Transportation Research Part F 14 (2011) 435–446

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Transportation Research Part F

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Driving simulator validation with hazard perception

Geoffrey Underwood ⇑, David Crundall, Peter ChapmanSchool of Psychology, University of Nottingham, Nottingham NG7 2RD, UK

a r t i c l e i n f o

Article history:Received 19 July 2010Accepted 15 April 2011

Keywords:Driving simulatorSituation awarenessHazard perceptionVisual searchEye movements

1369-8478/$ - see front matter � 2011 Elsevier Ltddoi:10.1016/j.trf.2011.04.008

⇑ Corresponding author. Tel.: +44 115 951 5313;E-mail address: geoff.underwood@nottingham.ac

a b s t r a c t

How should we assess the comparability of driving on a road and ‘‘driving’’ in a simulator?If similar patterns of behaviour are observed, with similar differences between individuals,then we can conclude that driving in the simulator will deliver representative results andthe advantages of simulators (controlled environments, hazardous situations) can beappreciated. To evaluate a driving simulator here we compare hazard detection while driv-ing on roads, while watching short film clips recorded from a vehicle moving through traf-fic, and while driving through a simulated city in a fully instrumented fixed-base simulatorwith a 90 degree forward view (plus mirrors) that is under the speed/direction control ofthe driver. In all three situations we find increased scanning by more experienced andespecially professional drivers, and earlier eye fixations on hazardous objects for experi-enced drivers. This comparability encourages the use of simulators in drivers trainingand testing.

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1. Introduction

Practical driving can be assessed by a range of component-task laboratory tools – reaction time tests, spatial-ability tests,and judgement tests, for example, as well as with more inclusive tasks involving driving simulators. Simulators are essentialtools of driver assessment, for ethical reasons above all others, in any task where drivers may be exposed to actual drivinghazards such as high probability of collision. When we place inexperienced novice drivers in a roadway situation with other,unpredictable road users we are putting them at risk, and simulators eliminate the consequences of these risks. Drivers maybehave in similar ways in simulators and on real roads, but questions have been raised about the validity of the measurestaken, with Kemeny and Panerai (2003) pointing out that simulators do not present all of the most relevant visual cues fordrivers (especially binocular cues and motion parallax), and with Owsley and McGwin (2010), for example, pointing to crudevisual display with poor fidelity that cannot represent the visual complexity or range of lighting conditions experienced inactual driving. Simulator validation studies have tended to compare driving on a road against driving in a simulator, assess-ing speed and speed adaptation, and lane-keeping (e.g., Bella, 2008; Godley, Triggs, & Fildes, 2002; Lee, Cameron, & Lee,2003; Törnros, 1998). Results have generally shown good correspondence, but Godley et al. (2002) distinguished betweenrelative validity (similar patterns of behaviour), which they did establish, and absolute validity (similar speeds), whichwas not established. Speed and lane-keeping are undoubtedly importance measures when validating a simulator, but theyshould be regarded as necessary conditions rather than sufficient conditions. They measure relatively low-level vehicle con-trol, being perceptual-motor measures of driving, and given our current knowledge of the factors that influence performanceit is now appropriate to included higher-level cognitive measures in the assessment. As well as controlling the vehicle we canalso assess the comparability of the driver’s situation awareness by looking for behavioural change in roads and in specificsituations associated by heightened levels of visual search. If an experienced driver is aware that a situation is likely to

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436 G. Underwood et al. / Transportation Research Part F 14 (2011) 435–446

present difficulties from other road users, then their search of the roadway changes (Crundall & Underwood, 1998; Under-wood, Chapman, Brocklehurst, Underwood, & Crundall, 2003). Also, when watching movies filmed from a driver’s perspec-tive, their behaviour towards a potential hazard is distinctive. Differences between drivers with different abilities can beused as a measure for the assessment of simulator validity. This paper asks whether behaviour towards potential hazardsis comparable in a simulator and in other driving tasks.

