subscribe: Posts | Comments | Email

Instrumentation: the driving simulator

Instrumentation: the driving simulator

This is Chapter 3 of the Thesis from Adaptive control to adaptive driver behaviour, by van Winsum, 1996.

Other chapters of this thesis can be found here:


3.1. Background

The preparation of the experiments discussed in this thesis required a substantial amount of software design and implementation for the TRC driving simulator. A full description of the functionality and implementation of the simulator is beyond the scope of this chapter. The reader is referred to other documents for more detail and background (for example Van Wolffelaar & Van Winsum, 1992; Van Wolffelaar & Van Winsum, 1994).

The driving simulator of the Traffic Research Center (TRC) was developed as an instrument for behavioural research of driving. The GIDS project in which the TRC was involved at that time required a simulation testbed for mathematical driving modeling. Because of the dynamic complexities of driver tasks in multi-vehicle traffic, a dynamic traffic simulation was required as a test tool (Van Winsum and Van Wolffelaar, 1993). The objective of GIDS, an acronym for Generic Intelligent Driver Support, was ‘to determine the requirements and design standards for a class of intelligent co-driver (GIDS) systems that are maximally consistent with the performance requirements and performance capabilities of the human driver.’ (Michon and Smiley (1993). A prototype system was developed as a demonstrator for the essential features of the GIDS concept. One of the functions of the GIDS system was to compare required driving behaviour with actual behaviour. Required behaviour was modeled for a number of driving tasks and implemented in a computer system (Van Winsum, 1991; McLoughlin et al., 1993). It was decided at that time to design and implement a dynamic traffic simulation model and connect this with the driving simulator under development. From that moment the driving simulator evolved as a dynamic driving simulator with an interacting traffic world that could be connected to the GIDS system to serve as a test bed. After this, the simulator was also used as a testbed for other external driver support systems.

However, most importantly, the simulator is an instrument for the study of driver behaviour. Because it is used by researchers with very different questions and requirements, flexibility in software design has been considered to be important. This was accomplished by using the object-oriented computer language C++, and a multi-purpose UNIX machine instead of a single purpose dedicated simulation machine. To further increase flexibility for the researchers and to facilitate the design and testing of the experiments reported in this thesis, a fourth generation simulation language, SSL (Scenario Specification Language), was developed for the specification of experiments, together with a specification language (NSL, Network Specification Language) and software tools for roadnetwork creation. Data-sampling and data-processing facilities were added to facilitate experimentation.


3.2 The structure of the simulator

The simulator is composed of a number of software and hardware components that are connected via interfaces. The ‘conventional’ driving simulator consists of a physical car mockup, a car model implemented in software and a graphics system, together with a static road network environment. A dynamic traffic environment is added to this. The structure of these components as well as the object relations are shown in figure 1. In this figure several types of relations can be seen. An “is-a” relation specifies that a certain object type is a subtype of an other more abstract object type. For example, a BMW-car is some kind of car. This means that it inherits the functionality of the more abstract object type car. A “has-a” relation specifies that a certain objects has another object as a member. For example, a car has a traverser. The heavy printed arrows specify the direction of the flow of information. For example, there is an information flow from the object roadnet to the object sensor. This means that a sensor requests information from a certain instantiation of the object roadnet.



Figure 1. Logical structure of components of the driving simulator and relations between objects.


In addition to this, a number of facilities related to data-sampling and processing and experimental control are added to the simulator.


Car cabin. The steering wheel, clutch, gear, accelera­tor, brake and indica­tors of the car (a BMW 518) are connected to a Silicon Graphics Skywriter 340VGXT compu­ter (IRIS). Electromotors and other electronic appliances are built in the car to excert forces on the pedals and steering wheel and to send data on the steering wheel, accelerator pedal, brake pedal and indicators to the IRIS computer for further processing by the car model.


Car model. The IRIS computer processes these signals in a separate process referred to as the car model. The car model is described in more detail in Spaargaren (1994). It computes the longitudinal and lateral speed and acceleration that are the result of physical characteristics of the car and the input from the car cabin. From this the new coordinate position in the artificial world the car is driving in is computed. The output of the car model is used by the car traverser and by the graphics system. The traverser constitutes the link with the dynamic traffic process while the graphics system presents the output of the full system in a real-time visual format to the driver.


Graphics system. On a projection screen, placed in front, to the left and to the right of the driver, 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 that are 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.

In addition to this, the sound of the engine, wind and tires is presented by means of a digital soundsampler recei­ving input from the simulator computer.


Logical network (Roadnet). The logical network is the static environment in which the simulator car and traffic operate. The static environment consists of a database with a network of roads, traffic signs, traffic lights and buildings. This database is used for the visualization of the environment by the graphics system and by the artificially intelligent traffic to evaluate the present situation. The database can be generated in two ways:

– by NSL (Network Specification Language). This is a user specification language, created for the TRC simulator (Van Winsum, SSL/NSL specification release 1.2, 1994), by which a network of roads can be specified as an ASCII text. This text is processed by an NSL interpreter program that generates a road network database that is used by the simulator (Van Winsum, 1994, NSL scanner/ parser/interpreter computer program).

– by means of an interactive graphical program written in C++/OSF Motif (Van Winsum, 1993, program WORLDED). The user can specify a network of roads by means of click and point operations. The output of the NSL interpreter can also be used as input for this program to visualize and change the network.


The network consists of a structure of three base tables: a table with intersections, a table with paths and a table with segments. An intersection is a point in the network coordinate system with 1..n, {n >= 1}, outgoing paths. Coordinates are in meters. The following relations hold:


– n = 1: the intersection forms a terminal point in the network. If cars approach this intersection they cannot proceed beyond the intersection and provisions are made to ensure that the car turns around in the opposite direction as soon as the intersection is reached. The intersection has no physical layout and has the appearance of an ending road. The implication is that it is not possible for cars to move off the logical world.

– n = 2: the intersection is a virtual intersection in the sense that it has no specific layout and is not treated as an intersection by the traffic. The only purpose of creating such an intersection is for the convenience of the network constructor.

– n > 2: the intersection has more than two branches.

An intersection is of a certain type (f.i. a roundabout), it can be controlled by traffic lights with a certain control strategy, and it contains a list of references to outgoing paths. This list is ordered such that the path connections to the intersections are counterclockwise. In addition to this the intersection contains information about the layout, which is used by the graphics system and by the traffic.


A path is a logical connection between two intersections and always has one direction. It must start at one intersection A and end at one intersection B, where A may be equal to B. If A=B then the path is logically a circular path. All paths  have precisely one path in the opposite direction, referred to as a counterpath. It has a list of references to segments with 1..n elements, {n>=1} . This list is ordered such that the segments are in successive order. A path also contains information on right-of-way at the intersection at the end of the path, whether entry into this path is allowed, a reference to a traffic light at the end of the path if there is one, and information on buildings on the right side of the segments on the path.

To every path an ordered list with references to cars is attached. This list is ordered such that it reflects the order of the cars on the path and it may be empty. Cars can be added or removed at any time during the simulation process. In this way the simulator car and the computer controlled cars are connected to the static environment. Because every car is an object in the software-engineering conception that it has its own functions and data-structures, every car performs its own administration of detailed position (coordinates, distances from the last intersection and from the edge of the road etc.) in relation to the logical network.

The concept of path corresponds to the terminology of graph theory. Using that terminology, intersections are nodes.


The combination of nodes and paths may be described as a directed graph with the following properties:

– Suppose the network is represented as the graph G=(P,Z), with P being the set of intersections or nodes and Z being the set of ordered relations between the intersections, then P = {0..n} with n > 0. The fact that all intersections are member of a set ensures that all members occur once. The number of the intersections are in successive order. A set of intersections is, for example, {0,1,2,3}, meaning that there are 4 intersections. The set {0,1,3,4} is incorrect because the number 2 is missing.

– Z contains the relations between two nodes A and B, for example {{1,2}, {1,3}, {1,1}}. If {A, B} is a member of Z then {B,A} is also a member. This shows that all paths have a counterpath. A road can be traveled in two directions and this is the reason that every path has a counterpath. If only one-way traffic is allowed there are still two paths because physically it is possible to enter a one-way street into the wrong direction although legally it is not allowed.

– The fact that Z is described as a set suggests that the member {A,B} may occur only once. This restriction has been abandoned for practical purposes. There may be more than one instantiation of the relation {A,B}. In that sense Z is not a set but a collection. This restriction was loosened because sometimes there is more than one road between two intersections.

– A further restriction to the graph specification is that all nodes must occur in at least one relation, that is, a node that is fully unconnected is not allowed.


A segment is represented as a line through the middle of a roadpiece. It can be either straight or curved and is undirected. Segments are members of ordered lists connected to a path and the ordered list must contain al least one segment. A segment must be a member of one and only one ordered list. Segments represent the physical layout of the road, while a path represents the logical presence of a road. The direction depends on the path the segment is in. If the segment is straight the two end points are given in coordinates. If it is curved the segment contains the necessary information on the curvature, such as the radius, the centerpoint of the arc etc. A segment has a certain lane-width. At present only two-lane segments are allowed.

Traffic signs, buildings and traffic lights are connected to the network and have a certain position, angle, and type. Within the simulator program this roadnet representation is implemented as the separate object class in the roadnet module (Van Winsum, 1992, computer program class c_roadnet, roadnet.c). This object performs its own administration and can be queried from outside via an interface.

The following is an example of a definition of a simple network with NSL.

Define Inter[0] {

X := 100; Y:= 100;


Define Segment[0] {

Type   := Straight;

StartX := Inter[0].X;

StartY := Inter[0].Y

Length := 100;

Angle  := 90;



In this definition a straight road of 100 meters with an absolute angle of 90 degrees is created, starting at coordinate position [100, 100]. Paths are added automatically by the system. Since this definition of a network would result in a path without an end node, the system creates an end node (intersection number 1). Since the lane-width is not specified, the segment is assigned the default lane-width of 3 meters by the NSL system. In this case the NSL interpreter creates 2 intersections, 2 paths and 1 segment, no traffic signs, traffic lights or buildings. NSL contains a number of geometric transformation methods and rules to assists the user and to make it easier to build the network.


Traffic. Traffic consists of a list of cars that may be controlled by a human driver (the simulator car) or by an artificially intelligent ‘driver’. Every car has a number of properties, such as a length, a width, a wheel-base and so on and a number of objects that are needed for driving in the logical world. These objects are a traverser, a sensor and a decision (control) mechanism. In the case of a human-controlled car the decision mechanisms is of course the human driver who, together with the car model, determines the movement of the car. In the case of a computer controlled car the decision mechanism is composed of a set of decision rules. Traffic is implemented in the simulator program as a separate object container class (Van Winsum, 1992, computer program class c_traffic, traffic.c). It contains all kinds of methods for adding or removing cars from a traffic list. The class traffic contains references to cars which may be very different in type. The mechanisms of late binding and virtual classes and inheritance, which are properties of the object-oriented methodology used, ensure that in the future all kinds of other moving objects such as pedestrians and bicyclists may be added to traffic. Every car has its own instantiation of a traverser, sensor and control object. These objects also may be of different types. For example, a human controlled car (the simulator car) would need a somewhat different traverser than a computer controlled car or maybe a pedestrian.

In the case of a human driver, the output of the car model, i.e. the speed and the angle of lateral displacement, are input for the traverser. For computer-controlled cars, the output of the artificially intelligent decision mechanism is the input for the traverser. The traverser calculates the lateral position (with respect to the right side of the road), the longitudinal displacement with respect to the road, it connects the car to the network of roads, checks which path is selected if the car is on an intersection and performs a number of other checks to maintain the position of the car accurate with respect to other traffic. It uses deadreckoning techniques in this process. The traverser is the interface between traffic and the road network and it also connects the simulator car with the interactive traffic world. The traverser is implemented as a separate object class in the simulator program, such that every car has a reference to its own instantiation of a traverser object (Van Winsum, 1992, computer program class, c_traverser, travers.c)

The sensor is an object that really consists of a set of sensors. Both the human controlled car and the computer controlled cars have a sensor object but they use it differently. In general, the sensor is used to ‘look’ into the network. In this way every car, including the simulator car, can evaluate the present type of road and curvature, evaluate the distance and speed of traffic in front etc. This means that the sensor is an interface between the network and the car in that it requests information from the network. The human controlled car uses this information for data storage purposes and to give input to driver support systems. The computer controlled cars use this information for the decisions they are required to make concerning their speed and course. Sensor is implemented as a separate object class in the simulator program (Van Winsum, 1992, computer program class c_sensor, sensor.c). Every car has a reference to its own instantiation of a sensor object.

The control mechanism for the human driver is the human information processing system that uses visual information received via the graphics system, to exert the controls in the car cabin. These car control signals are processed by the car model. The output of the car model is used to update the graphics and as input for the traverser that connects the simulator car to the network. The control mechanism of the computer controlled cars consists of a set of decision rules. Every computer controlled car has rules for different driver tasks on the tactical level. These tasks are related to curve negotiation, car-following, overtaking, negotiating intersections, speed choice on straight roads and processing road sign information. The car evaluates which tasks are presently performed and computes a required speed and lateral position. Since multiple tasks can be performed at the same time, a decision mechanism selects the appropriate speed and lateral position together with the required acceleration and wheel-angle to reach this state, after all tasks have been evaluated for the present car . This results in a natural and human-like behaviour that contributes in an important way to the fidelity of the simulator. For computer controlled robot cars the artificial intelligence is implemented in a separate object class in the simulator program (Van Winsum, 1992, computer program class c_control, control.c).

3.3 Data collection and processing

A large quantity of performance data can be collected with any sampling frequency. Examples are time-to-collision, time-to-intersection, time-to-line crossing, lateral position, speed, acceleration, brake force and so on. The user creates an ASCII text with keywords that specify the sample frequency and the data to sample with that frequency. The data are then sampled during a simulation run and stored into a binary file. The real-time handling of data-storage during a simulator run is controlled by a separate object class c_data that is implemented in the simulator program (Van Winsum, 1992, computer program class c_data, newdata.c).

After a simulator run the data can be visualized and preprocessed with a graphical program written in C++ and X-windows/OSF motif (Van Winsum, 1994, program DATAPROC).


For the experiments described in this thesis the real-time sampling of time-based information was required. The variables used are TTC (time-to-collision), TLC (time-to-line crossing) and THW (time-headway during car-following). These measures are defined and implemented as follows:


– THW is defined as D/u

for u > 0, else THW = infinite (undefined)

with D = bumper to bumper distance in meters along the path between

the simulator car and the lead vehicle, and

u = speed of simulator car in m/s


– TTC is defined as D/(u – ulead)

for (u – ulead) > 0, else TTC = infinite (undefined)

with ulead = speed of lead vehicle in m/s


– TLC is calculated differently depending on whether the car is on a straight road or in a curve.

In general, TLC = DLC/u,

for u > 0, else TLC = infinite (undefined)

with DLC = distance to line crossing along the vehicle path and

u = speed of simulator car in m/s.


DLC is solved goniometrically using the cosine rule. Normally, the car is not driving in a straight line but it alternates between curves to left and to right. The radius of the vehicle path is calculated using the coordinates of the centerpoint of the curve the car is driving. This centerpoint is calculated as the point where the perpendicular lines through the frontwheel and the rearwheel, with slipangles added to the wheelangles, intersect. Rv, the vehicle radius, is then computed as the distance between the center of gravity of the car and the centerpoint of the vehicle curve. Rv1 then is the distance between the front (left or right) wheel and the centerpoint of the vehicle curve. TLC then measures the time until either the left or right front wheel crosses the left or right lane boundary, given the current vehicle path and speed.

First the case for straight roads is described if the vehicle makes a left turning curve, see figure 2. DLC is computed as a*Rv1. Since Rv1 is known, only a has to be computed, using the cosine rule.



Figure 2. Determination of the length of the arc DLC for driving on straight roadsections.

