Introduction: Novice drivers’ inability to appropriately anticipate and respond to hazards has been implicated in their elevated crash risk. Our goal was to develop a driving hazard prediction task using naturalistic videos from the U.S. context that could distinguish between novice and experienced drivers. Method: Using the query builder from the SHRP 2 InSight Data Access Website, we identified a sample of 1034 videos for further review. Task criteria reduced these to 30 videos of near-crash events that were split into event and non-event segments and were used to develop the driving hazard prediction task (task). Participants, aged 16–20 years-old (22 novice and 19 experienced drivers) completed the task during which they watched event and non-event videos and were asked, “How likely was the driver of this car to get into a crash?” after each video. Overall ratings for hazardousness were calculated for experienced and novice drivers as well as a group difference score for hazardousness. Results: All participants rated event videos as more hazardous than non-event videos, but there was no main effect of group. Rather, there was a significant EventbyGroup interaction in which there were no group differences in hazard ratings for non-event videos, but experienced drivers rated event videos as more hazardous than novice drivers. Specific characteristics of the event videos, such as the hazard development period, were related to differences between novice and experienced drivers’ hazardousness ratings. Conclusion: To the best of our knowledge, this is the first use of naturalistic driving videos from an existing database as experimental stimuli. We found that the task discriminated between novice and experienced drivers’ ratings of hazardousness. This distinction suggests naturalistic driving videos may be viable stimuli for experimental studies. Practical Applications: The application of naturalistic driving video database for experimental research may hold promise.
- Hazard detection
- Teenage driver
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality