Maintaining task set under fatigue: a study of time-on-task effects in simulated driving

Transportation Research Part F 4 (2001) 103±118

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Maintaining task set under fatigue: a study of time-on-task e€ects in simulated driving Monique van der Hulst a,*, Theo Meijman b,1, Talib Rothengatter b a

Department of Work and Organisational Psychology, University of Nijmegen, P.O. Box 9104, 6500 HE Nijmegen, Netherlands b University of Groningen, Groningen, Netherlands

Received 21 March 2000; received in revised form 15 January 2001; accepted 18 February 2001

Abstract An experiment was carried out in a driving simulator in order to study time-on-task e€ects in driving with special attention to distance keeping and hazard avoidance performance. As expected, increases of fatigue in the course of sustained performance were associated with a deterioration of perceptual-motor performance and an increase of safety margins. In general, the results indicate that performance in less central task components such as steering deteriorates in the course of time, whereas performance in highpriority sub-tasks such as hazard avoidance remains intact. Time-schedule instructions disrupted the adaptation of safety margins in prolonged driving. This study has practical implications for the design of driver impairment monitoring systems. Ó 2001 Elsevier Science Ltd. All rights reserved. Keywords: Fatigue; Sustained performance; Time pressure; Coping strategies; Driving behaviour

1. Introduction In order to maintain coherent performance, operators in complex task environments need to maintain the appropriate `task set' (Hockey, 1986a; Allport et al., 1994). Task set can be de®ned as concentration on task-relevant information to maintain task goals and priorities. Sustained attention in complex dynamic tasks involves not only passive detection of relevant changes in the task environment, but also active search of relevant information, planning of actions and

*

Corresponding author. Tel.: +31-24-3612687; fax: +31-24-3615937. E-mail address: [email protected] (M. van der Hulst). 1 Theo Meijman participates in the Netherlands Concerted Research Action `Fatigue at Work', ®nanced by the Netherlands Organisation for Scienti®c Research (NWO). 1369-8478/01/$ - see front matter Ó 2001 Elsevier Science Ltd. All rights reserved. PII: S 1 3 6 9 - 8 4 7 8 ( 0 1 ) 0 0 0 1 7 - 1

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prediction of future circumstances (Bainbridge, 1997; Hockey and Tattersall, 1989). Driving is an example of a complex task that requires continuous attention in order to detect possible hazards. Brown (1994) pointed out that the main time-on-task e€ect in driving is a progressive withdrawal of attention from road and trac demands. A familiar environment is likely to induce more sustained attention problems than an unfamiliar or unpredictable environment (Hancock and Verwey, 1997; Nelson, 1997). Although driving in monotonous situations does not require many complex decisions, drivers should be prepared to respond to threats, and therefore continuous attention is required. As Brown (1994) states it, e€ort is required to cope with the challenge of stimulating task but also to stave o€ boredom in a monotonous task. Therefore, prolonged driving in monotonous conditions should be characterised as a demanding task that is associated with e€ort costs (Hancock and Verwey, 1997; De Waard, 1996) even though the task demands in terms of working-memory load and controlled information processing (Shi€rin and Schneider, 1977) are low. In the course of prolonged task performance, it generally becomes increasingly dicult to maintain task set and performance can be impaired. Performance decrements after prolonged task execution are generally referred to as fatigue e€ects. However, fatigue is an ill-de®ned concept and there has been ample discussion in the literature about the nature of fatigue (Meijman, 1991). Cameron (1973) pointed out that time is probably the only relevant variable that is uniquely associated with fatigue. Thus, fatigue results from prolonged performance. This de®nition is highly circular, and therefore it seems better to use the more neutral term `time-on-task e€ect' in order to refer to changes in task execution over time. In sustained performance, increased control activity is needed to maintain task orientation and activation (Hockey, 1986a, 1993). Thus, sustained attention is maintained against (possibly increasing) e€ort costs, and if the motivation to invest e€ort is low, performance can deteriorate. Therefore, fatigue is often de®ned as subjectively experienced aversion to invest further e€ort in the task (e.g., Thorndike, 1900; Broadbent, 1979; Meijman, 1991; Craig and Cooper, 1992; Brown, 1995). The motivation to invest e€ort in a monotonous driving task is likely to decrease in the course of time. Nilsson et al. (1997) found considerable di€erences between individuals in the time span in which they reached a critical level of aversion and wanted to stop driving. In this video-based driving-simulation study, subjective fatigue symptoms began to develop within 60 min. During prolonged driving, steering performance deteriorates gradually (e.g., Riemersma et al., 1977). Performance decrements in steering have been found within the ®rst half hour of driving for drivers who were not tired beforehand (O'Hanlon, 1981). O'Hanlon and Kelley (1977) found a relation between subjectively experienced fatigue and deterioration of steering performance. Individuals who performed poorly, reported higher levels of subjective fatigue. In a study of time-on-task e€ects on car-following performance, Brookhuis et al. (1994) found that coherence between the speed of the following car and the speed of the lead car diminished slightly after 2.5 h of continuous driving. This indicates that accuracy in following the lead car's speed changes was reduced. It can be concluded that prolonged driving is typically accompanied by a decreased motivation to continue driving and reduced accuracy of lateral and longitudinal vehicle control. Although motivation is important in sustained performance, it is incorrect to state that e€ort investment can and will always counteract performance decrements. As Brown (1995) pointed

