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Accident Analysis and Prevention 40 (2008) 851–860

Alertness maintaining tasks (AMTs) while driving

Tal Oron-Gilad ∗, Adi Ronen, David ShinarBen Gurion University of the Negev, P.O. Box 653, Beer Sheva 84105, Israel

Received 18 July 2007; received in revised form 21 September 2007; accepted 24 September 2007

bstract

We evaluated the effectiveness of alertness maintaining tasks (AMTs) on driver performance, subjective feelings, and psychophysiological staten monotonous simulated driving in two experiments. In the first experiment, 12 professional truck drivers participated in five sessions of simulatedriving: driving only, driving with one of three AMTs (counterbalanced), and driving while listening to music. AMTs were not equally effectiven maintaining alertness. The trivia AMT prevented driving performance deterioration, and increased alertness (measured by standardized HRV).he choice reaction time AMT was least demanding but also increased subjective sleepiness and reduced arousal (measured by alpha/beta ratio).he working memory AMT caused a significant decrement in driving speed, increased subjective fatigue, and was regarded by the participantss detrimental to driving. Trivia was preferred by the majority of the drivers over the other two AMTs. Experiment 2 further examined the utility

f the trivia AMT. When the drivers engaged in the trivia AMT they maintained better driving performance and perceived the driving duration ashorter than the control condition. The two experiments demonstrated that AMTs can have a positive effect on alertness. The effect is localized inhe sense that it does not persist beyond the period of the AMT activation.

2007 Elsevier Ltd. All rights reserved.

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eywords: Simulated driving; Alertness maintaining; Monotonous drive; Profe

. Introduction

Drivers adhere to different behaviors to cope with fatiguehile driving; behaviors that they think will help them in main-

aining their alertness. The most common behaviors includeistening to the radio (and increasing the volume when fatigued),pening a window, following the lane markers, talking to a pas-enger, and drinking coffee (Nguyen et al., 1998; Oron-Giladnd Shinar, 2000). However, empirical studies show that most ofhese behaviors are not effective in maintaining alertness (Hornend Reyner, 1995; Reyner and Horne, 1998). Furthermore, a rel-tively high rate of drivers who reported that they fell asleep athe wheel had their radio turned on, which also indicates thathis popular countermeasure is insufficient at times (Oron-Giladnd Shinar, 2000).

Brown (1994) distinguishes physical fatigue from men-al/psychological fatigue. Physical fatigue is directly related toynamic and/or static muscular work and is usually followed by

∗ Corresponding author at: Department of Industrial Engineering and Man-gement, Ben Gurion University of the Negev, P.O. Box 653, Beer Sheva 84105,srael. Tel.: +972 8 6472227; fax: +972 8 6472958.

E-mail address: orontal@bgu.ac.il (T. Oron-Gilad).

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001-4575/$ – see front matter © 2007 Elsevier Ltd. All rights reserved.oi:10.1016/j.aap.2007.09.026

al drivers

loss of muscle strength, pain, discomfort, headaches, nauseand blurred eyes. Psychological fatigue, on the contrary, is aubjective experience of disinclination to continue performinghe task at hand. According to Brown (1994), fatigue counter-easures will be successful to the extent that they correlateith the driver’s subjective experiences of fatigue. Desmond

nd Hancock (2001) define fatigue in terms of active and pas-ive states, where active fatigue is derived from continuous andbligatory high perceptual-motor demands, and passive fatigue,evelops over time when there appears to be little or no inter-sting stimulation. Driving requires the driver to maintain highevels of alertness even with little or no interesting stimulation.s the road becomes more monotonous and familiar drivingemands decrease and the driver is more susceptible to passiveatigue symptoms (Thiffault and Bergeron, 2003). In such situ-tions, drivers become more dependent on the environment toaintain wakefulness and therefore more vulnerable to envi-

onmental monotony (Dinges and Graeber, 1989; Dinges andribbs, 1991). We claim that there is a difference in treatmentf fatigue caused by the demands of the driving task itself (i.e.,

assive fatigue) and fatigue caused by the lack of sleep or exer-ion (Mackie and Wylie, 1992). Thus, fatigue can be causedy two sources; (a) the driver’s initial state before starting therive, (b) the characteristics of the drive. The two sources can

852 T. Oron-Gilad et al. / Accident Analysis a

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ig. 1. A model portraying the demand of the driving task as a function ofituational demands (context) and driver state.

ave a cumulative effect. Passive and active states require dif-erent means of intervention. Active fatigue caused by lack ofleep can be corrected by sleep, but this strategy will not nec-ssarily impact passive fatigue (inherent boredom) caused by aonotonous road.Dynamic models of stress and performance (Hancock and

arm, 1989; Wiener et al., 1984) are based on adaptation toask demands which is particularly relevant in driving. For sim-lification one can divide the level of demand of a drive into threeross categories; underload, optimal, and overload, as shown inig. 1. The vertical axis represents the contextual demands of

he drive. The horizontal axis portrays the driver state. Drivertate includes, but is not necessarily limited to, driving expe-ience, personality, and fluctuations in mood, motivation andtress (work-related and non-related). Both axes are a unifiedimplification of a larger multi-dimensional space. The driverims to be within an optimal performance zone where demand

s “comfortable”, (i.e., neither too high nor too low). While therean be large fluctuations in the contextual demands of the driveue to unexpected events or hazards (y-axis), only minor fluc-uations are expected in driver state within a drive. A ‘less fit’

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ig. 2. Countermeasures to fatigue in under load (a) and overload (b) situations. Inhe task upward to the comfort zone. In Figure (b) other types of tasks or substances

nd Prevention 40 (2008) 851–860

river (e.g. a novice driver, etc.) will probably be less tolerant tohanging levels of demand, and might be overloaded in a situa-ion in which a ‘fit’ driver would not be. A ‘less fit’ driver mightlso experience fewer episodes of underload and more episodesf overload than a ‘fit’ driver. When considering homogeneousroups of drivers such as professional truck drivers it is mostikely that they will be closely positioned on the horizontal axis.

