Category Archives: driver behaviour

Treatment of phobias with Virtual Reality techniques

A relatively new method to treat phobias is by means of virtual reality techniques. A number of research studies have demonstrated the positive effects of VR in the treatment of several types of phobia, such as fear of heights, fear of spiders, fear of flying and claustrophobia, as well as agoraphobia. New clinics have been established, specialized in virtual reality techniques to treat various phobias.

A car driving simulator is similar to a Virtual reality system, except for the helmet mounted displays (HMD’s). In both types of system, a computer generate world is presented to the client and the client can interact with this world. In the case of a driving simulator, you drive through the computer generated world as if you are seated in a real car and you interact with other traffic participants and the road infrastructure in a very realistic but also safe way.

For driving, the HMD as used in VR is not very well suited, because of the risk of cybersickness that comes with the use of HMD’s. This is aggravated because of two independent types of motions in driving a car:

  • the vehicle moves through the world, controlled by the steering wheel and pedals of the driver.
  • the client looks around in the environment by turning the head (head tracking)

These two types of motions in combination give a high risk of cybersickness in a VR system. The brain is not very well able to distinguish these movements. This is the most important reason that a driving simulator is a much better solution for the treatment of driving phobia or fear of driving: in a driving simulator the risk of ‘simulator sickness’ is a lot smaller, especially if the simulations are designed to keep lateral and longitudinal accelerations to a minimum.


Drinking while driving

Drinking while driving (DUI) is still one of the main factors in accident causation. Alcohol is involved in  over 25% of all car crashes in the US and a number of European countries. This makes alcohol the most important factor in accident causation in traffic. Especially young male drivers are affected. Young male drivers generally are inexperienced drivers and they tend to overestimate their driving skills and underestimate the effects of alcohol on their driving performance. Also, people under 20 often don’t ‘feel’ the effects of alcohol which causes them to drink much more than others. To read more about the accident risk of young drivers, click here… To read more about the effects of alcohol on driving performance, click here….

A car simulator that simulates the effects of alcohol will make them more aware of the negative effects of alcohol on driving performance. The effects of driver impairment, or driving under the influence (DUI) are simulated by:

  • blurred vision
  • slowed reactions and impaired motor performance resulting in swerving and reaction delays in braking

Alcohol also increases overconfidence which, ofcourse, can not be simulated easily. The impaired driving simulation practice shows the effects of driver impairment by comparing driver performance in two conditions:

  • normal driving
  • impaired driving

by showing the results on number of collisions and driving off the road, and crossing the center line. During the drive, a number of different hazards have to be coped with, such as a dog that crosses the street, a passenger that crosses the street from behind a stopped bus, people on a zebra crossing near a school, cars driving off from the side of the road, oncoming traffic, a lead vehicle that brakes unexpectedly etc. This practice will make young drivers aware of the dangers of impaired driving (DUI) in a way that can’t be demonstrated in a real car on public roads.

Effects of alcohol on driving performance

There’s abundant scientific evidence for the following:

  • Alcohol affects perceptual-motor skills required for safe driving and has been demonstrated to result in poorer steering control and choice of higher driving speeds
  • Young drivers are generally not aware of the dangerous effects of alcohol on driving performance
  • Because of this lack of awareness, they fail to compensate for the negative effects by driving more careful, choosing a lower speed or investing more effort in the driving task
  • Which results in a dramatically increased accident risk

Because especially young and inexperienced drivers are unaware of the negative effects of alcohol on driving and they tend to overestimate their driving skills, a simulation of the effects of alcohol in a car driving simulator can be an eye opener for this group, hopefully resulting in refraining from drinking while driving. A part of the evidence is presented below.

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 Casswell (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 results in an choice of higher speed. A lack of compensation for impairments in performance is the probable 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 is probably the cause for the absence of compensatory speed changes and effort.

Because of these effects of alcohol on a group of drivers that is already more at risk (young and inexperienced drivers), a driving simulator is an excellent instrument to make young drivers become more aware of the effects of alcohol on their driving behaviour.

The following literature was referred to:

