research-article

HeGA: Heterogeneous Graph Aggregation Network for Trajectory Prediction in High-Density Traffic

Authors:

Kai ZhengAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 1319 - 1328

https://doi.org/10.1145/3511808.3557345

Published: 17 October 2022 Publication History

Abstract

Trajectory prediction enables the fast and accurate response of autonomous driving navigation in complex and dense traffics. In this paper, we present a novel trajectory prediction network called <u>He</u>terogeneous <u>G</u>raph <u>A</u>ggregation (HeGA) for high-density heterogeneous traffic, where the traffic agents of various categories interact densely with each other. To predict the trajectory of a target agent, HeGA first automatically selects neighbors that interact with it by our proposed adaptive neighbor selector, and then aggregates their interactions based on a novel two-phase aggregation transformer block. At last, the historical residual connection LSTM enhances the historical information awareness and decodes the spatial coordinates as the prediction results. Extensive experiments on real data demonstrate that the proposed network significantly outperforms the existing state-of-the-art competitors by over 27% on average displacement error (ADE) and over 31% on final displacement error (FDE). We also deploy HeGA in a state-of-the-art framework for autonomous driving, demonstrating its superior applicability based on three simulated environments with different densities and complexities.

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Cited By

Cao YLi LChen XXu XHuang ZYu YSerra ESpezzano F(2024)Hypergraph Hash Learning for Efficient Trajectory Similarity ComputationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679555(175-186)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679555
Jin GLiang YFang YShao ZHuang JZhang JZheng Y(2024)Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.333382436:10(5388-5408)Online publication date: Oct-2024
https://doi.org/10.1109/TKDE.2023.3333824
Mao HSun ZQin N(2024)Heterogeneous Augmentation Based Spatio-Temporal Graph Convolutional Network for Traffic Forecasting2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651254(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651254
Show More Cited By

Index Terms

HeGA: Heterogeneous Graph Aggregation Network for Trajectory Prediction in High-Density Traffic
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
  2. World Wide Web
    1. Web mining
      1. Traffic analysis

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Copyright © 2022 ACM.

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Published: 17 October 2022

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Shenzhen Municipal Science and Technology R&D Funding Basic Research Program
National Natural Science Foundation of China

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CIKM '22: The 31st ACM International Conference on Information and Knowledge Management

October 17 - 21, 2022

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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Cao YLi LChen XXu XHuang ZYu YSerra ESpezzano F(2024)Hypergraph Hash Learning for Efficient Trajectory Similarity ComputationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679555(175-186)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679555
Jin GLiang YFang YShao ZHuang JZhang JZheng Y(2024)Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.333382436:10(5388-5408)Online publication date: Oct-2024
https://doi.org/10.1109/TKDE.2023.3333824
Mao HSun ZQin N(2024)Heterogeneous Augmentation Based Spatio-Temporal Graph Convolutional Network for Traffic Forecasting2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651254(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651254
Guo HPeng YFan ZZhu HSong X(2024)HHGNN: Heterogeneous Hypergraph Neural Network for Traffic Agents Trajectory Prediction in Grouping Scenarios2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10611535(14101-14108)Online publication date: 13-May-2024
https://doi.org/10.1109/ICRA57147.2024.10611535
Quan XLuo DYang QXia TZhang G(2024)Multidimensional graph transformer networks for trajectory prediction in urban road intersectionsJournal of Membrane Computing10.1007/s41965-024-00161-0Online publication date: 9-Jul-2024
https://doi.org/10.1007/s41965-024-00161-0
Sundari KThilak A(2024)A deep learning approach to predicting vehicle trajectories in complex road networksInternational Journal of Data Science and Analytics10.1007/s41060-024-00575-0Online publication date: 3-Jun-2024
https://doi.org/10.1007/s41060-024-00575-0
Xue QYang FLi SLi XLi GZhang W(2024)CiPN-TP: a channel-independent pretrained network via tokenized patching for trajectory predictionThe Journal of Supercomputing10.1007/s11227-024-06462-680:18(26512-26536)Online publication date: 28-Aug-2024
https://doi.org/10.1007/s11227-024-06462-6
Sundari KThilak A(2024)A unified vehicle trajectory prediction model using multi-level context-aware graph attention mechanismThe Journal of Supercomputing10.1007/s11227-024-06393-2Online publication date: 8-Aug-2024
https://doi.org/10.1007/s11227-024-06393-2
Lian WWang YZeng XLiu SXia YTang HSu H(2023)EGL: Efficient Graph Learning with Safety Constrains for Heterogeneous Trajectory PredictionDatabase Systems for Advanced Applications. DASFAA 2023 International Workshops10.1007/978-3-031-35415-1_5(67-78)Online publication date: 28-Sep-2023
https://doi.org/10.1007/978-3-031-35415-1_5

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