Citation

Zhang Z, Yan X, Wang H, Ding C, Xiong L, Hu J. Transp. Res. C Emerg. Technol. 2023; 156: e104363.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.trc.2023.104363

PMID

unavailable

Abstract

This research proposes an integrated behavioral and motion planner with proactive right-of-way acquisition capability. The proposed planner overcomes the shortcomings of conventional planner and is able to: (i) integrate both behavioral and motion planning for enhanced optimal maneuver; (ii) support real-time implementation; (iii) proactively acquire right-of-way for improved travel efficiency; (iv) take a person-by-person driving countermeasure for strengthened safety. In order to realize the proactive right-of-way acquisition, the game-based interaction-aware decision-making is designed, and Stackelberg competition is adopted as the game rule. The problem is formulated as a differential-game-based Receding Horizon Control (RHC) to integrate both behavioral and motion planning. An online Inverse Reinforcement Learning (IRL) is leveraged to identify the individual driving behavior of the competing vehicle in order for the enabling of person-by-person countermeasures. The problem is solved by a highly-efficient quadratic programming algorithm. A simulation evaluation is conducted on the proposed planner. The results demonstrate that the proposed planner is with millisecond-level computational efficiency. It improves travel efficiency by 4.78-25.77% with the proactive right-of-way acquisition, and enhances driving safety by 5.22-36.08% via taking person-by-person countermeasures. The range is caused by different congestion levels.

Language: en