– The length of the linepiece A is computed as Rv1-(dleft/cos(ha)), with dleft being the distance between the left frontwheel and the lane boundary (in a line perpendicular on the road) and ha the angle between the line perpendicular on the road and the line from the front wheel to the centerpoint of the vehicle curve.

– The length of the linepiece C is computed as  (2*A*cos(ß)+Ö((2*A*cos(ß))2-4*(A2-Rv12)))/2

Then a = arccos((A2 + Rv12 – C2)/(2*A*C))

and DLC = a*Rv1


Figure 3. Determination of the length of the arc DLC for driving on curved roadsections.


Figure 3 shows the situation for determining the TLC while the car is negotiating a road curve. Again, DLC is determined as a*Rv1. In this case a is computed differently.

– The length of linepiece A represents the distance between the centerpoint of the roadcurve and the centerpoint of the vehicle curve.

– Angle ß is computed as the angle difference between the line from the centerpoint of the vehicle curve to the centerpoint of the roadcurve and the line from the centerpoint of the vehicle curve to the left front wheel (if the vehicle turns towards the inner lane boundary).

– Angle a1 is computed as arccos((A2 + Rv12 – Rr2)/(2*A*Rv1))

– a= ß – a1 and DLC = a*Rv1


In addition to this, vehicle control information was required for the experiments. The position of the accelerator pedal, expressed as a percentage pressed, the position of the brake pedal and the force excerted by the foot on the braking pedal were used in the studies on car-following, while steering wheel angle was used in the study on steering performance and curve negotiation. The results of these control actions, such as speed, acceleration, heading angle and lateral position, were sampled and processed as well.

For every experiment automatic data processing programs were written to extract and process the required data. These data were then transformed into a format suitable for processing by SPSS.


3.4 Scenario Specification Language (SSL)

SSL is a user specification language that was defined and implemented as a tool for specification and design of experiments. It contains most of the functionality of the simulator. A description of SSL then essentially gives a description of the functionality of the TRC simulator. For a full specification of the language the reader is referred to the SSL/NSL specification document (Van Winsum, 1994).

An ASCII file with SSL commands is analyzed by a scanner and parser module during initialization of the simulator program and syntactical errors are reported to the user. (Van Winsum, 1994, SSL scanner/parser/interpreter modules). If no syntactical errors are found, the SSL commands are converted to an internal data-structure that is interpreted in real-time by the SSL-interpreter during execution of the simulation process. Since the simulation process is a dynamic process in which the state is determined by SSL specifications, the human driver, the behaviour of traffic and by the process operator who interacts with the computer via the user interface, the course of events is not deterministic. However, SSL commands can be used to force identical situations for all subjects in an experiment. Since SSL commands are often conditional, the state of the traffic world can be queried and events can be triggered if some condition is true or false.

Scenarios are defined in a SSL text file. A scenario is a predefined list of situations with a start and an end condition: the scenario starts when the start condition is fulfilled and terminates when the end condition is fulfilled. A scenario may involve 0..n cars, referred to as participants, in addition to the simulator car. A participant is a car that performs conditional actions. A scenario may be used for controlling traffic and traffic lights, for indicating when data must be stored, for communication with the driver with spoken or written messages, for sending messages to other devices, and so on. SSL is not exclusively a language for specification of traffic situations during an experiment. It also may be used for rapid prototyping of driver support systems, for creating test situations and for debugging. It is important to note that SSL is often used to override default settings and default behaviour. For example, if a computer-controlled car is created with SSL, the car follows its own rules unless specified differently with SSL.

The following is an example of an SSL description.


Define Scen[1] {

Var { time; }

Start {

When ( Part[MainTarget].LeadCar != Absent and

Part[MainTarget].DisToLeadCar < 50 );

Scen[].NrTimes := 1;   time := runtime();


End {

When ( runtime() – time > 20 );


Define Part[1] {

Start {

Part[].CarNr  := Part[MainTarget].LeadCar;

Part[].MaxVelocity := 50/3.6;


End {

Part[].MaxVelocity := 100/3.6;





This scenario specifies that if there is a lead vehicle and the distance to it is less than 50 meters then this lead vehicle starts to drive with a maximum speed of 50 km/h during 20 seconds. After 20 seconds (at the end of the scenario) this vehicle pulls up to a speed of 100 km/h.

SSL files contain the full script for an experiment and are thus complete specifications of an experiment. This ensures repeatability and detailed documentation of experiments. Since researchers are able to make their own SSL script files they can design and test experiments with a minimal dependency on technical staff and computer programmers.


3.5 The use of the simulator in the experiments

The driving simulator offers a number of advantages compared to studying driver behaviour on the road.

1) The sensors of the simulator car and of other cars used in the car-following experiments contain important information that is much harder to obtain with current technology in a real world test situation. This information is vital as input for the control of experiments and data-sampling. For the experiment on curve driving the measurement of TLC information during curve negotiation would be very hard to obtain in real world driving. A simulator is the only practical way to obtain complex measures such as the TLC in curves. Although time-to-collision information may be obtained during on-road experiments it is measured more practically and efficiently in the simulator.

2) All kinds of situations can be generated and tested that would be very hard or impossible to generate in the real world. In the curve negotiation experiment the drivers are required to negotiate a number of different road curves with a specific lane width and radii. Roads with the precise characteristics required by this experiment are very hard to find in the real world. During the car-following experiments the lead vehicle was sometimes required to drive with a certain fixed time-headway in front of the simulator car. This would be difficult to establish in on-road experiments.

3) The responses of drivers to maneuvers too dangerous to be tested in real world driving can easily be tested in the simulator. This is especially important in the car-following and braking experiments discussed in the chapters 5 to 9.

4) Situations can be brought under experimental control. This is important for the comparability of the results since all subjects have encountered precisely the same situations. In on-road experiments traffic density and weather conditions are hard to control. In this respect a simulator has important advantages compared to real world experiments.


In the experiments performed in the context of this thesis, the time-based safety margins TLC and TTC play an important role. The perception of TTC has been studied in a large number of experiments (see chapter 6). These studies have given strong support for the idea that TTC information is extracted from the optic flow field. The expansion of the image on the retina gives sufficient information for the extraction of TTC information without requiring the driver to assess speed or distance information. Since the graphical properties of optical perspective, visual angle and optical expansion rate are the same in the TRC simulator as in real world driving, there is reason to assume that the driving simulator is suitable for the type of research discussed in the chapters 5 to 9. An important prerequisite for a smooth optic expansion is a high graphical frame rate. In order to obtain a high frame rate, the visual scenes in all experiments are limited to the essential components while substantial effort has been invested in the design of fast algorithms for traffic handling and experimental control.


Chapter 2: Thesis traffic psychology

Chapter 2: Thesis traffic psychology

This is chapter 2 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:


                                                Models of driving behaviour


2.1 Introduction


A wide range of models of driving behaviour has been described in the literature that typically emphasize a specific aspect of car driving. Some models emphasize operational performance while others stress the importance of behaviour on the tactical level. Also, some models focus on individual differences while others emphasize situational factors. The number of serious attempts at categorization of driver behaviour models is limited. The problem is that the categorizations are almost always too limited, exclude important models or are wrong according to the advocates of some models. However, an important attempt is the one proposed by Michon (1985). Michon has made a distinction between taxonomic and functional driver behaviour models. Taxonomic models are inventories of facts while functional models specify relations between components. The best known example of a taxonomic model is the task analysis of the driving task developed by McKnight and Adams (1970). This task analysis specifies the driving task in terms of behaviour requirements (for a distinction between several types of task analyses, see Hackman, 1969). This means that it describes what the driver should do. The task analysis of McKnight and Adams is not aimed at understan­ding driver behaviour or at describing how the driver actually drives. Given the purpose of driver education, this is not surprising. However, since it is not aimed at under­standing driver behaviour, it is not discussed here.

In the sixties, the early concept of accident proneness was replaced by studies of indivi­dual differences in accident involvement that focused on psychologi­cal abilities. Together, these studies have become known as the skill model. The term ‘skill model’ is actually a misnomer, because the model centers around psychological abilities instead of skills. The use of the word ‘skill’ suggests that the model focuses on car driving performance while in fact psychological abilities are tested and correlated with accident involvement. Proponents of this model assume a relation between psychological abilities and car driving skills but generally fail to test this relation explicitly. Still, because of the general use of the term in traffic psychology, skill model is used in this thesis. Situational factors were hardly given any consideration in this approach. The dependent variable has been accident involvement instead of actual car driving behaviour. The studies in the tradition of accident proneness and differential accident involvement belong to the taxonomic models and focus on indivi­dual differen­ces measured by psychological ability tests.

Individual differences are also stressed in studies on the effects of aging. Since elderly road users differ in car driving performance from younger drivers and are assumed to suffer from skill degradation having an effect on accident involvement, the studies on this issue are grouped under the heading of the skill model in this thesis. However, it must be stressed that these studies differ strongly from the correlation approach of the differential accident involvement studies. Also, behaviour on the strategic and the tactical level of car driving is expli­citly incorporated.

The effects of temporary states induced by alcohol and drugs are assumed to affect car driving skills. Traditionally, studies on the effects of temporary states are not part of the skill model if this  is envisaged as being equiva­lent to the correlation approach. Also, the focus is neither on individual diffe­rences nor on situational factors. Since temporary states may be seen as within-subjects manipulations of skill level they may give some insights in the workings of skill level on driver behaviour. This is why studies on the effects of temporary states are discussed under the heading of skill models in the present thesis.

Michon (1985) has categorized the motivational models as functional models. In most motivational models the emphasis is on risk. Since they have been introduced to a certain extent as a reaction to the skill model, the emphasis is on situational factors instead of individual differences. Skills and abilities are not regarded as important in motivational models and behaviour on the operational level has a low priority in this approach, or as Näätanen and Summala (1976) put it: “crucial to traffic safety is what the driver actually will do in any given situation, rather than his maximum level of performance and the environmental demands”. Motivational models mainly study behaviour on the tactical level, especially speed choice. Although individual differences have a low priority in motivational models, young (male) drivers are regarded as a subgroup of the driving population that deserves special attention because of their high accident involvement rate. Studies of the young male driver are grouped under the heading of motivational models, since in the literature motivati­o­nal factors are emphasized in the behaviour of the young driver.

The adaptive control models, also categorized as functional models by Michon (1985), focus on behaviour on the operatio­nal level and especially on steering behaviour. Individual differences are ignored while effects of situational and vehicle-related factors on operational behaviour are emphasized.

In the next paragraphs, and especially in the paragraphs 2.2.2 and 2.2.3, a number of experimental studies on driver behaviour are discussed.  However, it is not intended to give a comprehensive review. The results of studies on driver behaviour are merely referred to as illustrative examples for the model of driver behavior that is developed during the course of the next paragraphs.


2.2 Skill models


  • From accident proneness to differential accident involvement.


 The concept of accident proneness has been in vogue from the 1920s up until the 1960s, and played an important role in theories of driver behaviour. McKenna (1983) presented a conceptual analysis of accident proneness. The idea was that some individuals are more liable to be involved in accidents than others. The statistical techniques that have been applied to resolve this issue have given rise to substantial contro­versy. One of the problems mentioned by McKenna is that differential accident liability can always be attributed to differences in exposure to risk. Moreover, the lack of a clear definition of accident proneness has resulted in confusion. Several meanings have been assigned to the concept of accident proneness. Some have understood it as that most accidents are caused by a few people. This is associated with the definition of accident proneness as a disproportionate involvement in accidents in a statistical sense. However, the mere randomness of accidents suggests that some people have been involved in more accidents than others because of ‘bad luck’. Others have regarded it as an individual property, or as a persona­lity characteristic or disposition leading to a disproportional accident involvement. In that case accident proneness is a trait. However, the connection between these (personality) characteristics and actual car driving behaviour resulting in a higher accident involvement is unclear.

McKenna (1982) proposed the differential accident involvement approach as an alternative to the concept of accident proneness because this would offer a better theoretical understanding of the psychological abilities and characteristics associated with human error. Further advantages of this approach are that it does not suffer from the moral and emotional connota­tions associated with accident proneness, and that it is based on psycholo­gical testing instead of statistical modeling. The differential accident involvement approach evaluates the contribution of psychological abilities instead of personality factors to accident involvement. Although this approach has become known as an important representative of the so-called skill model, it is important to note that it is not driving skill as such that is being evaluated but psychological abilities that are assumed to be related to driving skills. Efforts were made to identify the psychological abilities critical to safe car driving. A substanti­al amount of research was devoted to the study of corre­lati­ons between performance on perceptual-motor tasks that were assumed to measure abilities required for safe driving on the one hand and acci­dents on the other hand.

Unfortunately, because this approach is purely correlational, the nature of the relation between psychological abilities and accident involvement is not made explicit at all. The existence of such a relation is assumed on intuitive grounds and based on face validity. Because the process controlling this assumed relation was not investigated, the effects of psychological abilities on operational driving performance and on behaviour on the tactical level have not been examined. Therefore, accident involvement has been the only dependent variable in this line of research. The results were generally disappointing. A small overview of some of the extensive relevant literature gives the following results:

Vision is generally accepted as being of central impor­tance in driving. Yet correlations between several visual performan­ce tests such as static acuity, dynamic acuity, visual field, glare recovery and recognition on the one hand and accident rate on the other, are typically lower than 0.05 (Rumar, 1988).

The psychological test that has probably been studied most often in relation to accident involvement is the embedded figures test (EFT) of Witkin. This test measures the cognitive style of ‘field independence’ and it requires that a simple form is found within a background. The EFT has been presented as predicting accident rate. Mihal and Barrett (1976) reported a correlation of 0.24 between EFT performance and accident involvement. Loo (1978) obtained a correlation of 0.42 with self-reported accident rate. However, Harano (1970) found a correlation of only 0.001 and McKenna et al. (1986) found a non-significant correlation of 0.19 between EFT per­formance and accident rate. Also, Quimby and Watts (1981) failed to obtain a significant correlation with accident involvement.

Other psychological tests, such as the dichotic listening test, Stroop test and reaction time tests also have been reported to be poorly related to accident involvement (McKenna et al., 1986; Quimby and Watts, 1981).

Noordzij (1990) reviewed the German literature on individual differences and accident liability. Perfor­mance measures on a wide range of tests failed to predict safe driving in any of the reviewed studies. Some studies even reported relations contrary to the expected direction, such that better performance in the laboratory and on the road was associated with poorer accident histories.


McKenna et al. (1986) gave two explanations for the low correlations. The reliability of accident scores is low when these are obtained over only a few years. This makes it impossible to obtain high correlati­ons between accident rate and test per­formance. Furthermore, accident rate probably reflects diffe­rent psychological abili­ties that cannot be captured in a limited number of tests. Häkkinen (1979) demonstrated that the reliability of acci­dent scores increases by lengthening the time over which accidents are measured. He argued that the lack of significant relations between test scores and accident involvement in so many studies was caused by short exposure periods and poor control of environmental risk. Häkkinen studied accident involvement of professional bus and streetcar drivers and found significant differences between safe drivers and acci­dent involved drivers on a number of psychological tests measuring, for example, eye-hand coordination, choice reaction time and psychomotor personality factors. The correlations were over 0.40. The study of Häkkinen has often been referred to a evidence for the skill model, and it is one of the few studies that supports the model.

In summary, psychological abilities assumed to be related to driving skills have proven to be unrelated to accident involvement, except perhaps for professional drivers. Summala (1985) explained the results of Häkkinens’ study by the forced-paced nature of the driving task for this group of drivers. The task of pro­fessional bus drivers is paced by time-schedules and differs from the task of private drivers who are able to decrease the speed, overtake less often or avoid bad conditi­ons. The explanation suggested in this thesis is that the driver adapts behaviour on the tactical level to the level of operational performance if the driving task is self-paced. This prevents a higher accident involvement for drivers with poorer psycho-motor abilities. This ofcourse assumes that drivers with poorer psycho-motor abilities are characterized by poorer operational performance. However, when the task is forced-paced adaptation is not possi­ble. Unfortunately, the effects of psycho-motor abilities on operatio­nal performan­ce and on tactical behaviour, such as speed choice, have not been studied, thus making it impossible to prove the existence of such an adaptive mechanism from the data presented so far. There is however evidence that adaptive processes play an important role in accident causation of elderly drivers, who suffer from age-related performance decrements and in some transient state-related performance decrements. In that case the term compensati­on is applied instead of adaptation.