Early studies with simulated driving tasks were very promising in demonstrating a relationship between self-reportedaccident history and laboratory behaviour. Currie (1969) recruited 26 pairs of volunteers composed of a safe driver and an‘‘accident repeater’’ (at least three accidents per 100,000 miles) who were matched for age, occupation, and driving expe-rience. Their control of an electric model car (1:32 scale) was recorded as it travelled around a circuit while another carconverged on a collision course during overtaking, junction-crossing or when pulling across the driver’s path. To makethe study important to the drivers they appeared to be wired up to receive an electric shock in case of any ‘‘inappropriateaction’’. Currie commented on the efficacy of this threat, pointing out that many of the participants were seen to flinchwhen collisions occurred, and some reported mild nausea. The results suggested that safe drivers recognised the dangersfrom other cars earlier than accident-repeaters, by braking when a collision was likely, and they had fewer collisions over-all. Even in simple driving-related tasks then, drivers exhibit patterns of behaviour that are consistent with their on-roadbehaviour. To validate the measures taken from simulators, however, we need to know how drivers behave in the situa-tions that are simulated. A number of studies have looked for such comparisons, recording individual differences in mea-sures including driver-selected road speed, braking, traffic sign compliance, non-signed rule compliance, steering, and useof vehicle controls (e.g., Behr et al., 2010; Godley et al., 2002; Lee et al., 2003; Reed & Green, 1999), but direct comparisonsbetween in-simulator and in-car driving are relatively rare. In the present review we compare hazard perception responsesin a driving simulator, with hazard responses while driving and while participating in a conventional hazard perceptiontest.

Hazard perception is regarded here as a driver’s situation awareness for a dangerous configuration of roadway and roadusers, and will be used as the test-bed for comparing behaviour in different environments. Situations that require a driver toadapt their behaviour by changes of speed or direction are hazardous, and safer drivers will anticipate these situations beforeextreme braking or swerving is necessary to avoid a collision. For example, if driving along an otherwise unoccupied urbanstreet with a group of children playing with a ball on the footpath ahead, there is a chance of the ball and possibly one of thechildren running into the road. The children therefore present a potential hazard well before there is a risk of a collision, anda driver may adjust the car’s speed to allow gentle braking in case the ball and child do appear on the roadway. In Endsley’s(1995) three-level model of situation awareness there is a basis for distinguishing between drivers with different skill, andfor identifying the causes of differences in hazard perception. In this model the lower two levels of situation awareness cor-respond to perception of the current environment and knowledge of how the current situation has arisen (see also Endsley,2004; Horswill & McKenna, 2004; Underwood, 2007). Drivers who are able to predict the behaviour of other road users,anticipating how the current situation might develop as other vehicles manoeuvre around them, or what a group of childrenon the footpath ahead might do, would correspond to awareness at the third and highest level in Endsley’s model. Hazardperception tests that are used for driver evaluation ideally test these anticipation skills and are now used for driver trainingand assessment. Typical hazard perception tests involve movies filmed from a driver’s perspective in a car that travels alonga range of roadways. Events occur that would require braking or steering changes, such as the car in front of the camera carslowing sharply, or another road user moving into the path of the car. The participant is required to press a response buttonwhenever one of these events would require a driving response, or in some cases a continuous recording is taken by the par-ticipant moving a lever between settings marked ‘‘safe’’ to ‘‘dangerous’’ (e.g. Crundall, Chapman, Phelps, & Underwood, 2003;Pelz & Krupat, 1974).

Results with the hazard perception test have shown sensitivity to individual driving ability, effects of sleepiness, and age-related decrements. In Pelz and Krupat’s (1974) early study 60 drivers were shown a 5 min movie with 10 hazardous events,and differences in the driver’s accident record were associated with selected settings on the continuous recordings of the‘‘apprehension meter’’. Drivers with fewer accidents tended to be more cautious overall and to respond faster to the onsetof a hazard. More recent studies with discrete button-press responses have confirmed the tendency of inexperienced or nov-ice drivers to respond slower to hazards than older more experienced drivers (Borowsky, Shinar, & Oron-Gilad, 2010; Wallis& Horswill, 2007; Wetton et al., 2010), and that sleepiness slows the detection of hazards, especially in novice drivers (Smith,Horswill, Chambers & Wetton, 2009). There have been reports of an insensitivity of hazard perception tests, and the cause ofthis inconsistency is unclear. Chapman and Underwood (1998a, 1998b) and Sagberg and Bjørnskau (2006) found weak rela-tionships between driving experience and hazard perception responses, and one possibility for the discrepancy with otherresults might lie with the types of hazards shown. Some hazards are abrupt and attention-capturing, as when a pedestriansteps into the roadway from behind a parked vehicle. These types of hazards, which were certainly used in the Chapman andUnderwood studies, are potentially unavoidable and do not necessarily discriminate between good and bad drivers becausethey capture attention whatever is the driving experience of the observer. These abrupt or exogenous hazards differ fromanticipated hazards that call upon level-three situation awareness, which require the driver to understand what might hap-pen in the immediate future if other road users behave in hypothetical ways. These gradual onset hazards are more sensitiveto driving experience, as when we notice that an oncoming car might move into our path in order to manoeuvre past a sta-tionery obstacle, for example. It is possible that failures to report differences in hazard perception responses between noviceand experienced drivers stems from the selection of the types of hazards for inclusion in the study. We will use responses to

G. Underwood et al. / Transportation Research Part F 14 (2011) 435–446 437

hazards and hazardous situations here as a method of comparing driving and driving-related behaviour on the road, withhazard perception tests, and in a driving simulator.

