Autonomous Vehicle Overtaking: Modeling and an Optimal Trajectory Generation Scheme

Yu Yamada, Abu Saleh Md Bakibillah, Kotaro Hashikura, Md Abdus Samad Kamal and Kou Yamada
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Yu Yamada: Graduate School of Science and Technology, Gunma University, Kiryu 376-8515, Japan
Abu Saleh Md Bakibillah: School of Engineering, Monash University, Bandar Sunway 47500, Malaysia
Kotaro Hashikura: Graduate School of Science and Technology, Gunma University, Kiryu 376-8515, Japan
Md Abdus Samad Kamal: Graduate School of Science and Technology, Gunma University, Kiryu 376-8515, Japan
Kou Yamada: Graduate School of Science and Technology, Gunma University, Kiryu 376-8515, Japan

Sustainability, 2022, vol. 14, issue 3, 1-14

Abstract: Traffic congestion or accidents may occur as a consequence of the difficulty of performing a safe, comfortable, and efficient overtaking in a timely manner when there is a slow or stopped vehicle, cyclist, or partial lane blockage on the road. Specifically, most drivers find it challenging to overtake a sluggish vehicle on a single-lane road in the presence of vehicles coming from other directions. To resolve such overtaking concerns, this paper proposes a novel optimal trajectory generating scheme for autonomous vehicle overtaking that is both smooth and safe and can be used in a variety of traffic scenarios. The proposed scheme is based on the solution of an optimal predictive problem with the goal of minimizing driving costs while limiting collision risks in the presence of any opposite vehicle on the overtaking lane. The computational burden of the scheme is almost negligible and can be implemented in real-time. The scheme is evaluated in a variety of traffic conditions, including stopped and slow vehicles in the lane, as well as the presence or absence of a nearby opposite vehicle. The simulation results show that the proposed scheme effectively obtains the optimal trajectories even in the difficult overtaking contexts considering various constraints imposed by the road curve, opposite vehicles, and slow preceding vehicles. Finally, the optimal overtaking costs are obtained for various states of the associated vehicles, which provide an indication of the best state to initiate the overtake. The proposed technology can be employed as a fully automated system or an advanced driver assistance system (ADAS) to improve the vehicle flows at challenging driving conditions and enhance transportation sustainability.

Keywords: autonomous vehicle overtaking; collision avoidance; driving costs; optimal trajectory generation; traffic accidents (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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