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18 (12 pages)


A PC-based training program (Road Awareness and Perception Training or RAPT; Pradhan et al., 2009), proven effective for improving young novice drivers' hazard anticipation skills, did not fully maximize the hazard anticipation performance of young drivers despite the use of similar anticipation scenarios in both, the training and the evaluation drives. The current driving simulator experiment examined the additive effects of expert eye movement videos following RAPT training on young drivers' hazard anticipation performance compared to video-only and RAPT-only conditions. The study employed a between-subject design in which 36 young participants (aged 18-21) were equally and randomly assigned to one of three experimental conditions, were outfitted with an eye tracker and drove four unique scenarios on a driving simulator to evaluate the effect of treatment on their anticipation skills. The results indicate that the young participants that viewed the videos of expert eye movements following the completion of RAPT showed significant improvements in their hazard anticipation ability (85%) on the subsequent experimental evaluation drives compared to those young drivers who were only exposed to either the RAPT training (61%) or the Video (43%). The results further imply that videos of expert eye movements shown immediately after RAPT training may improve the drivers' anticipation skills by helping them map and integrate the spatial and tactical knowledge gained in a training program within dynamic driving environments involving latent hazards. © 2018 by the authors.


This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.

Original Publication Citation

Yamani, Y., Bicaksiz, P., Palmer, D. B., Hatfield, N., & Samuel, S. (2018). Evaluation of the effectiveness of a gaze-based training intervention on latent hazard anticipation skills for young drivers: A driving simulator study. Safety, 4(2), 18. doi:10.3390/safety4020018


0000-0001-8990-0010 (Yamani), 0000-0002-4876-2819 (Palmer)