2. Scanning the roadway

Inadequate scanning of the roadway will inevitably result in a collision. In his review of 50 years of safety research, Lee(2008) concluded that drivers crash into each other because they ‘‘fail to look at the right thing at the right time’’ (p. 525). Afailure of visual search is a prominent feature in surveys of police reports of crashes (Lestina & Miller, 1994) and in-car obser-vations of the precursors to crashes and near-misses in the Virginia Tech Transportation Institute (VTTI) group’s100-car nat-uralistic driving study (Klauer, Dingus, Neale, Sudweeks, & Ramsey, 2006). Failing to scan the roadway is a common cause ofthe driver colliding with another vehicle or having to brake or swerve suddenly to avoid a crash. Laboratory observations ofdriving-related performance provide the basis for many of our studies, although third-party reports and in-car surveillancemethods have also made valuable contributions. On-road studies of hazard perception are limited for ethical reasons, ofcourse, and much of what we know about the visual scanning behaviour of drivers has come from free-driving situations.Experiments reported by the VTTI group (Lee et al., 2008; Olsen, Lee, & Simons-Morton, 2007) and by Pradhan, Pollatsek,Knodler, and Fisher (2009), provide important exceptions to this generalization, observing the driver’s visual attention asthey encountered potentially hazardous situations that were staged with the assistance of actors. The VTTI studies took nov-ice and experienced drivers onto a test track in an instrumented vehicle that enabled the recording of eye movements as thehazardous events occurred. The 17 year old drivers were tested within a few weeks of gaining their licences, and were com-pared with a set of 40 year old experienced adults. As they drove around a 2 mile test track several hazards were set-up foreach driver – a stop-sign that was not visible until an occluding van had been passed, and pedestrians appeared, who mightor might not walk into the path of the driver. The experienced drivers were more likely to respond to the occluded stop-signthan the novices, and this finding was supported by more glances towards to the sign by the older drivers. Novices alsolooked at and responded to the pedestrians less frequently than did the experienced drivers. In a second study on the VTTItest track novices were observed shortly after gaining their licence and then again 6 months later (Olsen et al., 2007). Themeasures included eye fixations on the mirrors with and without a range of in-car secondary tasks such as operating theradio or a cell phone. In all measures new novices glanced into their mirrors less than experienced drivers, but after 6 monthssome of the differences had been eliminated – the novices had learnt the need to survey the roadway and were able to do so.

The VTTI novices used their mirrors less than more experienced drivers, suggesting one of two possibilities: either theywere unaware of the need to monitor events behind their vehicle, or they were so over-loaded with the task of vehicle con-trol that the collection of information about other road users was of secondary importance. A similar result came from one ofthe Nottingham studies of novice drivers in which eye fixations on mirrors were recorded during lane-change manoeuvres(Underwood, Crundall, & Chapman, 2002). Novices did look in their internal rear-view mirror in this study, but did not usethe more appropriate external right door mirror as much as experienced drivers when checking that a lane was clear as theymoved into it. Recarte and Nunes (2000) recorded drivers’ glances at their mirrors while driving on a range of roads, andfound that increases in mental workload acted to reduce the use of mirrors. This supports the idea that drivers restrict theirscanning for roadway information when driving becomes more difficult. Scanning for hazards is necessary for interactionswith other road users, but novices either do not understand the need to observe others and anticipate their actions, or theydo not have the available resources to control their vehicle and simultaneously think about what might happen in a few sec-onds. These alternatives are not mutually exclusive, of cours, but when novice and experienced drivers watched moviesfilmed from a car travelling along the same roads, a similar pattern of scanning was seen as when they drove along the roadsthemselves (Underwood, Chapman, Bowden, & Crundall, 2002). The experienced drivers scanned a video of a demanding ur-ban motorway more than the novices, suggesting that they were more aware of the potential dangers when travelling onsuch roads.

