceleration of a lead vehicle, are emphasized as important skills. Also, the ability to initiate braking as fast as possible is seen as an important factor in rear-end collisions. Starting from the existing literature, it was inves­tigated whether choice of time-headway is related to the ability to initiate braking as fast as possible. Using the logic of the additive factor method the locus of effect for differences in reaction time was examined. The stimulus encoding stage of the information-processing chain was manipulated by switching the brake lights of the lead vehicle on or off. This resembles a manipulation of the factor stimulus degradation. The motor-adjustment stage was manipulated by the presence or absence of a warning signal 1 second in advance of stimulus presenta­tion (deceleration of the lead vehicle). The presentation of a warning signal affects time uncertainty, a factor that is known to affect the motor-adjustment stage. The manipulations both had statistically significant additive effects on reaction time. This confirms the results reported in the experimental psychological literature that different stages are selectively affected by these two manipulations. However, no signifi­cant effect of preferred time-headway was found on reaction time. Also, no significant interactions of preferred time-headway with either the brake lights or the warning signal manipulations were found on reaction time. This indicates that choice of time-headway is not related to reaction time. It also indicates that choice of time-headway is not related to the speed at which a decele­ration is detected or to the speed at which the state of motor-readiness is reached.

The results on movement time (MT) revealed a different pattern. The factor warning signal had a significant effect on movement time; presentation of a warning signal resulted in a larger movement time. This result is difficult to explain. Generally, in laboratory experiments no effects of time uncertainty on movement time are found (see f.i. Frowein, 1981). A possible explanation is that the absence of a warning signal resulted in a longer reaction time and thus a higher criticality at the moment the motor response was initiated. This required the subjects to speed up the motor response. However, the absence of a significant effect of the factor brake lights on movement time makes this explanation highly unlikely because the brake lights manipulation had much stronger effects on reaction time. If  there are effects of criticality on movement, the manipulation of brake lights is expected to have a greater effect on movement time than the warning signal manipulation. This obviously was not the case. Also, since the subjects were instructed to brake as fast as possi­ble, criticali­ty effects were not expected. There was no significant effect of preferred time-headway on movement time. This means that there is no evidence that short followers differ from drivers with a larger preferred time-headway in the ability to generate a faster motor response per se. However, the interacti­on between preferred time-headway and the brake lights manipulation on movement time was significant. Only when the lead vehicle carried brake lights, short followers moved their foot faster to the brake maximum than drivers with a larger preferred time-headway. The relation between preferred time-headway and movement time was absent when the lead vehicle did not carry brake lights. This is partly consistent with the results reported by Marteniuk et al. (1988) in a study of motor learning. They found that as the perfor­mer is more skilled in the execution of a motor task, changing the feedback conditions strongly interferes with motor execution. The absence of brake lights may be regarded as a strong change in feedback conditions, since the brake lights of the lead vehicle are an important cue for the driver in braking. Post-hoc analysis revealed that the interaction of preferred time-headway with the brake light manipulation on movement time was mainly caused by a trial order effect. In the first braking maneuver there was a strong effect of preferred time-headway on movement time, only if brake lights of the lead vehicle were switched on during braking. This effect was absent in later braking maneuvers. The first braking maneuver differs in one important aspect from later braking trials. During later braking trials the subjects knew the level of deceleration of the lead vehicle and the duration of its deceleration, while this information was not available to the driver during the first braking trial. This suggests that preferred time-headway is related to the skill to transfer visual feedback to a required motor response. During the first trial visual feedback had to be interpreted during the course of braking, while during later trials the required motor response was known even before the response was generated. This means that for later trials a standard learned fast response could be generated while in the first trial the transformation of visual feedback to the motor-response may have played some role. This suggests that the differences in response execution speed as a function of preferred time-headway are restricted to braking situations characterized by uncertainty concerning the braking by the lead vehicle, the required dece­leration and the duration of braking, as is the case in normal car-following situations.

 


EXPERIMENT 1: Speed Choice and Steering Behaviour in Curve Driving

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EXPERIMENT 1: Speed Choice and Steering Behaviour in Curve  Driving

This is chapter 4 of the thesis “From adaptive control to adaptive traffic behaviour” about traffic psychology and behavioural adaptation of drivers, by Wim van Winsum. The thesis is from 1996. It describes a number of behavioural experiments into car driving that were performed in a research driving simulator.