  • Casswell, S. (1977).Cannabis and alcohol: Effects on close course driving behaviour. In: Johnson, L. (Ed.),Seventh International Conference on Alcohol, Drugs, and Traffic Safety. Melbourne, Australia.
  • Evans, L.E. (1989). What fraction of all traffic deaths are due to alcohol? In: M.W.B. Perrine (Ed.). Alcohol, drugs and traffic safety-T89. National Safety Council Chicago, Illinois, 424-430.
  • Evans, L.E. (1991). Traffic Safety and the driver. Van Nostrand Reinhold, New York.
  • Guthrie, S. and Linnoila, M. (1986). Epidemiological amd laboratory studies on alcohol, drugs, and traffic safety. In: Noordzij, P.C. Roszbach, R. (Eds.). Alcohol, drugs and traffic safety-T86. Elsevier Sciende Publishers B.V. North-Holland, 63-70.
  • Hansteen, R.W., Miller, R.D., Lonero, L., Reid, L.D. and Jones, B. (1976). Effects of cannabis and alcohol on automobile driving and psychomotor tracking. Annals of the New York Academy of Sciences, 282, 240-256.
  • Kennedy, R.S., Wilkes, R.L. and Rugotzke, R.S. (1989). Cognitive performance deficit regressed on alcohol dosage. In: M.W.B. Perrine (Ed.).Alcohol, drugs and traffic safety-T89. National Safety Council Chicago, Illinois, 354-359.
  • Louwerens, J.W., Gloerich, A.B.M., Vries, G. de, Brookhuis, K.A. and O’Hanlon, J.F. (1986). The relationship between drivers’ blood alcohol concentration (BAC) and actual driving performance during high speed travel. In: Noordzij, P.C. Roszbach, R. (Eds.). Alcohol, drugs and traffic safety-T86. Elsevier Sciende Publishers B.V. North-Holland, 183-186.
  • Moskowitz, H. and Robinson, C.(1986). Driving-related skills impairment at low blood alcohol levels. In: Noordzij, P.C. Roszbach, R. (Eds.).Alcohol, drugs and traffic safety-T86. Elsevier Sciende Publishers B.V. North-Holland, 79-86.
  • Robbe, H.W.J. (1994). Influence of marijuana on driving. Thesis. State University Limburg, Maastricht, The Netherlands.
  • Smiley, A. (1986). Marijuana: On-road and driving simulator studies. Alcohol, drugs, and driving: Abstracts and reviews, 2, 135-154.
  • Smiley, A.M. (1989). The issue of BAC limits: Interpreting findings of experimental studies. In: M.W.B. Perrine (Ed.). Alcohol, drugs and traffic safety-T89. National Safety Council Chicago, Illinois, 116-120.
  • Stein, A.C. (1986). A simulator study of the effects of alcohol and marijuana on driving behaviour. In: Noordzij, P.C. Roszbach, R. (Eds.).Alcohol, drugs and traffic safety-T86. Elsevier Sciende Publishers B.V. North-Holland, 197-201.
  • Stein, A.C. and Allen, R.W. (1986). The effects of alcohol on driver decision making and risk taking. In: Noordzij, P.C. Roszbach, R. (Eds.).Alcohol, drugs and traffic safety-T86. Elsevier Sciende Publishers B.V. North-Holland, 177-181.
  • Wilde, G.J.S., Trimpop, R.M. and Joly, R. (1989). The effects of various amounts of ethanol upon risk taking tendency and confidence in task performance. In: M.W.B. Perrine (Ed.). Alcohol, drugs and traffic safety-T89. National Safety Council Chicago, Illinois, 494-499.


Accident risk of young drivers

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.

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. 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. Extensive practice on relevant driving tasks can be improved in driver training and a car driving simulator can be a useful tool to accomplish that.

The following literature was referred to:

  • Beirness, D.J. and Simpson, H.M. (1986). Alcohol use and lifestyle factors as correlates of crash involvement amongst the youth. In: T. Benjamin (Ed.). Young drivers impaired by lcohol and other drugs. Royal Society of Medicine Services, London, 141-148.
  • Bragg, W.E. and Finn, P. (1982). Young driver risk-taking research: Technical report of experimental study. Contract No. DTNH 22-80-R-07360. National Highway Traffic Safety Administration, Washington D.C.
  • Brown, I.D. and Groeger, J.A. (1988). Risk perception and decision taking during the transition between novice and experienced driver status.Ergonomics, 31, 585-597.
  • Evans, L.E. (1991). Traffic Safety and the driver. Van Nostrand Reinhold, New York.
  • Evans, L.E. and Wasielewski, P. (1983). Risky driving related to driver and vehicle characteristics.Accident Analysis & Prevention, 15, 121-136.
  • Finn, P. and Bragg, B.W.E. (1986). Perception of the risk of an accident by young and older drivers.Accident Analysis & Prevention, 18, 289-298.
  • Haight, F.A. (1986). Risk, especially risk of traffic accident. Accident Analysis & Prevention, 18, 359-366.
  • Jessor, R. (1986). Risky driving and adolescent problem behaviour.: Theoretical and empirical linkage. In: T. Benjamin (Ed.). Young drivers impaired by lcohol and other drugs. Royal Society of Medicine Services, London, 97-110.
  • Jonah, B.A. (1986). Accident risk and risk-taking behaviour among young drivers. Accident Analysis & Prevention, 18, 255-271.
  • Kretschmer-Bäumel, E. and Kroj, G. (1986). Drinking and driving data from the Federal Republic of Germany. In: T. Benjamin (Ed.). Young drivers impaired by lcohol and other drugs. Royal Society of Medicine Services, London, 29-36.
  • Matthews, M.L. and Moran, A.R. (1986). Age differences in male drivers’ perception of accident risk: The role of perceived driving ability.Accident Analysis & Prevention, 18, 299-313.
  • Mayhew, D.R., Beirness, D.J., Donelson, A.C. and Simpson, H.M. (1986). Why are young drinking drivers at greater risk of collision? In: T. Benjamin (Ed.). Young drivers impaired by lcohol and other drugs. Royal Society of Medicine Services, London, 65-71.
  • Saad, F. (1989). Risk-taking or danger perception. Recherche Transports Securite, 4, 51-58.
  • Simpson, H.M. (1986). Young drivers’ alcohol- and drug impairment: Magnitude, characteristics and significance of the problem. In: T. Benjamin (Ed.). Young drivers impaired by lcohol and other drugs. Royal Society of Medicine Services, London, 1-7.
  • Soliday, S.M. (1974). Relationship between age and hazard perception in automobile drivers. Perceptual and Motor Skills, 39, 335-338.
  • Spolander, K. (1982). Inexperienced drivers’ behaviour, abilities and attitudes. Swedish National Road Traffic Research Institute Report.
  • Wasielewski, P. (1984). Speed as a measure of driver risk: Observed speeds versus driver and vehicle characteristics. Accident Analysis & Prevention, 16, 89-104.


