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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.

 

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