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out, the ability to monitor the eciency of one's own performance and judgement of one's own abilities might deteriorate as a result of fatigue. It is dicult to determine whether performance decrements in the course of prolonged task execution are the e€ects of decreased motivation or the results of an involuntary deterioration of performance monitoring. Operators evaluate the quality of their performance and the e€ort costs associated with this performance level (Bainbridge, 1974; Welford, 1978; Hockey, 1993). If the maintenance of operational performance is associated with increasing e€ort costs, strategy shifts can be expected to occur (see Hockey, 1997). In general, persons who are fatigued are likely to choose strategies that cost little e€ort (Hockey, 1986b; Craig and Cooper, 1992). Hockey (1997) suggested that fatigue might have a general adaptive role in shifting behaviour towards less e€ort-demanding modes of response. Thus, strategy shifts should be interpreted as economising on e€ort while trying to maintain adequate performance and protect task priorities. Coherent and adequate driving performance is characterised by the anticipatory avoidance of hazards and responses that are adapted to the criticality of the situation. Although drivers cannot control the frequency of relevant events that demand intervention, they can increase the amount of time that is available to react to hazards by decreasing speed and increasing headway. Increased safety margins allow drivers to decrease the task demands of prolonged driving while protecting the adequacy of collision avoidance. Interestingly, Fuller (1981) found that headway in closing-braking manoeuvres was longer in the last hour of prolonged driving in convoy in 11-h shifts, particularly in the late shift. In a later paper, Fuller (1984) reports that, at the end of the late shift, drivers reported symptoms of performance deterioration, drowsiness and exhaustion and were more inclined to want to stop driving. Thus, the increase of safety margins might at least partially re¯ect a compensatory adjustment that is related to fatigue and aversion. The study described below focused on changes in performance strategy in driving in relation to feelings of fatigue and aversion. Relatively little is known about changes in hazard avoidance performance in the course of a few hours of driving. Driving simulators provide the possibility to study time-on-task e€ects on hazard avoidance in highly controlled situations. In particular, we were interested in time-on-task e€ects on the choice of safety margins and reactions to predictable and unpredictable critical situations within the limited time span of about 2.5 h. Although this may not seem to be a long drive, the literature reviewed above shows that symptoms of fatigue and inaccuracy of vehicle control performance can develop within an hour of driving. Therefore, it is important to study whether these e€ects are accompanied by changes in hazard avoidance behaviour. The study focused on three research questions. The ®rst question was whether drivers would increase their headway in the course of prolonged driving and whether there would be a relation between subjectively experienced fatigue and possible changes in the choice of headway. Second, we were interested in changes in perceptual-motor performance and the accuracy of reactions in critical situations as a function of time on task, also in relation with subjectively reported fatigue. Finally, the third research question was whether time pressure instructions would a€ect the development of fatigue and whether time pressure would lead to changes in performance strategy in the course of prolonged driving. As was pointed out earlier, drivers can restrict the task demands by relaxing their performance criteria in vehicle control and by increasing their safety margins if the task constraints allow them to do so. However, strategy changes in driving depend on motivation and task

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constraints. In this study, the stringency of task constraints was manipulated by means of time pressure instructions. Time pressure causes a motivational pressure to maintain high speed and short safety margins, which implies that fast and accurate reactions to hazards are necessary (see Van der Hulst et al., 1998). Thus, time pressure is associated with increased task demands and therefore, it is likely to a€ect strategy shifts and e€ort investment during prolonged driving. 2. Method 2.1. Apparatus The study was carried out in an advanced driving simulator (Van Wol€elaar and Van Winsum, 1995). This ®xed-base simulator consists of a normal passenger car with original controls. All vehicle control actions taken by the driver are fed into a computer programme that runs on a graphical workstation (Silicon Graphics Skywriter). The workstation calculates the position of the simulator car in the virtual world and generates the images, which are projected on a panoramic screen (165° wide, 45° high). Other vehicles interact with the simulator car and with each other, and behave according to hierarchically structured decision rules that simulate human driving behaviour. 2.2. Task The simulated road environment consisted of rural roads with side roads. The circuit was 32 km long and the speed limit was 80 km/h. Several times during each ride, lead cars merged in front of the subject's vehicle and drove at a speed of 75 km/h. Participants were free to choose the headway they preferred but they could not overtake the lead car due to the presence of oncoming trac. During part of the ride, there were no lead vehicles present. This created opportunities for participants in the time-schedule group to keep to the schedule. In deceleration scenarios, the lead cars decelerated from their initial speed of 75 km/h to a new target speed of 55 km/h, with a deceleration level of 1 m=s2 (gradually) or 2 m=s2 (abrupt). The lead car drove 55 km/h until the participants' car had decelerated to 60 km/h and then accelerated to their original speed of 75 km/h. The brake lights did not light up, in order to simulate a situation in which the lead car released the accelerator pedal. There were two di€erent deceleration scenarios. In the unpredictable deceleration scenario, there were no contextual cues that the car in front was likely to decelerate. In the predictable deceleration scenario, the lead car had to give way to a vehicle coming from a side road on the right. Participants could see this car well in advance, and therefore they could expect the car in front to decelerate. Thus, deceleration level and predictability were varied, resulting in four deceleration conditions. In total, participants encountered 16 decelerations of lead cars in clear visibility conditions, four in each deceleration condition. In addition to the distance-keeping scenarios described above, there was a closing-scenario that involved closing in on a slow car that was driving in front. This car drove at a constant speed of 40 km/h and could not be overtaken due to the presence of oncoming trac. Thus, this scenario