A failure to scan the roadway at the most appropriate time was seen when moving into an outside lane on a dual-car-riageway (urban motorway) in the Underwood et al. (2002) study with mirror inspections. This pattern was also seen inthe eye movement study of novice and experienced drivers reported by Crundall and Underwood (1998) and Underwoodet al. (2003). The measure of scanning used was the variance of fixation locations. High variance indicates greater distancesbetween fixations, and the highest variance was found for experienced drivers on a dual-carriageway – the same type of roadwhere experienced drivers looked into their external door mirrors when making a lane-change manoeuvre (see Fig. 1). Thevariance for experienced drivers fluctuated according to road type, with low variance on relatively quiet roads containingfew hazards, or other road users in predictable locations. On the dual-carriageway, however, other vehicles were making lanechanges immediately in front of the driver, and there were slip roads entering the road from both sides. The experienceddrivers exhibited a high variance of fixation locations on this section of road, indicating that they were looking around them.Checking that the lanes were clear was an important part of negotiating this part of the test route, but the novice driversbehaved in just the same way as they did on the other roads. They tended to look straight ahead, focusing on maintainingtheir lane position, as reported by Mourant and Rockwell (1972). Experienced drivers, in contrast, showed sensitivity to thedemands of the roadway and to the behaviour of other road users.

When drivers are observed on actual roadways, their scanning behaviour reflects their experience. Novices look aroundless than older drivers, and they do not inspect their mirrors selectively. These studies did not use staged hazards, unlike the

Fig. 1. The range of scanning three types of roads, by novice and by experienced drivers (data from Crundall & Underwood, 1998).

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VTTI, and we are inferring that when experienced drivers look around them more than the novices that this is a product oftheir enhanced situation awareness prompting them to search for developing hazards. Experienced drivers increase theirroadway scanning at times of increased interaction with other drivers, however, and the most likely explanation is that theyare anticipating the need to avoid conflict. In assessing the use of driving simulators we will consider the sensitivity of sim-ulator-derived measures in demonstrating these same patterns of behaviour, and particularly the variations in inspectionbehaviour when hazards appear.

3. Scanning while watching hazard perception movies

Studies of actual behaviour while driving a vehicle in realistic environments are ideal in terms of providing ecologicalvalidity. Unfortunately the expense of such studies, and the practical difficulties in terms of participant safety and ethicalconsiderations precludes the routine testing of driver performance in actual hazardous situations. The VTTI studies withstaged hazards on a test track, and the Nottingham studies demonstrating enhanced scanning in experienced drivers on ur-ban motorways are exceptions to this generalisation. The observation of novices is particularly important because it is likelythat these drivers will have limited exposure to hazardous situations (Groeger & Clegg, 2000) and that any deficits in per-formance in such situations are likely to be directly implicated in the high accident rates of novice drivers. To explore per-formance in such situations researchers have often asked participants to watch videos of hazardous situations from thedriver’s perspective and respond to the levels of hazard present. Such responses can be by summative ratings (Groeger &Chapman, 1996), continuous ratings (Pelz & Krupat, 1974) or button presses (McKenna & Crick, 1994). One of the attractionsof using such hazard perception tests for understanding drivers’ visual search is that large numbers of participants can watchidentical hazardous events and we can aggregate eye movement measures over participants and time. This lets us examinethe way that visual search in hazardous situations differs from normal visual search when driving and to explore the pos-sibility that such differences are particularly pronounced for inexperienced drivers.

Fig. 2 shows sample data taken from 85 participants viewing a hazard perception video (see Chapman & Underwood,1998a). Along the bottom of the Figure we have plotted the proportion of respondents who pressed a hazard response buttonwithin each 200 ms time period. This video shows the view from a car driving through an urban environment in which apedestrian abruptly steps out into the road about 10 s into the video. Later in the video (between 18 and 23 s) other pedes-trians appear partly obscured by parked vehicles and one of these steps out into the road approximately 24 s into the video.Most drivers press the response button in response to each of the two pedestrians stepping into the road, and most viewersalso press the response button at some point between 18 and 23 s, though these responses to the developing hazard aremore spread out in time than those to the abrupt events. Fig. 2 also shows two measures of visual search – the mean fixationduration, and the mean saccade amplitude. Both these measures are averaged in each 200 ms bin over the 85 participantsand thus show typical changes in visual search and how they are sometimes related to hazards. It is very clear from the Fig-ure that the first two hazard periods (at around 10 and 20 s) are closely associated with increases in average fixation dura-tion. Long fixation durations are typically associated with high processing load and it thus makes sense to think that duringthese hazards viewers are spending longer extracting information from their point of gaze. It is important to note that thethird hazard (at 25 s) does not appear to follow this pattern and is not associated with a clear increase in fixation duration.One possibility for this is that the pedestrian has already been spotted during the previous hazard period (18–23 s) and theviewers have already anticipated that he might step out. The increase in fixation duration therefore takes place in theanticipatory period where potential hazards are being assessed rather than when the actual hazard occurs. Chapman and

Fig. 2. Changes in mean saccade amplitude, mean fixation duration, and proportion of respondents pressing a hazard response button over the course of a40 s video of hazardous driving (data from Chapman & Underwood 1998a).

