Learning Dynamic Graph for Overtaking Strategy in Autonomous Driving

doi:10.1109/TITS.2023.3287223

	Learning Dynamic Graph for Overtaking Strategy in Autonomous Driving
	Hu, Xuemin 1; Liu, Yanfang 1; Tang, Bo 2; Yan, Junchi 3; Chen, Long4,5
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
	2023-06-27
页码	13
通讯作者	Chen, Long(long.chen@ia.ac.cn)
摘要	Automatic overtaking is a challenging task for self-driving vehicles. Traditional rule-based methods for overtaking in autonomous driving heavily rely on many predefined rules and are difficult to apply in complex driving scenarios. Learning-based methods usually use convolutional networks, recurrent networks, and multilayer perceptrons, etc., to extract features from environments, but they fail to effectively represent geometric and interactive information among traffic participants. Classic graph convolutional networks (GCNs) have the ability of represent graph-structural information but are limited to stable relationship representation due to the fixed adjacency matrix when applied in autonomous driving. In this paper, we propose a novel dynamic graph learning method based on a graph convolutional network with a trainable adjacency matrix (TAM-GCN) to enable the learning of dynamic relationships among different nodes in an ever-changing driving scene. In addition, we develop a planning method for overtaking strategy in autonomous driving, where the proposed TAM-GCN is used to extract the spatial graph-structural features, select appropriate overtaking time, and generate efficient overtaking actions. The proposed model is trained using the imitation learning method. We conduct comprehensive experiments in both closed-loop and open-loop testing in the CARLA simulator and compare our method with state-of-the-art methods. Experimental results demonstrate the proposed method achieves better accuracy, safety and overtaking performance than existing methods.
关键词	Autonomous driving graph convolutional network trainable adjacency matrix overtaking dynamic graph
DOI	10.1109/TITS.2023.3287223
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62273135] ; National Natural Science Foundation of China[2021CFB460] ; Natural Science Foundation of Hubei Province in China[61806076]
项目资助者	National Natural Science Foundation of China ; Natural Science Foundation of Hubei Province in China
WOS研究方向	Engineering ; Transportation
WOS类目	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:001025524700001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53673
专题	多模态人工智能系统全国重点实验室
通讯作者	Chen, Long
作者单位	1.Hubei Univ, Sch Artificial Intelligence, Wuhan 430062, Hubei, Peoples R China 2.Worcester Polytech Inst, Dept Elect & Comp Engn, Worcester, MA 01609 USA 3.Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 5.Waytous Inc, Beijing 100083, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Hu, Xuemin,Liu, Yanfang,Tang, Bo,et al. Learning Dynamic Graph for Overtaking Strategy in Autonomous Driving[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2023:13.
APA	Hu, Xuemin,Liu, Yanfang,Tang, Bo,Yan, Junchi,&Chen, Long.(2023).Learning Dynamic Graph for Overtaking Strategy in Autonomous Driving.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,13.
MLA	Hu, Xuemin,et al."Learning Dynamic Graph for Overtaking Strategy in Autonomous Driving".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2023):13.