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forced drivers to adapt their speed. The slow car left the road at the ®rst possible occasion. Participants approached a slow car once in a 30-min ride. 2.3. Procedure After arrival at the institute, participants were asked to complete a short questionnaire that included questions about age, driving experience, etc. The experimental procedure started with a training ride to habituate participants to driving in the simulator. After the training ride, participants drove in a monotonous rural environment for 30 min. During this ride, participants encountered the distance-keeping scenarios that were described above. After the ®rst monotonous ride, all drivers participated in a route-memorisation experiment. This experiment was carried out in the same driving simulator, and involved the memorisation of complex routes while driving through a city. The route-memorisation experiment took about 1 h and 15 min. Finally, participants drove in the monotonous rural environment again. The second monotonous ride included the same distance-keeping scenarios as the ®rst ride (although in a di€erent order). Again, this ride took 30 min. In total, the procedure took about 2.5 h. Thus, the e€ects of time on task were studied in a before±after design and involved the comparison of driving behaviour in the ®rst and the second monotonous ride. As was mentioned earlier, all participants were experienced drivers of the simulator, which minimises the possibility of the occurrence of a learning e€ect that might interact with the e€ects of time on task. The instructions were varied between participants. Participants in the control group were instructed to drive as they would normally do. Participants in the time-schedule group were instructed to complete the ride within 30 min. It was possible to complete the ride in time without exceeding the speed limit. Four times during the ride feedback messages were projected on the simulator screen, in order to inform participants in the time-schedule group whether they drove ahead of or behind schedule. Participants in the control group did not receive feedback about their progress. All participants were told that the circuit was 32 km in length. Both groups were instructed to respect the speed limit and to give way to trac coming from the right. No speci®c instructions about distance keeping were given. 2.4. Measures For all participants, the time needed to arrive at the destination (driving time) was registered in order to check whether the time-schedule instructions had the intended e€ect. Thus, it was expected that driving time would be shorter for the time-schedule group than for the control group. Before and after the two experimental rides, several subjective measures were collected. Participants were asked to give fatigue ratings before and after each ride. The fatigue rating scale was a continuous scale (Zijlstra, 1993), with a minimum value of 0 and a maximum value of 150. In addition to fatigue, sleepiness was also rated before and after the rides (Stanford Sleepiness Scale; Hoddes et al., 1973). Fatigue and sleepiness were measured in order to check whether the time-ontask manipulation was e€ective, that is, whether participants were more fatigued and sleepier in the second ride than they were in the ®rst ride. Aversion ratings and mental e€ort ratings were collected in order to assess whether fatigue is associated with increased task aversion and

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increased e€ort costs. Before the two rides, participants were asked to give an aversion rating that measured the motivation to continue driving. Aversion was rated on a continuous scale (see also Borg, 1978) with a minimum value of 0 (no aversion at all) and a maximum value of 10 (extremely strong aversion). The e€ort scale used was the unidimensional Rating Scale Mental E€ort (Zijlstra, 1993; Verwey and Veltman, 1996). This scale has a minimum rating of 0 and a maximum rating of 150. Participants were asked to give e€ort ratings after the two distance-keeping rides. Furthermore, subjective ratings of performance quality were collected in order to test whether participants thought their performance was impaired after prolonged driving. The Driving Performance Quality Scale (Brookhuis et al., 1985) is a continuous scale that ranged from `much better than usual' to `much worse than usual', corresponding to values of 100 and )100, respectively. As was mentioned in the introduction, steering performance (lateral vehicle control) usually deteriorates in the course of prolonged driving. Standard deviation of the lateral position on the road was used as a measure of steering performance. Time headway in stable car-following situations in which the lead car had a constant speed was registered in order to study tactical adaptations of driving behaviour related to fatigue and time-schedule instructions. Time headway is de®ned as the distance to the lead vehicle divided by the speed of the participants' car. Longitudinal vehicle control was evaluated by means of measurements of time headway in the deceleration scenarios described above. The accuracy of reactions in critical situations was indicated by time-to-collision, which can be de®ned as the distance to the lead car divided by the speed difference between the cars. Minimum time-to-collision in emergency situations is an indicator of criticality: a short time-to-collision indicates a near accident. Due to data registration problems, a few measurements of steering performance and time headway were missing. Participants for whom some measures were missing were included in the analyses concerning all other variables (casewise exclusion). Subjective data and performance measures were analysed by means of analysis of variance with repeated measurements. The between-subjects factor was instruction and the within-subjects factor was time on task. For the measures of longitudinal vehicle control (i.e., the deceleration scenarios), additional within-subjects factors were deceleration level and predictability. 2.5. Participants Twenty-four participants, 14 men and 10 women, were recruited from the institute's participants pool. Participants were between 28 and 46 years of age and held a driving licence for at least four years. All participants had driven in the driving simulator before. Twelve participants were assigned to the time-schedule group and 12 to the control group. Both groups consisted of seven men and ®ve women. The average age of the time-schedule group was 33.4 years (S.D. 5.5 years) and the average age of the control group was 32.3 years (S.D. 5.1 years). Participants in the timeschedule group held their driving licence for 13.2 years on average (S.D. 5.5 years) and drove about 15,700 km annually (S.D. 11,000 km). The control group held their driving licence for 13.3 years (S.D. 3.6 years) and drove about 15,800 km/year (S.D. 10,500 km). T-tests revealed that there were no signi®cant di€erences in age, number of years licensed nor annual kilometrage between the groups.

