干旱区生态环境知识资源中心(Arid): Multi-Vehicle Collaborative Learning for Trajectory Prediction With Spatio-Temporal Tensor Fusion

Arid

DOI	10.1109/TITS.2020.3009762
	Multi-Vehicle Collaborative Learning for Trajectory Prediction With Spatio-Temporal Tensor Fusion
	Wang, Yu; Zhao, Shengjie; Zhang, Rongqing; Cheng, Xiang; Yang, Liuqing
通讯作者	Zhao, SJ (corresponding author)，Tongji Univ, Key Lab Embedded Syst & Serv Comp, Minist Educ, Shanghai 201804, Peoples R China.
来源期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
EISSN	1558-0016
出版年	2022
卷号	23 期号:1
英文摘要	Accurate behavior prediction of other vehicles in the surroundings is critical for intelligent transportation systems. Common practices to reason about the future trajectory are through their historical paths. However, the impact of traffic context is ignored, which means the beneficial environment information is deserted. Although a few methods are proposed to exploit the surrounding vehicle information, they simply model the influence according to spatial relations without considering the temporal information among them. In this paper, a novel multi-vehicle collaborative learning with spatio-temporal tensor fusion model for vehicle trajectory prediction is proposed, which introduces a novel auto-encoder social convolution mechanism and a fancy recurrent social mechanism to model spatial and temporal information among multiple vehicles, respectively. Furthermore, the generative adversarial network is incorporated into our framework to handle the inherent multi-modal characteristics of the agent motion behavior. Finally, we evaluate the proposed multi-vehicle collaborative learning model on NGSIM US-101 and I-80 benchmark datasets. Experimental results demonstrate that the proposed approach outperforms the state-of-the-art for vehicle trajectory prediction. Additionally, we also present qualitative analyses of the multi-modal vehicle trajectory generation and the impacts of surrounding vehicles on trajectory prediction under various circumstances.
英文关键词	Trajectory Predictive models Generative adversarial networks Collaborative work Tensile stress Intelligent vehicles Gallium nitride Collaborative learning spatio-temporal tensor fusion vehicle trajectory prediction generative adversarial networks
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000735517000021
WOS关键词	FRAMEWORK
WOS类目	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS研究方向	Engineering ; Transportation
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376828
作者单位	[Wang, Yu] Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China; [Wang, Yu; Zhao, Shengjie] Tongji Univ, Key Lab Embedded Syst & Serv Comp, Minist Educ, Shanghai 201804, Peoples R China; [Zhang, Rongqing] Tongji Univ, Sch Software Engn, Shanghai 201804, Peoples R China; [Cheng, Xiang] Peking Univ, State Key Lab Adv Opt Commun Syst & Networks, Beijing 100871, Peoples R China; [Yang, Liuqing] Colorado State Univ, Dept Elect & Comp Engn, Ft Collins, CO 80521 USA
推荐引用方式 GB/T 7714	Wang, Yu,Zhao, Shengjie,Zhang, Rongqing,et al. Multi-Vehicle Collaborative Learning for Trajectory Prediction With Spatio-Temporal Tensor Fusion[J],2022,23(1).
APA	Wang, Yu,Zhao, Shengjie,Zhang, Rongqing,Cheng, Xiang,&Yang, Liuqing.(2022).Multi-Vehicle Collaborative Learning for Trajectory Prediction With Spatio-Temporal Tensor Fusion.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,23(1).
MLA	Wang, Yu,et al."Multi-Vehicle Collaborative Learning for Trajectory Prediction With Spatio-Temporal Tensor Fusion".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 23.1(2022).