Evaluation of the Effectiveness of a Gaze-Based Training Intervention on Latent Hazard Anticipation Skills for Young Drivers: A Driving Simulator Study

Abstract

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.

An Expert Derived Feedforward Histology Module Improves Pattern Recognition Efficiency in Novice Students

“…The task performance findings are not consistent with the results in previous studies evaluating the efficacy of feedforward training in other digital or simulated environments, (Sadasivan et al, ; Nalanagula et al, ; Litchfield et al, ; Yamani et al, ). In these studies, novices who received the expert eye gaze‐based feedforward training performed the subsequent tasks at a significantly higher accuracy.…”

“…In these studies, novices who received the expert eye gaze‐based feedforward training performed the subsequent tasks at a significantly higher accuracy. Additionally, in studies of Sadasivan et al () and Yamani et al (), no significant difference in the eye movement parameters between the experimental and control group was observed. In comparison, the feedforward trained students in the present study utilized more controlled, efficient search patterns indicated by fewer, longer fixations, but in the same time and with the same accuracy as the students in the control group.…”

“…On subsequent defect identification tasks, the group that was provided with feedforward training performed the tasks with greater accuracy. Additionally, the incorporation of expert eye movements into training has been found to improve performance in printed computer board (PCB) defect identification tasks (Nalanagula et al, ) and improve hazard identification anticipation abilities in young drivers (Yamani et al, ). In the more relevant field of radiology, a feedforward training study has provided evidence that viewing the eye movements of an expert radiologist when searching for pulmonary nodules on an X‐ray improves subsequent diagnostic performance of novices on other radiographs (Litchfield et al, ).…”

Effects of Inaccurate Gaze Behavior on Young Drivers’ Hazard Anticipation

“…This video was shown to the RAPT-EV group. For the RAPT-NV group, an eye tracking video of a participant in Yamani et al (2018) who failed to anticipate all the hazards (did not correctly glance at the target zones while in the launch zone) was used.…”

“…In a previous study, Yamani et al (2018) examined the effects of showing young drivers how experts distribute their visual attention when navigating a dynamic driving scenario. Young drivers completed RAPT and drove four unique simulated driving scenarios with one hazard anticipation scenario each.…”