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3. Results 3.1. Driving time On average, the time-schedule group completed the experimental rides in 30 min, whereas the control group needed about 30 min and 50 s in order to complete the rides. The di€erence in driving time between the two groups was statistically signi®cant …F …1; 22† ˆ 25:54; P < :001†. In the ®rst ride, eight participants in the time-schedule group arrived on the destination within 30 min, whereas only one participant in the control group did. In the second ride, seven participants in the time-schedule group and two participants in the control group completed the ride within 30 min. Thus, the time-schedule instructions were e€ective but the di€erence in driving time between the groups was small because free speed choice was limited due to the presence of other trac and the 80 km/h speed limit. There was no di€erence in driving time between the ®rst and the second ride (F …1; 22† ˆ 1:53, ns) nor an interaction between the e€ects of instruction and the ®rst versus the second ride …F < 1†. 3.2. Subjective measures All subjective measures were assessed repeatedly. The di€erence between the ®rst and the last measurement was calculated in order to provide information about the changes in the subjective measures as a function of time on task. Table 1 provides the change scores for all subjective measures and the correlations between these change scores. Multivariate tests revealed a signi®cant e€ect of time on task on fatigue ratings …F …3; 20† ˆ 17:70; P < :001†. In the course of the experiment, fatigue ratings changed from 21.8 (between almost not fatigued and a little fatigued, S:D: ˆ 15:7) before the start of the ®rst ride to 45.4 (between somewhat fatigued and rather fatigued, S:D: ˆ 21:9) at the end of the second ride. Univariate tests showed that the increase of fatigue could be described by a linear trend …F …1; 22† ˆ 43:43; P < :001†, which means that fatigue increased monotonously in the course of the four measurements. The time-schedule group and the control group did not di€er in their fatigue ratings …F < 1†.

Table 1 Change scores (di€erence between the ®rst measurement and the last measurement of a variable, with positive change scores indicating an increase and negative change scores indicating a decrease as a function of time on task) and the subjective variables and correlations between these change scores Fatigue Sleepiness Aversion Performance quality Mental e€ort *

P < :05: P < :01:

