Bicyclists and motor vehicles share the same roads, yet there is currently no reliable technology available that assists drivers in safely overtaking bicyclists while also being acceptable to bicyclists themselves. Additionally, there is a lack of clarity regarding the critical factors involved in overtaking, as perceived by the various stakeholders.

To address these issues, this study aimed to develop computational decision-making models for car-to-bike overtaking and assess relevant factors influencing this overtaking behavior. The models considered the presence of oncoming traffic and designated bike lanes. An experiment was conducted using simulation technology, gathering subjective assessments from both drivers and bicyclists. The findings revealed disparities in satisfaction and perception of different overtaking scenarios between drivers and bicyclists.

Furthermore, the study identified significant factors influencing their subjective ratings and investigated the reasons behind these inconsistencies. The research emphasizes the importance of considering the perspectives of both drivers and bicyclists when developing car-to-bike overtaking features. The insights gained from this study will contribute to the establishment of guidelines aimed at protecting vulnerable bicyclists on the road.

About this research: https://ccat.umtri.umich.edu/research/u-m/a-data-driven-autonomous-driving-system-for-overtaking-bicyclists/
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Speaker Bios:
Dr. Brian Dr. Lin earned his BS, MS, and Ph.D. in Industrial Engineering from National Tsing Hua University in Taiwan. Dr. Lin has 11 years of experience in automotive human factors research at UMTRI after his Ph.D. His current research is focused on mining naturalistic driving data using statistical and machine-learning methods, driver-assist-system evaluation, driver performance and behavior assessment, and driver distraction and workload mitigation. His most recent work includes human driver’s lane-change maneuvers, drivers’ decisions at intersections, and passengers’ motion discomfort in moving vehicles. Dr. Lin has much experience in conducting experiments to evaluate advanced automotive systems, including auto-braking, lane departure, driver-state monitoring, electronic head units, car-following and curve-assist systems on L2 automation, and lane-change and intersection assist on L3 automation on public roads, test tracks, or simulation. He is familiar with the methods to investigate driver distraction, workload, and human-machine interaction with in-vehicle technologies and safety features. He serves as a peer reviewer for Applied Ergonomics, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Intelligent Vehicles, Transportation Research Part F, and Transportation Research Record.

Dr. Shan Bao earned her Ph.D. in mechanical and industrial engineering from the University of Iowa in 2009. Dr. Bao has led multiple, large, simulator and naturalistic-driving studies for industry and government sponsors. Her areas of expertise include the statistical analysis of crash datasets and naturalistic data, vulnerable road user safety, experimental design, algorithm development to identify driver states and movement, evaluation of driving-safety technologies, measurement of driver performance, driver decision-making, and statistical and stochastic modeling techniques. She has given multiple keynote speeches and served on expert panels at different conferences or meetings. She has also made technical presentations on scientific project results at many international conferences with a wide range of audiences. Dr. Bao is the author of recent IEEE e-learning course of “Human Factors in Automated Vehicles”.