  • Individual differences in skill: the elderly driver.


It is well documented that older drivers have to cope with declining vision and exhibit poorer performance on a wide range of tests of perceptual and motor ability and response speed (see for example Ysander and Herner, 1976). Ranney and Pulling (1990) found that older drivers (74-83 years of age) score lower on laboratory tasks requiring rapid switching of attention. Rackoff and Mourant (1979) reported poorer performance of older drivers on motor tests and especially on the embedded figures test.

Yet the accident rate of elderly drivers is lower than expected on the basis of the skill model, although the fatality amongst elderly drivers is quite high due to their physical frailness (Evans, 1988; Brouwer, 1989). Hakamies-Blomqvist (1994) found that older drivers had fewer accidents at nighttime and under bad weather and road-surface conditions compared to younger drivers. Older dri­vers were also less often in a hurry, alcohol intoxicated or distracted by non-driving activities compared to younger drivers. These results were interpreted as evidence that older drivers avoid more diffi­cult conditions. Ranney and Pulling (1990) reported that complex traffic situations pose problems for elderly drivers. They are more often involved in multiple vehicle intersection accidents, while they are less involved in single-vehicle accidents. They questioned the idea that older drivers have higher accident rates than middle-aged drivers. Although drivers over 65 make up 11.2% of the driving population in the United States, they are involved in only 7% of all accidents. A study of Cerelli (1989) was cited reporting that drivers over 75 have a crash involvement rate that is 2.5 times lower than that of drivers aged 40, and 5 times lower than that of 20 year old drivers. According to Brouwer and Ponds (1994) the fatality risk for drivers of age 70 is about three times as high compared to drivers at age 20, due to physical changes such as osteoporosis and decreased cardio­vascular efficiency resulting in an increased physical vulnerability. Correction for this increased vulnerability gives a better impression of actual accident involvement of older drivers compared to younger drivers. Application of this correction factor resulted in almost equal casualty risks for 35 and 70 year old drivers in the Netherlands in the eighties. Evans (1988) also found that when correcting for increased vulnerability, fatalities for older drivers are less than for male drivers under 20.

The results suggest that, although older drivers suffer from decreased performance on most tests of psycho-motor and attentional abilities, their accident risk is not dramatically different from drivers of other age groups. In situations with high time-pressure and situations beyond the control of the driver accident risk appears to increase for older drivers. A possible cause for this phenomenon may be found in the distinction between self-paced and forced-paced driving situations. When the driving task is self-paced, the situation allows the driver to compen­sate for performance deficits. However, compensation is impossible in forced-paced situations. In that case the driver is subjected to higher levels of time-pressure. The results may then be explained in terms of a process of adaptation: older drivers may compensate for their degradations of psycho-motor abilities by changing their behaviour both at the strategic level and the tactical level. There are a number of research findings in support of adaptive mechanisms.

The ultimate decision at the strategic level is to give up driving. Kosnik et al. (1990) found that older drivers who had recently given up driving reported more visual problems compared to older drivers who had not given up driving. The results suggested that older drivers are aware of their visual deficits and that this awareness influenced decisions about driving. At the strategic level decisions are also made regarding the time of driving. Planek and Fowler (1971) and Ysander and Herner (1976) found that older drivers avoided driving in the dark, on icy roads and in unknown cities more than younger drivers. According to these authors, self-selecti­on seems to be a factor of great importance when judging the traffic safety risks of elderly drivers. Older drivers also may compensate for their age-related impair­ments by limiting their driving and avoiding risky situations and rush hours (Ranney and Pulling, 1990). In addition to this, there is some evidence in support of compensation at the tactical level. Ranney and Pulling found that older drivers drive slower compared to younger drivers. This was also reported by Rackoff and Mourant (1979). They cited the studies of Case et al. (1970) and Rackoff (1974) in which it was found that the vehicle speed of older drivers, in an instrumented vehicle, was about ten percent less than the speed of younger drivers. The tendency of older drivers to drive at lower speeds was also referred to by Rumar (1987). The proportion of accidents where speed is below average increases as a function of age.


  • Variations in skill as a function of temporary states.


Both the consumption of marijuana and alcohol result in temporary state changes. This is generally assumed to temporarily affect perceptual-motor abilities. Because of this, the literature on temporary states is discussed under the heading of the skill model, although it must be stressed that in practice this field of research is treated as a separate problem domain, while the results of these studies are normally not related to a specific driving model. The line of reasoning in this thesis is that marijuana and alcohol may effect psycho-motor abilities which may affect operational driving performance. The factors marijuana and alcohol may be considered as a natural experiment in which perceptual-motor abilities are manipulated within the driver. This then offers interesting opportunities to study the effects on tactical behaviour and the relation with accident involvement. The effects of this within-subjects manipulation on driving skills and driving behaviour may then give important information about the workings of the process of adaptation.



Marijuana. Moskowitz (1985) reviewed a large number of studies on the effects of marijuana on psychological abilities. In reaction time experiments neither the speed of initial detection nor the speed of responding appears to be affected by marijuana, although the frequently reported increase of RT variability suggests that attentional mechanisms are impaired by marijuana. Tracking is significantly affected by marijua­na. Also, perceptual functi­ons and vigilance are negatively affected by this drug.

However, based on a review of a number of epidemiological studies, Moskowitz (1985) concluded that there is little evidence for an increased risk of accident involvement under marijuana. Robbe (1994) reviewed the epidemiological literature as well and concluded that some people do drive after cannabis use and that drivers involved in accidents often show the drug’s presence. However, because alcohol has been a severe confounding factor in all surveys of accident-involved drivers, the independent contribu­tion of marijuana to accidents remains unclear.

The effects of marijuana on driving behaviour has been examined in many experiments. According to Robbe (1994), the foremost impression one gains from reviewing the literature is that no clear relationship has been demonstrated between marijuana and either seriously impaired driving performance or the risk of accident involvement. Smiley (1986) compared simulator and on-road studies of marijuana effects on car driving perfor­mance. In simulator studies with realistic car dynamics and in interactive simulators strong effects of marijuana on operational performance were found. In a study of Smiley et al. (1981) in an inter­active driving simulator variability of veloci­ty and lateral position increased during curve negotiation and while following cars and in windgusts. Variability of headway and lateral position while following cars also increased under marijuana. However, a larger headway was chosen during car-following under marijuana. In a study by Stein et al. (1983) with an interactive simulator, performance effects of marijuana were examined in a number of driving tasks such as car control during windgusts, curve following and lane changes. Although there were effects on steering performance, mean driving speed was lower under marijuana.

Several other studies have presented behavioural evidence suggesting that drivers may adapt their tactical behaviour to deteriorated operational performance by choosing a lower speed or by increasing headway in car-following. In an on-road study by Caswell (1977) drivers under marijuana drove more slowly. In an on-road study by Smiley et al. (1986) the effects of marijuana on several tasks such as car-following, curve following, open road driving, emergency decision making and obstacle avoidance were measured. Marijuana only had a few effects, but it signifi­cantly increa­sed headway in the car-follo­wing task. Smiley (1986) concluded that all studies indicate that when the driver under marijuana has the possibi­lity to choose a lower speed, there are no effects on lane position control while speed is reduced. Stein (1986) studied the effects of marijuana on driving behaviour in a number of driving tasks in a simulator. A dose dependent effect of marijuana on speed was found; drivers decreased speed more with higher doses. In a task requiring the driver to compensate for random wind gusts, a strong effect of marijuana was found on mean speed and speed variability. Drivers were also required to control speed and steering during the negotiation of curves. Again, marijuana decreased speed. The speed reduction was also found in an obstacle avoidance task. No effects of marijuana on steering behaviour were found.

Robbe (1994) performed three on-road experiments in which the effect of marijuana on car driving was examined. In a study with driving on a restricted highway it was found that marijuana affected steering performan­ce as indicated by an increased standard deviation of lateral position (SDLP). Subjects were instructed to maintain a constant speed of 90 km/h, or less if they felt incapable of driving safely at that speed. The greater the dose, the harder the subjects attempted to compensate as indicated by perceived effort and increased heart rate. Despite the instruction, there was a small reduction in mean speed under marijuana. Drivers rated the quality of their own driving performance lower with higher doses, suggesting that they were aware of the effects of marijuana.

In another experiment, Robbe (1994) had subjects drive on a highway with other traffic under the instruction to maintain a speed of 95 km/h. This also involved a car-following test in which subjects were instructed to maintain a 50 meter headway. A marijuana dose-dependent increase in SDLP was found and a decrease in speed under marijuana. Also, under marijuana headway increased although the increase was highest with the smallest dose. Reaction time to speed changes in the preceding vehicle increased under marijuana. However, reaction time was confounded with headway, such that RT increased with increased headway.

In a third experiment, Robbe (1994) examined the effects of marijuana in a city driving task. Driving performance was evaluated by trained observers (driving instructor). No effects of marijuana were found on driving performance. Under marijuana it took more time to complete the circuit, suggesting a lower speed, although this was not significant. Drivers under marijuana perceived their driving quality as poorer compared to placebo and perceived their effort as higher.

In conclusion, the studies of the effects of marijuana suggest that, firstly, it affects perceptual and psycho-motor skills, secondly, it affects performan­ce on the operational level, and thirdly, it affects behaviour on the tactical level, especially when the task is self-paced. Evidence was presented that the drivers are aware of performance decrements under marijuana. It may be hypothesized that the perception of feedback of these performance decrements is a necessary prerequisite for such a compensation strategy. However, the nature of the perception of feedback, whether it is conscious or unconscious, is at present unclear. When the task is self-paced instead of prescribed by the experimenter (by instructing the subject to maintain a fixed speed), effects of marijuana on operational performance may be limited due to compen­sation for decreased skills: when drivers are allowed to choose their speed, effects of marijuana on steering behaviour are generally absent, while effects on steering behaviour are found when speed is prescribed by the experimenter. This compensation mecha­nism may explain why epidemiolo­gical studies have been unable to find a relation between marijuana and acci­dent involvement.



Alcohol. A substantial part of the literature on accidents and driver behaviour concerns the effects of alcohol. The effects of alcohol on performance are well documented for a large number of tests. Only a few examples are given here. Moskowitz and Robinson (1986) reviewed the literature on the effects of alcohol on task performance. They analyzed the results of 178 studies that fulfilled regular methodological criteria. Forty-five percent of the studies indica­ted impairment at 0.04% BAC (blood alcohol concentration) or less. The majority of studies reported impair­ment at below 0.07% BAC. Impairments were found in tracking, divided atten­tion, information processing, eye movements and psycho-motor skills, especial­ly in tasks requiring skilled motor performan­ce and coordination. Divided attention deterio­ra­ted already at very low BAC levels. Signal detection, visual search and recognition tasks also showed impairments at low BAC levels. Kennedy et al. (1989) measured the effect of BAC level on performance in a battery of nine tests measuring motor speed, symbol manipulation/reasoning, cognitive proces­sing speed and speed of response selection. Performance on eight out of nine tests was strongly and monotonously affected by BAC.

Evans (1991) estimated that 47% of fatal accidents, 20% of injuries and 10% of property damage are attributa­ble to alcohol. This means that alcohol contributes importantly to traffic accidents with the contri­bution increasing as crash severity increases. Evans (1989) concluded that eliminating alcohol would reduce traffic fatalities in the United States by 47±4 percent. Guthrie and Linnoila (1986), suggested that epidemiological studies indicate a disproportionate number of alcohol related fatal crashes involving young male drivers below 24 years of age. The majority of alcohol related accidents occur during the weekend, especially at evening hours, and in summer. According to Smiley (1989), alcohol is involved in 62 percent of all fatal single vehicle accidents.

There is also overwhelming evidence that alcohol affects operational driving performance. Louwerens et al. (1986) studied the effects of four doses of alcohol in a task where subjects were required to drive with a constant speed of 90 km/h with a constant lateral position between the right lane bounda­ries. Standard deviation of lateral position (SDLP) increased in a dose dependent manner as a function of alcohol. The subjective assessment of driving performance by the driver correlated poorly with SDLP and BAC level. This suggests that drivers were unaware of performance decrements under alcohol. In a simulator study with several driving tasks, Stein (1986) found that alcohol increased the number of accidents. Also, in a task requiring the driver to compensate for windgusts while following a winding road, steering behaviour was significantly affected by alcohol, and lane position variability was increased under alcohol. No effects of alcohol on mean speed were found, although speed variability increased under alcohol. Stein and Allen (1986) reported the results of an experiment that aimed to unravel the effects of alcohol on performance and risk taking. This is important because the effect of alcohol on accident involvement has often been attributed to an increase in deliberate risk taking. The effects of alcohol on driver behaviour was studied in a driving simulator and on a closed course. Both methods gave essentially the same results. Alcohol increased speed variability and the number of times the speed limit was exceeded. As drivers were well aware of the speed limit and the probability of detecti­on, and since speed feedback was available both visually and aurally, the increased variability suggested decrements in the driver’s perception and/or speedometer monito­ring. Also the frequency of running red lights was increased by alcohol. The subjective probability of running a red traffic light was affected by alcohol while risk acceptance was not affected by alcohol. Stein and Allen saw these results as evidence that the driver’s perception of speed and distance was impaired by alcohol, and that the drivers were unaware of this impairment. They concluded that the locus of effect of alcohol on risk taking is on the perceptual level instead of the risk acceptance level. Wilde et al. (1989) investigated the effect of BAC on performance on a response timing task and a general knowledge quiz. The findings did not support the hypothesis that alcohol increases deliberate risk taking. A significant increase in overconfidence in the cognitive task was observed under alcohol, but overconfidence and risk taking were not correlated.

In an on-road study by Caswell (1977) drivers performed several tasks such as overtaking, driving on straight road sections and curves and through narrow gaps while responding to road signals, traffic signals and auditory signals in a subsidiary task. Alcohol resulted in increased speeds and poorer tracking performance. In an on-road study of Smiley et al. (1986), alcohol at 0.05% BAC was asso­ciated with significantly higher speed on straight roads and in curves. Also, alcohol decrea­sed the number of peripheral stimuli detected. According to Smiley (1986), in three of the four studies reviewed, where effects of alcohol on speed were recorded, alcohol was associated with an increase in speed while it significantly affected steering performance in a number of studies (Smiley, 1989). In a study of Hansteen et al. (1976), alcohol increased the number of cones hit and the amount of ‘rough vehicle handling’ while it increased speed. Robbe (1994) tested the effect of alcohol on driving performance during city driving. Alcohol decreased performance in ‘vehicle handling’ and ‘action in traffic’, while speed was increased. Subjects thought, however, that they had driven as well as following placebo and there was no effect of alcohol on effort invested in the driving task.

In summary, alcohol strongly affects perceptu­al and psycho-motor skills as well as performance on the operational level of car driving. At the same time, alcohol increases speed. In this the effect of alcohol is opposite to the effect of marijuana. It may then be hypothesized that a lack of compensation for impair­ments in performance is the cause for the very strong role of alcohol in accident involvement. Evidence was presented that suggests that drivers are unaware of performance decrements under alcohol. This may be somehow related to the absence of compensatory speed changes and effort.


  • Conclusions and consequences for the present model.


Correlation studies in the tradition of the differential accident involvement approach have been unable to demonstrate a relation between psycho-motor abilities and accident involvement, with the possible exception of professional drivers. This may be explained by the self-paced nature of the driving task for private drivers which allows compensation for poorer skills, such that effects of psycho-motor abilities on accident involvement are decreased. Because the driving task for professional drivers is often controlled by time schedules and fixed driving times and routes, their task is more of a forced-paced nature. In the differential accident involvement approach a relation between psycho-motor abilities and driving performance was assumed instead of tested and driving behaviour on both the operational and the tactical level typically was not examined by this approach.

