Cooperative Lane Changing via Deep Reinforcement Learning

In this paper, we study how to learn an appropriate lane changing strategy for autonomous vehicles by using deep reinforcement learning. We show that the reward of the system should consider the overall traffic efficiency instead of the travel efficiency of an individual vehicle. In summary, cooperation leads to a more harmonic and efficient traffic system rather than competition

eess.SY cs.AI cs.LG Computer Science Engineering

Paper

References (38)

01A Right-of-Way Assignment Strategy to Ensure Traffic Safety and Efficiency in Lane ChangeCan Zhao, Yadong Xing, Zhiheng Li et al.2019 · arXiv.org · 5 citations

02Benchmarks for reinforcement learning in mixed-autonomy trafficEugene Vinitsky, Aboudy Kreidieh, Luc Le Flem et al.2018 · Conference on Robot Learning · 179 citations

03A dynamic lane-changing trajectory planning model for automated vehiclesD. Yang, Shiyu Zheng, Cheng Wen et al.2018 · Transportation Research Part C: Emerging Technologies · 223 citations

04A situation-aware collision avoidance strategy for car-followingLi Li, Xinyu Peng, Feiyue Wang et al.2018 · IEEE/CAA Journal of Automatica Sinica · 62 citations

05Naturalistic Lane Change Analysis for Human-Like Trajectory GenerationDonghao Xu, Zhezhang Ding, Huijing Zhao et al.2018 · 2018 IEEE Intelligent Vehicles Symposium (IV) · 15 citations

06Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic StatesPeter Wolf, Karl Kurzer, Tobias Wingert et al.2018 · 2018 IEEE Intelligent Vehicles Symposium (IV) · 59 citations In Library

07Highway Traffic Modeling and Decision Making for Autonomous Vehicle Using Reinforcement LearningChangxi You, Jianbo Lu, Dimitar Filev et al.2018 · 2018 IEEE Intelligent Vehicles Symposium (IV) · 48 citations

08Deep Q Learning Based High Level Driving Policy DeterminationKyushik Min, Hayoung Kim2018 · 2018 IEEE Intelligent Vehicles Symposium (IV) · 45 citations

09A Reinforcement Learning Based Approach for Automated Lane Change ManeuversPin Wang, Ching-yao Chan, A. D. L. Fortelle2018 · 2018 IEEE Intelligent Vehicles Symposium (IV) · 270 citations In Library

10Impact of cooperative adaptive cruise control on multilane freeway merge capacityHao Liu, X. Kan, S. Shladover et al.2018 · Journal of Intelligent Transportation Systems / Taylor & Francis · 137 citations

11Analysis of Cooperative Driving Strategies for Nonsignalized IntersectionsYue Meng, Li Li, Feiyue Wang et al.2018 · IEEE Transactions on Vehicular Technology · 148 citations

12Carrot and stick: A game-theoretic approach to motivate cooperative driving through social interactionMarkus Zimmermann, David Schopf, Niklas Lutteken et al.2018 · 70 citations

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