Learning Dynamic Graph for Overtaking Strategy in Autonomous Driving
文献类型：期刊论文
| 作者 | Hu, Xuemin2; Liu, Yanfang2; Tang, Bo3; Yan, Junchi1; Chen, Long4,5
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| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
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| 出版日期 | 2023-06-27 |
| 页码 | 13 |
| ISSN号 | 1524-9050 |
| 关键词 | Autonomous driving graph convolutional network trainable adjacency matrix overtaking dynamic graph |
| DOI | 10.1109/TITS.2023.3287223 |
| 通讯作者 | Chen, Long(long.chen@ia.ac.cn) |
| 英文摘要 | 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. |
| 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] |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001025524700001 |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Hubei Province in China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53672]  |
| 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.Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China 2.Hubei Univ, Sch Artificial Intelligence, Wuhan 430062, Hubei, Peoples R China 3.Worcester Polytech Inst, Dept Elect & Comp Engn, Worcester, MA 01609 USA 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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