We propose here that different interventions are necessaryo counteract the two types of fatigue. In underload situations,e propose to increase demand by adding additional cognitive

asks to the drive thereby increasing the overall task demandshile driving, as shown in Fig. 2(a). The effectiveness of adding

uch non-target stimuli has been demonstrated before in vigi-ance research (Salas et al., 1996; Scerbo, 1998). However, ifhe driver concentrates too much on the distracter, his or herttention may not capture a drive-related potentially hazardoustimulus (Reid, 1997). Thus, the challenge is to create additionalasks that increase the overall demands without distracting theriver from the primary driving task.

In contrast, increasing task demand to fatigued-overloadedituations is not recommended. In the case of the fatigued-verloaded driver, one should aim to improve the driver’stness level (by changing the driver state variables as shown

n Fig. 2(b)) rather than changing the demands of the drive.Our study focused on passive fatigue and active fatigue was

ot within the scope of this study. Thus, we examined the possi-ility of adding cognitive demanding tasks to the drive in ordero increase the overall task demand and allow the driver to

aintain a comfortable stress level (within the comfort zone)as shown in Fig. 2(a)). Our goal was to find alertness main-aining tasks (AMTs) that can increase the overall demandsf the drive to create a ‘comfortable’ stress level without dis-racting the driver from the primary task of driving. Littleas been done in finding technological solutions to increaserivers’ alertness (as opposed to alert the drivers to their drowsi-ess). Verwey and Zaidel (1999) provided initial evidence that

nder certain driving conditions and with specific tasks, per-orming a secondary task while driving can increase alertnessnd task engagement. They examined the effectiveness of anlertness maintaining device on drivers in a driving simulator,

figure (a) the alertness-maintaining task (AMT) “moves” the total demands ofcan “move” the driver’s fitness “rightward” to the comfort zone.

lysis and Prevention 40 (2008) 851–860 853

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sing a speech-controlled ‘gamebox’ system. Their gameboxllowed drivers to engage in various cognitive tasks. Theiresults demonstrated that the gamebox counteracted the nor-al performance deterioration typical of monotonous driving.owever, they did not track the specific games that the drivers

elected nor did they compare the gamebox to traditional fatigueountermeasures.

Because demanding driving tasks – such as driving on aurved road – actually help drivers counteract fatigue (Oron-ilad and Ronen, 2007; Desmond and Matthews, 1997), we

xamined the effects of the AMTs on mostly straight roads withery little traffic.

. Experiment 1

The purpose of the first experiment was to evaluate theffectiveness of three different AMTs on driver performance, rel-tive to driving without any distractions and relative to drivinghile listening to music. The AMTs that we chose are famil-

ar types of tasks that vary in cognitive demands; a vigilancehoice reaction time (CRT) task, a working-memory (WM)ask, and a ‘Trivia’ long-term memory verbal task. WM andRT tasks have been examined before while driving. Desmondnd Matthews (1997) suggested that when workload was low,peed working-memory tasks will increase task engagements,ecause the person responds to the challenge with increasedffort, while vigilance tasks will tend to reduce task engage-ent. Radeborg et al. (1999) used a verbal working memory

ask under variable road conditions and observed a negativeorrelation between steering accuracy and working memory per-ormance. Thus, adding tasks that exploit working memory maye detrimental to driving.

We hypothesized that while engaged in an AMT, the expectedeterioration in driving performance due to sustained perfor-ance will not occur. Furthermore, we expected to find fewer

ymptoms of fatigue in the AMT conditions than in the controlonditions, and we expected that the three AMTs would differ inheir effectiveness. Previously (Oron-Gilad and Ronen, 2007),e found that drivers had fatigue symptoms (measured by an

ncrease in heart rate variability (HRV), subjective reports, eye-id closures, and head nods) relatively quickly in a simulatedrive (within less than 50 min of simulated driving) even thoughhey were not sleep deprived or fatigued prior to the experiment.

e therefore chose to operate the AMT in accordance with thesendings: on straight road segments after approximately 50 minf driving.

.1. Method

.1.1. ParticipantsTwelve male truck drivers (aged 32–53 years), all from the

ame trucking company, and all with at least 10 years of truckriving experience volunteered to participate in the experiment.rivers received payment for each session and a bonus upon

ompletion of the experiment (five sessions).

Fig. 3. The fixed base simulator used in the study.

.1.2. Apparatus

.1.2.1. Equipment and tasks. The driving was conducted inSTI-SIM fixed based driving simulator (System Technology,

nc.), a personal computer (PC)-based interactive simulator withnteractive gas and brake pedals and an interactive steeringheel. The driving simulator was integrated into a passenger car

SEAT Malaga 1988) that provides the look and feel of driving incar as seen in Fig. 3. The simulation includes vehicle dynamics,isual and auditory displays, and a performance measurementystem. The driving tasks and events were programmed withScenario Definition Language (SDL) that allows the experi-enter to specify the sequence of tasks, events and performanceeasurement intervals. The visual display of the road is pro-

ected on 3 m × 3 m screen at a distance of 3 m from theriver’s eyes, providing the driver with a true horizontal fieldf 40◦.