**

Change score

1

2

3

4

23.6 1 1.4 )4.1 3.2

.85 .52 ).55 .63

.62 ).46 .51

).22 .34

).73

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There was no signi®cant di€erence in sleepiness ratings between the two groups (F …1; 22† ˆ 2:37, ns). Multivariate tests revealed a signi®cant e€ect of time on task on sleepiness ratings (F …3; 22† ˆ 4:92; P < :01). Univariate tests showed that the increase in sleepiness in the course of the experiment could be described by a linear trend …F …1; 22† ˆ 13:14; P < :001†. The Stanford Sleepiness Scale is a 7-point scale and therefore it can be argued that a normal analysis of variance is not allowed. Additional tests (Friedman's two-way ANOVA) con®rmed that sleepiness ratings increased signi®cantly in the course of the experiment (v2 ˆ 13:33, d.f. ˆ 3, P < :005). Before the ®rst ride, the mean sleepiness rating was 2.1 …S:D: ˆ 0:83†, and after the second ride the mean rating had increased to 3.1 …S:D: ˆ 1:1†. There was a high and signi®cant correlation between the increase of sleepiness and the increase of subjective fatigue …r ˆ 0:85; n ˆ 24; P < :001†. Time-schedule instructions did not a€ect aversion ratings (F …1; 22† ˆ 1:43, ns). Aversion to continue driving was lower before the ®rst experimental ride (after the training session) than before the second ride …F …1; 22† ˆ 37:4; P < :001†. The scores increased from 0.7 (very little aversion, S:D: ˆ 0:8) to 2.1 (light aversion, S:D: ˆ 1:2). The di€erence in aversion between the rides correlated with the increase of fatigue during the experiment …r ˆ 0:52; n ˆ 24; P < :01† and the increase of sleepiness …r ˆ 0:62; n ˆ 24; P < :001†. Participants rated their performance to be about equal to their normal driving performance. The mean performance ratings were 5.6 …S:D: ˆ 24:4† and 1.5 …S:D: ˆ 21:5† for the ®rst and the second ride, respectively. Self-rated performance quality did not di€er signi®cantly between the groups (F …1; 22† ˆ 2:31, ns), nor between the rides (F …1; 22† ˆ 1:03; ns). However, there was a signi®cant correlation between the di€erence in performance quality ratings between the rides and the increase of fatigue during the experiment …r ˆ :55; n ˆ 24; P < :01† and the increase of sleepiness …r ˆ :46; n ˆ 24; P < :05†. Thus, participants who were more fatigued and sleepier tended to report a decrease of performance quality. There were no di€erences in e€ort ratings between the two groups …F < 1† nor between the two rides …F < 1†. However, there was an interaction between group and ride …F …1; 22† ˆ 4:65; P < :05†. The time-schedule group invested a little less e€ort in the second ride as compared to the ®rst ride (a decrease from 43 to 37, S:D: ˆ 15:5 and 22.5, respectively), whereas the control group invested more e€ort in the second ride than in the ®rst ride (an increase of 37 to 49, S:D: ˆ 12:4 and 21.0, respectively). The di€erence in e€ort investment between the rides correlated with the increase of fatigue during the experiment …r ˆ :63; n ˆ 24; P < :001† and the increase of sleepiness …r ˆ :51; n ˆ 24; P < :05†. Thus, decreases of e€ort investment were associated with small increases of fatigue and sleepiness, whereas increases of e€ort investment were associated with large increases of fatigue and sleepiness. 3.3. Steering performance Steering performance was measured on two straight stretches of road during the ®rst 10 min and two straight road sections during the last 10 min of each ride. Fig. 1 shows the standard deviation of the lateral position in the course of the two rides. Standard deviation of the lateral position on the road was larger in the second ride (F …1; 20† ˆ 6:21; P < :05). Furthermore, the standard deviation of the lateral position was larger in the last 10 min of each ride than in the ®rst

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Fig. 1. Standard deviation of the lateral position on the road (mean and S.D.) at the start and at the end of both rides.

10 min …F …1; 20† ˆ 7:75; P < :05†. Thus, steering performance deteriorated in the course of the experiment and in the course of each 30-min ride. Correlations were calculated between the change in the standard deviation of the lateral position and the change in fatigue and sleepiness. The increase of standard deviation of the lateral position in the course of the experiment (the di€erence between performance at the beginning of the ®rst ride and performance at the end of the second ride) correlated with the increase of subjective fatigue …r ˆ :59; n ˆ 23; P < :01† and the increase of sleepiness …r ˆ :58; n ˆ 23; P < :01†. Thus, the deterioration of steering performance was stronger if fatigue and sleepiness increased more during the experiment. 3.4. Choice of headway Two stretches of road were selected where the lead car drove at a constant speed of 75 km/h and did not decelerate. Average time headway on these stretches was measured as an indicator of preferred headway. Stretches on which average time headway was longer than 5 s were considered to be non-car-following situations and were excluded from the analyses. Fig. 2 shows time headway chosen in the ®rst and the second ride for both groups. The time-schedule group maintained a shorter time headway than the control group …F …1; 21† ˆ 6:74; P < :05†. There were no signi®cant di€erences in choice of time headway between the two rides (F …1; 21† ˆ 2:81, ns). As can be seen in Fig. 2, average time headway increased in the second ride only for the control group, but this interaction e€ect was not signi®cant (F …1; 21† ˆ 2:09, ns). Presumably, the interaction failed to reach signi®cance because of a considerable increase of variability of the choice of time headway within the control group (S.D. increased from .69 in the ®rst ride to 1.41 in the second ride), whereas there was only a slight increase of variability within the time-schedule group (S.D. increased from .59 in the ®rst ride to .76 in the second ride). Interestingly, the correlation between time headway in the ®rst and the second ride was signi®cant for the time-schedule group …r ˆ :64; n ˆ 11; P < :05† but not for the

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Fig. 2. Choice of time headway (mean and S.D.) in steady-state situations with a constant speed of the lead car.

