Transportation Research Part F

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1 Transportation Research Part F 14 (2011) Contents lists available at ScienceDirect Transportation Research Part F journal homepage: Driving simulator validation with hazard perception Geoffrey Underwood, David Crundall, Peter Chapman School of Psychology, University of Nottingham, Nottingham NG7 2RD, UK article info abstract Article history: Received 19 July 2010 Accepted 15 April 2011 Keywords: Driving simulator Situation awareness Hazard perception Visual search Eye movements 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 and the advantages of simulators (controlled environments, hazardous situations) can be appreciated. To evaluate a driving simulator here we compare hazard detection while driving on roads, while watching short film clips recorded from a vehicle moving through traffic, and while driving through a simulated city in a fully instrumented fixed-base simulator with a 90 degree forward view (plus mirrors) that is under the speed/direction control of the driver. In all three situations we find increased scanning by more experienced and especially professional drivers, and earlier eye fixations on hazardous objects for experienced drivers. This comparability encourages the use of simulators in drivers training and testing. Ó 2011 Elsevier Ltd. All rights reserved. 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 essential tools of driver assessment, for ethical reasons above all others, in any task where drivers may be exposed to actual driving hazards 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 may behave in similar ways in simulators and on real roads, but questions have been raised about the validity of the measures taken, with Kemeny and Panerai (2003) pointing out that simulators do not present all of the most relevant visual cues for drivers (especially binocular cues and motion parallax), and with Owsley and McGwin (2010), for example, pointing to crude visual display with poor fidelity that cannot represent the visual complexity or range of lighting conditions experienced in actual driving. Simulator validation studies have tended to compare driving on a road against driving in a simulator, assessing 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 between relative validity (similar patterns of behaviour), which they did establish, and absolute validity (similar speeds), which was not established. Speed and lane-keeping are undoubtedly importance measures when validating a simulator, but they should be regarded as necessary conditions rather than sufficient conditions. They measure relatively low-level vehicle control, being perceptual-motor measures of driving, and given our current knowledge of the factors that influence performance it is now appropriate to included higher-level cognitive measures in the assessment. As well as controlling the vehicle we can also assess the comparability of the driver s situation awareness by looking for behavioural change in roads and in specific situations associated by heightened levels of visual search. If an experienced driver is aware that a situation is likely to Corresponding author. Tel.: ; fax: address: geoff.underwood@nottingham.ac.uk (G. Underwood) /$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi: /j.trf

2 436 G. Underwood et al. / Transportation Research Part F 14 (2011) present difficulties from other road users, then their search of the roadway changes (Crundall & Underwood, 1998; Underwood, Chapman, Brocklehurst, Underwood, & Crundall, 2003). Also, when watching movies filmed from a driver s perspective, their behaviour towards a potential hazard is distinctive. Differences between drivers with different abilities can be used as a measure for the assessment of simulator validity. This paper asks whether behaviour towards potential hazards is comparable in a simulator and in other driving tasks. Early studies with simulated driving tasks were very promising in demonstrating a relationship between self-reported accident 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 experience. Their control of an electric model car (1:32 scale) was recorded as it travelled around a circuit while another car converged on a collision course during overtaking, junction-crossing or when pulling across the driver s path. To make the study important to the drivers they appeared to be wired up to receive an electric shock in case of any inappropriate action. Currie commented on the efficacy of this threat, pointing out that many of the participants were seen to flinch when collisions occurred, and some reported mild nausea. The results suggested that safe drivers recognised the dangers from other cars earlier than accident-repeaters, by braking when a collision was likely, and they had fewer collisions overall. Even in simple driving-related tasks then, drivers exhibit patterns of behaviour that are consistent with their on-road behaviour. To validate the measures taken from simulators, however, we need to know how drivers behave in the situations that are simulated. A number of studies have looked for such comparisons, recording individual differences in measures including driver-selected road speed, braking, traffic sign compliance, non-signed rule compliance, steering, and use of vehicle controls (e.g., Behr et al., 2010; Godley et al., 2002; Lee et al., 2003; Reed & Green, 1999), but direct comparisons between in-simulator and in-car driving are relatively rare. In the present review we compare hazard perception responses in a driving simulator, with hazard responses while driving and while participating in a conventional hazard perception test. Hazard perception is regarded