Based on pilot testing with truck drivers, and to be moreompatible with the drivers’ professional experience, vehicleynamics (i.e., behavior of the steering wheel, throttle pedal,nd brake pedal) were adjusted to those recommended by theimulator manufacturer to fit the dynamics of a small-scale truckather than a private car. While some may find this combinationnon-conventional” our main concern here was to make drivers’eel more comfortable with (and less concerned/distracted with)he simulator dynamics throughout the prolonged drive. Since inhe analysis of results, we compare driving performance acrosshe five driving sessions, we did not expect this setup to have aignificant effect on the results.

.1.2.2. Driving scenario. The driving scenario consisted of aingle sequence of two road segments:

1) A two-lane straight rural road, 13.3 km long. The road is asimulation of the geometry of one of the main routes thatthese drivers drive, with desert scenery and almost no slopes,

minor grade changes of less than 10% over the total dis-tance, moderate curves (100–300 m radius), and low trafficdensity of 9 passing and oncoming vehicles over the 58 kmdrive.

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54 T. Oron-Gilad et al. / Accident Ana

2) A two-lane winding downhill road, 9.2 km long, with 22sharp curves (15–220 m radius), low traffic density of eightoncoming cars and five passing cars, and no intersections.

Lane width was 2.5 m. The width of the paved shoulderas 0.5 m and an unpaved shoulder extended beyond it. Lanearkings were either dashed lines segments or solid lines, solidarkings appeared prior to curves and slopes.To allow for variations in speed on the two roads, the road

egments differed in length to ensure at least 10 min of drivingn each segment. All drivers drove a single sequence consistingf five segments in which the straight road appeared three timesn the following order (1a)–(2a)–(1b)–(2b)–(1c). Slight varia-ions occurred in the scenarios from day to day, in the type ofncoming and passing vehicles, in the timing of these vehicles,nd in their driving speed. Total length of the drive was 58 km.he winding road segments were included in the drive in order

o lengthen the overall driving time.Speed limit signs appeared at the beginning of each seg-

ent: 88 k/h for segments (1) and 72 k/h for segments (2). Also,urve advisory signs appeared approximately 300 m prior tourves. Speed during the drive was limited to 90 k/h becausehen fatigued drivers tend to adopt higher speed (de Waard androokhuis, 1997) and we wanted to prevent drivers from increas-

ng speed as an alertness maintaining alternative. Drivers weresked to maintain the posted speed limits.

.1.2.3. Questionnaires and subjective measures. Perceivedatigue related to the driving task was assessed by the Swedishccupational Fatigue Inventory-20 (SOFI) (Aahsberg, 1998).his inventory is composed of five dimensions; Physical dis-omfort, Physical exertion, Lack of energy, Lack of motivation,nd Sleepiness, and is designed specifically to examine fatigue-elated symptoms. The questionnaire includes 20 questions (4or each dimension) with a Likert scale of 0–6 (0 – not at all,

– extremely). The score on each dimension is the averagef the responses to the four questions. The SOFI was adminis-ered just before and immediately after the drive. In addition, therivers also gave a uni-dimensional assessment of their fatigueevel on a scale of 1–100 (1 -totally awake, 100 – extremenlyleepy/exhausted), a simple method of rating perceived exer-ion (Oron-Gilad and Ronen, 2007) and estimated the level ofifficulty and the overall effect of each AMT on their perceivedriving performance. This was also administered just before andmmediately after the drive.

At the end of each drive, drivers were given a questionnairehat included questions regarding their attitude toward the par-icular AMT that they had just used: its level of interest and itserceived effectiveness on their alertness and on their driving.t the end of the fifth session, after each driver had experienced

ll three AMTs, drivers were asked about their general attitudeowards AMTs.

.1.2.4. Electrophysiological monitoring. ECG signals wereecorded from two skin surface electrodes at a sampling rate of50 Hz using an ‘Axon’ (cyberamp 380) amplifier and filteringystem connected to a computer. Heart rate (HR) was calculated

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nd Prevention 40 (2008) 851–860

y measuring R–R intervals. Heart rate variability (HRV) wasalculated using the calculation of standard deviation (S.D.) of–R intervals (time domain). EEG signals were recorded from

hree skin surface electrodes. Two electrodes were placed onosterior-central positions (C3–C4) and one on the forehead.ignals were recorded at 100 Hz using the system mentionedbove. Spectral analysis was performed to calculate α, θ and β

nergy levels.Attempts to quantify the EEG spectrum have been generally

isappointing except for situations where the overall activa-ion changes as a function of the load imposed (O’donnell andggemeier, 1986). In such situations, the power of the alphand the theta waves is expected to decrease as the driving pro-eeds due to the sustained performance nature of driving. Anncrease in cognitive demands will be immediately pronounceds an energy drop in the combined alpha-theta band (Brookhuisnd de Waard, 1993; Mouloua et al., 2000).