The effects of individual differences in psycho-motor abilities on behaviour on the strategic and the tactical level was illustrated by the case of the elderly driver. Accident involvement of the elderly driver is much lower than expected from the skill model. A possible cause for this may be that behaviour on the strategic level and the tactical level is adapted to poorer psycho-motor abilies of elderly drivers, in the sense that these drivers often refrain from driving in the dark, under bad weather conditions and so on, and that they drive with lower speeds.

Variati­ons of skill within the driver as a function of temporary states were illustrated by examining the effects of marijuana and alcohol. These studies have supplied strong evidence for effects on psycho-motor abilities and operational performance, together with effects on tactical behaviour. Evidence was presented that supports the hypothesis that effects of marijuana on accident involvement are tempered because of compensation of behaviour on the tactical level for degradations of perceptual-motor abilities and operational performance under marijuana, and that the driver perceives the feedback of poorer operational performance. Alcohol also strongly affects perceptual-motor abilities but the driver appears to be unable to perceive this. This may explain why the driver does not compen­sate behaviour on the tactical level which may be the cause for a strongly increased accident risk. The results are consistent with the idea that behaviour on the tactical level is adapted by decreasing speed or increasing headway when feedback of operational performance decrements is perceived by the driver.

The results presented so far, are summarized in figure 1 as a first attempt to describe a model of driver adaptation. Individual differences in psycho-motor abilities, for example as a function of age, and effects of temporary states induced by marijuana or alcohol on these abilities affect operational car driving performance.



Figure 1. Model of driver adaptation, derived from the discussion of skill models


Normally, these effects are monitored by the driver and the driver perceives the feedback of these effects, although this may be inhibited by alcohol. Two kinds of adaptation may occur. Either behaviour on the tactical level is adapted to compensate for decreased operational performance, if driving is self-paced. If driving is not self-paced the driver may also increase effort to improve operational performance or, alternatively, the driver may adapt behaviour on the strategic level by deciding to give up driving for a while or altogether, or by avoiding driving in bad conditions.

Thus far, only the effects of poorer operational performance have been examined, resulting mainly in decreased speeds. The reverse, better performance resulting in increased speed has not been examined. However, in the motivational models, discussed in the next paragraph, it is argued that behaviour on the tactical level, such as higher speed or smaller headway during car-following, are the result of motiva­tional factors instead of improved performance.


2.3 Motivational models of car driving


During the seventies driver behaviour modeling shifted to motivational approaches as alternatives for the skill model. The main reason for the rise of motivational models was the rejection of accident proneness as an explanatory concept and the disappointing results of the differential accident involvement approach (Summala, 1985; Näätänen and Summala, 1974). The fact that increasing driver skills and decreasing environmental demands did not result in increased traffic safety in a straightforward manner was attributed to the self-paced nature of the driving task in which the driver is able to control task difficulty. The emphasis of the motivational models on transient or situational factors came as a response to the individual difference approach of the skill model (Ranney, 1994). The actual behaviour of the driver in any given traffic situation was given more importance than the maximum level of performance. The motivational models that emerged in the seventies were based to a large extent on a few articles, written in the sixties, that stressed the self-paced nature of the driving task. Taylor (1964) claimed that galvanic skin response (GSR) per unit of time was constant. He regarded GSR as a measure for subjective risk and hypothesized that the driver adjusts the level of risk taking to keep emotional responses on a constant level. In this view, speed was adjusted to keep subjective risk on a constant level. This notion of compensation by adjusting speed differs from compensation as discussed in previous paragraphs. Taylor (and the motivational models in general) conjectured that speed was adjusted to compensate for subjective risk, while the viewpoint expressed in previous paragraphs suggests that speed is adjusted to compensate for degradations or improvements in operational performance. Both viewpoints stress the importance of the self-paced nature of the driving task. The Risk Homeostasis Theory of Wilde can be seen as a descendant of the principle formulated by Taylor. Cownie and Calderwood (1966) argued that accidents are the product of a simple closed-loop process in which feedback from the consequences of driver actions and decisions play an important role. They emphasized the importance of finding a good balance between motivating and inhibitory forces of positive and negative motivating events. This viewpoint has played an important role in the model of Näätänen and Summala.

Motivational models of driver behaviour have become synonymous with models of risk taking. The most important variants are Wilde’s Risk Homeostasis Theory, Näätänen and Summala’s Zero Risk Model and Fuller’s Threat Avoidance Model. The relation between motivations other than risk taking and driving behaviour has been examined in a limited number of studies, for example French et al. (1993), Rothengatter and de Bruin (1986) and Rothengatter (1988). French et al. (1993) investigated the relation between decision-making style, driving style and accident rates. The results of this study do not give an indication how behaviour on the tactical level is affected by motivational factors. Speed is described as an aspect of driving style together with more motivational concepts such as social resistance and deviance. This indicates that the concept of “driving style” is not clearly defined since it mixes overt behavioural manifestations with covert motivational constructs. Because of this it is difficult to integrate with other approaches discussed in this thesis. Rothengatter and de Bruin (1986) and Rothengatter (1988) examined the relation between speed choice and motivational factors within the framework of Fishbein and Ajzen’s model of reasoned action. It was found that speed choice is determined by four motivational factors: pleasure in driving, risk, travel time and costs. Pleasure in driving proved to be the strongest determining factor of speed choice, such that the subjects with the highest speed scored highest on pleasure in driving. However, pleasure in driving was also related to the top speed of the vehicle and thus to vehicle characteristics: drivers of high performance cars scored higher on pleasure in driving compared to drivers of low performance cars. This was explained by suggesting that drivers with more pleasure in driving, as a characteristic of the person, are more inclined to buy high performance cars. However, the reverse could also be true: drivers of high performance cars may enjoy driving fast more than drivers of low performance cars because the car allows better control at higher speeds. This issue should be considered in further studies.


  • Risk Homeostasis Theory.


Risk compensation models propose a general compensatory mechanism whereby drivers adjust their driving (e.g. speed) to establish a balance between what happens on the road and their level of accepted subjective risk. Wilde’s Risk Homeostasis Theory (RHT) is based on the assumption that the level of accepted subjective risk is a relative­ly stable personal parameter. An important implication is that drivers will nullify the effects of safety improvements by driving faster or behaving less cautious in general. This has resulted in considerable controversy. RHT (see for example Wilde, 1982), previously known as risk compensation theory (for example Wilde, 1976), consists in fact of two models; an individual model of driver behaviour and an aggregate model that relates driver behaviour to accident rate. In the individual model the driver is assumed to have a target level of risk that represents the amount of accident risk the driver accepts. This is continuously compared to the perceived level of risk which is an estimate of the accident risk in the immediate future. The perceived level of risk is determined by the vehicle path, the road environment and paths of other road users (the stimulus situation) and anticipations regarding the development of the stimulus situation in time. When there is a discrepancy between perceived risk and target level of risk the driver makes a behavioural change, either in the direction of reducing the level of risk if perceived risk is larger than target level of risk, or in the direction of increasing the level of risk if the reverse is true. This results in a homeostatic process in which the driver aims to match the perceived level of risk with the constant target level of risk.

The name ‘risk homeostasis’ theory is, however, essentially derived from the aggregate model, see figure 2. Again, drivers compare the perceived level of risk with the target level, resulting in adjustment actions. Aggrega­ted over all road users over a given time span, these adjustment actions will produce the rate of accident frequency and severi­ty. This has a lagged feedback on perceived risk. Decreased accident rates then decrease percei­ved risk after some time, resulting in adjustment actions that increase accident risk in a homeostatic way. The only factor that affects accident risk then is the target level of risk. In the aggregate model, skills play some role, although improvements in skills are unlikely to have a lasting effect on accident frequency and severity (Wilde, 1981). Perceptual skills determine the extent to which subjective risk corresponds to objective risk. It is important to note here that Wilde does not think that improving risk perception skills will improve traffic safety. Decision skills refer to the drivers’ ability to bring about the desired adjustment, while vehicle handling skills determine the extent to which the planned actions are executed properly. In the individu­al model of risk compensation skills only have a modest influence on perceived level of risk and on the transformation of sensory input. Thus, the concept of skill, as discussed previously, plays no role in Wilde’s theory. Individual differences are restricted to individual differences in motivational states that may affect the target level of risk.



Figure 2. Aggregate model of Risk Homeostasis Theory (from Wilde, 1982).


The effects of motivations on choice of time-headway (THW) during car-following has been studied by Heino et al. (1992). Drivers were classified as sensation seekers or sensation avoiders depending on their scores on personality inventories. It was expected that sensation seekers seek more risk and are thus characterized by a higher level of target risk. It was found that sensation seekers followed at a smaller THW which is associa­ted with higher objective risk by several authors. However, Heino found that this smaller THW was not associated with a higher subjective risk. It appeared then that both sensation groups accepted the same level of risk, and thus did not differ in target level of risk, but for sensation seekers this perceived level of risk was achieved at a smaller THW compared to sensation avoiders.

Meanwhile the discussions concerning the validity of RHT have gone on for years. Evidence provided by Wilde supporting RHT has been refuted by various counterexamples (for example, McKenna, 1982; Evans, 1985). Even more controversy has arisen over Wilde’s hypothesis that safety improve­ments will not work unless it affects the target level of risk (McKenna, 1988). The ability of drivers to monitor accident risk has been questioned and the assertion that drivers experience or accept risk has been challen­ged (for example, Evans, 1991). The plausibility of seeking some level of risk has been seriously doubted and according to several authors drivers seek the lowest possible, or zero, level of risk.


2.3.2 Zero Risk Theory.


Näätänen and Summala (1974, 1976) have presented a model of the driver’s decision making that has become known as the zero-risk theory. In this model motivational factors such as subjective risk, other motivations and vigilance determine driver decision-making and behaviour, see figure 3. The subjective risk monitor is a crucial element in this model. It was conceptualized as a monitor that generates subjective risk or fear depen­ding on the experienced risk in the present or expected traffic situation. Activation of subjective risk inhibits ongoing behaviour in the sense that it results in behaviour such as slowing down. It also has an inhibitory effect on subsequent behaviour in the sense that drivers learn to behave more cautiously in similar situations. However, most of the time subjective risk equals zero. Other motivations provide an excitatory component resulting in increased speed. These other motives are affected by persona­lity factors such as aggressiveness and the state of mind of the driver. Several changes in the traffic environment may affect subjective risk. Drivers may drive faster or overtake other cars more frequently before subjective risk is experienced. The best traffic safety measures are those that decrease objective risk but increase subjective risk.



Figure 3. The zero-risk model of Näätänen and Summala (from Näätänen and Summala, 1976).


An important difference with the risk homeostasis theory is that in the zero-risk theory the driver is assumed to accept no risk at all, that is, the target level of risk is zero. Näätänen and Summala state on the one hand that subjective risk is an important determinant of driver behaviour (as an inhibitory factor) while on the other hand most of the time subjective risk equals zero. Fuller formulated his Threat Avoidance Model, to be discussed later, partly in response to this inconsistency. In later publications by Summala (1985, 1988) the concept of subjective risk was more or less abandoned as an explanatory factor in driver behaviour: it is not risk that the driver attempts to control, but instead, drivers control and maintain safety margins, since normally the driver gives no consideration to risk (Summala, 1988). The concept of subjective risk should be reserved for ‘arm-chair estimates’ of the risks of, for example, traffic scenes shown to subjects for research purposes (Summala, 1988). He stated that the output of the subjective risk monitor was meant to represent a fear response resulting from the perception or expectation of loss of control over the car or of being on a collision course (Summala, 1986). The concept of ‘perceived loss of control’ thus replaced the continuous control of subjective risk. Taylor (1976) postulated that subjective risk is equivalent to the percep­tion of loss of control. The relative unimportance of subjective risk in the zero-risk theory was further exemplified by Summala by his observation that as drivers become more experienced driving becomes more automated and feelings of uncert­ainty or fear related to perceived loss of control decrease because confidence in control skills increases. He regarded the fact that drivers are not very well able to take account of the objective variance in the traffic system as the most important point of the zero-risk theory. The second main point is that different kinds of motives push drivers towards higher speeds and if the traffic system provides (environ­mental) opportunities to satisfy these motives, drivers are inclined to use them. This is not risk compensation but merely the result of a tendency for satisfaction of motives. Examples of such motives are that high speed as such is motivating and higher speeds mean shorter travel times. Also the conserva­tion of effort is seen as an important motive resulting in a reluctance to slow down. Speed also provides outlets for many other ‘extra motives’ such as the motive to demonstrate driver skills to peers. In order to improve traffic safety, drivers should be prevented to satisfy their motives by introducing speed limits.

Thus, the zero-risk theory has undergone significant changes in time. The role of the inhibitory forces associated with subjective risk has diminished while the excitatory motivational components are emphasized more strongly. Also, the concept of safety margins has replaced the concept of subjective risk and ‘perceived loss of control’ has become an important factor in the control of safety margins. Although this is not stated explicitly by Summala, loss of control is strongly related to performance on the operati­onal level. Because safety margins appear to be underlying controlling variables of driver behaviour, different subtasks such as lane keeping, car-following, curve negotiation, gap acceptance and overtaking should be analyzed in more detail (Summala, 1988). The concept of safety margin will be discussed later.

Yet, a large number of studies have focused on one aspect of the early version of the zero-risk model: the assumption of a discrepancy between subjective and objective risk. A threshold for risk perception is assumed, and risk compensation occurs only if this threshold is exceeded. Below this threshold subjective risk is experienced as zero. The idea then became in vogue that risk perception may be distorted as a function of several factors such as age or driving experience. Most studies on risk perception have focused on the young driver and these will be reviewed later.


  • Threat Avoidance Model.


Although the Threat Avoidance Model of Fuller is typically classified as a motivational theory it is actually more a theory of learning applied to car driving. Fuller (1984) has formulated his threat avoidance model of driving behaviour partly in an attempt to solve two problems associated with the zero-risk theory. The first problem was the dissociation between subjective and objective risk in the zero-risk model. The second problem was that subjective risk reactions constitute an important determinant of decision making while at the same time the driver feels no subjective risk most of the time. Since the experience of subjec­tive risk is aversive drivers are motivated to escape from situations that elicit subjective risk or to avoid those situations. Thus, subjective risk reactions are important determinants of behaviour assuming that drivers are able to anticipate and make appropriate adjustments to upcoming hazards. If driving consists to a large extent of learned avoidance reactions drivers will rarely experience any subjective risk at all.

Figure 4 gives a representation of the model. Because, in general, the driver’s own actions determine whether or not interactions with the road environment will be punishing, stimuli in the road environment have an aversive potential. A discriminative stimulus is some precursor of a potential aversive stimulus which has been learned by association. Several consequences are experienced as aversive. These may be very common consequences such as loss of self-pacing in the driving task and a state of high arousal, or less common consequences such as loss of vehicle control, physical injury, material damage, loss of self esteem and so on. The driver then is motivated to prevent these negative consequences and not just to avoid the experience of subjective risk. The discriminative stimulus is a function of the drivers’ perception of speed, the road environment and skill. It is an integration of these features projected into the future. For example, the combination of a particu­lar speed, a curve that is approached and an estimate of present vehicle handling skills determine together whether this constitutes a discriminati­ve stimulus. When perceived capability is a primary factor underlying the discriminative stimulus some compensatory response is generated that may consist of raising the performance level or some behavioural adjustment such as lowering the speed or increasing headway during car-following. It is then important to note that the issue of feedback and compensation is solved in Fuller’s model by assuming behavioural anticipatory avoidance responses to a discriminative stimulus. A delayed avoidance response may occur if the anticipatory avoidance response is inadequate. If there is a discriminative stimulus, the probabi­lity of an anticipatory avoidance response or a non-avoidance response is both determined by the subjective probability of expected threat and by the rewards and punishments of the response alternatives. If no discriminative stimulus is detected then either no threat is realized or a threat occurs demanding a delayed avoidance response of the driver.