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Underwood (1998a) averaged results across 13 different films and report an overall moment by moment correlation be-tween fixation duration and hazard response probability of 0.457, indicating that the two variables are closely, thoughnot inevitably linked. The remaining line in Fig. 2 is the mean saccade amplitude – that is the distance between successivefixations. Where this is large it indicates that viewers are scanning widely, and where it is small it suggests that viewers areconcentrating multiple fixations within a relatively small area. Although it is clear from Fig. 2 that saccade amplitude varieswidely throughout the film, there is also evidence that it is reduced around the first two hazard periods. Across 13 videosChapman and Underwood (1998a) found a significant but relatively small negative correlation between saccade amplitudeand hazard responses of �0.162. These findings, along with those using other spread of search measures such as horizontaland vertical point of gaze variances (e.g. Chapman & Underwood, 1998a, 1998b; Underwood, Phelps, Wright, van Loon, &Galpin, 2005) build on the findings of on-road studies such as Crundall and Underwood (1998) to support the idea that cer-tain types of hazard are associated with a general reduction in the spread of visual search, with hazardous events bringingabout longer fixation durations and less scanning of the environment, particularly in terms of horizontal spread of search.

The observed restriction in visual search in hazardous situations has clear advantages. When a hazardous area has beenpotentially identified it is clearly important that information in this region is processed in depth and there may be advan-tages to monitoring that location for further unfolding events. Nonetheless there is a potential danger in restricting search insuch situations in that over-focusing attention on one region may prevent the viewer from noticing and processing potentialhazards elsewhere in the environment. In this context it is interesting to look closer at the data from Chapman and Under-wood (1998b). In this study we compared novice and experienced drivers viewing and responding to a series of hazard per-ception videos. Novice drivers were all tested within 3 months of gaining a full British driving licence, while experienceddrivers had held a licence for between 5 and 10 years at the time of testing. As can be seen in Fig. 3, both groups showedclear reductions in spread of search, and increases in fixation duration during hazardous events, there was also an interestinginteraction between experience and the presence or absence of a hazard. Novice drivers increased their fixation durationsduring hazards significantly more than more experienced drivers did. This is consistent with the idea that experienced driv-ers may have learned information about hazards that allows them to process them relatively quickly and resume their nor-mal search strategies sooner than novice drivers can. A study by Chapman, Underwood, and Roberts (2002), in which novicedrivers were trained in hazard anticipation and visual search, showed that such training was successful in increasing spreadof search and reducing fixation durations when novice drivers subsequently viewed hazard perception videos.

While young novice drivers show clear impairments in visual search while watching hazard perception videos, the pic-ture for older drivers is less clear. Although there have been suggestions that elderly drivers might be impaired in hazard-related visual search tasks (e.g. Maltz & Shinar, 1999) larger studies such as Horswill et al. (2008) suggest that elderly driversshow only minor impairments in hazard perception ability. A study by Underwood et al. (2005) looked in detail at the visual

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Fig. 3. Mean fixation durations while watching hazard perception movies on three types of roads, for novice and for experienced drivers (data fromChapman & Underwood, 1998a,b).

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search patterns of elderly drivers viewing hazard perception videos. Fig. 4 shows data from this study that clearly illustratethe typical increase in fixation durations around the hazard and show that it is of similar magnitude in older (61–76 years)

Fixation Relative to Hazard Onset (HO)

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Fig. 4. Fixation durations recorded while younger and and older drivers watch hazard perception movies. The fixation at point HO is the first fixationrecorded after hazard onset, with the previous four fixations and following four fixations also shown (data from Underwood, Phelps, Wright, van Loon, &Galpin, 2005).

G. Underwood et al. / Transportation Research Part F 14 (2011) 435–446 441

and younger (31–44 years) drivers. Although older drivers may be impaired in some visual driving-related tasks it is possiblethat this is more to do the novelty of some lab tasks (e.g. change detection – Wetton et al., 2010, visual overlays – Maltz &Shinar, 1999) than an overall deficit in visual search in traditional video-based hazard perception tasks (Borowsky et al.,2010; Underwood et al., 2005).

