A thousand years of crash experience in three hours: An online hazard perception training course for drivers.

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.

The development and validation of video-based measures of drivers' following distance and gap acceptance behaviours.

The distance at which drivers follow other vehicles has been found to be linked to crash risk. Tailgating (i.e. driving at an unsafe following distance) is both endemic and a leading cause of rear-end crashes. Similarly, drivers' decisions about when to merge with a stream of traffic are likely to influence crash risk. Consistent with this, it has been shown that crashes are more common at intersections where drivers more frequently have to slow for vehicles pulling out into insufficient gaps. Therefore, the development of reliable and valid measures of both of these driving behaviours would facilitate further crash prevention research. Given the problems associated with assessing these behaviours during real driving, we developed new video-based measures. In our new following distance measure, participants view videos shot from the perspective of a driver who is following another vehicle at a range of distances across a variety of traffic environments. On each trial, participants report their own minimum comfortable following distance relative to the following distance depicted in the video. In our new test of gap acceptance behaviour, participants view a series of video clips and indicate when they would pull out into the approaching stream of traffic shown in each clip. The two new measures each yielded reliable data, and we found that young drivers made riskier choices than older drivers for both following distance and gap acceptance. These age-related differences are consistent with those found in observational studies of real driving, supporting the proposal that the new tests could potentially be used as proxies for these crash-related driving behaviours in both lab-based research and large-scale online studies.