Other chapters of this thesis can be found here:

 

The relation between speed choice and steering performance during curve negotiation was studied in a driving simulator. The hypothesis was that curve radius and steering competence both affect steering error during curve driving resulting in compensatory speed choice. In this, the control of safety margins was assumed to operate as a regulatory mechanism. Smaller curve radii resulted in a larger required steering wheel angle while steering error increased linearly with required steering wheel angle. This was compensated for by choosing a lower speed, such that the time-to-line crossing to the inner-lane boundary was constant over all curve radii examined. Steering competence was measured during straight road driving. Poorer steering competence also resulted in larger steering errors that were compensated for by choosing a lower speed such that the safety margin to the inner-lane boundary was unaffected by steering competence.

4.1 Introduction

 

Car driving behaviour in curves may be regarded as an interesting case where steering, as an example of operational performance, is intimately related to behaviour on the tactical level, in this case the choice of speed as a function of curve radius. The distinction between the operational and the tactical level of car driving behaviour has been made by several authors (c.f. Michon, 1985) and might form a fruitful basis for the development of modern driver behaviour theories (see c.f. Ranney, 1994). Until now, studies of car driving behaviour in curves have focused either exclusively on speed choice or on steering behaviour while no attempts have been made to integrate these two lines of research.

A consistent finding in studies on speed choice in curves is that speed has a curvilinear relation with curve radius (see c.f. Kanellaidis et al., 1990) and an inverse relation with lateral acceleration. This means that with smaller radii speed is lower but lateral acceleration is higher compared to larger radii (c.f. McLean, 1981). Sometimes an inverse linear relation is reported (Ritchie et al., 1968) while other studies have found an inverse non-linear relation between speed and lateral acceleration (Herrin and Neuhardt,1974; Macura, 1984). These results have encouraged the idea that lateral acceleration is used by drivers as a cue in speed choice in which a smaller lateral acceleration is accepted as a safety margin at higher speeds (and thus larger radii).

In studies of steering behaviour during curve negotiation, speed is usually held constant. Donges (1978) presented a two-level steering control model that incorporated negotiating curves. Anticipatory open-loop control starts with a steering action some time before the curve is entered followed by a steering-wheel angle maximum, dsa, in the curve. Then a period of stationary curve driving begins during which the driver generates correcting steering actions in a compensatory closed-loop mode. In a survey of models of steering behaviour Reid (1983) argued that driver models should incorporate both lane tracking and speed control. In Donges’ model the parameters estimated to fit the model on experimental data were influenced by vehicle speed and confounded with road curvature. Curve radius and speed during curve negotiation affect required operational performance because both factors affect the required steering-wheel angle. Godthelp (1986) described this phenomenon as follows: the required steering-wheel angle for a particular curve can roughly be characterized as dsr = GL(1+Ku²)/Rr. In this, dsr represents required steering-wheel angle, Rr the road radius in meters, G the steer-to-wheel ratio, L the wheel base, K a vehicle related stability factor and u represents longitudinal speed in m/s. For any given speed, required steering-wheel angle then increases with smaller radii, but for a given radius it increases with higher speed, if K is larger than zero, which is the case for a normal understeered car.

If the steering-wheel angle during curve negotiation matches the required steering-wheel angle perfectly, speed is only restricted by an upper limit where the vehicle starts skidding. The speed at which this occurs is generally much higher than actual speed in curves. The hypothesis of the present study is that steering errors play an important role in speed choice, such that speed is adapted to operational performance. There is some evidence that steering errors increase linearly with required steering-wheel angle, see c.f. Godthelp (1985, 1986). Since negotiating curves with a smaller radius requires a larger steering-wheel angle, the implication is that steering error is larger in curves with smaller radii compared to wider curves. If steering error is a linear function of required steering-wheel angle, the fraction defined as steering error divided by required steering-wheel angle should be constant over radii.

There is also evidence that steering error is affected by steering competence. Cavallo et al. (1988) found that, under visual occlusion, experienced drivers estimated the correct required steering-wheel angle better than inexperienced drivers. Also, experienced drivers exhibited less variation in steering-wheel amplitude during closed-loop control compared to inexperienced drivers. These results suggest that experienced drivers generate smaller steering errors.