here as a driver s situation awareness for a dangerous configuration of roadway and road users, and will be used as the test-bed for comparing behaviour in different environments. Situations that require a driver to adapt their behaviour by changes of speed or direction are hazardous, and safer drivers will anticipate these situations before extreme braking or swerving is necessary to avoid a collision. For example, if driving along an otherwise unoccupied urban street with a group of children playing with a ball on the footpath ahead, there is a chance of the ball and possibly one of the children running into the road. The children therefore present a potential hazard well before there is a risk of a collision, and a 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, and for identifying the causes of differences in hazard perception. In this model the lower two levels of situation awareness correspond 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 children on the footpath ahead might do, would correspond to awareness at the third and highest level in Endsley s model. Hazard perception tests that are used for driver evaluation ideally test these anticipation skills and are now used for driver training and assessment. Typical hazard perception tests involve movies filmed from a driver s perspective in a car that travels along a range of roadways. Events occur that would require braking or steering changes, such as the car in front of the camera car slowing sharply, or another road user moving into the path of the car. The participant is required to press a response button whenever one of these events would require a driving response, or in some cases a continuous recording is taken by the participant 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 agerelated 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 onset of a hazard. More recent studies with discrete button-press responses have confirmed the tendency of inexperienced or novice 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 of this inconsistency is unclear. Chapman and Underwood (1998a, 1998b) and Sagberg and Bjørnskau (2006) found weak relationships between driving experience and hazard perception responses, and one possibility for the discrepancy with other results might lie with the types of hazards shown. Some hazards are abrupt and attention-capturing, as when a pedestrian steps into the roadway from behind a parked vehicle. These types of hazards, which were certainly used in the Chapman and Underwood studies, are potentially unavoidable and do not necessarily discriminate between good and bad drivers because they capture attention whatever is the driving experience of the observer. These abrupt or exogenous hazards differ from anticipated hazards that call upon level-three situation awareness, which require the driver to understand what might happen in the immediate future if other road users behave in hypothetical ways. These gradual onset hazards are more sensitive to driving experience, as when we notice that an oncoming car might move into our path in order to manoeuvre past a stationery obstacle, for example. It is possible that failures to report differences in hazard perception responses between novice and experienced drivers stems from the selection of the types of hazards for inclusion in the study. We will use responses to

3 G. Underwood et al. / Transportation Research Part F 14 (2011) hazards and hazardous situations here as a method of comparing driving and driving-related behaviour on the road, with hazard 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). A failure of visual search is a prominent feature in surveys of police reports of crashes (Lestina & Miller, 1994) and in-car observations of the precursors to crashes and near-misses in the Virginia Tech Transportation Institute (VTTI) group s100-car naturalistic driving study (Klauer, Dingus, Neale, Sudweeks, & Ramsey, 2006). Failing to scan the roadway is a common cause of the driver colliding with another vehicle or having to brake or swerve suddenly to avoid a crash. Laboratory observations of driving-related performance provide the basis for many of our studies, although third-party reports and in-car surveillance methods have also made valuable contributions. On-road studies of hazard perception are limited for ethical reasons, of course, 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 as they encountered potentially hazardous situations that were staged with the assistance of actors. The VTTI studies took novice and experienced drivers onto a test track in an instrumented vehicle that enabled the recording of eye movements as the hazardous events occurred. The 17 year old drivers were tested within a few weeks of gaining their licences, and were compared with a set of 40 year old experienced adults. As they drove around a 2 mile test track several hazards were set-up for each driver a stop-sign that was not visible until an occluding van had been passed, and pedestrians appeared, who might or might not walk into the path of the driver. The experienced drivers were more likely to respond to the occluded stop-sign than the novices, and this finding was supported by more glances towards to the sign by the older drivers. Novices also looked at and responded to the pedestrians less frequently than did the experienced drivers. In a second study on the VTTI test track novices were observed shortly after gaining their licence and then again 6 months later (Olsen et al., 2007). The measures included eye fixations on the mirrors with and without a range of in-car secondary tasks such as operating the radio or a cell phone. In all measures new novices glanced into their mirrors less than