Drivers’ Reaction to Connected and Automated Vehicle Safety Applications in the Vicinity of a Work Zone: A Driving Simulator Study

Highway safety is still a concerning issue despite different safety strategies, improvements in infrastructure, and in-vehicle technologies. Connected and automated vehicle (CAV) technology offers the possibility of improving highway safety through data exchange. Using a driving simulator and an eye-tracking system, this study investigates drivers’ behavior while influenced by certain CAV functions near a work zone. The variables obtained from the driving simulator computer include driving speed, acceleration/deceleration, and takeover reaction time (ToRt) when approaching work zone and driving through a work zone with and without CAV capabilities are compared. The eye-tracking heat map generated with gaze data acquired from eye-tracking glass proves that all participants noticed the takeover request (TOR) and responded accordingly. The analysis of variance (ANOVA) results indicate that the driving performance in two scenarios is different within the work zone and a buffer zone segment. The jerk analysis results suggest that the participants tend to drive with a lower frequency of acceleration and deceleration rate in the base scenario that has no autonomous feature in comparison to CAV scenario. Regression analysis shows that participants who drive more mileages annually have a shorter ToRt. Interestingly, ToRt decreases by age, and participants who trust CAV features have longer ToRt. The overall results suggest that CAV feature is capable of completing the primary intention of reducing speed in the vicinity of work zone to improve safety.


  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 274-285
  • Monograph Title: International Conference on Transportation and Development 2023: Transportation Safety and Emerging Technologies

Subject/Index Terms

Filing Info

  • Accession Number: 01902296
  • Record Type: Publication
  • ISBN: 9780784484876
  • Files: TRIS, ASCE
  • Created Date: Dec 15 2023 8:49AM