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Trajectory prediction for autonomous driving based on multiscale spatial‐temporal graph.

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Title: Trajectory prediction for autonomous driving based on multiscale spatial‐temporal graph.
Authors: Tang, Luqi1,2,3 (AUTHOR), Yan, Fuwu1,2,3 (AUTHOR), Zou, Bin1,2,3 (AUTHOR) zoubin@whut.edu.cn, Li, Wenbo1,2,3 (AUTHOR), Lv, Chen4 (AUTHOR), Wang, Kewei5 (AUTHOR)
Source: IET Intelligent Transport Systems (Wiley-Blackwell). Feb2023, Vol. 17 Issue 2, p386-399. 14p.
Subjects: Decision making, Forecasting, Mathematical convolutions, Autonomous vehicles, Driverless cars
Abstract: Predicting the trajectories of surrounding heterogeneous traffic agents is critical for the decision making of an autonomous vehicle. Recently, many existing prediction methods have focused on capturing interactions between agents to improve prediction accuracy. However, few methods pay attention to the temporal dependencies of interactions that there are different behavioural interactions at different time scales. In this work, the authors propose a novel framework for trajectory prediction by stacking spatial‐temporal layers at multiple time scales. Firstly, the authors design three kinds of adjacency matrices to capture more genuine spatial dependencies rather than a fixed adjacency matrix. Then, a novel dilated temporal convolution is developed to handle temporal dependencies. Benefiting from the dilated temporal convolution, the authors' graph convolution is able to aggregate information from neighbours at different time scales by stacking spatial‐temporal layers. Finally, a long short‐term memory networks (LSTM)‐based trajectory generation module is used to receive the features extracted by the spatial‐temporal graph and generate the future trajectories for all observed traffic agents simultaneously. The authors evaluate the proposed model on the publicly available next generation simulation dataset (NGSIM), the highway drone dataset (highD), and ApolloScape datasets. The results demonstrate that the authors' approach achieves state‐of‐the‐art performance. Furthermore, the proposed method ranked #1 on the leaderboard of the ApolloScape trajectory competition in March 2021. [ABSTRACT FROM AUTHOR]
Copyright of IET Intelligent Transport Systems (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 161658292
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Trajectory prediction for autonomous driving based on multiscale spatial‐temporal graph.
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  Data: <searchLink fieldCode="JN" term="%22IET+Intelligent+Transport+Systems+%28Wiley-Blackwell%29%22">IET Intelligent Transport Systems (Wiley-Blackwell)</searchLink>. Feb2023, Vol. 17 Issue 2, p386-399. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+convolutions%22">Mathematical convolutions</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomous+vehicles%22">Autonomous vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Driverless+cars%22">Driverless cars</searchLink>
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  Data: Predicting the trajectories of surrounding heterogeneous traffic agents is critical for the decision making of an autonomous vehicle. Recently, many existing prediction methods have focused on capturing interactions between agents to improve prediction accuracy. However, few methods pay attention to the temporal dependencies of interactions that there are different behavioural interactions at different time scales. In this work, the authors propose a novel framework for trajectory prediction by stacking spatial‐temporal layers at multiple time scales. Firstly, the authors design three kinds of adjacency matrices to capture more genuine spatial dependencies rather than a fixed adjacency matrix. Then, a novel dilated temporal convolution is developed to handle temporal dependencies. Benefiting from the dilated temporal convolution, the authors' graph convolution is able to aggregate information from neighbours at different time scales by stacking spatial‐temporal layers. Finally, a long short‐term memory networks (LSTM)‐based trajectory generation module is used to receive the features extracted by the spatial‐temporal graph and generate the future trajectories for all observed traffic agents simultaneously. The authors evaluate the proposed model on the publicly available next generation simulation dataset (NGSIM), the highway drone dataset (highD), and ApolloScape datasets. The results demonstrate that the authors' approach achieves state‐of‐the‐art performance. Furthermore, the proposed method ranked #1 on the leaderboard of the ApolloScape trajectory competition in March 2021. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of IET Intelligent Transport Systems (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1049/itr2.12265
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      – Code: eng
        Text: English
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        PageCount: 14
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        Type: general
      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Mathematical convolutions
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      – SubjectFull: Autonomous vehicles
        Type: general
      – SubjectFull: Driverless cars
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      – TitleFull: Trajectory prediction for autonomous driving based on multiscale spatial‐temporal graph.
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            NameFull: Tang, Luqi
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            NameFull: Yan, Fuwu
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              M: 02
              Text: Feb2023
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              Y: 2023
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