experienced drivers, but after 6 months some 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 they were unaware of the need to monitor events behind their vehicle, or they were so over-loaded with the task of vehicle control that the collection of information about other road users was of secondary importance. A similar result came from one of the 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 use the more appropriate external right door mirror as much as experienced drivers when checking that a lane was clear as they moved into it. Recarte and Nunes (2000) recorded drivers glances at their mirrors while driving on a range of roads, and found that increases in mental workload acted to reduce the use of mirrors. This supports the idea that drivers restrict their scanning for roadway information when driving becomes more difficult. Scanning for hazards is necessary for interactions with other road users, but novices either do not understand the need to observe others and anticipate their actions, or they do not have the available resources to control their vehicle and simultaneously think about what might happen in a few seconds. These alternatives are not mutually exclusive, of cours, but when novice and experienced drivers watched movies filmed from a car travelling along the same roads, a similar pattern of scanning was seen as when they drove along the roads themselves (Underwood, Chapman, Bowden, & Crundall, 2002). The experienced drivers scanned a video of a demanding urban motorway more than the novices, suggesting that they were more aware of the potential dangers when travelling on such roads. A failure to scan the roadway at the most appropriate time was seen when moving into an outside lane on a dual-carriageway (urban motorway) in the Underwood et al. (2002) study with mirror inspections. This pattern was also seen in the eye movement study of novice and experienced drivers reported by Crundall and Underwood (1998) and Underwood et al. (2003). The measure of scanning used was the variance of fixation locations. High variance indicates greater distances between fixations, and the highest variance was found for experienced drivers on a dual-carriageway the same type of road where experienced drivers looked into their external door mirrors when making a lane-change manoeuvre (see Fig. 1). The variance for experienced drivers fluctuated according to road type, with low variance on relatively quiet roads containing few hazards, or other road users in predictable locations. On the dual-carriageway, however, other vehicles were making lane changes immediately in front of the driver, and there were slip roads entering the road from both sides. The experienced drivers 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 drivers behaved in just the same way as they did on the other roads. They tended to look straight ahead, focusing on maintaining their lane position, as reported by Mourant and Rockwell (1972). Experienced drivers, in contrast, showed sensitivity to the demands 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 around less than older drivers, and they do not inspect their mirrors selectively. These studies did not use staged hazards, unlike the

4 438 G. Underwood et al. / Transportation Research Part F 14 (2011) Fig. 1. The range of scanning three types of roads, by novice and by experienced drivers (data from Crundall & Underwood, 1998). VTTI, and we are inferring that when experienced drivers look around them more than the novices that this is a product of their enhanced situation awareness prompting them to search for developing hazards. Experienced drivers increase their roadway scanning at times of increased interaction with other drivers, however, and the most likely explanation is that they are anticipating the need to avoid conflict. In assessing the use of driving simulators we will consider the sensitivity of simulator-derived measures in demonstrating these same patterns of behaviour, and particularly the variations in inspection behaviour 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 ecological validity. Unfortunately the expense of such studies, and the practical difficulties in terms of participant safety and ethical considerations precludes the routine testing of driver performance in actual hazardous situations. The VTTI studies with staged hazards on a test track, and the Nottingham studies demonstrating enhanced scanning in experienced drivers on urban motorways are exceptions to this generalisation. The observation of novices is particularly important because it is likely that these drivers will have limited exposure to hazardous situations (Groeger & Clegg, 2000) and that any deficits in performance in such situations are likely to be directly implicated in the high accident rates of novice drivers. To explore performance in such situations researchers have often asked participants to watch videos of hazardous situations from the driver 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 attractions of using such hazard perception tests for understanding drivers visual search is that large numbers of participants can watch identical hazardous events and we can aggregate eye movement measures over participants and time. This lets us examine the way that visual search in hazardous situations differs from normal visual search when driving and to explore the possibility 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 button within each 200 ms time period. This video shows the view from a car driving through an urban environment in which a pedestrian abruptly steps out into the road about 10 s into the video. Later in the video (between 18 and 23 s) other pedestrians 