.1.2.5. Alertness maintaining secondary tasks. Three AMTsere designed for the experiment: a choice reaction time task, aorking memory task (that resembles the game ‘Simon’) (work-

ng memory), and long-term memory trivia questions (Trivia).asks were presented both auditorily and visually. The primaryurpose of this setup was to examine the loading effect of the taskegardless of its modality. In the choice reaction time task, theriver was required to register which one of three possible cuesas appeared; visually the cues were presented as three arrowsointing left, upward or right, and each arrow was accompa-ied by a unique sound. In the working-memory task, the driveras required to repeat a sequence of cues; visually the cues

ppeared as colored rectangles in yellow, red and blue and eachectangle had a unique sound. The sequence of cues increasedn length: at first, only one cue appeared, if the driver identi-ed it successfully two cues were presented sequentially, ando on until the sequence was five cues long. If the driver failedo repeat the signals correctly, the sequence was repeated for andditional time, if the driver was successful, the length of theequence increased, if not, it was repeated again. If the driverailed to reproduce the sequence correctly for the third time, theystem informed the driver that a new sequence will begin andnew sequence (starting with one cue) began. In the trivia task,

he driver was asked to choose one of three possible answers tolternative-choice questions. The question and possible answersere displayed on the screen and read out by the system at the

ame time. The questions were selected from a variety of topicsncluding sports, geography, and music.

Each task was designed to last approximately 10 min (timearied slightly from one driver to the other due to differencesn their response times). Tasks were presented on a screen of

laptop computer that was placed on the car’s hood slightlyo the right of the driver’s straight line of sight (as shown inig. 4). Responses were keyed on a set of three buttons (left,iddle, and right), located on the center of the steering wheel.

he application logged responses and response times.

.1.2.6. Control conditions. Two control conditions were used:riving without any AMTs or distractions (no AMT) and driving

T. Oron-Gilad et al. / Accident Analysis a

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ig. 4. The AMT screen was positioned on the hood of the vehicle and wasdjusted to appear at the right edge of the simulation screen.

hile listening to music (Music). For the music condition, wesked each driver to bring his own music cassette; one that heistens to regularly while driving.

.1.2.7. Procedure. Drivers were tested individually. Eachriver participated in five sessions, each lasting approximatelyh, and always at the same time of day. The order of the threeMTs was counterbalanced among drivers. The first session

no AMT condition) included an adaptation period to the sim-lator and to the physiological monitoring equipment, followedy driving the entire scenario. In the 2–4th sessions, prior tohe beginning of the drive, drivers were given a training ses-ion on the AMT. Then during the drive AMT was added to therive during road segment (1c) (the third straight road segment).he fifth session was the same for all drivers, and consisted ofriving while listening to music (for the entire duration of therive). The format of all sessions was the same: upon arrival theriver filled the informed consent and was given a trial sessionon the AMT, when applicable), then he filled an initial ques-ionnaire. Upon completion of the questionnaire the driver wasonnected to the ECG and EEG electrodes and read (again) the

riving instructions. After 5 min of rest in the vehicle while thehysiological measures were being recorded, the drive began.he drivers drove the entire scenario consisting of the five roadegments (with the AMT when applicable), and then they filled

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able 1riving performance measures for the (1b) and (1c) road segments in each driving co

easure/condition No-AMT CRT

verage lane position [cm] (1b) 139 (8) 140 ((1c) 140 (13) 140 (

.D. lane position [cm] (1b) 24 (5) 24 (4(1c) 25 (7) 26 (6

.D. steering wheel rate [deg/s] (1b) 5.22 (4.70)a 2.93(1c) 8.48 (8.42) 4.64

verage speed [km/h] (1b) 70 (13)a 72 (1(1c) 66 (15) 72 (1

a Duncan post-hoc test significant (p < .05) for the difference between (1b) and (1c

nd Prevention 40 (2008) 851–860 855

final questionnaire. If the driver fell asleep repeatedly at theheel the driving session was terminated.

.2. Results

Results were analyzed separately for performance measures,ubjective measures and physiological measures.

.2.1. Driving performance measuresDriving performance was measured by four parameters

ecorded by the simulator: the average lane position (lane posi-ion is measured relative to the center of the road, lane widthas 2.5 m), standard deviation of the lane position, S.D. of the

teering wheel reversal rate, and the average speed (maximumpeed was limited to 90 k/h).

Overall, we found great variability in driving performanceeasures among participants and within each participant across

he five sessions. To minimize the effects of individual dif-erences and variations among sessions we analyze only theifference in performance between identical road segments (1b)nd (1c); i.e., the difference in performance between the secondtraight segment of the drive, and the third straight segment of therive in which the AMT was employed (in the AMT conditions).ummary of the results is given in Table 1.

A repeated measures two-way analysis of variance of condi-ion (five conditions) × road segment (1b and 1c) on the differentependent variables yielded significant effects for the steeringheel reversal rate and the speed [F(4, 32) = 2.65, p = .05, and(4, 32) = 3.17, p = .03, respectively]. Post-hoc analysis showed

hat the steering wheel control was significantly worse in theo-AMT condition compared to all other conditions (p < .05),s shown in Fig. 5. Post-hoc analysis on speed showed thatpeed was significantly lower in the working memory AMTondition than in all other conditions (p < .05), as shown inig. 6.

.2.2. Subjective measures

.2.2.1. Subjective occupational fatigue inventory (SOFI).verall, the average ratings on each dimension in each of the

ve conditions, before and after the drive were low (below theid scale value) – indicating very moderate subjective sense of

atigue in all five dimensions (a maximum mean value of 1.0,.8, 0.6, 1.9, and 1.3, respectively for the five dimensions). This

ndition in Experiment 1

Memory Trivia Music

15) 144 (8) 146 (12) 140 (14)7) 139 (8) 144 (14) 151 (13)

) 24 (4) 27 (7) 27 (5)) 24 (8) 24 (5) 28 (4)

(1.03) 3.35 (1.62) 3.72 (2.55) 3.48 (1.20)(5.16) 4.52 (3.06) 3.77 (2.48) 3.86 (1.81)

4) 76 (14)a 77 (9) 78 (8)6) 70 (15) 75 (11) 80 (8)

).