control group …r ˆ :16; n ˆ 12; ns†. The increase in variability within the control group and the absence of a correlation between time headway chosen in both rides indicate that most but not all of the participants in the control group have increased their headway in the second ride. Indeed, in the control group, nine out of twelve participants maintained a longer time headway in the second ride than in the ®rst ride (average increase of 1.35 s for these nine participants). In the timeschedule group, six out of twelve participants maintained a longer time headway in the second ride than in the ®rst ride (average increase of .46 s). In order to study the relationship between the change in choice of headway and fatigue levels, an average fatigue score and an average sleepiness score were calculated for the ®rst and the second ride (the average of the measurements before and after the ride), as an indicator of fatigue and sleepiness during the rides (note that this calculation di€ers slightly from the change scores reported in Table 1). There was a signi®cant positive correlation between the di€erence in average headway in the ®rst and second ride and the difference in fatigue ratings between the rides …r ˆ :47; n ˆ 23; P < :05†. Thus, the larger the increase of fatigue, the more time headway increased. However, the correlation between the di€erence in sleepiness ratings between the rides and the change in time headway was not signi®cant (r ˆ :18; n ˆ 23, ns). 3.5. Reactions in critical situations In order to determine whether participants anticipated predictable decelerations of the lead car, time headway was registered three seconds before the lead car started to decelerate and at the start of the deceleration. Increases in time headway in the three seconds prior to the deceleration were interpreted as anticipatory responses. There was no di€erence in anticipation of predictable decelerations of the lead car between the two groups (F …1; 22† ˆ 1:71, ns) nor between the two rides …F < 1†. As expected, time headway at the start of predictable decelerations was longer than time headway at the start of unpredictable decelerations …F …1; 22† ˆ 6:53; P < :05†, as a result of an anticipatory increase in headway in the predictable deceleration scenarios.

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The decrease of time headway in deceleration scenarios, that is, the di€erence between time headway at the moment the lead car started to decelerate and minimum time headway, was used as an indicator of the accuracy of reactions. Smaller reductions in time headway were interpreted as more accurate. Because it is likely that reactions to speed changes of a car in front are more accurate if time headway is shorter (e.g., Brookhuis et al., 1994), time headway at the moment the lead car started to decelerate was included in the analysis as a covariate. The time-schedule group reacted more accurately than the control group, but this e€ect could be explained by the di€erence in time headway at the start of the deceleration. Reactions to predictable decelerations were more accurate than reactions to unpredictable decelerations …F …1; 21† ˆ 12:11; P < :01†. Fig. 3 shows the accuracy of reactions to abrupt and gradual decelerations of the lead car. As can be seen in Fig. 3, the di€erence in accuracy of reactions between abrupt and gradual decelerations is larger in the second ride (signi®cant interaction between deceleration level and time on task, F …1; 21† ˆ 8:19; P < :01†. As we were particularly interested in the e€ects of time on task on the tuning of reactions to potential threats and the interaction between time on task and time-schedule instructions, we decided to carry out some additional analyses in order to explore the interaction e€ect. These post-hoc analyses revealed that the di€erence in accuracy of reactions between abrupt and gradual decelerations failed to reach statistical signi®cance in the ®rst ride …F …1; 21† ˆ 3:13; P < :1†, but was highly signi®cant in the second ride …F …1; 21† ˆ 9:58; P < :005†. In the second ride, reactions to gradual decelerations were less accurate than reactions to abrupt decelerations. For each deceleration of the lead car, minimum time headway was registered. Decelerations for which minimum time headway was smaller were considered to be more critical. Fig. 4 shows minimum time headway for predictable and unpredictable decelerations. Minimum time headway was shorter for unpredictable decelerations …F …1; 21† ˆ 22:91; P < :001†. There were no di€erences in minimum time headway between the two rides …F < 1†, and minimum time headway did not di€er between the two groups (F …1; 22† ˆ 3:56, ns). However,

Fig. 3. Accuracy of reactions (decrease of time headway in deceleration episodes, mean and S.D.) to abrupt and gradual decelerations of the lead car, averaged over predictable and unpredictable decelerations.

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Fig. 4. Minimum time headway for predictable and unpredictable decelerations (mean and S.D.), averaged over abrupt and gradual decelerations.

there was a signi®cant interaction between instruction and time on task …F …1; 22† ˆ 5:09; P < :05†. Again, additional analyses were carried out for both rides separately in order to explore this interaction. These post-hoc analyses revealed that there was no signi®cant di€erence in minimum time headway between the groups in the ®rst ride …F < 1†, but there was a signi®cant di€erence in the second ride …F …1; 22† ˆ 7:45; P < :05†. In the second ride, the minimum time headway was longer for the control group than for the time-schedule group. In both rides, participants encountered a vehicle that drove at a speed of 40 km/h and forced them to adapt their speed. There was no di€erence in minimum time to collision between the two rides …F < 1†, nor between the two groups …F < 1†. Thus, there were no indications that the accuracy of reactions in critical situations deteriorated in the course of time.

4. Discussion Prolonged task execution is associated with decreased motivation and inaccuracy of control actions. As expected, subjective fatigue and sleepiness increased as a function of time on task, even in the limited 2.5-h time-span of this study. The increase of fatigue and sleepiness was accompanied by an increased aversion to continue driving and a deterioration of steering performance. Interestingly, larger increases of fatigue and sleepiness were associated with larger increases of aversion and a greater deterioration of steering performance. These ®ndings suggest that fatigue is accompanied by a decreased motivation to continue with the task. The gradual deterioration of steering performance and the correlation between performance decrements and subjective ratings of fatigue are consistent with the results of on-the-road studies (e.g., O'Hanlon, 1981; O'Hanlon and Kelley, 1977; Riemersma et al., 1977). There was no di€erence between the rides in performance quality ratings and e€ort ratings. Thus, although the aversion to continue driving was higher in the second ride, this was not re¯ected in reported e€ort investment. On the contrary,