One issue with studies that look at hazard perception ability using video-based hazard perception tests is that the field ofview is generally much smaller than that available in real driving, thus in the studies by Chapman and Underwood (1998a,1998b) the hazard videos subtended only 15.4 degree of visual angle when presented. This creates two potential problems,the first is that it is possible that a compression of the field of view during the hazard perception test biases visual search andmay actually make it easier for participant to spot peripheral events than it might be in actual driving. The second problem isthat some types of hazard cannot be realistically simulated using such a limited field of view. Thus any event where a vehiclepulls out from a junction would usually require the driver to engage in wide scanning incorporating significant head move-ments. Typically such events are simply not included in conventional hazard perception tests. A recent exception to this isthe study by Shahar, Poulter, Clarke, and Crundall (2010) where they developed a three-screen hazard perception test. In thistest a full 180 degree of visual angle was recorded from a moving vehicle and presented to people over three large videomonitors subtending approximately 112 degree of visual angle during testing. They found that when all three screens werepresented participants performed significantly better than when viewing was limited to a central screen subtending 42 de-gree of visual angle, even when the hazard occurred only on the central screen. This highlights a potential difficulty withvideo-based hazard perception testing in that hazards may be deliberately chosen in which spread of search has been arti-ficially limited. If anything, we would predict that difference in search in hazards, and differences as a function of trainingand expertise would be even larger in studies that use a wider selection of hazards and more realistic fields of view.

On the roadway experienced drivers respond to increased interaction with other road users with increased scanning andwhen watching hazard perception movies they increase their scanning when looking at more demanding roads. This patternof novice-experienced driver differences will now be considered with drivers in a simulator.

4. Hazard perception in a driving simulator

While the majority of hazard perception research has historically been concerned with the use of video clips of driving toinvoke and assess hazard perception skill, there has been an increasing trend over the past decade to turn to simulation. AsBoyle and Lee (2010) pointed out in a recent prologue to a special issue of Accident Analysis and Prevention on driving sim-ulation, the average number of papers reporting the use of a driving simulator rose from 124 papers published between 1965and 1999, to 572 papers in the subsequent decade (and that did not include the 25 papers they were introducing in theirspecial issue). Many of these papers however are not to do with hazard perception per se, but instead focus on a varietyof topics including the evaluation of new interfaces for entertainment systems (e.g. Garay-Vega et al., 2010); developingwarning systems to combat fatigue (e.g. Vadeby et al., 2010); investigating the effects of alcohol or drugs on basic drivingperformance (e.g. Lenné et al., 2010); and assessing low level visual cues to steering (e.g. Coutton-Jean, Mestre, Goulon, &Bootsma, 2009). There is however a growing interest in the specific use of driving simulation to investigate and assess hazardperception skill (Allen, Cook, & Park, 2005; Garay, Fisher, & Hancock, 2004; Garay-Vega & Fisher, 2005. Garay-Vega, Fisher, &Pollatsek, 2007; Fisher, Pollatsek, & Pradhan, 2006; Pradhan et al., 2005).

For any topic within the field of driving research it has been noted that simulation will provide advantages over any on-road study in terms of safety, cost and experimental control (Reed & Green, 1999). Certainly the ethical and pragmatic prob-lems of placing drivers in hazardous situations in the real world render any such research difficult. While some researchershave used some ingenious methods to record hazard perception skill on public roads (e.g. Olson & Sivak, 1986), the use offoam rubber hazards is still ethically challenging, as well as being costly to set-up and limiting in the range of hazards thatcan be investigated. However all of these advantages of simulators can also be attributed to the video-based methodologiesused in much hazard perception research. Clips of driving are relatively cheap to produce (and can be much cheaper thanmany high-fidelity simulators), usually only requiring a video camera on a suction mount. They certainly provide experimen-tal control, as participants always see the same route and the same events – something that a simulator cannot guarantee,and unless the simulated hazards include personal injury, the ethical implications for the participants are minimal and tendto revolve around issues of motion sickness (Brook et al., 2010).

There are however a number of advantages that simulators have over video-based hazard perception tests. Perhaps themost important is the addition of interactivity. Whereas a video-based hazard perception test typically collects only re-sponse times to the appearance of a hazard, simulators can record a much more complex behavioural response, includingpreparatory behaviours (slowing in anticipation of a hazard, changing lane position to avoid a potential hazard) as wellas emergency manoeuvres to avoid the actual hazard. Speed, braking, steering angle and lane position can provide multiplemeasures on the approach to a hazard, providing a behavioural signature that not only indicates that a driver has spotted thehazard but also that what behaviour they have chosen to avoid it.

In a recent simulator study of hazard perception we used these behavioural signatures to distinguish between two groupsof learner drivers, one of which had received professional commentary training (Crundall, Andrews, van Loon & Chapman,2010). Commentary training requires the drivers to produce an on-line verbal record of what they are seeing and what theyare thinking. Typically it is seen as a training tool reserved for advanced drivers and police drivers (primarily because the act

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Fig. 5. The reduction in speed across two simulated drives on the approach to hazards for a control group and a group trained in commentary driving(adapted from Crundall et al., 2010).

