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Highlights • Multiple methods were used to demonstrate the validity of a hazard perception test. • Scores yielded experience-level differences and correlated with an existing measure. • Crucially, scores predicted the frequency of heavy-braking during real driving. • Heavy-braking events had been measured using low-cost consumer-level dashcams. • Such devices are therefore a viable means of measuring real-world driving behaviour. Abstract Computer-based hazard perception tests are used in a number of countries as part of the driver licensing processes, and hence evaluating the validity of such tests is crucial. One strategy for assessing the validity of the scores generated by a hazard perception test is to determine whether they can predict on-road driving performance. Only a few prior studies have attempted this, all relying on the subjective ratings of an examiner who was present during a single brief drive and was not blind to the driver's demographic characteristics, potentially contaminating the outcomes. Additionally, only one such study focused on the most relevant participant group with respect to the validity of tests used in licencing processes, namely young drivers. We sought to remedy this situation in the present project by measuring young drivers' performance over an extended period of everyday driving via g-force triggered video cameras ("dashcams") installed in their own vehicles. As a precursor to the dashcam study itself, we developed a new computerized hazard perception test and assessed the validity of its scores by more traditional means (Study 1). As expected, test scores distinguished between high-risk and lower-risk driver groups, and correlated with scores on an established hazard perception test previously shown to predict crash risk. In the subsequent dashcam study (Study 2), the frequency of heavy-braking events (controlling for distance driven) was used as a more objective measure of driving performance. Results indicated that drivers with higher rates of heavy braking had slower hazard perception response times, further supporting the use of these scores as a valid measure of drivers' ability to exercise hazard perception skill during real driving. More generally, this study also demonstrates the viability of using low-cost off-the-shelf dashcams to measure real-world driving behaviour. [ABSTRACT FROM AUTHOR]

Drivers’ hazard perception ability has been found to predict crash risk, and novice drivers appear to be particularly poor at this skill. This competency appears to develop only slowly with experience, and this could partially be a result of poor quality performance feedback. We report an experiment in which we provided high-quality artificial feedback on individual drivers’ performance in a validated video-based hazard perception test via either: (1) a graph-based comparison of hazard perception response times between the test-taker, the average driver, and an expert driver; (2) a video-based comparison between the same groups; or (3) both. All three types of feedback resulted in both an improvement in hazard perception performance and a reduction in self-rated hazard perception skill, compared with a no-feedback control group. Video-based and graph-based feedback combined resulted in a greater improvement in hazard perception performance than either of the individual components, which did not differ from one another. All three types of feedback eliminated participants’ self-enhancement bias for hazard perception skill. Participants judged both interventions involving video feedback to be significantly more likely to improve their real-world driving than the no feedback control group. While all three forms of feedback had some value, the combined video and graph feedback intervention appeared to be the most effective across all outcome measures. [ABSTRACT FROM AUTHOR]

The current study aims to obtain knowledge about the nature of the processes involved in Hazard Perception, using measurement techniques to separate and independently quantify these suspected sub-processes: Sensation, Situation Awareness (recognition, location and projection) and decision-making. It applies Signal Detection Theory analysis to Hazard Perception and Prediction Tasks. To enable the calculation of Signal Detection Theory parameters, video-recorded hazardous vs. quasi-hazardous situations were presented to the participants. In the hazardous situations it is necessary to perform an evasive action, for instance, braking or swerving abruptly, while the quasi-hazardous situations do not require the driver to make any evasive manoeuvre, merely to carry on driving at the same speed and following the same trajectory. A first Multiple Choice Hazard Perception and Prediction test was created to measure participants’ performance in a What Happens Next? Task. The sample comprised 143 participants, 47 females and 94 males. Groups of non-offender drivers (learner, novice and experienced) and offender drivers (novice and experienced) were recruited. The Multiple Choice Hazard Perception and Prediction test succeeded in finding differences between drivers according to their driving experience. In fact, differences exist with regard to the level of hazard discrimination (d′ prime) by drivers with different experience (learner, novice and experienced drivers) and profile (offenders and non-offenders) and these differences emerge from Signal Detection Theory analysis. In addition, it was found that experienced drivers show higher Situation Awareness than learner or novice drivers. On the other hand, although offenders do worse than non-offenders on the hazard identification question, they do just as well when their Situation Awareness is probed (in fact, they are as aware as non-offenders of what the obstacles on the road are, where they are and what will happen next). Nevertheless, when considering the answers participants provided about their degree of cautiousness, experienced drivers were more cautious than novice drivers, and non-offender drivers were more cautious than offender drivers. That is, a greater number of experienced and non-offender drivers chose the answer “I would make an evasive manoeuvre such as braking gradually”. [ABSTRACT FROM AUTHOR]

In 2008, the state of Queensland in Australia introduced a video-based hazard perception test as part of the licensing process for new drivers. A key validity check for such a test is whether scores are associated with crash involvement. We present data demonstrating that drivers who failed the hazard perception test (based on a ROC curve-derived pass mark) were 25% [95% confidence interval (CI) 6%, 48%] more likely to be involved in an active crash (defined as a crash occurring while the driver’s vehicle was moving but they were not engaged in parking or reversing) during a one year period following the test (controlling for driving exposure, age, and sex). Failing drivers were also 17% (95% CI 6%, 29%) more likely to have been involved in active crashes prior to the test, in the period since obtaining their provisional license. These data support the proposal that the hazard perception test is a valid measure of crash-related driving performance. [ABSTRACT FROM AUTHOR]