856 T. Oron-Gilad et al. / Accident Analysis and Prevention 40 (2008) 851–860

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ig. 5. The difference in standard deviations of the steering wheel rate betweenoad segments (1c) and (1b) in Experiment 1. Vertical bars denote 0.95 confi-ence intervals.

as not unreasonable to expect from drivers who are used toriving heavy trucks 8 h or more per day.

The only significant difference was found in the Sleepinessimension [F(1, 9) = 7.23, p = .025], where the ‘before’ ratingsere significantly lower than the ‘after’ ones. In particular,ost-hoc comparisons (Duncan) showed significant differencesetween the ‘before’ and ‘after’ ratings of the no AMT and CRTonditions (p < .05). Fig. 7 shows the difference in subjectiveatings of sleepiness before and after the drive.

.2.2.2. Uni-dimensional estimate of fatigue. Drivers weresked to rate their subjective feeling of fatigue prior to the pro-onged drive and at the end of the driving session on a scalef 1–100 (1 – totally awake, 100 – totaly sleepy). Overall, theeported fatigue ratings were low (the mean score was 24, 19,7, 24, and 24 for the no-AMT, CRT, WM, Trivia, and musiconditions, respectively) and no significant main effect on dif-

erences was found. Post-Hoc analysis revealed that the WMondition was the only condition where the difference betweenhe before and after ratings was significant with a 15% increasen the after ratings.

ig. 6. The difference in speed between road segments (1c) and (1b) in Experi-ent 1.

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rive in Experiment 1. [*] denote significant differences between the before andfter ratings.

.2.2.3. Subjective opinion on AMTs. At the end of each driven which the AMT was employed (2–4th sessions), drivers weresked to estimate the level of influence that the AMT had onheir driving performance and on their alertness. A one-wayNOVA on the effect of the tasks on the driving was significant

F(2,18) = 5.20, p < .02) and post-hoc comparisons (Tukey HSD)howed that of the three tasks, the memory task was perceiveds the most detrimental to the driving. There were no significantifferences among the tasks in their perceived effects on therivers’ alertness. At the end of the fifth session, the driversere asked to state their attitude toward AMTs. All of the drivers

tated that they listen to music while driving and that they thinkusic has a positive effect on their driving. Based on their limited

xperience with AMTs in the experiment, 60% (7) of the driverstated that they would like to try an AMT device in their vehicle.f the three tasks they had experienced, the one that they wereost interested in installing in their vehicle was the trivia task.his task received positive responses from 50% of the drivers,hile the other two received positive responses from 25% of therivers.

.2.3. Physiological measurements

.2.3.1. Heart rate (HR) and heart rate variability (HRV). HRnd HRV were used to assess the driver mental workload. Mosttudies show that heart rate – if it changes at all – increasesnd HRV decreases during effortful mental processing (e.g.ancock and Meshkati, 1988; de Waard and Brookhuis, 1997).R and HRV were calculated for every road segment in each

ession. These measurements were standardized relative to therst driving segment (1a) in each trial. As expected, there wereo significant changes in HR throughout the drive. For HRV,here were significant effects for the condition [F(4, 32) = 2.99,= .005], and for the interaction condition × road segment [F(4,

2) = 6.39, p = .0001], as shown in Fig. 8. A rating closer to 100%ndicates that the driver remained at the same workload level asn the beginning of the drive. Post-hoc analyses revealed that inhe absence of an AMT (no-AMT) HRV increased significantly

T. Oron-Gilad et al. / Accident Analysis and Prevention 40 (2008) 851–860 857

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ig. 8. The difference in standardized HRV (relative to the beginning of therive (1a) (100%)) between segments (1c) and (1b) in Experiment 1.

etween segments (1b) and (1c), and when AMT was present itecreased significantly, especially for ‘trivia’ task.

.2.3.2. EEG measurements. Spectral analysis was performedo calculate theta (4–7 Hz), alpha (7–13 Hz) and beta (13–25 Hz)nergy levels. Because the EEG signals, we obtained were notqual in quality, we used the ratio of alpha/beta instead of theatio of (alpha + theta)/beta relative to the rest period prior to theeginning of the drive. This ratio was less sensitive to noise, ashown in Fig. 9, which represents data obtained from one typicalrive. This ratio was calculated three times for each session: inoad segment within the last minutes of the segment (1b) andwice for road segment (1c); while the AMT was operated andmmediately after it was terminated. There were no significantifferences between conditions for (1b) and (1c) after the AMTas terminated (the last minute of the drive). However, post-hoc

nalysis revealed a significant difference (p < .05) between thehange in the CRT AMT (an increase of 70%) and the change

n the other two AMTS, which were not significantly differentrom zero. Thus, indicating that the power of the alpha wavesncreased when the AMT was on.

stit

able 2ummary of significant results in Experiment 1

riving performance measuresSteering wheel controlSpeed

ubjective measuresSOFI sleepiness scaleUni-dimensional fatigue estimate 1–100Subjective preference for AMT

hysiological measuresEEG (alpha/beta ratio) difference between (1b) and (1c) after AMT was terminatedEEG (alpha/beta ratio) difference between (1b) and (1c) when AMT was operatedHeart rate variability

ost-hoc comparisons, NC, no change; SC, significant change (at p < .05). The arrowfrom the value of the first segment of the pair.

ig. 9. The arousal ratio of the entire drive calculated for each minute separatelyor one particular participant in Experiment 1.