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 viewers also press the response button at some point between 18 and 23 s, though these responses to the developing hazard are more spread out in time than those to the abrupt events. Fig. 2 also shows two measures of visual search the mean fixation duration, and the mean saccade amplitude. Both these measures are averaged in each 200 ms bin over the 85 participants and thus show typical changes in visual search and how they are sometimes related to hazards. It is very clear from the Figure that the first two hazard periods (at around 10 and 20 s) are closely associated with increases in average fixation duration. Long fixation durations are typically associated with high processing load and it thus makes sense to think that during these hazards viewers are spending longer extracting information from their point of gaze. It is important to note that the third 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 the viewers have already anticipated that he might step out. The increase in fixation duration therefore takes place in the anticipatory period where potential hazards are being assessed rather than when the actual hazard occurs. Chapman and

5 G. Underwood et al. / Transportation Research Part F 14 (2011) Fig. 2. Changes in mean saccade amplitude, mean fixation duration, and proportion of respondents pressing a hazard response button over the course of a 40 s video of hazardous driving (data from Chapman & Underwood 1998a). Underwood (1998a) averaged results across 13 different films and report an overall moment by moment correlation between fixation duration and hazard response probability of 0.457, indicating that the two variables are closely, though not inevitably linked. The remaining line in Fig. 2 is the mean saccade amplitude that is the distance between successive fixations. Where this is large it indicates that viewers are scanning widely, and where it is small it suggests that viewers are concentrating multiple fixations within a relatively small area. Although it is clear from Fig. 2 that saccade amplitude varies widely throughout the film, there is also evidence that it is reduced around the first two hazard periods. Across 13 videos Chapman and Underwood (1998a) found a significant but relatively small negative correlation between saccade amplitude and hazard responses of These findings, along with those using other spread of search measures such as horizontal and 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 certain types of hazard are associated with a general reduction in the spread of visual search, with hazardous events bringing about 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 been potentially identified it is clearly important that information in this region is processed in depth and there may be advantages to monitoring that location for further unfolding events. Nonetheless there is a potential danger in restricting search in such situations in that over-focusing attention on one region may prevent the viewer from noticing and processing potential hazards elsewhere in the environment. In this context it is interesting to look closer at the data from Chapman and Underwood (1998b). In this study we compared novice and experienced drivers viewing and responding to a series of hazard perception videos. Novice drivers were all tested within 3 months of gaining a full British driving licence, while experienced drivers had held a licence for between 5 and 10 years at the time of testing. As can be seen in Fig. 3, both groups showed clear reductions in spread of search, and increases in fixation duration during hazardous events, there was also an interesting interaction between experience and the presence or absence of a hazard. Novice drivers increased their fixation durations during hazards significantly more than more experienced drivers did. This is consistent with the idea that experienced drivers may have learned information about hazards that allows them to process them relatively quickly and resume their normal search strategies sooner than novice drivers can. A study by Chapman, Underwood, and Roberts (2002), in which novice drivers were trained in hazard anticipation and visual search, showed that such training was successful in increasing spread of 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 picture for older drivers is less clear. Although there have been suggestions that elderly drivers might be impaired in hazardrelated visual search tasks (e.g. Maltz & Shinar, 1999) larger studies such as Horswill et al. (2008) suggest that elderly drivers show only minor impairments in hazard perception ability. A study by Underwood et al. (2005) looked in detail at the visual

6 440 G. Underwood et al. / Transportation Research Part F 14 (2011) Fixation Duration ( msec ) Urban Suburban Rural Road type Hazard (Experienced) Hazard (Novice) Safe (Experienced) Safe (Novice) Fig. 3. Mean fixation durations while watching hazard perception movies on three types of roads, for novice and for experienced drivers (data from Chapman & Underwood, 1998a,b). search patterns of elderly drivers viewing hazard perception videos. Fig. 4 shows data from this study that clearly illustrate the typical increase in fixation durations around the hazard and show that it is of similar magnitude in older (61 76 years) Mean Fixation Duration (msec) Fixation Relative to Hazard Onset (HO) Fig. 4. Fixation durations recorded while younger and and older drivers watch hazard perception movies. The fixation at point HO is the first fixation recorded after hazard onset, with the previous four fixations and following four fixations also shown (data from Underwood, Phelps, Wright, van Loon, & Galpin, 2005).