Skills and 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 could be 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. A research driving simulator is an excellent research tool to investigate these issues further.

The following literature was referred to:

  • Häkkinen, S. (1979). Traffic accidents and professional driver characteristics: a follow-up study. Accident Analysis & Prevention, 11, 7-18.
  • Harano, R.M. (1970). Relationships of field dependence and motor vehicle accident involvement.Perceptual and Motor Skills, 31, 272-256.
  • Loo, R. (1978). Individual differences and the perception of traffic signs. Human Factors, 20, 65-74.
  • McKenna, F.P. (1982). The human factor in driving accidents. An overview of approaches and problems.Ergonomics, 25, 867-877.
  • McKenna, F.P. (1983). Accident pronesness: a conceptual analysis. Accident Analysis & Prevention, 15, 65-71.
  • McKenna, F.P.; Duncan, J. and Brown, I.D.. (1986). Cognitive abilities and safety on the road: a re-examination of individual differences in dichotic listening and search for embedded figures. Ergonomics, 29, 649-663.
  • Mihal, W.L. and Barrett, G.V. (1976). Individual differences in perceptual information processing and their relation to automobile accident involvement. Journal of Applied Psychology, 61, 229-233.
  • Noordzij, P. (1990). Individual differences and accident liability: a review of the German literature. TRRL Contractor Report, 195, Crowthorne.
  • Quimby, A.R. and Watts, G.R. (1981). Human Factors and driving performance. TRRL Laboratory Report 1004, Crowthorne.
  • Rumar, K. (1988). Collective risk but individual safety. Ergonomics, 31, 507-518.
  • Summala, H. (1985). Modeling driver behaviour: A pessimistic prediction? In: EVans L. and Schwing, R.C. (eds.). Human behaviour and traffic safety. Plenum Press, New York, 43-61.


Accident risk of 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. The number of studies on driver behaviour of elderly drivers in driving simulators is limited, because this age group is more susceptible to simulator sickness.

The following literature was referred to:

  • Brouwer, W.H. (1989). Bejaarden in het verkeer. In: C.W.F. van Knippenberg, J.A. Rotherngatter and J.. Michon (eds.). Handboek sociale verkeerskunde. Van Gorcum, Assen/Maastricht, 331-349.
  • Brouwer, W.H. and Ponds, R.W.H.M. (1994). Driver competence in older persons. Disability and Rehabilitation, 16, 149-161.
  • Case, H.W.; Hulbert, S. and Beers, J. (1970). Driving ability as affected by age. Final Report No. 70-18. Institute of Transportation and Traffic Engineering. University of California, Los Angeles.
  • Cerelli, E. (1989). Older drivers, the age factor in traffic safety. Report DOT-HS-807-402. U.S. Department of Transportation.
  • Evans, L.E. (1988). Older driver involvement in fatal and severe traffic crashes. Journal of Gerontology, Social Sciences, 43, 186-193.
  • Hakamies-Blomqvist, L. (1994). Compensation in older drivers as reflected in their fatal accidents.Accident Analysis & Prevention, 26, 107-112.
  • Kosnik, W.D.; Sekuler, R. and Kline, D.W. (1990). Self-reported visual problems of older drivers. Human Factors, 32, 597-608.
  • Planek, T.W. and Fowler, R.C. (1971). Traffic accident problems and exposure characteristics of the aging driver. Journal of Gerontology, 26, 224-230.
  • Rackoff, N. (1974). An investigation of age related changes in driver’s visual search patterns and driving performance and the relation to tests of basic functional capacities. Ph.D. dissertation, The Ohio State University, Columbus, Ohio.
  • Rackoff, N.J. and Mourant, R.R. (1979). Driving performance of the elderly. Accident Analysis & Prevention, 11, 247-253.
  • Ranney, T.A. and Pulling, N.H. (1990). Performance differences on driving and laboratory tasks between drivers of different ages. Transportation Research Record 1281, 3-10.
  • Rumar, K. (1987). Elderly drivers in Europe. In: Proceedings of roads and traffic safety on two continents, VTI report 331A, Linkoping, Sweden.
  • Ysander, L. and Herner, B. (1976). The traffic behaviour of elderly male automobile drivers on Gothernburg, Sweden. Accident Analysis & Prevention, 8, 81-86.


