A Survey on Trajectory-Prediction Methods for Autonomous Driving

被引：233

作者：

Huang, Yanjun ^{[1
]}

Du, Jiatong ^{[1
]}

Yang, Ziru ^{[1
]}

Zhou, Zewei ^{[1
]}

Zhang, Lin ^{[1
]}

Chen, Hong ^{[1
]}

机构：

[1] Tongji Univ, Sch Automot Studies, Shanghai 201804, Peoples R China

来源：

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES | 2022年 / 7卷 / 03期

基金：

国家重点研发计划;

关键词：

Trajectory; Predictive models; Learning systems; Vehicle dynamics; Computational modeling; Kalman filters; Intelligent vehicles; Autonomous driving; trajectory prediction; machine learning; deep learning; reinforcement learning; MOTION PREDICTION; DECISION-MAKING; DRIVER BEHAVIOR; VEHICLE; MODEL; RECOGNITION; ATTENTION; FRAMEWORK; PATH; INTERSECTIONS;

D O I：

10.1109/TIV.2022.3167103

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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.

引用

页码：652 / 674

页数：23

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