Conditional Goal-Oriented Trajectory Prediction for Interacting Vehicles

doi:10.1109/TNNLS.2023.3321564

	Conditional Goal-Oriented Trajectory Prediction for Interacting Vehicles
	Li, Ding1,2 ; Zhang, Qichao1,2 ; Lu, Shuai 3; Pan, Yifeng 3; Zhao, Dongbin1,2
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2023-10-17
页码	13
通讯作者	Zhang, Qichao(zhangqichao2014@ia.ac.cn)
摘要	Predicting future trajectories of pairwise traffic agents in highly interactive scenarios, such as cut-in, yielding, and merging, is challenging for autonomous driving. The existing works either treat such a problem as a marginal prediction task or perform single-axis factorized joint prediction, where the former strategy produces individual predictions without considering future interaction, while the latter strategy conducts conditional trajectory-oriented prediction via agentwise interaction or achieves conditional rollout-oriented prediction via timewise interaction. In this article, we propose a novel double-axis factorized joint prediction pipeline, namely, conditional goal-oriented trajectory prediction (CGTP) framework, which models future interaction both along the agent and time axes to achieve goal and trajectory interactive prediction. First, a goals-of-interest network (GoINet) is designed to extract fine-grained features of goal candidates via hierarchical vectorized representation. Furthermore, we propose a conditional goal prediction network (CGPNet) to produce multimodal goal pairs in an agentwise conditional manner, along with a newly designed goal interactive loss to better learn the joint distribution of the intermediate interpretable modes. Explicitly guided by the goal-pair predictions, we propose a goal-oriented trajectory rollout network (GTRNet) to predict scene-compliant trajectory pairs via timewise interactive rollouts. Extensive experimental results confirm that the proposed CGTP outperforms the state-of-the-art (SOTA) prediction models on the Waymo open motion dataset (WOMD), Argoverse motion forecasting dataset, and In-house cut-in dataset. Code is available at https://github.com/LiDinga/CGTP/.
关键词	Trajectory Predictive models Behavioral sciences Pipelines Task analysis Feature extraction Vehicle dynamics Conditional prediction goal-oriented trajectory prediction hierarchical vectorized representation joint trajectory prediction marginal prediction
DOI	10.1109/TNNLS.2023.3321564
关键词[WOS]	MODEL
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2022YFA1004000] ; National Natural Science Foundation of China (NSFC)[62173325] ; China Computer Federation (CCF) Baidu Open Fund ; National Key Research and Development Program of China[2022YFA1004000] ; National Natural Science Foundation of China (NSFC)[62173325] ; China Computer Federation (CCF) Baidu Open Fund
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China (NSFC) ; China Computer Federation (CCF) Baidu Open Fund ; National Key Research and Development Program of China ; National Natural Science Foundation of China (NSFC) ; China Computer Federation (CCF) Baidu Open Fund
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:001090718900001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/54386
专题	多模态人工智能系统全国重点实验室多模态人工智能系统全国重点实验室_深度强化学习
通讯作者	Zhang, Qichao
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 3.Baidu Inc, Beijing 100085, Peoples R China
第一作者单位	中国科学院自动化研究所
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	Li, Ding,Zhang, Qichao,Lu, Shuai,et al. Conditional Goal-Oriented Trajectory Prediction for Interacting Vehicles[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2023:13.
APA	Li, Ding,Zhang, Qichao,Lu, Shuai,Pan, Yifeng,&Zhao, Dongbin.(2023).Conditional Goal-Oriented Trajectory Prediction for Interacting Vehicles.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,13.
MLA	Li, Ding,et al."Conditional Goal-Oriented Trajectory Prediction for Interacting Vehicles".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023):13.