• The present study frames expert and non-expert drivers' situation awareness (SA) within the Perceptual Cycle Model. • SA is assessed along with driver gaze behaviour to correlate behavioural and verbal report measures. • Both verbal counts and fixation-related gaze are found to reliably differentiate expert and non-expert drivers. • Differences in expert and non-expert gaze behaviour informs an understanding of expert SA. Efforts to reduce road crash rates depend on a clear understanding of the factors that contribute to driver risk. Not all drivers are at equivalent risk. It is critical to identify the factors that contribute to the development of expertise in the driving environment. The rapid development of a driver's situation awareness (SA) is central to the safe performance of the driving task. Therefore, SA must be clearly operationalised in order to better assess its role in the development of expertise. This study employs an existing scheme based on the Perceptual Cycle Model (PCM) used for post hoc incident analysis and adapts it to the driving context. We attempted to correlate performance on coded verbalisations indicative of SA with non-invasive objective gaze metrics. Gaze metrics and the verbal counts were shown to differentiate between both expert and experienced (non-expert) drivers, but these measures failed to correlate with one another. Findings indicate differences in the way expert and experienced drivers update their schema of the driving task, with equivalent effort required to do so. The novel adaptation demonstrated in this paper allows for a domain-specific assessment of SA which reliably differentiates between drivers of varying expertise levels. Although selected gaze metrics were shown to be inadequate predictors of SA, additional analysis demonstrated key differences in gaze content. Combined, these findings enhance an understanding of expert SA development contributing to reduced crash risk. [ABSTRACT FROM AUTHOR]

• Learner drivers completed a 6-week online hazard perception training course. • Parents supervised the online training and real-world transfer exercises. • Both learners and parents had improved hazard perception skill after training. • Learners demonstrated safer following distance preferences after training. • All learners and parents who completed the course reported safer real driving. We previously found that a six-session online hazard perception training course, which incorporates evidence-based learning strategies and footage of over a hundred real crashes, improved hazard perception skill and reduced risk-taking intentions in novice drivers who had passed their on-road driving test within the previous three years. However, one issue with targeting crash-prevention training at individuals who are already driving unsupervised is that drivers are at their highest crash risk immediately after they pass their on-road driving test. That is, the training may arrive too late to protect drivers while they are at their most vulnerable. It is also possible that it may prove difficult to persuade drivers to complete an unsupervised training course if they are already licensed to drive independently. Given that learner drivers cannot drive unsupervised, and that they are typically supervised by a parent, one potential strategy is to target the training at learners and to ask their parents to provide one-on-one mentoring throughout the course. We therefore recruited learner driver/parent-supervisor dyads to participate in a randomized control study, with the objective of examining the effects of the hazard perception training course on aspects of driving behaviour associated with crash risk (as measured using validated computer-based tests). Outcome measures included two hazard perception skill assessments (a response time hazard perception test and a verbal response hazard prediction test), and three tests assessing aspects of risk-taking propensity in driving (speed choice, following distance, and gap acceptance). Learners who completed the course (N = 26) significantly improved their scores on both hazard perception skill measures, and also chose safer following distances, compared with a waitlist control group (N = 23). However, the training did not significantly reduce learners' speed choice or gap acceptance propensity. The hazard perception skill of parent-supervisors, who observed the course but did not complete it, also improved on both hazard perception measures, relative to controls. Additionally, both learners and their parent-supervisors reported a range of positive effects on the learners' real-world driving performance. These results suggest that this type of hazard perception training could be beneficial if deployed during the learner phase of driver licensing. [ABSTRACT FROM AUTHOR]

• We created a 6-week video-based online hazard perception training course for drivers. • The course incorporated evidence-based training methods and >100 real crash videos. • Trainees' scores improved on validated hazard perception and hazard prediction tests. • The course led to safer vehicle following distance preferences in a video-based test. • All trainees reported improvements in their on-road driving safety after training. A key goal of driver training is to teach drivers to avoid crashes. However, in traditional driver training, drivers are unlikely to see even a single example of the class of event that we want them to learn to avoid. We developed a six-session automated online hazard perception training course for drivers, which incorporates a range of evidence-based strategies and employs extensive video footage of real crashes. We evaluated this course in a randomized control trial by examining its effects on previously-validated computer-based measures of hazard perception, hazard prediction, speed choice, following distance, and gap acceptance propensity, as well as self-rated measures of driver skill, safety, and real world transfer. We found that the course resulted in significant improvements in hazard perception response time and hazard prediction scores, and significantly longer vehicle following distances. Additionally, all participants in the trained group reported that their real world driving behaviour had improved. No significant training effects were found for the other measures. The results suggest that the course can improve key behaviours associated with crash risk. [ABSTRACT FROM AUTHOR]