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large increases of fatigue and sleepiness were associated with increases of e€ort investment. These results suggest that performance decrements in prolonged task performance are not caused by a decrease in the investment of e€ort. Rather, performance can deteriorate in spite of increased e€ort investment. There were no di€erences in fatigue and sleepiness nor in aversion between the control group and the time-schedule group. Apparently, prolonged driving on a strict time schedule does not make the driver more fatigued, at least within the duration of this study, but neither does it keep the driver alert. It was expected that drivers would try to restrict the e€ort costs of prolonged driving, possibly by increasing their headway. In general, there were no di€erences in choice of headway between the rides. However, participants who became more fatigued during the experiment increased their headway to a greater extent than participants who reported only slight increases of fatigue. Although it is impossible to infer a causal relation from a correlation, this study provides more direct evidence for a connection between feelings of fatigue and the choice of headway than Fuller's (1981, 1984) studies do because our ®ndings are not confounded with changing visibility conditions. The results suggest that drivers who su€er from fatigue do indeed increase their safety margins. It can be assumed that the avoidance of collisions has the highest priority in driving. On average, anticipation and the accuracy of reactions to predictable and unpredictable decelerations of the lead car were not a€ected by time on task. However, the tuning of responses to the deceleration level of the lead car did change as a function of time on task. The di€erence in accuracy of reactions between abrupt and gradual decelerations was larger in the second ride. This is consistent with the results of an on-the-road study by Brookhuis et al. (1994), in which it was found that the coherence between the speed of the subject's car and the speed of the lead car in a carfollowing task decreased after prolonged driving. In our simulator study, it was possible to control the deceleration level of the lead car very precisely, and the present ®ndings show that reactions to gradual decelerations were less accurate than reactions to abrupt decelerations after prolonged driving. Thus, this study provides further evidence that fatigue is associated with rather subtle changes in the ®ne-tuning of perceptual-motor actions in distance keeping. It should be noted that the accuracy of reactions to abrupt decelerations did not deteriorate. Furthermore, in situations in which participants approached a slow car, criticality did not increase as a function of time on task. Therefore, it seems unlikely that the subtle changes in the accuracy of perceptualmotor control that were found are caused by a deterioration of the drivers' abilities. This suggests that drivers give priority to hazard avoidance and relax their e€orts in perceptual-motor control. In general, these results indicate that operators protect performance in sub-tasks that have a high priority while they allow performance decrements in less central task components in the course of prolonged performance (see also Hockey, 1997). The impact of a simple manipulation of instructions on performance strategy was quite remarkable. In general, participants who received time-schedule instructions maintained a shorter time headway. After prolonged driving, the di€erences between the two groups became more pronounced. There were indications that the tendency to increase headway in the second ride was stronger for the control group. Presumably due to these larger safety margins, minimum time headway in deceleration scenarios was longer for the control group, particularly in the second ride. Hence, drivers in the control group increased their safety margins and managed to maintain an adequate performance level. These results indicate that the control group adopted a more

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defensive driving strategy in the second ride, whereas the time-schedule group did not adapt their performance strategy. The results show that, under normal circumstances, drivers are likely to increase their safety margins when they become fatigued. This is consistent with Brown's (1994) statement that prolonged driving will not necessarily cause serious problems if drivers can choose the speed and the safety margins they prefer and if they can stop driving if they want to. However, the time-span of the experiment was limited and we do not know whether the adaptation of driving strategy will still be e€ective after longer periods of continuous driving. The combination of time pressure and fatigue can be dangerous for several reasons. First, the study reported here shows that drivers maintain shorter safety margins if they have to drive according to a strict time schedule. Second, drivers who are in a hurry are reluctant to increase their safety margins when they are fatigued. Third, it is likely that drivers continue driving for longer periods of time despite the aversion to continue driving. It would be particularly interesting to study the relation between time pressure and the decision to stop driving. The results of the experiment reported here suggest that externally imposed time pressure overrules other goals in driving and induces a decreased ¯exibility of driving strategy. In general, we think that research of time-on-task e€ects in complex dynamic tasks should focus on adaptive strategies in relation to task constraints and the operator's priorities. Simulated task environments such as driving simulators provide possibilities for the manipulation of task conditions with a level of precision that would be impossible in the ®eld. Even though virtual reality is almost always a simpli®cation of the real task situation, we can assume that changes in performance strategy under high workload or fatigue re¯ect natural task behaviour. As Bainbridge (1978) pointed out, performance strategies in complex task performance are not only chosen for the control of a process or situation, but also to control the mental state of the operator. Thus, operators choose a strategy that allows them to maintain situation awareness and to control the process according to their own preferences and priorities. Our study suggests that fatigue is associated with a change in performance strategy that results in a restriction of e€ort costs while protecting primary performance goals. Similar strategy shifts have been found as an e€ect of sleep deprivation in a process control task (Hockey et al., 1998) and as a result of increased workload in air trac control (Sperandio, 1978). Thus, there are some indications that changes in performance strategy are not speci®c for a particular task domain. Although performance on low-priority task components is likely to deteriorate in cases of increased workload or fatigue, primary task performance remains adequate. The results of this experiment have practical implications for the design of information systems in road trac, in particular systems that aim at the detection of driver impairment due to fatigue. In the past decade, several international projects ®nanced by the European Union have focused on the development of performance-monitoring in-vehicle systems that can detect driver impairment on the basis of unobtrusive measures (De Waard and Brookhuis, 1991; Fairclough et al., 1995; Brookhuis et al., 1998). When such a monitoring system detects performance impairments, it can warn the driver and if the driver does not react it might even switch to an automatic vehicle control mode. A deterioration of steering performance is often used as an indicator of driver impairment. As we have seen in this study, there is indeed a correlation between the increase of fatigue and sleepiness and the deterioration of steering performance. However, steering performance is also used in the evaluation of mental workload in various driving conditions and it is

