CAV-Based Intersection Maneuver Assist Systems (CAVIMAS) and Their
Impact on Driver Behavior, Acceptance, and Safety
Anuj K. Pradhan, Assistant Research Scientist – The University of Michigan Transportation Research Institute
Shan Bao, Associate Research Scientist – The University of Michigan Transportation Research Institute
Associate Professor – The University of Michigan-Dearborn
Heejin Jeong, Postdoctoral Research Fellow – The University of Michigan Transportation Research Institute
40% of the estimated 5.8 million crashes in the US in 2008 were intersection-related, with most of these having driver-related reasons attributed as the critical reasons for the crashes. Most of these human-related reasons have the potential to be mitigated by leveraging thoughtful deployments of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) safety management solutions in tandem with human factors based interventions relating to the content and presentation of such solutions. This project designed and experimentally evaluated a conceptual system – Connected and Automated Vehicle based Intersection Maneuver Assist Systems (CAVIMAS) – aimed at assisting drivers with intersection maneuvers by leveraging connected infrastructure and providing real-time guidance and warnings, and active vehicle controls. This was undertaken in an advanced driving simulation environment, and the system was evaluated via a user study to investigate drivers’ interactions with such systems, including their perceptions, acceptance, and trust-related behaviors.
Research Thrust(s): Enabling Technology