.3. Experiment 1 discussion

Due to variability in performance among drivers and evenmong sessions of the same driver, we have limited ourselveso the discussion of differences within each drive rather than

aking the comparison of absolute values among sessions. Weid this in order to eliminate extraneous variables that are partf the task-related fatigue. Significant differences among sub-ects and sessions have also been noted by others (Tijerina etl., 1999; Hockey, 1986). Using the difference measures, wedentified different patterns among the five experimental condi-ions. Table 2 provides a summary of the significant differencesetween segments (1b) and (1c) – before and during the AMThase (Table 3).

With regard to the no-AMT baseline-condition, it was evidenthat we managed to create an underload situation in which driversxperienced symptoms of fatigue even though they were notired nor sleep deprived prior to the beginning of the drive. Theymptoms were consistent across the three types of measures,nd included deterioration of steering wheel control over time,

ignificant increase in sleepiness in the Sleepiness subscale ofhe SOFI, and a significant increase in HRV; all indicating anncrease in the level of fatigue relative to the initial segment ofhe drive. All measures point out that in the absence of an AMT

No-AMT CRT Memory Trivia Music

SC↓ 38% NC NC NC NCSC↓ 6% NC SC↓8% NC NC

SC↑83% SC↑54% NC NC NCNC NC SC↑15% NC NCN/A 25% 25% 50% N/A

NC NC NC NC NCN/A SC↑70% NC NC N/ASC↑81% NC NC SC↓53% NC

indicates the direction of change and numbers indicates the relative change in

858 T. Oron-Gilad et al. / Accident Analysis and Prevention 40 (2008) 851–860

Table 3Driving performance measures for Experiment 2

Dimension/condition/block Block 1 Block 2 Block 3 Block 4

Average lane position [cm] No-AMT 149 (25) 146 (17) 148 (17) 153 (16)Trivia 147 (17) 152 (14) 151 (17) 151 (16)

S.D. lane position [cm] No-AMT 34 (16) 36 (14) 36 (13)a 35 (13)Trivia 32 (13) 32 (10) 30 (10) 33 (11)

S.D. steering wheel rate [deg/s] No-AMT 4.76 (4.17) 4.84 (3.77) 4.82 (3.02) 3.95 (2.61)Trivia 3.80 (1.18) 4.15 (1.75) 4.13 (1.97) 4.05 (2.71)

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r music, symptoms of passive fatigue set in after driving for ahile (about 45 min in the present setting).When the drivers used music to counter monotony, there were

o indications of performance deterioration. There were also nopparent costs in subjective feeling of fatigue or in physiologicalffort. The lack of significant changes in any of the measureshile listening to music, indicates that driving with music wasgood method for maintaining alertness, or at least definitely

etter than driving without music. Music was also acknowledgednd approved by all drivers; possibly because drivers simplynjoyed driving with their favorite music.

When comparing the control conditions to the AMT con-itions, it became apparent that not all AMTs were equallyemanding. The choice reaction time task seemed to be lessemanding than the other two tasks as it did not affect drivingerformance measures but it increased feeling of sleepiness. Thelpha/beta ratio derived from the EEG showed that during theperation of the CRT the drivers were less aroused and moreatigued. In terms of the model depicted in Fig. 2, we suggesthat the CRT increased the overall demands but failed to bringhe drivers into the comfort zone. Thus, it actually worsened theituation rather than improved it.

The working memory AMT caused the highest decrement inpeed, perhaps indicating that it was too demanding for mostrivers to perform while driving. The drivers were also aware ofhis detriment: this was the only AMT that was associated withsignificant increase in the overall uni-dimentional measure of

atigue. In terms of the model (Fig. 2), we suggest that the WMMT increased the overall demand beyond the comfort zone

nto an overload situation, causing the drivers to divert theirttention from the driving to the AMT. Thus, the WM AMTctually worsened the driving situation rather than improved it.

The trivia task was the most effective means of mitigating per-ormance deterioration. On the basis of the standardized HRV,he alertness level with the trivia task in segment (1c) was signif-cantly higher and drivers also felt more alert. This is also wherehe trivia AMT had an advantage over the music condition ashere was no improvement in HRV in the music condition. Thus,n terms of our model (Fig. 2), the trivia AMT did exactly what

as expected from an AMT: it moved the driving situation into

he comfort zone. It was therefore not surprising that the triviaMT was preferred by more drivers than either one of the twother AMTs.

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86 (5) 85 (6) 86 (5)87 (4) 87 (5) 88 (5)

On the basis of these results, the trivia AMT appeared to be theMT with the greatest potential benefit, and the one most likely

o be accepted by drivers. We therefore conducted a validationtudy to further examine the effect of the trivia AMT on drivingerformance and on the subjective feelings of fatigue after austained period of driving in repetitive monotonous conditions.

. Experiment 2

.1. Method

.1.1. ParticipantsTwenty-one students aged 24–27 years (M = 26, S.D. = 2),

ith 5+ years of driving experience, who received course creditor their participation.

.1.2. Apparatus and tasksDriving was conducted in the same fixed base-driving simu-

ator used in Experiment 1. The driving scenario consisted of aour repetitions of the two-lane straight road segment developedor Experiment 1 (1). All drivers drove the same scenario. TheMT was the aural–visual trivia game described in Experiment.

