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Webinar Recording

Title:Impact of Connected Vehicle Technology on Traffic Safety under Different Highway Geometric Designs
Date Recorded:Apr 1, 2021
Project #:MPC-590
Report #:MPC-22-484

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To mitigate crash severity and reduce crash rate, advanced technologies such as connected automated vehicles (CAVs) have shown great potentials in preventing human driving errors. Using real-time data collected via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication platforms, CAV technology can greatly support many types of in-vehicle safety applications, such as forward collision warning (FCW), intersection movement assist (IMA), blind-spot warning (BSW), lane changing warning (LCW), do not pass warning (DNPW), and control loss warning (CLW). The intent of this study is to develop a system that can integrate CAV data and traffic sensor information to concurrently address the need to improve urban arterial safety and mobility. Under the mixed traffic pattern of CAVs and human-driven vehicles (HVs), the system aims to achieve three primary objectives: proactively preventing rear-end collisions, reactively protecting side-street traffic from red-light-running vehicles, and effectively facilitating speed harmonization along local arterials.

NDSU Dept 2880P.O. Box 6050Fargo, ND 58108-6050