A Vectorized Representation Model for Trajectory Prediction of Intelligent Vehicles in Challenging Scenarios

doi:10.1109/TIV.2023.3317032

	A Vectorized Representation Model for Trajectory Prediction of Intelligent Vehicles in Challenging Scenarios
	Guo, Lulu 1; Shan, Ce 1; Shi, Tengfei 2; Li, Xuan 3; Wang, Fei-Yue4
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
ISSN	2379-8858
	2023-10-01
卷号	8 期号:10 页码:4301-4306
通讯作者	Li, Xuan(lix05@pcl.ac.cn)
摘要	Trajectory prediction for challenging scenarios has always been a significant problem in the field due to the complexity of dynamic scenarios and interactions. Furthermore, there is often a dynamic gap between evaluating and validating methods on fixed datasets and real driving scenarios. This letter forms part of a series of reports emanating from the IEEE Transactions on Intelligent Vehicles's Decentralized and Hybrid Workshops (DHW) dedicated to the field of Scenarios Engineering. Our research proposes a scenario engineering-based calibration and validation framework for trajectory prediction of autonomous vehicles to more effectively validate the performance of the method in challenging scenarios. First, Scenarios Engineering (SE) uses OpenSCENARIO and real dataset to generate high-definition maps for challenging driving scenarios. Then, the vectorization approach is employed to extract contextual details from the scene and agent trajectory information from the HD map, and the graph neural network is used to model the high-order interaction to realize the interactive trajectory prediction. Compared with the traditional method, the trajectory prediction can be calibrated through SE so that the prediction process can use more traffic information and attribute characteristics, and improve the evaluation index of prediction. The DHW discusses a practical case to verify the potential of the trajectory prediction framework based on scenarios generation in improving the authenticity and accuracy of trajectory prediction.
关键词	Index Terms-Autonomous vehicles graph neural network HD maps OpenSCENARIO scenarios engineering trajectory prediction
DOI	10.1109/TIV.2023.3317032
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62203250] ; National Natural Science Foundation of China[62203334] ; Young Elite Scientists Sponsorship Program of China Association of Science and Technology[YESS20210289] ; Young Elite Scientists Sponsorship Program of China Association of Science and Technology[YESS20210397] ; China Postdoctoral Science Foundation[2020TQ1057] ; China Postdoctoral Science Foundation[2020M682823] ; Fundamental Research Funds for the Central Universities
项目资助者	National Natural Science Foundation of China ; Young Elite Scientists Sponsorship Program of China Association of Science and Technology ; China Postdoctoral Science Foundation ; Fundamental Research Funds for the Central Universities
WOS研究方向	Computer Science ; Engineering ; Transportation
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:001109113000015
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：2[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/173211/55126
专题	多模态人工智能系统全国重点实验室
通讯作者	Li, Xuan
作者单位	1.Tongji Univ, Dept Control Sci & Engn, Shanghai 201804, Peoples R China 2.Beihang Univ, Sch Comp Sci & Engn, Beijing 100083, Peoples R China 3.Peng Cheng Lab, Shenzhen 518000, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Guo, Lulu,Shan, Ce,Shi, Tengfei,et al. A Vectorized Representation Model for Trajectory Prediction of Intelligent Vehicles in Challenging Scenarios[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2023,8(10):4301-4306.
APA	Guo, Lulu,Shan, Ce,Shi, Tengfei,Li, Xuan,&Wang, Fei-Yue.(2023).A Vectorized Representation Model for Trajectory Prediction of Intelligent Vehicles in Challenging Scenarios.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,8(10),4301-4306.
MLA	Guo, Lulu,et al."A Vectorized Representation Model for Trajectory Prediction of Intelligent Vehicles in Challenging Scenarios".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 8.10(2023):4301-4306.