Figure 4. Fuller’s threat avoidance model (from Fuller, 1984).



This may occur because of perceptual errors, inadequate learning to recognize a discrimi­native stimulus, unpredictable behaviour of other road users or sudden mechanical failure. If the driver makes no anticipatory avoidance response a delayed avoidance response represents an escape from an aversive stimu­lus. Learner drivers are more likely to make delayed avoidance rather than anticipatory avoidance responses than experienced drivers because they have not yet developed associations between discriminative and potential aversive stimuli. The higher accident involvement of young drivers was explained by this lack of associations, while Näätänen and Summala (1976) attributed the high accident involvement of young drivers to the relative strength of ‘extra motives’ in this group, such as the desire to drive at high speeds.

Based on this framework, Fuller suggested that there may be drivers who are predominantly anticipatory avoidance responders while others are predo­minantly delayed avoidance responders. This may then be related to indivi­dual differences in the ability to detect hazards. This ability has been referred to as ‘hazard cognition’ by several authors and it is believed to be related to accident involvement of young and inexperienced drivers.



  • Individual differences in motivations: the young driver.


Young drivers, especially males, from 18 to 24 are dramatically more often involved in accidents compared to drivers of other age groups (Evans, 1991). This overinvolvement of young male drivers in the accident statistics is one of the most consistently observed phenomena in traffic throughout the world. A confounding factor is that young drivers usually are the least experienced. Simpson (1986) stated that the reason for the high involvement of young drivers in vehicle accidents, even when exposure to risk is controlled for, is not clear. While young people from 16 to 24 years of age represent 17% of the Canadian population, they account for 31% of all traffic fatalities, 33% of all traffic injuries and 58% of all driver fatalities in Canada. Because risk is usually applied as an explanatory concept for the high accident involvement of young drivers, studies on this issue are discussed here.

The meanings of the risk-related concepts will be discussed first as they are applied in the case of the young driver. Risk-taking is something which is usually inferred from observation of behaviour (Saad, 1989). Traffic researchers often assume that high speed and close following carry a higher objective risk. Drivers who display such behaviours are then assumed to take more risks. Jonah (1986) has given several examples of higher risk-taking in young drivers. Young drivers have been reported to drive at higher speeds (for example Wasielewski, 1984; Soliday, 1974), although the correlation between speed and age is generally very low. Also, younger drivers have been reported to follow at smaller headways (Evans and Wasielewski, 1983). This behaviour associated by a number of researchers with higher risk taking in young drivers, is often seen as evidence that young drivers either deliberately seek more risk or accept a higher target level of risk, and thus have a higher risk acceptance or risk utility, or have a deficient risk perception, i.e. they fail to see the risk involved with such behaviours. The former concept is associated with Wilde’s model while the latter is more closely associa­ted with the models of Näätänen and Summala and Fuller. Both concepts have been used as expressi­ons of subjective risk. One of the problems with risk research centers around the conceptual vagueness of the term ‘subjective risk’. It is not always clear whether it refers to a failure to perceive the potential danger (hazard perception), to an underestimation of the probability of a certain event (subjective estimation of objective risk), to the driver’s poor appreciation of his or her ability to cope with the situation, or to attitudes and motives regarding safety (risk acceptance) (Saad, 1989). Haight (1986) argued that the only valid meaning of the term ‘risk’ refers to empirical probability or expected cost. In that case risk is a statisti­cal concept referring to the outcome of behaviour on a highly aggregated level. In such a view there is little room for terms such as subjective risk, risk perception or risk acceptance. Another problem associated with some risk research is the circularity in reasoning. The explanati­on for behaviour associated with a higher objective risk, resulting in more accidents, is that drivers deliberately want a higher objective risk or fail to see the objective risk involved. So the behaviour to be explained is explained in terms of the outcomes of precise­ly the same behaviour.

The high accident involvement of young drivers has often been attributed to poorer risk perception, resulting in a larger discrepancy between subjective risk and objective risk for young male drivers. Jonah (1986) stated that, even though young drivers may perceive as much risk while driving as older drivers and thus do not deliberately seek more risk, they may be more confident in their ability to avoid an accident. In Jonah’s review, risk perception was meant to reflect the subjective estimation of objective risk. He presented some evidence that younger drivers had poorer risk perceptions in the sense that they estimated objective risk lower compared to other age groups. However, it is not clear what this means. Basically, the subjects were asked about their knowledge of statistical facts over which even traffic researchers are still debating. Wilde’s model is the only risk model that assumes that knowledge of drivers concerning statistical accident risk affects behaviour. It has been objected by many authors that it is highly unlikely that drivers are aware of accident statistics or that these play any role in driving behaviour. Finn and Bragg (1986) also measured subjective risk or risk perception as the estimation of objective risk as a statistical phenomenon by asking questions such as ‘how many people were killed in traffic accidents in Massachusetts last year’. Although it was found that young drivers see driving as more dangerous when general questions about accident risk were asked, and they recognize that their age group is at greater risk of accident involvement compared to other age groups, they see their own chances to be involved in an accident as lower compared to their own age group and older drivers when specific questions about their own risk are asked. Finn and Bragg saw this as evidence that young drivers differ from older drivers in lower risk perception and not in risk acceptance and that risk perception, or at least seeing less risk in driving situations compared to older male drivers, may account for the high accident involvement of young male drivers. Bragg and Finn (1982) found that specific behaviours such as speeding and tailgating were perceived as less risky by young drivers. They hypothe­sized that the lower perception of risk in young drivers may be attributa­ble to the greater confidence in their skill or belief in their ability to handle a particular hazardous situati­on. Risk perception was thus connected with confidence in driver skills.

Matthews and Moran (1986) assessed the relationship between perceived skill and perceived risk. In their study young (18-25) and middle-aged (35-50) male drivers completed a question­naire on accident risk and driving ability and gave subjective ratings of risk to videotaped traffic situations. Young drivers gave lower ratings of accident risk for driving situations which demanded fast reflexes or substantial vehicle handling skills. They rated their own risk of an accident and driving abilities as being the same as for older drivers. However, they saw their peers as being significantly more at risk and as having poorer abilities than themselves. The data suggested that risk perception is strongly related to perceived ability. Spolander (1982) found that drivers with three years of experience judged themselves to have better driving skills compared to other drivers. The drivers who gave the highest ratings on skill also reported  faster driving.

Brown and Groeger (1988) distinguished two inputs to the process of risk perception: information on potential hazards in the traffic environ­ment and information on the joint abilities of driver and vehicle to prevent that hazard potential being transformed into actual accident outcomes. Risk perception is the detection of any shortfall in the ability to avoid realizing the potential of immedia­te task and environmen­tal hazards.

This short review makes clear that the concept of risk perception has more than one meaning which makes the interpretation of results from these studies difficult. On the other hand, subjective risk has been linked more and more with (perceived) driving skills. This suggests that, at least in the mind of the driver, subjective risk really means fear of loss of control, as was suggested during the discussion of the zero-risk theory.


In another line or research, the high accident involvement rate of especially young male drivers has been associated with the use of alcohol and drugs as a lifestyle-related phenomenon. Although as many as 50% of fatally injured young drivers have been found to be positive for alcohol, this is slightly lower than the frequency for older drivers. Also, it has become clear from surveys that drinking and driving is widespread among younger drivers although they had typically consumed less alcohol than older drivers. In alcohol related crashes younger drivers tend to have lower BACs than older drivers (Simpson, 1986). Yet, the high accident involvement among young drivers has been attributed to risky driving behaviour as an aspect of adolescent lifestyle that is embedded in the same set of personality and behaviour aspects as other kinds of adolescent problem behaviour such as delinquency, problem drinking and illegal drug use and smoking (Jessor, 1986). Also, Beirness and Simpson (1986) found that accident involved young drivers score higher on thrill and sensation seeking, alcohol consumption and frequency of drinking while they score lower on traditional values and usage of seat belts. In short then, some authors believe that the high accident involvement of young, and especially male, drivers is a lifestyle related phenomenon resulting in a higher deliberate risk acceptance or higher target level of risk, using the terminology of Wilde. But in that case it would be expected that a higher percentage of accident involved young drivers are positive on alcohol and have higher BAC levels compared to older drivers. This obviously is not the case.

It has frequently been reported that the relative risk of becoming involved in a fatal accident rises faster as a function of BAC level for younger drivers compared to older drivers (Simpson, 1986; Kretschmer-Bäumel and Kroj, 1986). In other words, with increases in the amount of alcohol consumed, the accident risk increases for all age groups, but much more rapidly for the young. Although the typical explanation for this has been the relative inexperience of young drivers with alcohol, driving and the combination of these, there is no scientific evidence that inexperience with drinking and/or driving is the cause for the stronger impact of alcohol on accident rate for the young (Simpson, 1986; Mayhew et al. 1986). Although the reason for the interaction between age and BAC level on accident involvement is not clear, it suggests that both factors share a common locus of effect, in the sense that the factor that causes the higher accident rate of young drivers is aggravated by alcohol. In the discussion of the effects of alcohol it was suggested that the lack of compensation for impaired performance may be the cause for the large role of alcohol in accident causation. Evidence was presented that drivers are unaware of performance decrements under alcohol which is possibly the cause for the absence of compensatory speed changes and effort. From the same perspective it may be suggested that young and inexperienced drivers have not yet learned to recognize the effects of situational factors on their perfor­mance and thus fail to compensate for these effects resulting in speeds that are too high for the circumstances.


2.3.5 Conclusions and consequences for the present model.


In the literature on risk perception it is often suggested that young drivers are more involved in accidents because the discrepancy between subjective risk and objective risk is higher in this group. The reason proposed in a number of studies is that young drivers tend to overestimate their own abilities, although the overestimation of one’s abilities appears to be a general phenomenon for all age groups. A drawback of studies on young drivers is that the problem is often examined without simultaneously measuring operational and tactical behaviour. Following the line of reasoning of this thesis, it may be hypothesized that the high accident involvement of young drivers is caused by a failure of young drivers to adapt their speed, or tactical behaviour in general, to the traffic situation, because they overestimate their ability to cope with hazardous situations and fail in the perception of feedback of operational performance decrements induced by traffic situations, vehicle characte­ristics or environmental variations in general. The interaction between BAC level and age on accident involvement may then be suggestive of a common locus of effect for both the factors alcohol and young drivers. However, it must be stressed that the lack of studies that have examined explicitly the operational and tactical behaviour of young drivers prevents firm conclusions.

From the discussion of the motivational models, a second version of the model of driver adaptation is presented in figure 5.




Figure 5. Model of driver adaptation, derived from the discussion of motivational models.


Various situational factors may affect operational performance resulting in a sense of ‘loss of control’. In general, this effect is monitored, although for young drivers monitoring or recognition of these effects on operational performance may be hampered in some way. Associations between situational factors and the monitored effects on operational performance are formed that result in adaptations of behaviour on the tactical level (anticipatory avoidance responses, according to Fuller). There also is a direct effect of performance monitoring on tactical behaviour. The effects of age on accident involvement may also be explained in terms of fewer associations between situational effects and operational performance because of limited experience, resulting in fewer anticipatory avoidance respon­ses. However, the comments made on the young driver are highly hypothetical and need to be verified by more rigorous experimentation.


2.4 Adaptive control models


The adaptive control models, referred to by Michon (1985), deal primarily with the operati­onal level of car driving behaviour. These models have been inspired by the principle of adaptive control in which the human operator adapts his control behaviour to the characteristics of the system to be controlled. This concept resembles the use of the term adaptation in this thesis. An important difference lies in the behavioural level at which this process of adaptation occurs: in adaptive control models adaptation occurs on the operational level while in the model of adaptation discussed in this thesis adaptation occurs primarily on the tactical level.

Michon (1985) distinguished between two different classes of adaptive control models; the servo-control models and the information flow control models. The first class is primarily concerned with manual control in the context of signals that are continuous in time, while the second involves discrete decisions. In practice, the distinction has somewhat vanished, resulting in hybrid models. Servo-control models consider driving as a continuous tracking task. These models have been applied to operational performance of steering on straight roads and curves and to obstacle avoidance maneuvers. Input signals are transformed by transfer functions into a vehicle output. Transfer functions represent both driver and vehicle dynamics and contain lead components to account for preview or anticipation of the driver and lag components representing driver and vehicle inertia.

Young (1969) discussed a number of different types of adaptation to the system to be controlled. Input adaptation refers to the ability of the operator to detect familiar or repeated patterns in the input and track these in a predictive or open loop fashion.  The adaptive control models applied to the driver task mainly refer to input adaptation. The best known is the STI model described by McRuer et al. (1977). One variant of this model, see figure 6, refers to compensatory steering control on straight roads. In this model the driver is assumed to act as a regulator against external disturbances that arise from wind and road surface effects. Thus, operational performance is continuously adapted to system disturbances and vehicle characteristics. The steering wheel output is determined by transfer functions while the visual inputs to the model are lateral position and vehicle heading errors. In the ‘input adaptation’ models the predictable aspects of  the steering task, such as the required steering angle as determined by the road curvature, are described as precognitive tracking while the random components in the input signal are handled by compensatory tracking. Another important type of adaptation is referred to as controlled element adaptation.  This occurs when the operator changes his control strategy as an adaptation to changes in the dynamics of the system. If a driver normally drives a sedan but changes to a sports car he has to adapt his steering behaviour to the different steering ratio. In general, any change in vehicle characteristics or vehicle dynamics requires some form of controlled element adaptation.  In chapter 4 an experiment on steering during curve negotiation is discussed. In the introduction of chapter 4 it is stated that required steering wheel angle during curve negotiation is determined by curve radius and by speed.  Speed then changes the dynamics of the system to be controlled and requires an adaptation of steering wheel angle as a form of controlled element adaptation. In that sense speed  is considered as a ‘property’ of the system to be controlled  instead of  a form of  operator adaptation on its own.  In the adaptive control models, the operator is described as someone who responds to the task-characteristics  instead of someone who actively creates the task. However, because the driving task is self-paced most of the time, the behaviour of the driver affects the dynamics of the task.




Figure 6. STI compensatory steering model (from Reid, 1983).


  • Some properties of the adaptive control models.


A consistent feature in attempts to validate these models with human drivers is that subjects are instructed to drive with a fixed speed, thereby excluding possible effects of tactical behaviour on operational performance. Also, the parameters that are found using human drivers often apply to only one situation. Variation in speeds and curve radii will affect the parameters of the models (see for example Donges, 1978). It is argued here that operational performance and behaviour on the tactical level are interdependent and should both be incorporated into a single model.

There are a large number of examples that suggest that speed is used to compensate for detrimental effects of various task-related and situational factors on operational steering performance. For example, Good and Baxter (1986) used the STI model to study steering performance as a function of roadway delineation. The quality of steering was expressed, among other things, by the remnant that accounts for that part of the manual control output that is uncorrela­ted with the input. A smaller remnant then indicates better steering performance. Wider edge lines resulted in a smaller remnant because of improved vehicle guidance. However, wider edge lines also resulted in higher speed. Also, day time driving resulted in better steering performance and higher speed compared to night time driving. Thus, it appears that factors that improve steering performance result in higher speeds. However, the effects on speed are not accounted for by the model and are considered undesirable artifacts.

