Cooperative Lane Changing via Deep Reinforcement Learning

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

7 pages, 6 figures

References in corpus (4)

• Continuous control with deep reinforcement learning
• Playing Atari with Deep Reinforcement Learning
• End-to-End Deep Reinforcement Learning for Lane Keeping Assist
• Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States

Cited by in corpus (5)

• Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning
• Deep Reinforcement Learning and Transportation Research: A Comprehensive Review
• Distributed Vehicular Computing at the Dawn of 5G: a Survey
• Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon
• V2V Spatiotemporal Interactive Pattern Recognition and Risk Analysis in Lane Changes