A Data-Driven Autonomous Driving System for Overtaking Bicyclists

A Data-Driven Autonomous Driving System for Overtaking Bicyclists

Brian Lin Headshot
Brian Lin
Shan Bao Headshot
Shan Bao
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Principal Investigator(s):

Brian Lin, 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

Project Abstract:
Many bicyclists share the roadway with motor vehicles that drive much faster. Once an accident occurs with bicyclists involved, the death rate of the bicyclist is extremely high. To date there is no mature and reliable technology that helps drivers overtake bicyclists safely and can be as well as accepted by bicyclists. This study follows a systematic method to develop a prototype for an automated overtaking system, specifically for overtaking bicyclists. Naturalistic driving data based on pre-extracted overtaking events with more other critical factors will be mined to create three models that covers four phases of an overtaking: approaching, overtaking, passing, and returning. These models will then be implemented as an automated 2 overtaking prototype to a simulated platform for a motor vehicle to overtake bicyclists based on different strategies. An experiment of human study will be conducted to evaluate the prototype from both the viewpoints of the driver and the bicyclist that how they want to overtake and be overtaken safely. It is expected that the outcomes can offer the OEMs and suppliers who are keen on developing safe and human-centered automated vehicle systems with useful insights.
Furthermore, the insights can be helpful for legislation on the act or guidelines of protecting on-road vulnerable bicyclists.

Research Thrust(s): Human Factors, Modeling & Implementation

Project Form(s):
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