Tenkink (1988) studied the effects of sight distances of 27, 37 and 183 meters with fixed speeds. Standarddeviation of lateral position (SDLP) increased with higher fixed speeds over all sight distances with steeper increases for smaller sight distances. A smaller sight distance resulted in a larger SDLP at a given speed. Lowering sight distance thus deteriorated steering performance and this was aggravated with higher speeds. However, if drivers were allowed to choose their own speed, reductions in sight distance resulted in the choice of lower speeds while SDLP was maintained on a relatively constant level, except for very short sight distances of 27 meters where speed was not reduced enough to prevent an increase in SDLP. According to Tenkink, a safety margin based on time may have caused the speed reduction under reduced sight distance, because the speed-distance curve appeared to approach a line through the origin, with a slope corresponding to a minimum time of 1.2 seconds for driving on straight roads. Harms (1993) also studied the effect of reduced sight distance on speed choice and lane keeping. She found that reduced sight distance resulted in the choice of a lower speed, while SDLP was unaffected, even with the shortest sight distance of 30 meters. She suggested that the speed reducti­on had prevented a deterioration of lateral control performance as a function of sight distance.

These studies suggest that situational factors that affect operational steering performance are compensated for by speed choice if task conditions are self-paced. If drivers are not given the opportunity to adapt behaviour on the tactical level they are forced to improve behaviour on the operational level, and it is under these conditions that the adaptive control models are normally tested.


In most adaptive control models lateral position deviations, heading angle and anticipated curvature are treated as the input variables that are continuously transformed into a steering wheel angle. The validity of the input variables and the assumption of continuous minimization of errors has been challenged by a number of authors. Riemersma (1987) performed a number of experiments to find the visual cues that are used by the driver in steering control. He found that control of lateral position alone is not sufficient for lane keeping in straight road driving and that heading angle is not directly used as an input variable in steering control, in contrast to the assumption of adaptive control models.

Blaauw (1984) studied the multitasking aspects of car driving. A monitoring function was assumed to supervise manual control associated with steering and speed control on the operational level. Because of a supervisory function, perceptual and control actions are not executed continuously, in contrast to the assumption of the adaptive control models, thus allowing free time in-between control actions. Experienced drivers adjusted their steering control better to increased task demands invoked by driving with a constant speed or night time driving compared to inexperienced drivers. Also, in self-paced conditions where drivers were free to choose their own speed, increasing task demands by occlusion or night time driving resulted in the choice of lower speeds.

Godthelp (1984) questioned the assumption of the adaptive control models that the driver behaves in a closed-loop error-correction mode in which continuous attention is allocated to the steering task. He applied the Time-to-Line-Crossing (TLC) as a measure that reflects the time available for the driver before a correcting steering action is needed to prevent a lane boundary exceedence. The amount of time the driver voluntarily refrains from using visual feedback (occlusion time) correspon­ded closely with TLC values. This means that when the driver has less time available to postpone correcting steering actions, a request for visual feedback is made sooner. This implies that the driver is aware of the time available and that correcting steering actions are generated when some TLC criterion has been reached. Drivers chose occlusion times of about 40% of the available time, irrespective of speed. Also, if steering corrections during the occlusion interval were larger, the driver requested visual feedback sooner, suggesting awareness of the driver’s own steering behaviour and a compensatory effect on visual sampling. When, in Godthelp (1984), drivers were asked to switch to error-correction when vehicle motion could still comfortably be corrected to prevent a crossing of the lane boundary, it appeared that drivers chose a strategy where TLC on the moment of steering correction was about constant over different (fixed) speeds. This constancy of TLC over speed was obtained without occlusion, while the strategy of requesting visual feedback when 40% of available time was reached occurred under occlusion. This difference was explained as a result of the degree of uncertainty regarding the vehicle trajectory. Thus, Godthelp found strong evidence that steering control is not continuous, that drivers are sensitive to TLC and that TLC information is used in steering control.


The relation between vehicle dynamics and operational behaviour constitutes an important aspect of adaptive control models. Godthelp and Käppler (1988) found that changing the vehicle characteristics to heavy understeering resulted in increased steering control effort but similar lateral control performance, as evidenced from TLC control performance, compared to a normally understeered car, because drivers were able to develop an accurate internal representation of the vehicle dynamics. In both normal and heavy understeered cars the accepted occlusion times were about 40% of available time, independent of (fixed) speed. This suggests that drivers adapt their visual information intake and steering behaviour to the dynamic characteristics of the vehicle such that the same strategy is maintained. From the results of Godthelp and Käppler it may be inferred that drivers are sensitive to vehicle handling properties and change their operational behaviour as a function of this if the driver is required to drive with a fixed speed. This may be considered as an example of controlled element adaptation and thus as an example of adaptation of operational behaviour. A number of other studies have revealed effects of vehicle characteristics on tactical driver behaviour. Rumar et al. (1976) studied the effects of studded tires on speed choice in curves. Drivers with studded tires drove faster compared to drivers with unstudded tires in icy road conditi­ons. This did not result in lower safety, since the ‘safety margin’, defined as the difference between real and critical lateral acceleration, was larger with studded tires. Summala and Merisalo (1980) also found that drivers with studded tires chose higher speeds in curves in low-friction conditions and that the safety margin was greater for drivers with studded tires in slippery conditions. The higher speeds with studded tires in low friction conditions may be regarded as an adaptation of tactical behaviour to the increased friction coefficient induced by studded tires. Also, the acceleration capability of cars has been shown to affect behaviour. Evans and Herman (1976) found that drivers accepted smaller gaps with oncoming cars while negotiating intersec­tions if the acceleration capability of the car was higher. However, the physical safety margin was not negatively affected by acceleration capability. Also, newer cars used higher levels of deceleration compared to older cars when they stopped at signalized intersections (Evans and Rothery, 1976). This was explained as a possible adaptation of behaviour (on the tactical level) to compensate for reduced mechanical conditions in older vehicles. Evans and Wasielewski (1983) found that drivers of newer cars and cars with intermediate mass followed with a smaller time-headway. This may also be the result of better deceleration capabilities of newer cars. Evans (1991) postulated that improved braking and vehicle handling characteristics result in increased speeds, closer following and higher speeds in curves. When safety changes are invisible to the user as may be the case with seat belts and increased crashwor­thiness, there is no evidence of any measurable human behaviour feedback. A similar point was made by Lund and O’Neill (1986). Design changes that reduce the likelihood of a crash do have an effect on behaviour. They stated that how a car is driven depends on feedback to the driver about the car’s handling characteristics. Vehicle-related factors may then affect both operational and tactical driver behaviour depending on the visibility of the feedback.


  • Conclusions and consequences for the present model.


Adaptive control models study the effects of system characteristics on operational behaviour without establis­hing a link with behaviour on the tactical level. Also, adaptive control models assume that continuous attention is being allocated to the steering task resulting in continuous error correction.




Figure 7. Model of driver adaptation, derived from the discussion of adaptive control models and related research.


Under forced paced conditions effects of vehicle characteristics and situational factors generally affect operational behaviour as is predicted by the adaptive control models. However, car driving is a self-paced task most of the time and it is under these conditions that speed reducti­ons generally occur, possibly as an attempt to compensate for effects on operational performance. Evidence was presented that a time-based variable, TLC, is used by the driver as a criterion for generating corrective steering actions. TLC is determined by operational steering perfor­mance, vehicle characteristics, speed and lane width. Effects of situational and vehicle-related factors on steering performance and vehicle dynamics may then be compensated for by a speed reduction, such that a constant safety margin is maintained.

From the discussion of the adaptive control models, a third version of the model of driver adaptation is presented in figure 7. Various situational factors, driving experience and vehicle characteris­tics affect operational performance. This effect is monitored and adapted for either via allocation of effort in order to improve operational performance, or via an effect on behaviour on the tactical level.



2.5 Connecting operational and tactical behaviour: a driving model based on safety margins


The adaptation model as it emerges from the discussion of the literature on driver models and driving behaviour is presen­ted in figure 8. This model states that several factors affect operational performance. For example, temporary states, induced by alcohol or marijuana, affect psycho-motor abilities while psycho-motor abilities affect operational performance. Also, vehicle related factors situational factors and driving experience may affect operational performance in accordance with the adaptive control models. The effects on operational performance are perceived via a feedback loop by the driver, although alcohol and young age may inhibit this. If driving is self-paced, the driver adjusts behaviour on the tactical level by either increasing speed or decreasing headway during car-following if operational performance is improved, or by decreasing speed or increasing headway if operational performance deteriorates. If there are no opportunities to adapt behaviour on the tactical level, i.e. when the driving task is forced-paced, the driver may elect in allocate more effort to increase operational performance. Adaptation of tactical behaviour or effort allocation does not only occur as a response to momentary changes, but also in the form of an anticipatory response. This response is the result of learned associations between various factors and effects on operational performance allowing an adaptation of tactical behaviour in the absence of an effect on operational performance. For example, if the driver has learned the effects of rain on road friction and on operational steering performance, he may already choose a lower speed before these effects are actually experienced during a particular period of rain




Figure 8. Adaptation model of car driving.


However, the mechanisms by which this process works are still unclear. The extent to which speed is adapted cannot be predicted because of the lack of a unitary measure that incorporates both behaviour on the operational level and the tactical level. An organizing principle may be found in the operation of safety margins. Earlier it was mentioned that, according to Summala (1985), drivers maintain safety margins and that this process should be analyzed in more detail in subtasks such as lane keeping, car-following, curve negotiation, gap acceptance and overtaking. Rumar (1988) shares the point of view that drivers control safety margins instead of risk. He proposed that a safety margin may be operationally defined as an area of safe driving around the car, equivalent to the old idea of the subjective dynamic field that expands in front of the car if speed is increased (Gibson and Crook, referred to by Rumar, 1988). Safety margins can be operationally defined as distance or time related measures (Summala, 1988), although they have also been described in other terms such as a diffe­rence between actual and critical lateral acceleration. Summala has mentioned the time-to-line-crossing (TLC) and time-to-collision (TTC) as examples of safety margins.

Operational control in car driving is usually separated into lateral control and longitudinal control. Lateral control refers to keeping the car within the lane boundaries or to steering away from objects that block the path of the vehicle. Longitu­dinal control refers to activities related to the control of speed, such as braking and use of accelerator and clutch. It is proposed here that the driver uses TLC as a safety margin during lateral control, while TTC, or more general­ly time-to-object (TTO), is used as a safety margin during longitudinal control. Ofcourse, TLC is the same as TTO to either of the lane boundaries. Thus, safety margins are proposed to represent time-related measures. This has a number of advantages. Because driving is a dynamic task in which the driver and other traffic participants move with varying speeds, time may be used as a relatively constant parameter that can be controlled by means of tactical adaptations of speed or headway. In addition to this, there is abundant evidence that humans are very well equipped to perceive time to static and dynamic objects in dynamic situations.

Lee (1976) argued that 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 ability to use TTC informa­tion and the actual use of this information has been established in a number of studies, referred to in subsequent chapters on car-following and braking. Van der Horst (1990) showed that time-to-intersecti­on (TTI) is used by the driver in the decision when to start braking as well as in the control of braking. The TTI at which the driver starts braking appeared to be rather constant over speed. In stopping for a stationary object the minimum TTC during the approach was also about constant over different approach speeds. This suggests that time-to-object may be used as a safety margin the driver is not willing to exceed in longitudinal control tasks. Behavioural manife­stations of adaptation on the tactical level in longitudi­nal control tasks are adaptation of speed and of time-headway during car-following. It may be argued that poorer perfor­mance in operatio­nal control increases the chance that a TTO safety margin is exceeded. In approaching a stationary object such as a traffic light, for which the driver has to stop, the driver may decrease his speed earlier in order to compensate for this. During car-following, the driver may choose a larger time-headway. This allows more time to react if the lead vehicle decelerates and thus minimizes the chance that a critical TTC is exceeded.

As was already mentioned in the previous paragraph, drivers appear to be able to estimate the TLC in lateral control tasks, and there is evidence that TLC plays an important role in steering control. If TLC is too small, it can be increased by choosing a lower speed. Thus speed adaptations allow control of a TLC-based safety margin.

Several factors related to operational performance, vehicle characteris­tics, environment and behaviour on the tactical level affect these time-based safety margins. TLC is affected by vehicle dynamics, steering performance, speed, road width and curve radius. TTC is affected by braking characteristics of the vehicle, braking performance of the driver, initial headway, and behaviour of the lead vehicle. Thus, these measures of safety margin integrate many different aspects of the driving task, such as operational performance and tactical behaviour, and may be regarded as good candidates for the unitary measures that serve as an organi­zing principle in the model presented here.


The general idea underlying the adaptation model is that any factor that affects operational performance may result in adaptation of behaviour on the tactical level, if the driving task is self-paced and if the driver is able to perceive these effects on operational performance. In this, feedback of the effects on operational performance may have a direct effect on tactical behaviour. For example, windgusts affect operational performance which, if detected by the driver, result in the choice of a lower speed. Alternatively, feedback effects may result in learned associations of the effects of various factors on operational performance, resulting in anticipatory adaptation responses. If, for example, the driver detects a fog bank in the direction of the vehicle path, he may already decrease speed, although the effect of fog on operational performance has not been experienced yet. The speed reduction then is an anticipatory adaptation response resulting from associations learned in the past between fog and effects on operational performance. If the driving task is not fully self-paced, the driver may elect to increase effort in order to improve operational performance. Time-based safety margins are proposed as the regulating mechanisms of behavioural adaptation. The strategies involved in this are a matter of further experimentation.

In the experimental section of this thesis two driving tasks, curve negotiation and car-following, are analyzed in more detail. Curve negotiation is essentially a lateral control task, while car-following is predominantly a longitudinal control task. Figure 9 presents a model for lateral control tasks. Figure 10 does the same for the longitudinal control tasks of car-following. Both models are almost identical, although they differ in the kinds of safety margins and behavioural adaptations.



Figure 9. Model for the lateral control task.





Figure 10. Model for the longitudinal control task of car-following.



2.6 Experimental validation of the model: research questions


Two different car driving tasks, negotiating curves and car-following, are studied in detail in the chapters that follow. The goal of the six experiments discussed in the chapters 4 to 9 is to examine one aspect of the present model: the prediction that individual differences in operational performance affect behaviour on the tactical level. The experiments were performed in a car driving simulator.

In experiment 1 the driver task of curve negotiation is analyzed. It focuses on the relation between steering performance and speed choice in curves with different radii. Drivers differ in steering performance in that some drivers consistently commit larger steering errors than others. Curve radius is manipulated as an situational factor that affects operational performance. In general, steering errors are larger in curves with smaller radii. It is then investigated how speed is affected by curve radius and by individual differences in steering competence as an adaptative response to steering performance. Drivers already decrease speed before the curve is entered and, thus, before the effect of radius on steering performance is experienced. The adaptation of speed then is assumed to be an anticipatory adaptation response that has been learned by experience in curve negotiation. Time-to-line-crossing (TLC) is used as a safety margin and it is explored whether this safety margin is affected by curve radius and steering competence.

In the experiments 2 to 6 the longitudinal control task of car-following is analyzed. It is examined whether choice of time-headway (THW), as behaviour on the tactical level, is affected by operational braking performance. During car-following, the driver has to take account of the possibility that the driver of the lead vehicle might brake. However, the driver never knows when the lead vehicle will brake, and if it does, how hard it will brake and for how long. It is then assumed that the driver has learned the quality of his or her own braking performance from previous experiences and that this results in a preference for a specific THW. THW is the time available to the driver to reach the same level of deceleration as the lead vehicle in case it brakes, without becoming involved in a collision. Braking performance is assumed to affect the time required to reach the same level of deceleration as the lead vehicle. Adaptation of THW may then be regarded as a tuning of available time to required time that is determined by braking performance.

In experiment 2 it is investigated whether choice of THW is related to the ability to brake as fast as possible in situations where the driver knows that the lead vehicle will brake and the level of deceleration at which it will brake. In this experiment the locus of effect of differences in braking performance is examined as well.