If the driver compensates for larger steering errors induced by smaller radii or poorer steering competence by choosing a lower speed, some regulating mechanism or safety margin is required that determines how speed is adapted. It is suggested here that the time-to-line crossing (TLC), developed by Godthelp et al. (1984), is such a safety margin. TLC represents the time available for a driver until the moment at which any part of the vehicle reaches one of the lane boundaries. In a study of Godthelp (1988) drivers were instructed to generate correcting steering actions when vehicle heading could still comfortably be corrected to prevent a crossing of the lane boundary. Drivers made a corrective steering action at a constant TLC irrespective of vehicle speed.

The model on the relation between speed choice and steering performance may then be summarized as follows. Required steering-wheel angle is determined by curve radius and speed, while steering error is determined by required steering-wheel angle and steering competence. It is assumed that the driver has learned the effect of curve radius and speed on required steering-wheel angle and on steering error from previous experiences. Also, it is assumed that steering error is consistent and the driver is aware of his or her steering competence. When the driver approaches a curve, both radius and steering competence cause an anticipatory adjustment of speed, much like the anticipatory avoidance response in the threat avoidance model of Fuller (1984), in which the effects of radius and steering competence on steering error are traded off with speed such that the safety margin TLC remains constant and independent of radius and steering competence. Although mathematically TLC is determined by steering error as well as speed, the higher steering errors associated with smaller radii and poorer steering competence are assumed to result in lower speeds because of the constancy of preferred TLC as a guiding principle. This principle will then result in low or non-significant correlations of speed and steering error with TLC. The relation between lateral acceleration and speed is then assumed to be a by-product of this mechanism.

In the experiment steering competence was measured separately during straight road driving. Road radius was manipulated within-subjects with radii of 40, 80, 120 and 160 meters. Originally, lane-width was manipulated within-subjects as well, since lane-width was expected to affect TLC. However, the effects of lane-width are not reported since these are not of crucial importance to the issue addressed here. Also, subjects used only a part of the lane-width because they drove relatively close to the inner lane boundary. This counteracted possible effects of lane-width on TLC and speed choice. There is also evidence in the literature that drivers use the inner lane boundary as a reference for vehicle guidance, see c.f. Shinar et al. (1980), McDonald and Ellis (1975), Cohen and Studach (1977). Therefore, only TLC and steering behaviour data towards the inner lane boundary are reported in the present article.

 

4.2 Method

 

Apparatus. The experiment was performed in the Traffic Research Centre (TRC) fixed-based driving simulator. It consists of a car (BMW 518) with a steering wheel, clutch, gear, accele­ra­tor, brake and indica­tors connected to a Silicon Graphics Skywriter 340VGXT compu­ter. A car model converts driver control actions into a displacement in space. On a 2 x 2.5 meter projection s­creen, placed in front of the car mockup, an image of the outside world with a horizon­tal angle of 50 degrees is projec­ted by a graphical videopro­jector, controlled by the 3D-grap­hics software. Images are presented with a rate of 15 to 20 frames per second, resulting in a sug­gestion of smooth move­ment. The visual objects are buil­dings, roads, traffic signs, traffic lights and artificially intelligent traffic. The sound of the engine, wind and tires is presented by means of a digital soundsampler recei­ving input from the simulator computer. The simula­tor is described in more detail elsewhere (Van Wolffe­laar & Van Winsum, 1992 and Van Winsum & Van Wolf­felaar, 1993).

 

Procedure. A circuit of two-lane roads with a lane-width of either 3.0, 3.5 or 4.0 meters was used. Roads had deline­ation with broken center lines and continuous edge lines. Four left-turning curves with 90 degrees angle and radii of 40, 80, 120 and 160 meters were separated by straight road segments. After completing a questionnaire on driving experience and age, subjects practiced driving in the simulator for ten minutes. They were instructed to choose their own preferred speed but to adapt the speed for curves as they normally would and to stay in the right lane. There were three trials, one for every lane-width. Each trial consisted of five roundtrips. This means that in every trial all four curves were negotiated five times. The three trials are treated as multiple measurements here.

 

Data registration and analysis. Sample measurements (10 Hz) were taken on speed (m/s), lateral position, steering-wheel angle (degrees), TLC (seconds), and steering error (degrees).

The steering integral (Ids) during straight road driving was used as a measure for steering competence. This was computed as follows. The steering-wheel signal was divided into periods where the steering wheel was turned to left and periods where it was turned to right (relative to the zero angle). For every period the amplitude was integrated over time and these integrals were averaged resulting in Ids. Thus, this measure is affected by both steering-wheel amplitude and frequency. A smaller steering integral represents better steering performance. Steering error in curves, dse, was defined as the difference between the actual steering-wheel angle and required steering-wheel angle (ds – dsr).

