Cooperative Lane Changing via Deep Reinforcement Learning

2019/06/20 by Guan Wang, Jianming Hu, Wang, Guan +5 · 21 citations
Computer Science · Engineering · Psychology · #Artificial Intelligence (cs.AI) #Artificial intelligence #Autonomous Vehicle Technology and Safety #Computer science #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Psychology #Reinforcement #Reinforcement Learning in Robotics #Reinforcement learning #Social psychology #Systems and Control (eess.SY) #Traffic control and management #cs.AI #cs.LG #cs.SY #eess.SY #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1906.08662

published in arXiv (Cornell University) (Cornell University) · 7 pages, 6 figures

arxiv created 2019/06/20 · openalex publication_date 2019/06/20 · arxiv updated 2019/06/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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

Citations

• Playing Atari with Deep Reinforcement Learning
• Reinforcement learning in robotics: A survey
• Gradient-based learning applied to document recognition
• Human-level control through deep reinforcement learning
• Continuous control with deep reinforcement learning

Cited by

• CauTraj: A Causal-Knowledge-Guided Framework for Lane-Changing Trajectory Planning of Autonomous Vehicles
• V2V Spatiotemporal Interactive Pattern Recognition and Risk Analysis in Lane Changes

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