Effects of marijuana on driving performance

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 car 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 research driving 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.

The following literature was referred to:

  • Caswell , S. (1977). Cannabis and alcohol: Effects on closed course driving behaviour. In: Johnson, L. (ed.). Seventh International Conference on Alcohol, Drugs, and Traffic Safety. Melbourne. Australia.
  • Moskowitz, H. (1985). Marihuana and driving. Accident Analysis & Prevention, 17, 323-345.
  • Robbe, H.W.J. (1994). Influence of marijuana on driving. Thesis. State University Limburg, Maastricht, The Netherlands.
  • Smiley, A.M.; Moskowitz, H. and Zeitman, K. (1981). Driving simulator studies of marijuana alone and in combination with alcohol. Proceedings of the 25th conference of the American Association for Automotive Medicine, 107-116.
  • Smiley, A.M. (1986). Marijuana: On-road and driving simulator studies. Alcohol, drugs and driving: Abstracts and reviews, 2, 135-154.
  • Smiley, A.M.; Noy, I. and Tostowaryk, W. (1986). The effects of marijuana alone and in combination with alcohol on driving performance. In: Noordzij, P.C., Roszbach, R. (eds). Alcohol, drugs and traffic safety-T86. Elsevier Science Publishers BV, North-Holland, 203-206.
  • Stein, A.C.; Allen, R.W.; Cook, M.L. and Karl, R.L. (1983). A simulator study of the combined effects of alcohol and marijuana on driving behaviour. Report submitted to the National Highway Traffic Safety Administration. Hawthorne, CA: Systems Technology Inc.
  • Stein, A.C. (1986). A simulator study of the effects of alcohol and marihuana on driving behaviour. In: Noordzij, P.C., Roszbach, R. (eds).Alcohol, drugs and traffic safety-T86. Elsevier Science Publishers BV, North-Holland, 197-201.


Adaptive control models of driving

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. 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 below, 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 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.

adaptive control model

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 exampleDonges, 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. Standard deviation 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.

The following literature was referred to:

  • Blaauw, G.J. (1984). Car driving as a supervisory control task. Thesis. Institute for Perception TNO, Soesterberg, The Netherlands.
  • Donges, E. (1978). A two-level model of driver steering behaviour. Human Factors, 20, 691-707.
  • Evans, L.E. (1991). Traffic safety and the driver. Van Nostrand Reinhold, New York.
  • Evans, L. and Herman, R. (1976). Note on driver adaptation to modified vehicle starting acceleration.Human Factors, 18, 235-240.
  • Evans, L. and Rothery, R. (1976). Comments on effects of vehicle type and age on driver behaviour at signalized intersections. Ergonomics, 19, 559-570.
  • Evans, L.E. and Wasielewski, P. (1983). Risky driving related to driver and vehicle characteristics.Accident Analysis & Prevention, 15, 121-136.
  • Godthelp, J. (1984). Studies of vehicle control. Thesis. Institute for Perception TNO, Soesterberg, The Netherlands.
  • Godthelp, J. and Käppler, W.D. (1988). Effects of vehicle handling characteristics on driver strategy.Human Factors, 30, 219-229.
  • Good, M.C. and Baxter, G.L. (1986). Evaluation of short-range roadway delineation. Human Factors, 28, 645-660.
  • Harms, L. (1993). The influence of sight distance on subjects’ lateral control: a study of simulated driving in fog. In: Gale (eds). Vision in Vehicles-IV. Elsevier Science Publishers BV, North-Holland.
  • Lund, A.K. and O’Neill, B. (1986). Perceived risks and driving behaviour. Accident Analysis & Prevention, 18, 367-370.
  • McRuer, D.T.; Allen, R.W.; Weir, D.H. and Klein, R.H. (1977). New results in driver steering control models. Human Factors, 19, 381-397.
  • Michon, J.A. (1985). A critical view of driver behaviour models. What do we know, what should we do? In: Evans, L; Schwing, R. (eds.). Human behaviour and traffic safety. New York: Plenum Press.
  • Reid, L.D. (1983). A survey of recent driver stereing behaviour models suited to accident studies.Accident Analysis & Prevention, 15, 23-40.
  • Riemersma, J.B.J. (1987). Visual cues in straight road driving. Thesis. University of Groningen.
  • Rumar, K.; Berggrund, U.; Jernberg, P. and Ytterbom, U. (1976). Driver reaction to technical safety measure-studded tires. Human Factors, 18, 443-454.
  • Summala, H. and Merisalo, A. (1980). A psychophysical method for determining the effect of studded tires on safety. Scandinavian Journal of Psychology, 21, 193-199.
  • Tenkink, E. (1988). Lane keeping and speed choice with restricted sight distances. In: Rothengatter, T. and de Bruin, R. (eds). Road user behaviour: theory and research. Van Gorcum, Assen/Maastricht, The Netherlands.
  • Young, L.R. (1969). On adaptive manual control. Ergonomics, 12, 635-675.