Figure 1 presents a time-history of steering error and TLC during curve negotiation. The curve is entered at time 0. Positive values of steering error and TLC represent steering to the inner lane boundary (left) while negative values represent steering to the outer lane boundary (right). The steering error fluctuates around zero. If steering error is zero then the steering-wheel angle equals the required steering-wheel angle. The open-loop phase ends when the maximum steering-wheel angle, dsa is reached. In Figure 1 this is indicated by the first maximum for dse. This is followed by closed-loop steering control during which deviations from the required steering error are minimized by the driver.

The following variables were analyzed:

– The steering error dse on the moment dsa is reached. This represents the steering error during the open-loop phase.

– The required steering-wheel angle dsr. This was measured as the steering-wheel angle on the moment that steering error was zero just before dsa was reached.

– The steering error ratio, computed as dse/dsr. This ratio is a measure for the relative steering error.

– The steering error integral, Idse, during the closed-loop phase. This was computed as the average integral of all periods where the steering error was directed toward the inner lane boundary.

– The minimum TLC’s to the inner lane boundary, TLCmin during the closed-loop phase. These were determined and averaged for every radius/trial combination.

– The minimum speed during curve negotiation. This was determined and averaged for every radius/trial combination.

The effects of radius were analyzed with repeated measurements analysis of variance. The effects of steering competence were analyzed with correlation and regression analyses. The confidence level for significance was set at p£0.05.

Figure 1. Steering error and TLC time-history during curve negotiation.

Subjects. 16 subjects, 8 male and 8 female, participated in the experiment. The average age was 34 years (sd. 6.3, range 22-47). They were licensed drivers for 12 years on average (sd. 6.3, range 2-27). The average annual kilometrage was 10594 (sd. 8267, range 1500-30000).

 

4.3 Results

 

The correlation between steering integral Ids and drivers’ total kilometrage was -0.62 (p<0.01). This means that more experienced drivers steered more accurately on straight road segments.

The minimum speed during curve negotiation was significantly affected by radius (F(3,15)= 58.17, p<0.01). Required steering-wheel angle (dsr) was significantly affected by radius (F(3,15)=188.24, p<0.01) as was the steering error (dse) during the open-loop phase (F(3,15)=28.28, p<0.01) and the steering error integral (Idse) during the closed-loop phase (F(3.15)=14.29, p<0.01). The effect of radius on steering error ratio was not statistically significant. Also, the effect of radius on the minimum TLC (TLCmin) during the closed-loop phase was not significant. The averages of these dependent variables as a function of radius are presented in Table 1.

Table 1. Averages of dependent variables as a function of radius

 

Radius (m)

Dependent variable                40              80                120             160

 

speed (m/s)                            11.23        14.92        17.58              17.99

required angle (degrees)     121.44        74.64       56.56             43.47

steering error:

-open loop (degrees)           14.20         7.47           5.54              4.75

-closed loop (integral)        14.02         6.55           5.26              4.67

steering error ratio                 0.12         0.10           0.10           0.11

minimum TLC (s)                   2.52         2.70           2.89           2.79

 

 

It can be seen that a smaller radius resulted in a larger required steering-wheel angle, larger steering errors and a lower speed. However, TLC and the steering error ratio were constant over all radii. Both steering errors during the open and closed-loop phases were affected by radius in the same manner.

 

Table 2. Standardized alpha coefficients of dependent variables

 

Dependent variable              standardized alpha

 

Speed                                    0.93

required angle                      0.91

steering error:

-open loop                            0.88

-closed loop                         0.86

steering error ratio              0.91

minimum TLC                      0.90

 

In order to test effects of individual differences in steering competence on dependent variables it is required that these variables are consistent within the driver. In that case, it is justified to average over all measurements (4 radii x 3 repetitions). In that way, the effect of radius is canceled while the effect of individual differences is preserved. The reliability, or consistency, of the dependent variables was tested with the standardized alpha coefficient. This represents the estimated square of the correlation of scores on a collection of items, in this case the 12 measurements, with true scores (Nunnally, 1978). For basic research a reliability of 0.80 is generally regarded as a satisfactory level.