7 G. Underwood et al. / Transportation Research Part F 14 (2011) and younger (31 44 years) drivers. Although older drivers may be impaired in some visual driving-related tasks it is possible that 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 of view 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 and may actually make it easier for participant to spot peripheral events than it might be in actual driving. The second problem is that some types of hazard cannot be realistically simulated using such a limited field of view. Thus any event where a vehicle pulls out from a junction would usually require the driver to engage in wide scanning incorporating significant head movements. Typically such events are simply not included in conventional hazard perception tests. A recent exception to this is the study by Shahar, Poulter, Clarke, and Crundall (2010) where they developed a three-screen hazard perception test. In this test a full 180 degree of visual angle was recorded from a moving vehicle and presented to people over three large video monitors subtending approximately 112 degree of visual angle during testing. They found that when all three screens were presented participants performed significantly better than when viewing was limited to a central screen subtending 42 degree of visual angle, even when the hazard occurred only on the central screen. This highlights a potential difficulty with video-based hazard perception testing in that hazards may be deliberately chosen in which spread of search has been artificially limited. If anything, we would predict that difference in search in hazards, and differences as a function of training and 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 and when watching hazard perception movies they increase their scanning when looking at more demanding roads. This pattern of 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 to invoke and assess hazard perception skill, there has been an increasing trend over the past decade to turn to simulation. As Boyle and Lee (2010) pointed out in a recent prologue to a special issue of Accident Analysis and Prevention on driving simulation, the average number of papers reporting the use of a driving simulator rose from 124 papers published between 1965 and 1999, to 572 papers in the subsequent decade (and that did not include the 25 papers they were introducing in their special issue). Many of these papers however are not to do with hazard perception per se, but instead focus on a variety of topics including the evaluation of new interfaces for entertainment systems (e.g. Garay-Vega et al., 2010); developing warning systems to combat fatigue (e.g. Vadeby et al., 2010); investigating the effects of alcohol or drugs on basic driving performance (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 hazard perception skill (Allen, Cook, & Park, 2005; Garay, Fisher, & Hancock, 2004; Garay-Vega & Fisher, 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 onroad study in terms of safety, cost and experimental control (Reed & Green, 1999). Certainly the ethical and pragmatic problems of placing drivers in hazardous situations in the real world render any such research difficult. While some researchers have used some ingenious methods to record hazard perception skill on public roads (e.g. Olson & Sivak, 1986), the use of foam rubber hazards is still ethically challenging, as well as being costly to set-up and limiting in the range of hazards that can be investigated. However all of these advantages of simulators can also be attributed to the video-based methodologies used in much hazard perception research. Clips of driving are relatively cheap to produce (and can be much cheaper than many high-fidelity simulators), usually only requiring a video camera on a suction mount. They certainly provide experimental 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 tend to 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 the most important is the addition of interactivity. Whereas a video-based hazard perception test typically collects only response times to the appearance of a hazard, simulators can record a much more complex behavioural response, including preparatory behaviours (slowing in anticipation of a hazard, changing lane position to avoid a potential hazard) as well as emergency manoeuvres to avoid the actual hazard. Speed, braking, steering angle and lane position can provide multiple measures on the approach to a hazard, providing a behavioural signature that not only indicates that a driver has spotted the hazard 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 groups of 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 they are thinking. Typically it is seen as a training tool reserved for advanced drivers and police drivers (primarily because the act