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of producing a commentary can be quite demanding in itself), however in this particular study we identified benefits evenfor learner drivers when placed in a simulator. Fig. 5 shows the change in speed across two assessment drives in the simu-lator on the approach to the various hazards. The approach distance to the hazards is broken down into 10 m data bins, withlower numbers representing closer proximity to the hazard. While even the untrained group show a reduction in speedacross the two assessments on the approach to the hazards, the reduction is much more marked in the group who receivedcommentary training in-between the two assessments. Not only can we see a clear difference between the two groups wecan also see when this difference manifests (around 40–30 m before reaching the hazard).

A second argument for valuing the interactivity of a simulator over the passive nature of video clips is that it places amore realistic level of demand upon the visual system. Certain types of visual cues become extremely important for car han-dling, such as the suggestion that drivers need to fixate the tangent point when steering around curves (Land & Lee, 1994).The requirement to attend to these cues when controlling a car will invariably interrupt a visual search for hazards (cf. Schie-ber, Schlorholtz, & McCall, 2009). Video-based hazard perception clips place no such demands on the viewer, and may there-fore over-estimate the hazard perception abilities of individuals. To study hazard perception through simulation however,we need to assume some level of correspondence between the way we move our eyes in the simulator and the way we moveour eyes while on the road in a real driving situation.

Konstantopoulos, Crundall, and Chapman (2010) have recently demonstrated some relative validity between on-road eyemovements and those evoked within a simulator. They had two hypotheses derived from previous on-road and video-basedstudies. First, they predicted that eye movements in a simulated drive should differ between drivers of differing experience(in this case driving instructors were compared to learner drivers). As with the on-road findings reported above, they foundthat the more experienced drivers had a more efficient search strategy, with frequent short fixations that ranged across awider horizontal area than those of the learner drivers (see Fig. 6). The learners tended to produce longer fixations that weremore tightly clustered around the centre of the display. The second hypothesis was that visual efficiency would degrade un-der deteriorating visibility conditions. They found that simulated driving through rain or at night tended to increase thelength of fixation durations (suggesting increased processing difficulty due to reduced visibility), though the spread of searchwas not impacted. Konstantopoulos et al. argued, at least in regard to the experiential differences, that this suggested thesimulator had relative validity in that differences noted on real roads are also apparent in the simulator. Some researchershave gone even further however, claiming absolute validity between on-road and simulated drives, albeit within very tightconstraints (Shechtman, Classen, Awadzi, & Mann, 2009). Shechtman et al., compared the number and type of errors madewhen turning right or left at a junction (including visual scanning errors). They reported no differences between the on-roadand simulator conditions, which they claim is evidence of absolute validity.

Our recent work on eye movements evoked by simulated hazards has also suggested some relative validity between sim-ulator and other methods (Chapman, van Loon, Trawley, & Crundall, 2007; Crundall, Chapman, Underwood, van Loon, &Chapman, 2006). Using the same simulated hazards as reported in Crundall et al. (2010) we analysed eye movements acrossthree periods of time: before a hazard (essentially a safe period), on approach to a hazard (when the source of the hazardmight be visible though the hazard has not yet triggered), and during the hazard (the time window during which the hazard

Fig. 6. The fixation locations of one driving instructor and one learner across a 90 � 30� visual display during a section of daytime driving (top panel). Thebottom panel reflects the flattened display that the participants were seeing at the time (taken from Konstantopoulos et al., 2010).

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becomes apparent and the driver is required to make some form of evasive manoeuvre. As can be seen in Fig. 7, attentionappears to be captured on the approach to the hazard. During this time window fixation durations are at their longest, whilesaccade amplitude and spread of search are significantly decreased. This fits with our previous work using video-based haz-ards (e.g. Chapman & Underwood, 1998a, 1998b) that demonstrated a similar decrease in search and a corresponding in-crease in fixation duration in the presence of a hazard. However it also provides a further insight: attentional capture isat its greatest prior to the hazard being triggered. During the actual hazard window, participants appear to recover some-what from this focussing effect. This is possibly due to the interactive nature of the simulator. Once the hazard is identified,the driver then has to decide what manoeuvre to make. This may necessitate an emergency scan of the scene to ensure thatthe anticipated manoeuvre (e.g. changing lanes to avoid the hazard) does not cause conflict with other road users. This effect

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Fig. 7. Eye movement measures taken from participants driving through a series of hazardous scenarios in a driving simulator. The measures are (a) meanfixation durations, (b) mean saccadic amplitude, and (c) mean spread of search (adapted from Chapman et al., 2007).

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is unlikely to become apparent during video-based presentations that merely require a push button response. Thus it seemsthat while our data reflect those obtained using video-based methodologies, they also show differences that can be explainedby the more realistic nature of the simulator.