Drowsiness and impairment in car driving

In a research driving simulator the effects of time-on-task were measured on variables that measure drowsiness, driving performance and steering behaviour. It was found that the fraction of time during which the eyes are closed is a good measure of drowsiness that is sensitive to the effects of time-on-task. Of all single variables that measure driver performance and impairment, the percentage of time during which any part of the vehicle exceeded one of the lane boundaries was the most strongly affected by time-on-task. Also, with progressing drowsiness, the amplitude of steering corrections increased towards larger values. This was caused both by larger error corrections in response to larger errors and by an increase in coarseness of the steering response. Large steering corrections proved to be the single best indicator of progressing impairment by drowsiness and fatigue.


Falling asleep at the wheel and drowsiness are considered important factors in accident causation. Estimates of the involvement of these factors in accidents are higher when the statistics are based on in-depth accident studies (10-25%) compared to statistics based on general police databases (1-4%) (Horne & Reyner, 1995). This indicates that, although the scope of the problem is not clear, drowsiness and fatigue are significant risk factors. Because of this a large number of studies on drowsiness, fatigue and sleepiness have been reported in the literature. These studies differ widely in the variables used to measure drowsiness and impaired driving.

Drowsiness is a psychophysiological state that is assumed to result in an inability of the driver to drive safely. Drowsiness is often measured by eyelid closures. The percentage of time the eyes of the driver are 80 to 100% closed (PERCLOS) has been used as a variable for measuring drowsiness. Dingus, Hardee and Wierwille (1987) found that PERCLOS correlated better with driver impairment than other measures of eyelid closures that were examined. The use of eyelid closures as an indicator of drowsiness stems from the general observation that as people get drowsy they close their eyes more frequently and during longer periods, until they finally fall asleep. However, in an experiment by Wierwille, Lewin and Fairbanks (1996) it was found that PERCLOS did not predict drifting off the road very well. Since measures of eyelid closure were not sensitive enough, they advised to monitor lane position as well in order to detect driver impairment.

A number of other studies have focussed on driver behaviour instead of driver state. According to Bishop, Madnick, Walter and Sussman (1985), steering activity becomes more coarse when driving for long periods of time: the number of large steering movements increases while the number of smaller steering amplitudes decreases. Seko, Kataoka and Senoo (1985) also found that with reduced alertness caused by drowsiness, the number of steering corrections with large amplitudes increases. This suggests that with progressing drowsiness the number of lapses of attention increases resulting in longer periods during which there are no steering corrections. This would result in drifting to the edge of the lane which is corrected by a larger steering amplitude. This view is consistent with the theory of ‘blocking’ as proposed by Bertelson and Joffe (1963). They found that with progressing fatigue the occurrence of ‘blocks’ of large reaction times increases. After a ‘block’, performance returns to normal for a while. A blocking may express itself as a failure to commit a correcting steering action in time which results in smaller safety margins to the lane boundary, crossing the lane boundary or in moving off the road. In that case the occurrence of an error (i.e. a smaller safety margin or a lane boundary exceedance) is not the only indication of impaired performance because of drowsiness. Also the increase in the number of large steering amplitudes is an indication of error correction in response to a larger error. This error correction response then evidences progressing impairment. Error correction may then be defined as turning the steering wheel in the opposite direction, with a large peak-to-peak amplitude, when the driver notices that the lane boundary is about to be crossed or has been crossed. Error corrections prevent accidents to occur and they may partly prevent effects of drowsiness on measures such as the standard deviation of the lateral position (SDLP) or exceedance of the lane boundaries. From this perspective it may be that effects on steering amplitude-related measures as indicators of error correction show up earlier than effects on lane position related variables. However, with progressing drowsiness, error corrections may come too late resulting in increased swerving or running off the road.

This reasoning assumes that safety margins to the lane boundary are perceived by the driver and acted on by a correcting steering action. This principle has been demonstrated by Godthelp (1988) in a study where drivers were instructed to generate correcting steering actions when vehicle heading could still be corrected comfortably to prevent a crossing of the lane boundary. Safety margins were defined by the concept of Time-to-Line Crossing (TLC). This represents the time available until any part of the vehicle reaches one of the lane boundaries. This coupling of perception and action has also been demonstrated for driving in curves by Van Winsum and Godthelp (1996) and for the way drivers change lanes by Van Winsum (in press).