Table 2 presents the standardized alpha coefficients for all dependent variables. It can be seen that all variables are reliable and most alpha’s are higher than 0.90. The minimum speed, TLC, steering errors, required steering-wheel angle and steering error ratio were averaged over radii and repetitions. Figure 2 presents the results of multiple regression analyses. Only significant partial regression coefficients are displayed.

 

Figure 2. Path diagram with partial regression coefficients.

*=p<0.05, **=p<0.01, ns=not significant.

 

It can be seen that the measures for steering errors in the open-loop and the closed-loop phase are strongly intercorrelated, indicating that they measure the same phenomenon. Steering error is determined by required steering-wheel angle, while there is no direct path from speed to steering error. Required steering-wheel angle is strongly determined by speed. In addition to this, steering error is strongly determined by steering competence (Ids). But while a higher steering competence results in lower steering error it also results in higher speed. Because steering competence is an intermediary factor, there is no effect of speed or steering error on TLC. Also, there is no path from steering competence to TLC. This suggests that subjects with poorer steering performance maintain the same safety margin as subjects with better steering performance, and that they choose a lower speed in order to maintain that safety margin. The correlation between Ids and the steering error ratio was 0.74 (p<0.01).

 

4.4 Discussion and conclusions

The effects of curve radius as a road design factor and steering competence as an individual driver characteristic on speed choice in curves were studied from the perspective that effects on operational performance are compensated for on the tactical level. The implied mechanism in the case of curve negotiation is that both curve radius and steering competence affect steering errors on the operational level. In this, the preferred TLC was assumed to be a regulating mechanism that determines how speed is controlled in order to compensate for larger steering errors. Since TLC is mathematically determined by speed and steering error, higher steering errors can be compensated for by choosing a lower speed such that TLC is unaffected by radius or steering competence. The results supported this model. It was found that both required steering-wheel angle and steering error during the open and closed-loop phases increase with smaller radii, but that the relative steering error, defined as steering error divided by required steering-wheel angle, is constant over radii. This strongly suggests that steering error is linearly related to required steering-wheel angle and is consistent with the results of Godthelp (1985, 1986). Smaller radii resulted in the choice of a lower speed, but the minimum TLC’s during curve negotiation were not affected by radius. This suggests that larger steering errors are compensated for by choosing a lower speed such that a constant minimum TLC is maintained. This finding confirms the ideas of Summala (1988) and Rumar (1988) that drivers control safety margins that can be operationalized as distance or time related measures. The TLC as a safety margin then is controlled by the drivers’ speed choice. The results suggest that speed choice and steering performance are both intimately related in negotiating curves.

In this study, individual differences in steering competence strongly determined speed choice and steering performance in curves. Steering competence was measured with the steering integral during straight road driving. A larger steering integral is an indication of poorer steering performance. The quality of steering performance was related to driving experience. Steering performance, speed choice and minimum TLC were consistent within drivers during curve negotiation. Steering error was determined both by steering competence and by required steering-wheel angle while required steering-wheel angle was determined by speed. This confirms the model discussed in the introduction. Because drivers with poorer steering performance drove slower, while their steering errors were larger, no significant relations of speed and steering errors with TLC were found. This suggests that drivers with poorer steering competence compensated their larger steering errors, which decreased TLC, by choosing a lower speed, which increased TLC. Since steering competence did not affect TLC, it can not be concluded that drivers with poorer steering competence were less safe drivers. Steering error ratio correlated significantly with steering-competence as measured by the steering integral. The strong effect of steering competence on the steering errors during curve negotiation suggests that the steering integral is a good indicator for the quality of steering performance and that steering performance is consistent within the driver.

Based on the finding that steering error is a linear function of required steering-wheel angle and on the constancy of the minimum TLC to the inner lane boundary, the speed in curves as a function of radius was calculated using a mathematical model. From this, lateral acceleration was computed. Lateral acceleration proved to be an inverse function of speed as a by-product of the presented driver strategy.

Thus it appears that both radius as a road design element and steering competence as a driver characteristic exercise their influence on driving behaviour in the same manner. Both affect operational performance resulting in an adaptation of behaviour on the tactical level in an attempt to control safety margins. This is of theoretical significance for driving modeling in general since it suggests that effects of various factors related to the vehicle, weather, road, traffic, temporary states and the driver on behaviour on the tactical level (c.f. speed choice) may exercise their influence via an effect on operational performance. Most driver models are exclusively directed at either the operational or the tactical level. However, it is suggested that the relation between operational performance and behaviour on the tactical level should be a fundamental element in driver modeling.

 


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