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Trajectory Forecasting Based on Prior-Aware Directed Graph Convolutional Neural Network
Su, Yuchao1; Du, Jie2; Li, Yuanman3; Li, Xia1; Liang, Rongqin1; Hua, Zhongyun4; Zhou, Jiantao5
2022-01
Source PublicationIEEE Transactions on Intelligent Transportation Systems
ISSN1524-9050
Abstract

Predicting the motion trajectories of moving agents in complex traffic scenes, such as crossroads and roundabouts, plays an important role in cooperative intelligent transportation systems. Nevertheless, accurately forecasting the motion behavior in a dynamic scenario is challenging due to the complex cooperative interactions between moving agents. Graph Convolutional Neural Network has recently been employed to deal with the cooperative interactions between agents. Despite the promising performance of resulting trajectory prediction algorithms, many existing graph-based approaches model interactions with an undirected graph, where the strength of influence between agents is assumed to be symmetric. However, such an assumption often does not hold in reality. For example, in pedestrian or vehicle interaction modeling, the moving behavior of a pedestrian or vehicle is highly affected by the ones ahead, while the ones ahead usually pay less attention to the ones behind. To fully exploit the asymmetric attributes of the cooperative interactions in intelligent transportation systems, in this work, we present a directed graph convolutional neural network for multiple agents trajectory prediction. First, we propose three directed graph topologies, i.e., view graph, direction graph, and rate graph, by encoding different prior knowledge of a cooperative scenario, which endows the capability of our framework to effectively characterize the asymmetric influence between agents. Then, a fusion mechanism is devised to jointly exploit the asymmetric mutual relationships embedded in constructed graphs. Furthermore, a loss function based on Cauchy distribution is designed to generate multimodal trajectories. Experimental results on complex traffic scenes demonstrate the superior performance of our proposed model when compared with existing approaches.

KeywordAsymmetric Interactions. Cooperative Intelligent Transportation Systems Directed Graph Convolutional Neural Network Directed Graphs Feature Extraction Forecasting Generative Adversarial Networks Predictive Models Topology Trajectory Trajectory Prediction
DOI10.1109/TITS.2022.3142248
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000745449800001
Scopus ID2-s2.0-85123384974
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Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorLi, Yuanman
Affiliation1.Guangdong Key Laboratory of Intelligent Information Processing, College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China.
2.Health Science Center, School of Biomedical Engineering, Shenzhen University, Shenzhen 518060, China.
3.Guangdong Key Laboratory of Intelligent Information Processing, College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China (e-mail: yuanmanli@szu.edu.cn)
4.School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China.
5.State Key Laboratory of Internet of Things for Smart City and the Department of Computer and Information Science, University of Macau, Macau 999078, China.
Recommended Citation
GB/T 7714
Su, Yuchao,Du, Jie,Li, Yuanman,et al. Trajectory Forecasting Based on Prior-Aware Directed Graph Convolutional Neural Network[J]. IEEE Transactions on Intelligent Transportation Systems,2022.
APA Su, Yuchao,Du, Jie,Li, Yuanman,Li, Xia,Liang, Rongqin,Hua, Zhongyun,&Zhou, Jiantao.(2022).Trajectory Forecasting Based on Prior-Aware Directed Graph Convolutional Neural Network.IEEE Transactions on Intelligent Transportation Systems.
MLA Su, Yuchao,et al."Trajectory Forecasting Based on Prior-Aware Directed Graph Convolutional Neural Network".IEEE Transactions on Intelligent Transportation Systems (2022).
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