Another group of studies has focussed on the effects of fatigue on driver performance instead of psychophysiological measures and measures of driver control behaviour. The standard deviation of the lateral position (SDLP) and exceedance of the lane boundaries are generally referred to as indicators of the quality of driving behaviour and, thus, driver performance. It has been found that SDLP increases with time on task (Riemersma, Sanders, Wildervanck & Gaillard, 1977; De Waard & Brookhuis, 1991). SDLP measures swerving and control over lateral position. Another measure of driver performance is the proportion of time that any part of the vehicle exceeds the lane boundary. This has been referred to as ‘LANEX’ by Wierwille, Lewin and Fairbanks (1996). LANEX is a strong indicator of driver impairment. The minima of TLC are used in the present study as an additional measure of driver performance. These minima represent the safety margins to the lane boundaries that are maintained by the driver. Smaller TLC minima indicate poorer lateral control and suggest poorer driver performance and progressing impairment.

In summary, three types of variables are used in the present study. Variables that measure driver performance are LANEX, SDLP and TLC minima. Other variables measure drowsiness. These are related to eyelid closures, especially PERCLOS (i.e. the percentage of time that both eyes are closed). The third type of variable relates to driver behaviour or more specifically to steering behaviour of the driver.
The experiment was performed in the driving simulator of the TNO Human Factors Research Institute.


Eighteen paid subjects participated in the experiment. Age ranged between 24 and 70 years.  All subjects had held their drivers licence for more than 5 years and the annual kilometrage exceeded 5000 km. The experiment was performed in the driving simulator of the TNO Human Factors Research Institute, described in detail in Hogema and Hoekstra (1998), and Hoekstra, Van der Horst and Kaptein (1997).

For the detection of eyelid closures, EOG was measured for both eyes with electrodes attached just above and below each eye in line with the pupil. By this procedure the signal is affected only by artefacts caused by vertical eye movements and not by eye movements in the horizontal plane. The electrodes were connected to a physiological amplifier with a timeconstant of 10 s. The amplifier gave an output between –5 and +5 Volts. This was fed directly into the A/D converter of the simulator computer, where it was sampled and stored together with the driver behaviour data. This ensured a fixed synchronization in time between all signals.

All subjects were informed about the purpose of the experiment and that it took three hours of continuous driving. Subjects were free to stop driving at any moment without negative consequences. They were requested to stop if they were feeling uncomfortable or if they were too tired to continue to drive safely. After the instructions, the electrodes for EOG measurement were attached and the signal was tested with an oscilloscope. During the drive, speed was controlled by a cruise control that was set at a constant speed of 80 km/h. This was expected to facilitate the occurrence of drowsiness because of the relatively low speed in a visually boring environment. All subjects drove continuously for three hours during the daytime on a monotonous road under simulated evening lighting conditions. No other traffic was encountered. The road was a standard two-lane road with a lane width of 3.1 m, broken centerline and continuous edgelines. It consisted of straight and curved segments with a continuous horizontal radius of 2000 m that turned either to the left or to the right over an angle of 45º. Subjects were instructed to drive in the right lane without exceeding the lane boundaries. A mild side wind with varying force was simulated in order to necessitate a minimum amount of steering effort. 

Coordinate positions were stored as well as steering wheel angle, lateral position and EOG recordings of both eyes with a frequency of 10 Hz. Time-to-Line Crossing (TLC) was computed off-line according to the method described in Van Winsum, Brookhuis and De Waard (in press). Time-on-task (TOT), i.e. the effect of progressing time that is assumed to result in fatigue and drowsiness, was treated as a within-subjects factor as follows. The first 20 minutes of each run was not analyzed since this period was used to familiarize the subjects with driving in the simulator. After this, the remaining time was divided into 5 sequential blocks of equal duration. Usually these blocks each covered a period of 32 minutes. This means that there are 5 TOT blocks. Effects of fatigue and drowsiness on task performance are expressed as an effect of TOT. To evaluate time-on-task effects the dependent variables were averaged over each block and divided by the average value for the first block where appropriate. In this way all variables have the same units and can be compared directly in terms of the sensitivity to TOT effects. If it is assumed that drowsiness increases with increasing time on task, then the variables with the strongest statistical effect of TOT are most useful as indicators of drowsiness. This procedure of dividing the average by the data of the first time block can only be used if the data on the first block can never be zero. Therefore, not all variables are suitable for treatment by this procedure.