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therefore not a particularly diagnostic measure of fatigue. Moreover, our study shows that performance on high-priority subtasks, such as collision avoidance, is not a€ected, at least not within the time span of this study. Therefore, steering performance alone is not a very good measure of the ®tness of the driver to safely control a vehicle. Driver impairment monitoring systems should aim to detect patterns of changes in several variables rather than in a single measure. Furthermore, driver impairment monitoring systems might try to detect whether drivers adapt their behaviour to changing circumstances, such as reduced visibility conditions. This study shows that the adaptation of behaviour to changing circumstances is not a€ected due to fatigue. Time pressure, however, does disturb the adaptivity of task performance strategy, but it is unclear whether the driver is aware of this reduced adaptation. Moreover, drivers who have to drive on strict time schedules are likely to continue driving despite feelings of fatigue. It seems useful to include strategic factors such as time pressure in future studies of driver fatigue and driver impairment monitoring systems. References Allport, A., Styles, E. A. & Hsieh, S. (1994). Shifting intentional set: exploring the dynamic control of tasks. In C. Umilta & M. Moscovitch (Eds.). Attention and performance XV. Conscious and nonconscious information processing (pp. 421±452). Cambridge, MA: MIT Press. Bainbridge, L. (1974). Problems in the assessment of mental load. Le Travail Humain, 37, 279±302. Bainbridge, L. (1978). Forgotten alternatives in skill and work-load. Ergonomics, 21, 169±185. Bainbridge, L. (1997). The change in concepts needed to account for human behaviour in complex dynamic tasks. IEEE Transactions on Systems, Man, and Cybernetics ± Part A: Systems and Humans, 27, 351±359. Borg, G. (1978). Subjective aspects of physical and mental load. Ergonomics, 21, 215±220. Broadbent, D. E. (1979). Is a fatigue test now possible?. Ergonomics, 22, 1277±1290. Brookhuis, K. A., De Vries, G., Prins van Wijngaarden, P., & O'Hanlon, J. F. (1985). The e€ects of increasing dose of meptazinol (100, 200, 400 mg) and glaferine (200 mg) on actual driving performance. (Report VK 85-16). Haren, Netherlands: University of Groningen, Trac Research Centre. Brookhuis, K., De Waard, D., & Mulder, B. (1994). Measuring driving performance by car-following in trac. Ergonomics, 37, 427±434. Brookhuis, K. A., De Waard, D., Peters, B., & Bekiaris, E. (1998). SAVE ± System for detection of driver impairment and emergency handling. IATSS Research, 22, 37±42. Brown, I. D. (1994). Driver fatigue. Human Factors, 36, 298±314. Brown, I. D. (1995). Methodological issues in driver fatigue research. In L. Hartley (Ed.), Fatigue and driving: driver impairment, driver fatigue and driving simulation (pp. 155±166). London: Taylor and Francis. Cameron, C. (1973). A theory of fatigue. Ergonomics, 16, 633±648. Craig, A., & Cooper, R. E. (1992). Symptoms of acute and chronic fatigue. In A. P. Smith & D. M. Jones (Eds.), Handbook of human performance. Volume 3: state and trait (pp. 289±338). London: Academic Press. De Waard, D., & Brookhuis, K. A. (1991). Assessing driver status: a demonstration experiment on the road. Accident Analysis and Prevention, 23, 297±307. De Waard, D. (1996). The measurement of drivers' mental workload. Unpublished doctoral dissertation. Netherlands: University of Groningen. Fairclough, S., Planque, S., Martinez, D., & Brookhuis, K. A. (1995). Behavioural responses to a driver impairment monitoring system. In ERTICO (Ed.), Proceedings of the ®rst world congress on applications of transport telematics and intelligent vehicle highway systems. (pp. 2032±2038). Boston: Artech House.. Fuller, R. G. C. (1981). Determinants of time headway adopted by truck drivers. Ergonomics, 24, 463±474. Fuller, R. G. C. (1984). Prolonged driving in convoy the truck driver's experience. Accident Analysis and Prevention, 16, 371±382.

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