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Aware of the History: Trajectory Forecasting with the Local Behavior Data

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

The historical trajectories previously passing through a location may help infer the future trajectory of an agent currently at this location. Despite great improvements in trajectory forecasting with the guidance of high-definition maps, only a few works have explored such local historical information. In this work, we re-introduce this information as a new type of input data for trajectory forecasting systems: the local behavior data, which we conceptualize as a collection of location-specific historical trajectories. Local behavior data helps the systems emphasize the prediction locality and better understand the impact of static map objects on moving agents. We propose a novel local-behavior-aware (LBA) prediction framework that improves forecasting accuracy by fusing information from observed trajectories, HD maps, and local behavior data. Also, where such historical data is insufficient or unavailable, we employ a local-behavior-free (LBF) prediction framework, which adopts a knowledge-distillation-based architecture to infer the impact of missing data. Extensive experiments demonstrate that upgrading existing methods with these two frameworks significantly improves their performances. Especially, the LBA framework boosts the SOTA methods’ performance on the nuScenes dataset by at least 14% for the \(K=1\) metrics. Code is at https://github.com/Kay1794/LocalBehavior-based-trajectory-prediction.

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Acknowledgements

National Natural Science Foundation of China under Grant 62171276, the Science and Technology Commission of Shanghai Municipal under Grant 21511100900, CCF-DiDi GAIA Research Collaboration Plan 202112 and CALT 2021-01.

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Authors and Affiliations

  1. Department of Computer Science, University of Southern California, Los Angeles, CA, 90089, USA

    Yiqi Zhong & Ulrich Neumann

  2. Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University, Shanghai, China

    Zhenyang Ni & Siheng Chen

Authors
  1. Yiqi Zhong
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  2. Zhenyang Ni
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  3. Siheng Chen
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  4. Ulrich Neumann
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Corresponding author

Correspondence to Siheng Chen .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Zhong, Y., Ni, Z., Chen, S., Neumann, U. (2022). Aware of the History: Trajectory Forecasting with the Local Behavior Data. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13682. Springer, Cham. https://doi.org/10.1007/978-3-031-20047-2_23

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  • DOI: https://doi.org/10.1007/978-3-031-20047-2_23

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