In-Vehicle Dynamic Curve Speed Warnings at High-Risk Rural Curves
Thursday, March 29, 2018
2:30 p.m. – 3:30 p.m. Central
Mechanical Engineering Building
University of Minnesota
111 Church Street SE
About the Presentation
Lane-departure crashes at horizontal curves represent a significant portion of fatal crashes on rural Minnesota roads. Because of this, solutions are needed to aid drivers in identifying upcoming curves and inform them of a safe speed at which they should navigate the curve. One method for achieving this that avoids costly infrastructure-based methods is to use in-vehicle technology to display dynamic curve-speed warnings to the driver. Such a system would consist of a device located in the vehicle capable of providing a visual and auditory warning to the driver when approaching a potentially hazardous curve at an unsafe speed.
This presentation will discuss a project that explored the feasibility of in-vehicle dynamic curve-speed warnings as deployed on a smartphone app. The system was designed to maximize safety and efficacy to ensure that system warnings are appropriate, timely, and non-distracting to the driver. The developed system was then evaluated by 24 Minnesota drivers in a controlled pilot study at the Minnesota Highway Safety and Research Center in St. Cloud, Minnesota.
The results of the pilot study showed that, overall, the pilot study participants liked the system and found it useful. Analysis of quantitative driver behavior metrics showed that when receiving appropriately placed warnings, drivers navigated horizontal curves 8-10 percent slower than when not using the system. These findings show that such a curve-speed warning system would be useful, effective, and safe for Minnesota drivers.
About the Speaker
Brian Davis is a research fellow in the Intelligent Vehicles Laboratory at the University of Minnesota. His research focuses on the development of enabling technologies for intelligent vehicle applications. This has included the development and integration of systems including GPS/GNSS, cellular networks, LIDAR, RADAR, embedded computing, machine learning and computer vision for applications in roadway mapping, driver assist and lane-departure warning, vehicle tracking, work-zone safety, and connected vehicles.
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