Human-centered testing of rear-facing display to reduce vehicle collisions with snowplows
Principal Investigator(s):Nichole Morris, HumanFIRST Lab Director, Mechanical Engineering
- Rajesh Rajamani, Professor, Chair, Mechanical Engineering
Ensuring clear and safe roads during the winter is a key priority for Minnesota; however, the required process of plowing roads has inherent risks given slick road conditions, plow-vehicle speed differentials, and growing issues of risky driver behavior (e.g., speeding, following too closely). The winter of 2021-2022 saw an uptick of crashes with over 60 instances of snowplows being struck by members of the public, with many of these being rear-end collisions at night. A potential countermeasure against rear-end crashes of this nature is to enhance the conspicuity of snowplows through enhanced lighting and retroreflective markings. However, adding additional audiovisual warning systems may be required to capture the attention and earlier response time for inattentive or reckless drivers. This project aims to:
1) Examine various lighting configurations and patterns of rear lights on snowplows to maximize driver recognition and responsiveness at night.
2) Develop a rear-facing radar-based system to detect and warn drivers approaching snowplow at unsafe speeds (i.e., time to collision measurements).
3) Conduct a pilot study of snowplows equipped with sensing system to determine the success of the adapted lighting and warning system to encourage safe approach speeds and following distances.