8 442 G. Underwood et al. / Transportation Research Part F 14 (2011) C 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). of producing a commentary can be quite demanding in itself), however in this particular study we identified benefits even for learner drivers when placed in a simulator. Fig. 5 shows the change in speed across two assessment drives in the simulator on the approach to the various hazards. The approach distance to the hazards is broken down into 10 m data bins, with lower numbers representing closer proximity to the hazard. While even the untrained group show a reduction in speed across the two assessments on the approach to the hazards, the reduction is much more marked in the group who received commentary training in-between the two assessments. Not only can we see a clear difference between the two groups we can also see when this difference manifests (around 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 a more realistic level of demand upon the visual system. Certain types of visual cues become extremely important for car handling, 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. Schieber, Schlorholtz, & McCall, 2009). Video-based hazard perception clips place no such demands on the viewer, and may therefore 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 move our eyes while on the road in a real driving situation. Konstantopoulos, Crundall, and Chapman (2010) have recently demonstrated some relative validity between on-road eye movements and those evoked within a simulator. They had two hypotheses derived from previous on-road and video-based studies. 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 found that the more experienced drivers had a more efficient search strategy, with frequent short fixations that ranged across a wider horizontal area than those of the learner drivers (see Fig. 6). The learners tended to produce longer fixations that were more tightly clustered around the centre of the display. The second hypothesis was that visual efficiency would degrade under deteriorating visibility conditions. They found that simulated driving through rain or at night tended to increase the length of fixation durations (suggesting increased processing difficulty due to reduced visibility), though the spread of search was not impacted. Konstantopoulos et al. argued, at least in regard to the experiential differences, that this suggested the simulator had relative validity in that differences noted on real roads are also apparent in the simulator. Some researchers have gone even further however, claiming absolute validity between on-road and simulated drives, albeit within very tight constraints (Shechtman, Classen, Awadzi, & Mann, 2009). Shechtman et al., compared the number and type of errors made when turning right or left at a junction (including visual scanning errors). They reported no differences between the on-road and 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 simulator 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 across three periods of time: before a hazard (essentially a safe period), on approach to a hazard (when the source of the hazard might be visible though the hazard has not yet triggered), and during the hazard (the time window during which the hazard

9 G. Underwood et al. / Transportation Research Part F 14 (2011) Fig. 6. The fixation locations of one driving instructor and one learner across a visual display during a section of daytime driving (top panel). The bottom panel reflects the flattened display that the participants were seeing at the time (taken from Konstantopoulos et al., 2010). becomes apparent and the driver is required to make some form of evasive manoeuvre. As can be seen in Fig. 7, attention appears to be captured on the approach to the hazard. During this time window fixation durations are at their longest, while saccade amplitude and spread of search are significantly decreased. This fits with our previous work using video-based hazards (e.g. Chapman & Underwood, 1998a, 1998b) that demonstrated a similar decrease in search and a corresponding increase in fixation duration in the presence of a hazard. However it also provides a further insight: attentional capture is at its greatest prior to the hazard being triggered. During the actual hazard window, participants appear to recover somewhat 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 that the anticipated manoeuvre (e.g. changing lanes to avoid the hazard) does not cause conflict with other road users. This effect