In experiments 3 and 4 it is examined whether choice of THW is constant over different speeds and whether individual differences in choice of THW are consistent. In experiment 3 the role of time-to-collision (TTC) on the moment the lead vehicle starts to brake is examined in detail. More specifically, it is tested whether the sensitivity of the braking response to TTC information differs as a function of preferred THW. In experiment 4 the process of braking itself is examined in more detail and a model of braking is presented starting from modern theories of perceptual-motor performance. The process of braking is separated into three sequential phases: a reaction time (RT) phase, an open-loop phase covering the initial motor response, and a closed-loop phase during which visual feedback is used to control the process of braking. It is tested whether TTC on the moment the driver detects the braking of the lead vehicle affects the early motor phase (open-loop component) of the braking process and whether the motor response differs as a function of preferred THW in unexpected emergency braking situations.

In the experiments 5 and 6 it is tested explicitly whether short followers differ from long followers in the open- and closed loop phases of the braking response by manipulating both phases. However, in experiment 5 specific task-related factors induced startle responses and vigilance effects requiring some methodological changes in the final experiment. In experiment 6 the level of deceleration of the lead vehicle is manipulated. This affects the TTC on the moment the driver detects the deceleration of the lead vehicle and this procedure aims to manipulate the duration of the open-loop response. It is tested whether the open-loop response of short followers is more strongly affected by this manipulation compared to long followers. This would support the idea that the sensitivity of the motor response to TTC information differs as a function of preferred THW, and thus, that short followers differ in operational performance from long followers. It is also examined whether preferred THW is related to differences in performance in other tasks that require a fast dynamic perception-response coupling as a test of the hypothesis that preferred THW is  related to perceptual-motor abilities that are more general than braking performance.


Chapter 10: thesis traffic psychology


This is chapter 10 of the thesis from 1996 by van Winsum. 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:


General discussion and conclusions


10.1 Testing the adaptation model for the case of individual differences: discussion of results from the experiments


The adaptation model predicts that factors  that affect operational performance will normally result in an adaptation of behaviour on the tactical level, such that constant safety margins are maintained. Individual differences in operational performance are then predicted to be reflected in individual differences in behaviour on the tactical level. The results of the experiments support the adaptation model applied to the relation between individual differences in behaviour on the tactical level and the operational level for both the car-following task and the curve negotiation task. The general results from these experiments and the relevance for the adaptation model are discussed in this chapter.

If individual differences in skills and operational performance result in adaptation of behaviour on the tactical level then this behaviour must be consistent and characterized by individual differences. This implies that, in addition to the transient effects on tactical behaviour discussed in previous paragraphs, some level of consistency and constancy must exist in, for example, speed choice and choice of headway during car-following. If the adaptation model also applies to individual differences then at least some part of the between-subjects variance in behaviour on the tactical level must be explained in terms of the between-subjects variance in operational performance. The tasks of curve negotiation and car-following were selected for closer examination. Speed choice during curve negotiation is considered as an example of the effect of lateral control performance on behaviour on the tactical level. Choice of time-headway in car-following is described as an example of the effect of longitudinal control performance on tactical behaviour.

Experiment 1 deviates from the other five in that it not only examines the effect of individual differences in operational performance but also the effect of a situational factor, i.e. curve radius. The results have been published in Human Factors. The experiment was performed in a driving simulator that was programmed by the people from Carnetsoft. Explaining speed choice as a function of curve radius has been a long lasting problem that has been investigated in a large number of studies. The problem is usually described in terms of a relation between lateral acceleration and choice of speed. The underlying process has never become clear. However, the results of experiment 1 clearly suggest that the inverse relation between lateral acceleration and speed, often referred to in the literature, is the result of a process of adaptation of speed choice together with a strategy of maintaining constant safety margins. Speed choice in curves proves to be a consistent measure of tactical behaviour. Also measures of operational performance prove to be stable and consistent within the driver. This indicates that the important prerequisite for the validity of the adaptation model that both operational performance and behaviour on the tactical level are consistent and characterized by clear individual differences is fulfilled for the case of lateral control performance and speed choice in curves. Steering is discussed as the factor that affects choice of speed in curves. A model of steering is presented that suggests that steering errors are affected by individual differences in steering competence and by required steering-wheel angle. A larger required steering-wheel angle then results in larger steering errors. The situational factor road radius, together with speed, affects required steering-wheel angle. A smaller radius increases required steering-wheel angle and thus steering error, which is compensated or adapted for by choosing a lower speed. The same reasoning applies to individual differences in steering performance. This is measured independently during straight road driving. Drivers with poorer steering competence are characterized by larger steering errors which is compensated for by choosing a lower speed in curves according to the adaptation model. Summarizing, in experiment 1 the adaptation model is tested in two different manners for the case of speed choice in curves:

– curve radius affects operational performance which affects speed choice, and

– steering competence affects operational performance which affects speed choice.

These hypotheses are supported by the results of experiment 1. The results indicate that a smaller curve radius and poorer steering competence increase steering errors and result in such speed reductions that TLC is kept on a constant minimum value. These results then strongly support the adaptation model discussed in paragraph 2.5 and the value of the concept of a time-based safety margin that is controlled during driving.


Experiments 2 to 6 consider the task of car-following. During car-following the driver never knows whether the lead vehicle will brake, and if it does, how hard it will brake and for how long. It is then assumed that the driver has learned the quality of his or her braking performance from previous experiences and that this results in the choice of a preferred  time-headway (THW). THW is the time available to the driver to reach the same level of deceleration as the lead vehicle in case it brakes, without becoming involved in a collision. Braking performance is assumed to affect the time required to reach the same level of deceleration as the lead vehicle. Adaptation of THW may then be regarded as a compensation strategy for drivers with poorer braking performance.

The detailed examination of the car-following task introduces some specific problems. First of all, it is not immediately clear which aspects of operational performance play a role in choice of time-headway. This is examined in the experiments 2 to 6.

Secondly, the literature on choice of THW during car-following is not very extensive. The literature on braking is limited as well and mainly restricted to emergency braking (braking as fast as possible), see chapter 5. This implies that the theoretical perspective on braking and car-following had to be developed during the course of experimentation and that the number of experiments required to test the theoretical model is larger for the case of car-following than for speed choice in curves.

Thirdly, an important limitation in the study of car-following is that the details of operational braking performance can only be compared between different drivers if they start braking at the same distance- or time-headway. This means that, in studying braking performance, drivers will have to be forced into time-headway conditions they would not choose themselves, which may result in differential effort allocations as a function of the discrepancy between preferred THW and actual THW. This was illustrated by the results of an experiment by Heino et al. (1992). They found that particularly drivers who normally follow at a larger THW increase their mental effort, as measured by heart rate variability, when they are forced to follow at a smaller THW. This means that the methodological prerequisite of measuring braking performance in forced-paced situations may, to some degree, obscure individual differences in braking performance because of between-subjects differences in effort allocation. Nevertheless in the present studies, this method is preferred to the alternative where braking performance is measured while drivers follow at their preferred THW. Drivers who follow at a smaller THW would be forced to brake faster compared to drivers who follow at a larger THW, and this would damage the comparability of braking performance between drivers.

The results of experiments 3 and 4 demonstrate that choice of THW is consistent and constant over different speeds. In experiment 3 preferred THW is measured at speeds of 40, 50, 60 and 70 km/h. Speed has no significant effect on preferred THW and the within-subjects reliability of the THW’s is high. This is confirmed by the results of experiment 4. The high consistency in choice of THW has been confirmed in an on-road experiment by Heino et al. (1992). They reported a correlation of 0.85 between time-headways measured on two different stretches of road. Other studies on the consistency of THW are discussed in chapter 6. The results indicate that choice of THW is independent of speed and consistent within the individual driver and that clear and reliable individual differences exist in choice of THW. This is an important prerequisite for the application of the adaptation model to individual differences.


Experiment 2 examines the relation between preferred THW and the ability to brake as fast as possible,  the speed of stimulus encoding and response preparation. The additive factor logic (see Sternberg, 1969) is applied to examine the locus of effect of operational performance differences. The search for differences in the ability to brake as fast as possible stems from the tradition in the literature on braking where the quality of braking is generally examined in terms of the ability to brake as fast as possible. Experiment 2 may therefore be regarded as a search for individual differences in the limits of performance. The braking parameter that is studied in detail is reaction time (RT), defined as the interval between the moment the lead vehicle starts to brake and the moment the subject starts to release the foot from the accelerator. Again, this approach stems from the dominant view in the literature on braking, i.e. that differences in braking performance originate from perceptual factors measured by RT. Differences in the speed of stimulus encoding regarding the braking action of the lead vehicle would suggest that drivers with a smaller preferred THW (short followers) perceive the braking of the lead vehicle earlier. Differences in response preparation would suggest that the state of motor readiness is reached earlier by short followers compared to long followers. The results indicate that choice of THW is not related to individual differences in RT for a decelerating lead vehicle, to differences in stimulus encoding or to differences in response preparation. From this it is concluded that differences between short and long followers cannot be explained in terms of “limits of perceptual and motor skills”. However, differences in preferred THW appear to be related to braking performance in quite another way. Differences in response execution speed as a function of preferred THW are restricted to braking situations characterized by uncertainties concerning the braking of the lead vehicle, the required deceleration and the duration of braking, as is always the case in real world car-following situations. The results suggest that individual differences in the transformation of visual feedback to the motor response may be related to choice of THW. The results have been published in Ergonomics.


Experiment 3 considers these aspects in more detail and examines the use of time-to-collision (TTC) during braking and the way the braking response is executed. The process of braking is connected explicitly to the literature on 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 for example Van der Horst, 1990). In the literature it is often suggested that the perception of TTC from the optic flow field is an important skill related to the initiation of braking. But curiously, only a few experimental studies have connected the concept of TTC to the braking response. The general conclusion from the literature is that TTC is underestimated and that there are large individual differences in the ability to accurately estimate TTC. In experiment 3 the hypothesis is tested that preferred THW is related to the sensitivity to TTC information. According to this reasoning, drivers who are more sensitive to TTC information are better able to judge the moment to start braking while drivers who are less sensitive to TTC information run the risk of starting to brake too late. This may result in a compensatory larger preferred time-headway for these drivers. The results indicate that both the initiation and the control of braking are strongly determined by TTC on the moment the lead vehicle starts to brake. Short followers differ from long followers in the control of braking: short followers brake harder and more efficiently, and, most importantly, the intensity of braking is more sensitive to TTC differences, compared to long followers. Yet, a confounding factor may have affected the results. Because the absolute levels of TTC differ between short and long followers in this experiment, short followers may have been forced to brake more efficiently.


Experiment 4 explicitly controls this confounding factor. Braking performance is measured with identical initial time-headway for all subjects. The subjects are unaware of the fact that the lead vehicle will brake and of the required deceleration and the duration of braking. A model of braking is discussed in which the process of braking is divided into three separate phases: the RT phase, the open-loop ballistic phase and the closed-loop phase. The RT phase is defined as the interval between the moment the lead vehicle starts to brake and the moment the foot starts to be retracted from the accelerator pedal. The open-loop phase is operationally defined as the period that starts when the subject retracts the foot from the accelerator after the lead vehicle has started to decelerate and ends when the brake pedal is touched. During the closed-loop phase visual feedback is used to control the process of braking. It is defined operationally as the period between the moment the brake pedal is touched by the foot and the moment the maximum brake position is reached. It is hypothesized that the speed of the open-loop ballistic response is determined by TTC on the moment the driver detects the deceleration of the lead vehicle, while the duration of the closed-loop phase is determined by the number of decelerations in the brake pedal signal (movement corrections). The results show that reaction time is not related to preferred time-headway. This confirms the results of the experiment 2. The open-loop phase of the motor response appears to be very sensitive to TTC, and especially to TTC on the moment the foot is retracted from the accelerator pedal. This supports the hypothesis. Also, the results indicate that short followers are characterized by a faster open-loop response that is not caused by a smaller TTC. This suggests that short followers program their movement speed to a higher level compared to long followers. The duration of the closed-loop phase of the motor response is, in accordance with the hypothesis, strongly related to the number of movement corrections. Short followers exhibit a faster closed-loop response with fewer movement corrections. The results also indicate a strong effect of total movement time on preferred THW, strengthening the conclusion that short and long followers differ in both the open- and closed-loop phases of movement. This suggests that short follo­wers are more sensitive to the task requirements in braking situations, confirming the results of experiment 3.


Experiments 5 and 6 test the hypothesis that short followers differ from long followers in the sensitivity of the braking response execution to TTC information. Both experiments apply the model of braking discussed in chapter 7. In experiment 5, the RT phase, the open-loop and the closed-loop phases of the braking process are manipulated independently. If short followers differ from long followers in either of these phases then the factor “preferred THW” should interact with any factor that manipulates these phases. The RT phase is manipulated with the factor initial THW on the moment the lead vehicle starts to brake. The duration of the open-loop phase is manipulated by the factor initial deceleration. The level of deceleration (3 vs. 6 m/s²) of the lead vehicle is expected to affect the TTC on the moment the subject detects the braking of the lead vehicle and thereby the duration of the open-loop phase. The closed-loop phase is manipulated by the factor secondary deceleration: as soon as the foot touches the brake pedal (this is the moment the closed-loop phase starts) the deceleration of the lead vehicle changes. This requires the use of visual feedback in order to change the programmed motor response. Although the results show that the respective phases of the braking response are affected by the manipulations, the predicted interactions of preferred THW with the factors that manipulate the open- and the closed-loop phases are not statistically significant. The pattern of results suggests that task-specific factors resulted in undesirable startle reactions and vigilance effects.

Because of this the final experiment 6 applies multiple measurements per manipulated factor, a higher frame-rate and shorter task duration, in order to prevent startle reactions and vigilance effects. The main hypothesis is that short followers differ from long followers in the sensitivity of the motor response to TTC. TTC is manipulated with two levels of initial deceleration of the lead vehicle (3 vs. 6 m/s²), in random order. The results indicate, in support of the main hypothesis, that the open-loop response of short followers is more sensitive to differences in TTC compared to long followers. The assumed causal chain is that individual differences in some basic perceptual-motor skill affect the quality of the braking response. The driver is assumed to adapt the choice of THW 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 improved operational performance because of learning effects. To rule out this explanation it is examined whether short followers differ from long followers in perceptual-motor performance in tasks unrelated to braking. In order to test whether choice of THW is related to more general perceptual-motor skills that require the transformation of visual information to a motor response, performance on a lateral tracking task and a longitudinal tracking task is tested. The results clearly indicate that short followers perform better on both the lateral tracking tasks and the longitudinal tracking task. In addition to this, performance on both types of tracking tasks is significantly correlated. This strongly suggests 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.


10.2 General conclusions and next steps


The impact of vehicle factors and situational factors related to road, weather and temporary state on driver behaviour and the underlying mechanisms of behavioural effects have been addressed in this study. Mechanisms related to individual differences in driver behaviour have been tested from the perspective of the adaptation model. It is clear that the system components vehicle and environment have an important effect on driver behaviour, mediating the effects on accident involvement and traffic safety in general. Adaptation mechanisms are best studied by measuring driver behaviour as a function of vehicle factors, individual differences in skills, situational factors and temporary states instead of accidents, because these factors affect behaviour directly while they affect accident involvement indirectly. One of the reasons for the lack of progress in driver modeling, referred to in chapter 1, is the abundance of determinants and factors that operate simultaneously. This has resulted in several theories that apply only to a limited problem domain. The adaptation model integrates the operational and the tactical level of driver behaviour into one framework. As discussed in chapter 2, driver models and studies in traffic psychology usually examine only one of these levels. It is suggested that these levels should always be studied in their mutual relationship. For example, if the effect of a roadmeasure on speed is examined it should also examine the effects on operational performance at the same time. Of course practical problems may prevent this and this is one of the reasons why simulators may be useful. However, the results suggest that measurement of behaviour on one level may be meaningless when behaviour on another level is excluded from examination. Several other driving tasks such as speed choice on straight roads, gap acceptance at intersections, stopping for traffic lights, overtaking and so on need to be examined within this framework.