.1.2.1. Questionnaires and subjective measures. The sameni-dimensional self-assessment measure of fatigue used inxperiment 1 was administered before and immediately after

he drive. At the end of the drive (when applicable), driversere given an additional questionnaire regarding their attitude

owards the trivia AMT that they had been using: its level of inter-st, its effectiveness on their alertness and its perceived impactn their driving.

.1.3. ProcedureEach driver participated in two sessions of approximately

0 min of driving on 2 consecutive days, always at the sameime of the day. Each drive consisted of four repetitions of the0-min two-lane straight road segment. In the control session,he driver drove the four segments without any intervention (no-

MT) and in the treatment session the driver had the trivia AMTuring block three of the drive. Thus, in the treatment session therivers drove second segments without the AMT, followed by ahird segment with the AMT, and then a fourth segment without

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he AMT. The order of the treatment and control sessions wasounterbalanced across subjects.

After completing the opening questionnaire, the drivingegan and drivers were instructed that their main task was theriving task. At the end of the drive, drivers completed a closinguestionnaire.

.2. Results

.2.1. Driving performance measuresThe same four driving performance measures used in Exper-

ment 1 were analyzed in Experiment 2. Repeated measuresNOVAs on Block (4) × Condition (2) for each one of theerformance measures yielded significant differences in per-ormance only for the standard deviation of the lane positionF(8, 160) = 2.51, p = .014] and post-hoc comparisons revealedhat the significant difference occurred only in segment 3hen the AMT was operated. Thus, the variability in the

ane position in segment 3 was significantly greater with-ut the trivia AMT (M = 36.46, S.D. = 13.24) than with itM = 30.00, S.D. = 9.73).

.2.2. Subjective measuresIn both conditions, drivers estimated their subjective level of

atigue as significantly lower at the end of the drive than prioro the beginning of the drive [(M = 76, S.D. = 19) and (M = 61,.D. = 22) for the no-AMT condition and (M = 62, S.D. = 25)nd (M = 63, S.D. = 26) for the AMT condition, respectively],nd there was no difference in final estimates between the twoonditions.

Subjective questions were rated on a scale of 1–5. In bothriving conditions, drivers perceived the drive as monotonousM = 4.6, S.D. = 0.8). Drivers also rated their driving perfor-ance in both conditions as relatively good (M = 3.6, S.D. = 1.0)

nd there were no significant differences between conditions.he trivia questions were not perceived as detrimental to therive (M = 1.7, S.D. = 0.7). When asked to estimate whether therivia questions made them feel more alert during the drive;7 drivers (81%) reported that they made them more or muchore alert, three drivers (14%) reported that it made them

ess alert and one driver (5%) reported that it had no effect.welve (60%) of the drivers also reported that driving with the

rivia made the drive seem shorter. Twenty drivers (74%) ratedhe trivia questions as relatively pleasing, but only nine (40%)rivers said that they would like to have such a device in theirar.

.3. Experiment 2 discussion

In Experiment 2, there was a significant improvement inaintaining the lane position in the AMT condition, when theMT was operated. However, once the AMT was stopped,riving performance was similar to the no-AMT condition

ndicating a highly local effect of the AMT. From the sub-ective questionnaire, it is evident that the Trivia AMT hadn overall positive effect on the drivers and did not increaseheir perceived level of fatigue. However, as in Experiment

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nd Prevention 40 (2008) 851–860 859

, drivers did not have a clear-cut opinion on using an AMTegularly.

. Discussion

Our study provided data on the short-term effectivenessf the trivia AMT particularly in comparison with simulatedriving without countermeasures. What was also prominentn the results was that driving on a monotonous road with-ut using any countermeasure was very detrimental. However,istening to music was more beneficial than we expected; itaused no performance deterioration or subjective fatigue and itas better than the choice reaction time and working memory

asks.The two studies showed that the use of certain AMTs can

ave time-limited, positive effect on the driver’s physiologicaltate, subjective feeling of fatigue, and driving performance.verall, the trivia task was the most promising AMT of

he ones examined, providing consistent results across alleasures. However, the effectiveness of an AMT may also

epend on the driver’s preferences, which were not examinedn this study. The AMTs may require modifications. For therivia task, which was preferred by most drivers, it becamepparent that personalization in task content (i.e., fitting theubject to the driver’s interest, e.g., sports, politics, etc.) maynhance its attractiveness, improve its effect, and increase taskngagement.

Not all cognitive tasks are appropriate for maintaining alert-ess. The WM task interfered with the driving. The CRT actuallynduced fatigue. The lack of a consistent theoretical frameworkhat links the cognitive demands of the driving to other tasks

akes it difficult to systematically predict potential task charac-eristics for AMTs. Furthermore, it is not clear how performanceithin the optimal range of activation (see Fig. 1) can be sus-

ained over time; whether task demands should be kept constantr whether there should be fluctuations or rhythmic cycles inemand. If the latter is true, then a strategy of operating AMTsor short periods of time and then allowing time to recoverhould be the preferred approach to maintaining alertness andask engagement. However, if the latter is not true then the lim-ted experimentation we have conducted suggests that AMTsave a local effect that does not carry over beyond the durationf their operation.

