期刊
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
卷 156, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trc.2023.104328
关键词
Human-like autonomous driving system; Human-like decision making; Hierarchical reinforcement learning; Cognitive map; Lane change
This paper proposes a hierarchical reinforcement learning-based framework called Cog MP, which combines cognitive maps and motion primitives in human-like decision making. The framework integrates operational, decision-making, and cognitive levels in autonomous driving systems, and is used to make human-like decisions in lane-changing scenarios.
Human-like decision making is crucial to developing an autonomous driving system (ADS) with high acceptance. Inspired by the cognitive map, this paper proposes a hierarchical reinforcement learning (HRL)-based framework with sound biological plausibility named Cog MP, which combines the cognitive map and motion primitive (MP) in human-like decision making. In the proposed Cog-MP, three general levels involved in ADS are integrated in a top-bottom way, including operational, decision-making, and cognitive levels. The proposed Cog-MP is used to make human-like decisions in lane-changing scenarios, focusing on three aspects: human-like lane decision, human-like path decision, and decision optimization. The proposed framework is validated on two groups of realistic lane-change data, of which one group is used to train cognitions towards different styles of driving behaviors, and the other group is to provide validation scenarios. Experimental results show that the proposed framework can generate human-like decisions and perform soundly regarding the three considered aspects, demonstrating a promising prospect in developing a brain-inspired human-like ADS.
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