One research group that has been pioneering the role of hazard perception in simulators is that of Donald Fisher and hiscolleagues based in the University of Massachusetts. Many of their studies have participants drive through a series of poten-tially hazardous scenarios in a high-fidelity fixed-base simulator while eye movements are recorded (e.g. Pradhan et al.,2005; Fisher et al., 2006). They have consistently found that experienced drivers are more likely to glance at those areasof the driving scene that the experimenters have defined a priori as providing vital cues to the successful navigation a poten-tially hazardous scenario. For instance, Pradhan et al. (2005) had three groups of drivers navigate through 16 potentially haz-ardous situations in a driving simulator. Learners fixated the a priori hazard sources 35% of the time, experienced driversfixated the same regions 50% of the time, and highly experienced drivers fixated these areas 66% of the time. In work wehave recently completed (Crundall et al., 2010) we have also noted that learner drivers are less likely to fixate certain typesof hazards than more experienced drivers and driving instructors.

Despite these notable successes in the use of simulators in hazard perception research, one of the problems noted in stud-ies using video-based methodologies is still apparent. We have already reported on the inconsistencies of the video-basedhazard perception research to show replicable effects across different research groups, and it seems that the use of simula-tors does not necessarily improve this. For instance Liu, Hosking, and Lenné (2009), and Shahar et al. (2010) used the samemotorcycle simulator yet while Lui et al. found discriminatory effects of the simulated hazards, Shahar et al. did not. Cru-cially however they were using different hazardous scenarios.

As with explanations of null results in video-based hazard perception research (e.g. Sagberg & Bjørnskau, 2006), it seemsthat some simulated hazards are better than others. This problem however identifies what we believe is the ultimatestrength of the driving simulation in regard to hazard perception. We need to identify why some hazards discriminate be-tween good and bad drivers but others do not. There are likely to be many reasons, but one noted by Shahar et al. (2010) andGaray-Vega et al. (2007) is the need for the hazard to be foreshadowed in some manner. Garay-Vega et al. (2007) use this termto describe a non-hazardous event that might draw one’s attention to the subsequent hazardous event. For instance, seeingpedestrians crossing the road ahead might draw one’s attention to the fact that a parked truck is obscuring a pedestriancrossing. Thus the driver is more likely to be alert to the sudden emergence of pedestrians upon reaching the truck. We prefera more immediate foreshadowing element, which we term the precursor to the hazard (before the pedestrian steps into theroad they are a precursor; Crundall et al., 2010). Essentially however both terms refer to a cue that experienced drivers couldperhaps recognise and use to prepare for a potentially hazardous event. It is likely however that precursors will differ in theirdiscriminability. If a precursor is too obvious then both good and bad drivers may benefit to an equal degree (Garay-Vegaet al., 2007), while a lack of precursors will lead both experienced and novice drivers to be equally surprised by the onsetof a hazard (Shahar et al., 2010).

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5. Conclusion

There is comparability, then, between driving behaviour on the road, while watching hazard perception movies, and in adriving simulator. Experienced drivers search the roadway more and they have shorter eye fixation durations than less expe-rienced drivers. This is only relative validity in the sense used by Godley et al. (2002), in that we cannot create the samehazardous situations on a road as can be programmed in a simulator. Absolute validity would involve the same scenariosbeing used on the road and in the simulator, and the same responses recorded in each. The validity of the simulator is estab-lished here by the observation of similar patterns of behaviour in both experienced and novice drivers. In addition to com-paring perceptual-motor skills associated with speed and lane-keeping when assessing driving simulator validity, werecommend that cognitive skills are also assessed, and we suggest that hazard perception is a suitable candidate for inclusionany battery of validity tests.

Although we are satisfied that driving simulators can demonstrate similar patterns of driver differences as can be seen onactual roads and when watching hazard perception movies, further experiments are required to investigate the conditionsunder which hazards can discriminate between driver groups. For a thorough investigation of this, hazardous scenarios needto be manipulated and tested, one component at a time. Even the simplest hazard could have a myriad of configurations(how long is the pedestrian on the pavement before stepping into the road? Does the pedestrian look over her shoulder?When does she step into the road? Are we less likely to notice this if there is an oncoming vehicle?). It is impossible tomanipulate these variables using video-based stimuli: trying to film the same clip a second time but with only one differenceis unlikely to work. In simulation however, or even with the presentation of simulated videos (e.g. Hosking, Liu, & Bayly,2010), we can manipulate a huge range of variables relating to the hazard. Through this extreme level of experimental con-trol we will be able to identify those elements of a hazard that are crucial to discriminating between good and bad drivers.

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