This study was conducted in a driving simulator whereatigue symptoms occur rather quickly (Nilsson et al., 1997;ron-Gilad and Ronen, 2007) probably due to the enhancedigilance demand of the simulated driving, and the lack ofisk associated with potential hazardous events. These con-itions should not be mistaken for real-world driving whereuctuations in task demand occur suddenly and drivers need

o allocate more attention to potential hazards. As such,uture simulator studies should examine how fluctuations inask demand and implementation of unexpected events in the

xperimental design affect the use of AMTs. The results ofuch studies will enable the refinement of the gross modelhat we have presented in Figs. 1 and 2 by predicting howriver state changes over time in accordance with fluctuating

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60 T. Oron-Gilad et al. / Accident Ana

riving demands and added distraction. At last, experimentsn real driving conditions must be made before consideringmplementation.

cknowledgements

This study was supported in part by a grant from the Gen-ral Motors Foundation, a grant from the Israeli Ministry ofndustry, Trade, Commerce and Labor, and the Paul Ivanierenter for Robotics and Production Management at Ben-Gurionniversity.

eferences

ahsberg, E., 1998. Perceived Fatigue Related to Work. University of Stock-holm, Department of Psychology, Sweden, ISBN 91-7153-830-5.

rookhuis, K.A., de Waard, D., 1993. The use of psychophysiology to assessdriver status. Ergonomics 36 (9), 1099–1110.

rown, I.D., 1994. Driver Fatigue. Human Factors 36 (2), 298–314.esmond, P.A., Hancock, P.A., 2001. Active and passive fatigue states. In:

Hancock, P.A., Desmond, P.A. (Eds.), Stress, Workload, and Fatigue. LEAPublishers, London, pp. 455–465.

esmond, P.A., Matthews, G., 1997. Implications of task induced fatigue effectsfor in-vehicle countermeasures to driver fatigue. Accid. Anal. Prev. 29, 4,513–523.

e Waard, D., Brookhuis, K.A., 1997. On the measurement of driver mentalworkload. In: Rothengatter, J.A., Carbonell Vaya, E. (Eds.), Traffic andTransport Psychology. Theory and Application. Pergamon, Oxford, pp.161–171.

inges, D.F., Graeber, R.C., 1989. Crew fatigue monitoring. Flight Saf. Found.Flight Saf. Digest, 65–75.

inges, D.F., Kribbs, N.B., 1991. Performing while sleepy: effects ofexperimentally-induced sleepiness. In: Monk, J.H. (Ed.), Sleep, Sleepiness,Performance. John Wiley & Sons, Chichester, England.

ancock, P.A., Meshkati, N. (Eds.), 1988. Human Mental Workload. Amster-dam, North-Holland.

ancock, P.A., Warm, J.S., 1989. A dynamic model of stress and sustainedattention. Hum. Factors 31, 519–537.

orne, J.A., Reyner, L.A., 1995. Driver sleepiness. J. Sleep Res. 4 (2), 23–29.

V

W

nd Prevention 40 (2008) 851–860

ackie, R.R., Wylie, C.D. (1992), Driver fatigue: Accidents, Assessments, andCountermeasures, Report no. 92-00169, Human Factors Research Labora-tory, Essex Corporation.

ouloua, M., Hitt, J.M., Deaton, J., 2000. Automation and workload in aviationsystems. In: Hancock, P.A., Desmond, P.A. (Eds.), Stress, Workload, andFatigue. LEA Publishers, London, pp. 321–333.

guyen, L.T., Jauregui, B., Dinges, D.F., 1998. Changing Behaviors to PreventDrowsy Driving and Promote Traffic Safety: Review of Proven, Promising,and Unproven Techniques. AAA Foundation for Traffic Safety, WashingtonDC.

ilsson, T., Nelson, T.M., Carlson, D., 1997. Development of fatigue symptomsduring simulated driving. Accid. Anal. Prev. 29 (4), 479–488.

’donnell, R.D., Eggemeier, E.T., 1986. Workload assessment methodology.In: Boff, K.R., Kaufman, L., Thomas, J.P. (Eds.), Handbook of HumanPerformance. Wiley, N. Y., p. 42-1L 142-.

ron-Gilad, T., Ronen, A., 2007. Road characteristics and driver fatigue: asimulator study. Transport. Injury Prev. 8, 1–10.

ron-Gilad, T., Shinar, D., 2000. Driver fatigue among military truck drivers.Transport. Res. F 3 (4), 195–209.

adeborg, K., Briem, V., Hedman, L.R., 1999. The effect of concurrent taskdifficulty on working memory during simulated driving. Ergonomics 42,767–777.

eid, J. M. (1991). Dysfunctional driving behaviors: a cognitive approach to roadsafety research. Road safety research, ATSB Australian Transport SafetyBureau.

eyner, L.A., Horne, J.A., 1998. Evaluation of “In Car” countermeasures todriver sleepiness: cold air and radio. Sleep 21 (1), 46–51.

alas, E., Driskell, J.E., Hughs, S., 1996. Introduction: The Study of Stress andHuman Performance. In: Driskell, J.E., Salas, E. (Eds.), Stress and HumanPerformance. LEA Publishers, New Jersey, pp. 1–45.

cerbo, M.W., 1998. What’s so boring about vigilance? In: Hoffman, R.R.,Sherrick, M.F., Warm, J.S. (Eds.), Viewing psychology as a whole: The inte-grative science of William N. Dember. American Psychological Association,Washington DC, pp. 145–166.

hiffault, P., Bergeron, J., 2003. Fatigue and individual differences inmonotonous simulated driving. Pers. Individual Differences 34 (1),

erwey, W.B., Zaidel, D.M., 1999. Preventing drowsiness accidents by an alert-ness maintenance device. Accid. Anal. Prev. 31, 199–211.

iener, E.L., Curry, R.E., Faustina, M.L., 1984. Vigilance and task load: insearch of the inverted U. Hum. Factors 26, 215–222.