Lane change decision-making through deep reinforcement learning with rule-based constraints
文献类型:会议论文
作者 | Wang JJ(王俊杰)1,2; Zhang QC(张启超)1,2; Zhao DB(赵冬斌)1,2; Chen YR(陈亚冉)1,2 |
出版日期 | 2019-03 |
会议日期 | 2019-7 |
会议地点 | Budapest, Hungary |
关键词 | Lane Change Decision-making Deep Reinforcement Learning Deep Q-Network |
英文摘要 | 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. |
源URL | [http://ir.ia.ac.cn/handle/173211/51720] |
专题 | 复杂系统管理与控制国家重点实验室_深度强化学习 |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
推荐引用方式 GB/T 7714 | Wang JJ,Zhang QC,Zhao DB,et al. Lane change decision-making through deep reinforcement learning with rule-based constraints[C]. 见:. Budapest, Hungary. 2019-7. |
入库方式: OAI收割
来源:自动化研究所
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