EOG data were filtered off-line and the filtered signal was subtracted from the raw EOG signal to allow peak detection analysis by a computer program that detected eyeblinks and eyelid closures. These were transformed into the following indicators of drowsiness:
  • PERCLOS, i.e. the fraction of time during which both eyes are closed
  • BLINK, i.e. the blinkfrequency
For each TOT block, the average value was divided by the value of the first block.
The following indicators of driver performance were computed:
  • LANEX, i.e. the fraction of time during which any of the wheels exceeds the right lane boundary.
  • SDLP, i.e. the standard deviation of lateral position, with respect to the first block.
TLC minima to the left and right lane boundaries were determined and only minima of less than 20 s were analyzed. These minima were used to compute:
  • TLC1.0, i.e. the percentage of TLC minima smaller than 1.0 s.
The following indicators of steering behaviour were computed:
  • SDST, i.e. the standard deviation of steering wheel angle, with respect to the first block,
  • P3-6, i.e. the power of fast steering movements (in the domain of 0.3-0.6 Hz) as a fraction of all steering activity < 0.6 Hz, with respect to the first block.
  • STAMP, i.e. the average of peak-to-peak steering amplitudes, with respect to the first block
  • STDIS, i.e. the fraction of larger peak-to-peak steering amplitudes. This was computed as follows: for the first block the distribution of all peak-to-peak steering amplitudes was computed and the 80th percentile value (i.e. that value for which 80% of all values are smaller and 20% of all values are larger) was determined. Then, for all subsequent time blocks the percentage of values that was larger than the initial 80th percentile value was computed and divided by 20 (i.e. the percentage larger than the value in the first block). Figure 1 gives a graphical illustration of this principle. There are two bell-shaped distributions of peak-to-peak steering amplitudes. The left distribution refers to the first block, while the right distribution represents block i. The idea then is that as drowsiness progresses, the distribution of steering amplitudes shifts towards larger values (to the right). The vertical line represents the value of steering amplitudes, during the first block, that separates the 20% largest values from the 80% lowest values. The sum of the striped and black area represents the percentage of values that is larger than P80 of the first block. STDIS then is the sum of the black and striped area divided by the black area.
The following types of analyses were conducted:
1)     The effect of time on task was tested with analysis of variance using a within-subjects repeated measurement design. The aim of these analyses was to evaluate the relative sensitivity of the dependent variables to effects of drowsiness.
2)     For each block the magnitude of the TLC minima was related to the correcting steering wheel action in the opposite direction. This was realized as follows. TLC minima to the right lane boundary result in a path-correcting turning of the steering wheel to the left, while TLC minima to the left lane boundary result in a path-correcting turning of the steering wheel to the right. All TLC minima to either the left or the right were detected together with the accompanying correcting peak-to-peak steering amplitude to the opposite direction. Then the TLC minima were categorized into groups according to the magnitude of the TLC minima. For testing the relation between TLC minima and steering corrections, the following groups were distinguished:
1  =TLC minima >0.0 and <=1.5 s; 2  =TLC minima >1.5 and <=3.0 s
3  =TLC minima >3.0 and <=4.5 s; 4  =TLC minima >4.5 and <=6.0 s
5  =TLC minima >6.0 and <=7.5 s; 6  =TLC minima >7.5 and <=9.0 s
7  =TLC minima >9.0 and <=10.5 s; 8  =TLC minima >10.5 and <=12.0 s
9  =TLC minima >12.0 and <=13.5 s; 10 =TLC minima >13.5 and <=15.0 s
A smaller TLC minimum can be considered as a larger error that is compensated by a larger steering correction (larger peak-to-peak steering amplitude in the opposite direction). Analyses of variance were applied to test whether the sensitivity of the steering response to TLC information changes as a function of time on task.
Figure 1. Distributions of peak-to-peak steering amplitudes. STDIS is computed as the sum of the black and striped area divided by the black area.

