弹道
计算机科学
领域(数学)
人工智能
强化学习
机器学习
数学
天文
物理
纯数学
作者
Yanjun Huang,Jiatong Du,Ziru Yang,Zewei Zhou,Lin Zhang,Hong Chen
出处
期刊:IEEE transactions on intelligent vehicles
[Institute of Electrical and Electronics Engineers]
日期:2022-09-01
卷期号:7 (3): 652-674
被引量:110
标识
DOI:10.1109/tiv.2022.3167103
摘要
In order to drive safely in a dynamic environment, autonomous vehicles should be able to predict the future states of traffic participants nearby, especially surrounding vehicles, similar to the capability of predictive driving of human drivers. That is why researchers are devoted to the field of trajectory prediction and propose different methods. This paper is to provide a comprehensive and comparative review of trajectory-prediction methods proposed over the last two decades for autonomous driving. It starts with the problem formulation and algorithm classification. Then, the popular methods based on physics, classic machine learning, deep learning, and reinforcement learning are elaborately introduced and analyzed. Finally, this paper evaluates the performance of each kind of method and outlines potential research directions to guide readers.
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