Autonomous driving decision-making is a great challenge due to the complexity and uncertainty of the traffic environment. Combined with the rule-based constraints, a Deep Q-Network (DQN) based method is applied for autonomous driving lane change decision-making task in this study. Through the combination of high-level lateral decision-making and low-level rule-based trajectory modification, a safe and efficient lane change behavior can be achieved. With the setting of our state representation and reward function, the trained agent is able to take appropriate actions in a real-world-like simulator. The generated policy is evaluated on the simulator for 10 times, and the results demonstrate that the proposed rule-based DQN method outperforms the rule-based approach and the DQN method.
@article{arxiv.1904.00231,
title = {Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraints},
author = {Junjie Wang and Qichao Zhang and Dongbin Zhao and Yaran Chen},
journal= {arXiv preprint arXiv:1904.00231},
year = {2019}
}
Comments
6 pages, 5 figures, accepted at 2019 International Joint Conference on Neural Networks(IJCNN)