A Comprehensive Review of Main Methods in Autonomous Driving Behavior Decision-making

XVII International Conference on Systems, Automatic Control and Measurements, SAUM 2024 (pp. 92-95)

АУТОР(И) / AUTHOR(S): Jianxun Cui , Huidong Gao , Staniša Perić , Marko Milojković , Miroslav Milovanović

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DOI:  10.46793/SAUM24.092C

САЖЕТАК / ABSTRACT:

This paper reviews the core component of autonomous driving technology—the main methods of decision-making and planning. Special emphasis is placed on decision-making and planning as the key bridge connecting perception and control, and the development of behavior decision-making systems is thoroughly discussed. The article first introduces rule-based decision-making methods, including the three structures of finite state machines and their advantages and disadvantages. It then shifts to learning-based behavior decision-making methods, discussing in detail two strategies: imitation learning and reinforcement learning, including their advantages and limitations. Finally, the paper explores large model-based behavior decision-making methods, which leverage the general knowledge understanding and reasoning capabilities of large language models to provide decision support in the form of natural language.

КЉУЧНЕ РЕЧИ / KEYWORDS:

autonomous driving, reinforcement learning, language large model, behavior decision

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