3.     RESULTS

Table 1 gives an overview of the effects of time on task on the different dependent variables. It can be seen that all variables related to driver performance, drowsiness and steering behaviour are strongly affected by time of driving: performance deteriorates with increased driving time, and drivers become more drowsy while their steering becomes more coarse. The steering amplitude related variables, i.e. STDIS and STAMP, have the largest effect of time on task. This means that the effect of time-on-task on these variables is the most reliable, as indicated by the F-statistic. When comparing STDIS and STAMP, the effect size of time-on-task is the largest for STDIS. This can also be seen in figure 2. These results indicate that, of all variables examined in the present experiment, the fraction of large peak-to-peak steering amplitudes (STDIS) is the best indicator of drowsiness-related driving impairment.
Table 1. Effects of time on task on the dependent variables (F-statistics), together with average values
Time on task block
Dependent variable
Effect of time on task df=68,4
Driver performance
8.81  **
7.34 **
SDLP relative
8.30 **
5.90 **
9.51 **
Steering behaviour
14.60 **
15.25 **
11.49 **
9.00 **
** p < .001
From the previous analyses it appeared that with progressing drowsiness driver errors increased which resulted in more frequent crossing of the lane boundaries and smaller TLC minima. This may explain the shifting to larger steering corrections with time-on-task, since larger errors may be corrected by larger corrections. In order to test this, the magnitude of the TLC minima was related to the correcting steering wheel action in the opposite direction for each block according to the procedure described in paragraph 2. Analyses of variance were applied to test whether the sensitivity of the steering response to TLC information changes as a function of time on task. In these analyses the time-on-task effects of block 1 vs block 5 were tested. The effect of time-on-task on the amplitude of the steering corrections was significant (F(17,1)=30.43, p<.001), while the effect of TLC minimum on the amplitude of the corrective steering actions was significant as well (F(153, 9)=135.89, p<.001). This is illustrated in figure 3.
Figure 2. Variables related to steering behaviour as a function of time on task.
The results show that the magnitude of the correcting steering action is strongly related to the magnitude of the error, since smaller TLC minima are associated with larger correction peak-to-peak steering amplitudes in the opposite direction. However, in addition the effect of time-on-task of steering amplitude is highly significant, despite the fact that it has been controlled for TLC minima. This means that although the increase in larger steering corrections with time-on-task can partly be explained by the increase in errors (small TLC minima) with progressing time-on-task, there still is a substantial increase in the magnitude of peak-to-peak steering corrections which cannot be explained by larger errors. This suggests that as drowsiness increases, steering reactions to a movement of the car towards the lane boundaries become larger.
Figure 3. Peak-to-peak steering amplitude as a function of TLC minima for the first and the last time-on-task blocks.


In an experiment performed in the TNO driving simulator the effects of drowsiness on several measures of driving performance, drowsiness and steering behaviour were studied. The method used was prolonged driving under monotonous environmental conditions. The results reveal significant effects of time-on-task on variables that measure drowsiness. These variables were based on eyelid closures derived from EOG measurements. Both the fractions of time during which both eyes were closed (PERCLOS) and the frequency of eyeblinks (BLINK) were significantly affected by time-on-task. This suggests that the experimental setup resulted in the desired effect of inducing drowsiness in the subjects. In accordance with the literature, PERCLOS appears to be a valid indicator of drowsiness. This variable is easy to compute from the data of an eyelid monitor and may be a useful variable for driving impairment detection systems.

Driver performance was measured by means of variables that were derived from lateral position. Of all performance-related variables, the fraction of time during which any part of the vehicle exceeds one of the lane boundaries (LANEX) was the most sensitive to effects of time-on-task. Because of this, it is recommended to include this variable in systems that are aimed at driver impairment detection. Also, in accordance with the literature, the standard deviation of lateral position (SDLP) was significantly affected by time-on-task as were the TLC minima.

The results of the steering related variables show that the fraction of large peak-to-peak steering amplitudes that are larger than the 80th percentile value during the initial period of driving were the most sensitive to the effects of time-on-task. This indicates that the distribution of steering amplitudes shifts towards larger values with progressing time. This variable appears to be more sensitive than all other variables that were measured in this experiment, driver performance and drowsiness-related variables included. It is therefore recommended to include this variable in systems for detecting drowsiness-related driver impairment.

In a final analysis it was evaluated how the relation between the magnitude of the errors, measured by the TLC minima, and the magnitude of the correcting steering wheel movements to the opposite direction was affected by time-on-task. The shift to larger steering corrections with progressing time-on-task may partly be explained by the larger errors that are committed when drowsiness increases. It appeared that the amplitude of the correcting steering response is indeed strongly related to the momentary TLC minimum: a smaller TLC minimum is accompanied by a larger steering correction in the opposite direction. However, when corrected for the level of the TLC minima, steering corrections still increase significantly with time-on-task. This cannot be explained by larger errors with progressing fatigue. The results suggest that the larger steering corrections that occur with higher levels of drowsiness are both the result of larger errors and of increased coarseness of the steering responses. The mechanism responsible for this effect is unclear. A possible explanation may be that with progressing drowsiness drivers tend to look at a point on the road closer in front in an attempt to reduce visual input. This may then result in poorer lateral control. There is experimental evidence for the idea that lateral control performance deteriorates when the driver has less preview, as is the case when the driver looks at a point closer in front of the vehicle. For example, Tenkink (1988) studied the effects of sight distances of 27, 37 and 183 meters on lateral control performance. He found that a smaller sight distance resulted in a larger SDLP at a given speed. The hypothesis that drowsy drivers look at a point closer in front of the vehicle is consistent with the results of Kaluger and Smith (1970). They found, in a study of driver fatigue, that drivers looked closer in front of the vehicle after several hours of driving. Mourant and Rockwell (1972) have described this compensating strategy of fatigued drivers as a regression towards the visual scan behaviour of novice drivers, who also are characterized by looking closer in front of the car compared to experienced drivers. Alternatively, this change in visual scanning strategy may be an attempt to reduce the amount and complexity of visual input. However, this hypothesis needs to be tested in further research. 


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