10 444 G. Underwood et al. / Transportation Research Part F 14 (2011) Fixation durations (ms) Measures of spread (degrees) Before Hazard Approaching Hazard During Hazard 2.5 Fixation durations Saccadic amplitude Spread of horizontal search Fig. 7. Eye movement measures taken from participants driving through a series of hazardous scenarios in a driving simulator. The measures are (a) mean fixation durations, (b) mean saccadic amplitude, and (c) mean spread of search (adapted from Chapman et al., 2007). is unlikely to become apparent during video-based presentations that merely require a push button response. Thus it seems that while our data reflect those obtained using video-based methodologies, they also show differences that can be explained by 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 his colleagues based in the University of Massachusetts. Many of their studies have participants drive through a series of potentially 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 areas of the driving scene that the experimenters have defined a priori as providing vital cues to the successful navigation a potentially hazardous scenario. For instance, Pradhan et al. (2005) had three groups of drivers navigate through 16 potentially hazardous situations in a driving simulator. Learners fixated the a priori hazard sources 35% of the time, experienced drivers fixated the same regions 50% of the time, and highly experienced drivers fixated these areas 66% of the time. In work we have recently completed (Crundall et al., 2010) we have also noted that learner drivers are less likely to fixate certain types of 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 studies using video-based methodologies is still apparent. We have already reported on the inconsistencies of the video-based hazard perception research to show replicable effects across different research groups, and it seems that the use of simulators does not necessarily improve this. For instance Liu, Hosking, and Lenné (2009), and Shahar et al. (2010) used the same motorcycle simulator yet while Lui et al. found discriminatory effects of the simulated hazards, Shahar et al. did not. Crucially 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 seems that some simulated hazards are better than others. This problem however identifies what we believe is the ultimate strength of the driving simulation in regard to hazard perception. We need to identify why some hazards discriminate between good and bad drivers but others do not. There are likely to be many reasons, but one noted by Shahar et al. (2010) and Garay-Vega et al. (2007) is the need for the hazard to be foreshadowed in some manner. Garay-Vega et al. (2007) use this term to describe a non-hazardous event that might draw one s attention to the subsequent hazardous event. For instance, seeing pedestrians crossing the road ahead might draw one s attention to the fact that a parked truck is obscuring a pedestrian crossing. Thus the driver is more likely to be alert to the sudden emergence of pedestrians upon reaching the truck. We prefer a more immediate foreshadowing element, which we term the precursor to the hazard (before the pedestrian steps into the road they are a precursor; Crundall et al., 2010). Essentially however both terms refer to a cue that experienced drivers could perhaps recognise and use to prepare for a potentially hazardous event. It is likely however that precursors will differ in their discriminability. If a precursor is too obvious then both good and bad drivers may benefit to an equal degree (Garay-Vega et al., 2007), while a lack of precursors will lead both experienced and novice drivers to be equally surprised by the onset of a hazard (Shahar et al., 2010).

11 G. Underwood et al. / Transportation Research Part F 14 (2011) Conclusion There is comparability, then, between driving behaviour on the road, while watching hazard perception movies, and in a driving simulator. Experienced drivers search the roadway more and they have shorter eye fixation durations than less experienced drivers. This is only relative validity in the sense used by Godley et al. (2002), in that we cannot create the same hazardous situations on a road as can be programmed in a simulator. Absolute validity would involve the same scenarios being used on the road and in the simulator, and the same responses recorded in each. The validity of the simulator is established here by the observation of similar patterns of behaviour in both experienced and novice drivers. In addition to comparing perceptual-motor skills associated with speed and lane-keeping when assessing driving simulator validity, we recommend that cognitive skills are also assessed, and we suggest that hazard perception is a suitable candidate for inclusion any battery of validity tests. Although we are satisfied that driving simulators can demonstrate similar patterns of driver differences as can be seen on actual roads and when watching hazard perception movies, further experiments are required to investigate the conditions under which hazards can discriminate between driver groups. For a thorough investigation of this, hazardous scenarios need to 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?). 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