According to the adaptation model, drivers with poorer operational performance protect themselves by adapting behaviour on the tactical level, resulting in a lower speed or larger time-headway. The other side of this reasoning is that drivers with better perceptual-motor skills and good operational performance drive at higher speeds or follow at smaller time-headways. However, it is not by any means intended to suggest that drivers with higher speeds are not dangerous because they have a highly developed skill level. Undoubtedly, some drivers who follow other vehicles at a close distance or who drive faster than average are not characterized by better operational performance. The suggested relation is a probabilistic one, and not mechanistic. However, the line of reasoning makes clear that the concept of risk becomes more meaningful if skills and level of performance are added to the equation. This is to say that a certain speed may not be as risky for one person as for the other if they differ in certain required perceptual-motor skills, from the same perspective as the fact that flying an F16 fighter plane is considerably more risky for the author of this thesis than for an experienced pilot.

Also, it is often assumed that higher speeds and shorter following distances are associated with a high accident risk although a number of studies do not confirm this simple relation. The effect of variabi­lity within the traffic system on accidents is one of the reasons why Summala (1985) promoted the introduction of speed limits. This has greatly reduced the accident risk in a number of countries. Speed limits reduce the variability of speed in the system and this reduces accident risk. Brehmer (1990) predicted that accident probability is lowest for cars driving with the average speed, but increa­ses for drivers who deviate more from the average speed, either to lower or higher speeds. He referred to a study of Solomon (1964) on the relation between speed and accident rate on US highways, that supported this hypothesis. Munden (1967), referred to in Rooyers et al. (1992), repor­ted a U-shaped relation between speed and accident rate as well. Brehmer also predicted that accident rates are higher in environments where the variance of the speed distribu­tion is highest. A study of Greenberg (1964) was referred to which demon­strated a positive correlation between accident rate and speed distribution for a sample of US roads. Numerous authors have mentioned that it is an establis­hed fact that accident risk is related to driver speed, and that speeding therefore can be regarded as a form of driver error, related to poor speed perception skills or poor hazard perception. However, whether a higher speed is riskier compared to a lower speed with identical speed distributions is an unresol­ved matter. A similar point can be raised with regards to headways during car-following. Shorter time-headways are usually associated with a higher risk of rear-end collisions. In a large-scale study on the relation between time-headway and accident risk in several countries a relation was found between rear-end accident rates per 100 million vehicle kilome­ters and time-headway (Benjamin, 1980). This relation was however strongly affected by the flow of traffic or traffic density. Traffic volume affected both time-headway and the number of rear-end collisions so that a causal relationship between close following behaviour and rear-end acci­dents could not be established. It was already demonstrated in the fifties by the studies of Herman (referred to in Forbes, 1972) that, even in car-following situations with conservative headways, normal speeds and short response times of drivers, flow distur­bances by the platoon leader (the first car in the chain) may cause a chain reaction that makes it impossible for drivers downstream to avoid a collision. This indicates that the relation between speed choice, choice of time-headway and accident risk is not as straightforward as often suggested.

The general principle of behavioural adaptation demonstrates the inherent flexibility of human behaviour. This flexibility resembles the issue of ‘human behaviour feedback’, discussed in chapter 1, which has puzzled many traffic safety researchers and triggered fierce discussions about the effects of safety measures. The adaptation model may offer the concepts and methodology to clarify the issue of this ‘human behaviour feedback’ in more coherent terms. Driver adaptation of tactical behaviour to effects of safety measures on operational performance may be an important determinant for the success or failure of intended safety changes in the road-vehicle-driver system.

Although the process of adaptation appears to be ‘normal behaviour’, it also seems clear that certain factors prevent adaptation resulting in increased accident involvement. Examples of this are the consumption of alcohol and the case of the young male driver. Citing from paragraph 2.3.4: “The interaction of BAC level and age on accident involvement suggests that both factors share a common locus of effect, in the sense that the factor that causes the higher accident rate of young drivers is aggravated by alcohol. In the discussion of the effects of alcohol it was suggested that the lack of compensation for impaired performance may be the cause for the large role of alcohol in accident causation. Evidence was presented that drivers are unaware of performance decrements under alcohol which is possibly the cause for the absence of compensatory speed changes and effort. From the same perspective it may be suggested that young and inexperienced drivers have not yet learned to recognize the effects of situational factors on their perfor­mance and thus fail to compensate for these effects resulting in speeds that are too high for the circumstances”. Clearly this is an issue that needs to be investigated further. There are some indications that alcohol inhibits the perception of feedback from the driving task. The assumed lack of adaptation in young (male) drivers also needs to be explored further. The theory presented in this study offers a framework to examine these issues.

An important next step is the further validation and testing of the adaptation model. In the present study only a limited part of the model was tested. For example, the principle of effort allocation under forced paced conditions and the effects of this on operational performance need to be tested in further studies. The six experiments described in this study are only a first step in the direction of testing the limits and scope of the model of adaptation.


introduction: thesis traffic psychology


This is the introduction chapter 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:



During the last decade the field of driver behaviour modeling has suffered from a lack of progress. This has been attributed to a number of different causes, the most important one being the preoccupation in traffic psychology with accidents and accident causation (Ran­ney, 1994). As a result it has never been clear whether driver theories should explain accidents or everyday driving. Several authors have stressed that the actual traffic situations instead of accident analysis should be the main focus and that driver theories should explain everyday driving instead of accidents (for example Rumar, 1988).

The second factor behind the lack of progress in driver modeling is the fact that the motivati­onal models which are dominant today have failed to generate testable hypotheses (Ranney, 1994) mainly because of the confusion between individual and aggregate levels of analysis (Michon, 1989). Also, the continuing debate concerning the validity of the risk homeostasis theory has stalled progress.

Michon (1985) has attributed the lack of progress in driver behaviour modeling to the failure to incorporate the results from the ‘cognitive revolution’ in psychology. He divided the task of car driving into three levels of skills and control: strategic (planning), tactical (maneu­vering) and operational (control). On the strategic level, trip planning and the selection of trip goals and route occur. On the tactical level, sometimes referred to as the maneuvering level, the driver negotiates prevailing circumstances. It includes maneuvers such as obstacle avoidance, gap acceptance, overta­king, choice of headway during car-following and speed choice. The operational level relates to direct lateral and longitudinal vehicle control. Michon postulated that a comprehensive model of driving should take these levels into account, and specify the relations between them. However, all existing models have focused almost exclusively on one level. Ranney (1994) regarded the hierarchical control structure between these levels as one of the most significant developments in the field of driver modeling. It forms a basis for the development of modern driver behaviour theories.

Huguenin (1988) saw the abundance of determinants and factors that operate simultaneously as the cause for the lack of a general theoretical basis or a comprehensive model of driver behaviour which has resulted in several theories that apply only to a limited problem domain.

The causes for the limited progress in driver modeling referred to by Ranney, Michon and Huguenin converge in the problem of ‘human behaviour feedback’ which has puzzled many traffic safety researchers during the last decade and triggered fierce discussions about the effects of safety measures. This issue of the apparently unpredictable human behaviour effects following road safety measures was explored by Evans (1985). He compared the expected safety effects with the actual safety changes in 26 studies and found evidence of changes greater than expected, as expected, smaller than expected, no safety change and perverse effects (safety change opposite in sign to expected). Evans concluded that no behavioural model was available to predict effects of changes in the road-vehicle-driver system. The same issue was referred to as ‘behavioural adaptation’ instead of ‘human behaviour feedback’ in a report of the OECD (1990). Behavioural adaptation was defined as “those behaviours which may occur following the introduction of changes to the road-vehicle-user system and which are not consistent with the initial purpose of the change …”. Because behavioural adaptation may strongly affect the success or failure of road safety measures, collection of driver behaviour data is at least as important as accident data. Accident and fatality data do not contribute as much to an understanding of the process that produced them as driver behavior data because they are only a summary or a final result of a complicated process.

The effects of ‘human behaviour feedback’ as defined by Evans and of ‘behavioural adaptation’ as defined in the OECD report are limited to changes related to the road and the vehicle. For example, in the OECD report a number of studies are referred to that present evidence of increases in speed if lane width or shoulder width is increased while accidents are reduced. Also, the presence of edge lines has been associated with speed increases and accident reductions. However, in chapter 2 evidence will be presented that adaptation is a much more general phenomenon that can be observed in the fields of individual differences, transient states, effects of age and so on. At present no theory is able to predict or explain the changes in behaviour after the introduction of a road safety measure, although several theories have claimed to explain some of the effects. The theory that has been associated most often with the ‘human feedback effects’ is Wilde’s risk homeostasis theory (Wilde, 1982). The most important reason for the fierce discussions evoked by this theory centers around the explanation it provides for the ‘human feedback effects’. The emotional discussions over the reasons for this phenomenon and surrounding any attempt at theory development have incapacitated the progress in driver modeling.

In chapter 2 a model of driving behaviour is developed in which the process of adaptation plays a dominant role. Although the meaning of the term adaptation in this thesis differs from its meaning in the OECD report, the underlying process is thought to be the same. In both cases behaviour on the tactical level is changed as a function of some factor. ‘Behavioural adaptation’ as defined in the OECD report results in a smaller safety benefit than expected after the introduction of a safety measure, because behaviour becomes more ‘risky’. This is sometimes referred to as ‘negative adaptation’: drivers choose a higher speed or follow at a smaller headway. It is often assumed that the number of traffic accidents would have been reduced more if behaviour had remained constant. In contrast, adaptation as described in this thesis sometimes may result in a process in which system safety increases because behaviour becomes less ‘risky’, in the sense that drivers sometimes choose a lower speed or follow at a larger headway in response to some factors. It is then assumed that both effects on behaviour are two sides of the same coin.

Most driver models can be characterized by an emphasis on either indivi­dual differences or on situational factors and are limited in scope to either the tactical level or the operational level. The emphasis on either of these is in part determined by histori­cal reasons. A comprehensive driver model should be able to handle both indivi­dual differences and situational factors as well as the operational and the tactical level of car driving behaviour. In this, several factors related to the driver, the vehicle and the road-environment have to be incorporated into a comprehensive framework.

The purpose of the present study is to construct and validate a model of driving behaviour in which these requirements are met, starting from a discussion of existing theories and models of driving behaviour in chapter 2. The essence of the model that is derived in the course of the next chapter is that human drivers operate at different levels simultaneously. Several factors affect the quality of operational performance. These factors may be related to individual differences in perceptual-motor ability affected by age or to temporary state-related effects induced by marijuana or alcohol. But situational factors such as sight distance may also affect operational performance. The main point discussed in chapter 2 is that both individual and situational factors that affect performance on the operational level will ultimately result in an adaptation of behaviour on the tactical level and in some cases also on the strategic level. Therefore the model is referred to as the adaptation model of car driving behaviour. In chapter 2 evidence is presented that adaptation of behaviour on the tactical level to changes in performance on the operational level may be crucial from a safety point of view. Accident involvement appears to be highest in cases where adaptation fails. This makes the study of the process of adaptation not only important from a scientific point of view but also from a traffic safety point of view.


Outline of the study. Chapter 2 serves as a theoretical section and discusses a number of driver models from the perspective of adaptation. A general model of driver behaviour is presented that emphasizes the interactions between the operational and the tactical level of the car driving task. The skill models, motivational models and adaptive control models are analyzed in terms of their emphasis on either the operational or tactical level and situational factors or individual differences. During this discussion a number of central problems in traffic psychology is examined in detail. The problem of the elderly driver is analyzed in terms of individual differences in perceptual-motor abilities. The effects of alcohol and drugs (marijuana) are analyzed in terms of state-related factors that result in a transient degradation of operational performance. The problem of the young male driver is associated with motivational factors and discussed in terms of the motivational models. Paragraph 2.5 connects the operational and the tactical levels of behaviour with the concept of safety margins, and makes the adaptation model more explicit for the lateral and the longitudinal driving control tasks. Paragraph 2.6 serves as a link between the theoretical model and six experiments that were performed to test a number of elements from the adaptation model. In these experiments one important aspect of the adaptation model is examined in detail: the extent to which individual differences in behaviour on the tactical level are related to individual differences in operational performance and perceptual-motor skills.

The experiments were performed in the TRC driving simulator. Chapter 3 discus­ses this research instrument and the contribu­tion of the author to its development in more detail. Two car driving tasks, negotiating curves and car-following, are studied in the chapters that follow.

Experiment 1 analyzes the driver task of curve negotiation and this is discussed in chapter 4. It focuses on the relation between steering performance and speed choice in curves with different radii. The car-following task and its relation with braking performance is examined in the experiments 2 to 6. These are discussed in the chapters 5, 6, 7, 8 and 9 respectively.

The general results from the six experiments and the relevance for the adaptation model are discussed in chapter 10, together with a number of general conclusions and some next steps.


Driver safety and simulators


Young male drivers have by far the largest risk of becoming involved in a fatal traffic accident. One of the main reasons is that they overrate their driving skills and underestimate the risks on the road. This mismatch between skills and risk assessment has been studied a lot in traffic psychology and has been a long established fact. This problem has grown worse by two additional developments:

  1. The increase of smart phones and use of social media while driving has resulted in an increase in accidents in the Western world during the last few years because of the problem of distracted driving. This has broken the trend of decreasing numbers of on-road fatalities during the years before. When you overestimate your driving skills, as young male drivers do, its a small step to checking your smart phone while driving. This type of multitasking is impossible: if you have your eye off the road for more than 3 seconds, accident risk increases dramatically. Checking your phone normally takes much more time than 3 seconds.
  2. Young male drivers drive more often after alcohol consumption than other groups. They think they drive well enough to resist the performance degradations because of alcohol. One of the nasty effects of alcohol is that people are often unaware of the negative effects of alcohol on driving performance. It tends to enhance the overestimation of your own skills and thus increases the self evaluation problems that young male drivers already have.

Becoming more aware of the effects of distraction and alcohol on driving behaviour should be an important part of the driver training curriculum. These are things that must be experienced before they are internalized by young male drivers. For reasons of driver safety, these are things that are not easily learned in a learner car on public roads. However, in a car driving simulator, this can be easily demonstrated. The effects of alcohol on driving can be simulated without having to drink alcohol. And the effects of distraction by a phone can also be easily and safely demonstrated. When young drivers are fully aware of the negative effects of distraction and alcohol on driving by having had experiences themselves, this will hopefully reduce the number of traffic fatalities and improve driver safety.

Distracted driving


Distracted driving is one of the most important causes of traffic fatalities at the moment in most Western countries. Since the introduction of smart phones, texting or social media engagement while driving results in inattentive driving, and head-tail collisions, driving off the road or other types of traffic accidents. Several research studies have clearly demonstrated that using the telephone behind the wheel is dangerous. See for example this study of Patten et al., or this study of Thornros et al.

While driving, if you have your eyes off the road for only three seconds or more, the risk of becoming involved in accidents strongly increases. Most of these accidents occur with young inexperienced drivers. They are so engaged in their smart phone that they are

  • not aware of how long their eyes are off the road,
  • or how much attention these applications require.
  • Another important factor is that these young drivers generally overestimate their driving skills,
  • and underestimate the speed at which the situation around the vehicle may change.

In a driving simulator they have the opportunity to experience these dangers and see how this affects their driving behaviour. Several driving simulator programs are available to let young drivers experience the effects of distracted driving by:

  • letting them drive a vehicle on a curvy road with oncoming traffic and then engage them in a secondary task (such as using a cell phone) that distracts their attention to other things than montoring the environment
  • letting them experience that having your eyes off the road for only a few seconds can be a great hazard and frequently results in getting off the road

This can be a real eye opener for them that can save the lives of many young drivers and other road users that get involved in accidents caused by them.

Inattentive driving is only one of the hazards that young and inexperienced drivers have to become more aware of. Other safety awareness programs exist for the effects of alcohol and drugs on driving behaviour. When people become more experienced they learn to take account of the effects of distraction, alcohol or fatigue in prolonged driving, so they refrain from these dangerous activities. But young drivers haven’t had the opportunities yet to learn these associations and a driving simulator can be a great help in this respect.

« Previous Entries Next Entries »