Review of Research on Decision-making and Planning for Automated Vehicles

被引：0
作者：
Zhu B. [1 ]
Jia S.-Z. [1 ]
Zhao J. [1 ]
Han J.-Y. [1 ]
Zhang P.-X. [1 ]
Song D.-J. [1 ]
机构：
[1] State Key Laboratory of Automotive Simulation and Control, Jilin University, Jilin, Changchun
来源：
Zhongguo Gonglu Xuebao/China Journal of Highway and Transport | 2024年 / 37卷 / 01期
关键词：
automated driving technology; automotive engineering; behavior decision-making; behavior prediction; end-to-end decision-making and planning; motion planning; review;
D O I：
10.19721/j.cnki.1001-7372.2024.01.018
中图分类号：
学科分类号：
摘要：
Decision-making and planning are the core functions of automated driving systems and the key to improving the driving safety, driving experience and travel efficiency of automated vehicles. The main challenges faced by decision-making and planning are how to meet the extremely high reliability and safety requirements for automated driving, and how to effectively deal with scenario complexity, environmental variability, traffic dynamicity, game interactivity, and information completeness, as well as how to generate human-like driving behavior, so that vehicles can integrate into the traffic ecosystem naturally. A systematic and overall review of the technical points of decision-making and planning is presented in this paper to gain a comprehensive understanding of their frontier issues and research progress. Firstly, the research progress of situational awareness-oriented behavior prediction is reviewed from three perspectives, namely data-driven driving behavior prediction, probabilistic model driving behavior prediction, and personalized driving behavior prediction. Secondly, behavior decision-making is summarized into reactive decision-making, learning decision-making and interactive decision-making, all of which are analyzed in turn. Thirdly, motion planning and its applications are compared and analyzed from a methodological perspective, including graph search methods, sampling methods, numerical methods, interpolation and curve fitting methods, etc. Additionally, the key scientific issues and major research progress of end-to-end decision-making and planning are summarized and analyzed. Finally, the significant impact of decision-making and planning on improving the intelligent level of automated vehicles is summarized, and the future development trends and technical challenges are prospected. © 2024 Xi'an Highway University. All rights reserved.
引用
收藏
页码：215 / 240
页数：25
相关论文
共 190 条
[1]  
WALDROP M M., Autonomous vehicles: No drivers required [J], Nature, 518, 7537, pp. 20-23, (2015)
[2]  
XU H L, ZHANG Y, CASSANDRAS C G, Et al., A bi-level cooperative driving strategy allowing lane changes, Transportation Research Part C: Emerging Technologies, 120, (2020)
[3]  
DUARTE F., Self-driving cars: A city perspective [J], Science Robotics, 4, 28, (2019)
[4]  
PADEN B, CAP M, YONG S Z, Et al., A survey of motion planning and control techniques for self-driving urban vehicles [J], IEEE Transactions on Intelligent Vehicles, 1, 1, pp. 33-55, (2016)
[5]  
CHEN G X, HU L, ZHANG Q S, Et al., ST-LSTM: Spatiotemporal graph based long short-term memory network for vehicle trajectory prediction [C], national Conference on Image Processing (ICIP), pp. 608-612
[6]  
SUN J, KIM J., Joint prediction of next location and travel time from urban vehicle trajectories using long short-term memory neural networks, Transportation Research Part C: Emerging Technologies, 128, (2021)
[7]  
LI Z N, HUANG X H, MU T, Et al., Attention-based lane change and crash risk prediction model in highways [J], IEEE Transactions on Intelligent Transportation Systems, 23, 12, pp. 22909-22922, (2022)
[8]  
ZYNER A, WORRALL S, WARD J, Et al., Long short term memory for driver intent prediction [C], IEEE. 2017 IEEE Intelligent Vehicles Symposium (I V), pp. 1484-1489, (2017)
[9]  
XIN L, WANG P, CHAN C Y, Et al., Intention-aware long horizon trajectory prediction of surrounding vehicles using dual LSTM networks [C], IEEE. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 1441-1446, (2018)
[10]  
HOU L, XIN L, LI S E, Et al., Interactive trajectory prediction of surrounding road users for autonomous driving using structural-LSTM network [J], IEEE Transactions on Intelligent Transportation Systems, 21, 11, pp. 4615-4625, (2020)
12345678910