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PIEPredict++: An Improved Pedestrian Intention Estimation Model Incorporating Comprehensive Environment Information

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Advances in Visual Computing (ISVC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15046))

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Abstract

Predicting pedestrians’ highly variable crossing intentions has attracted considerable attention in autonomous driving research. This paper introduces PIEPredict++, a new model for predicting pedestrian intentions. It uses dynamic and static environmental features around pedestrians to infer intentions. To understand pedestrian behavior, we propose the EnvProcess module, incorporating the cross-attention mechanism to focus on key features in different environments. Evaluation of the publicly available pedestrian intention estimation dataset demonstrates that our model achieves exceptional results in terms of accuracy and F1 score. Our state-of-the-art performance in F1 score and recall is particularly noteworthy, underscoring the model’s exceptional ability to predict false-negative instances and its considerable implications for autonomous driving.

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Notes

  1. 1.

    https://github.com/aras62/PIE.

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Author information

Authors and Affiliations

  1. Ritsumeikan University, Ibaraki, Osaka, 567-8570, Japan

    Yikai Bao & Nobuhiko Nishio

  2. TIER IV, Inc., Shinagawa-ku, Tokyo, 140-0001, Japan

    Yikai Bao & Yukihiro Saito

Authors
  1. Yikai Bao
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  2. Yukihiro Saito
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  3. Nobuhiko Nishio
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Corresponding authors

Correspondence to Yikai Bao or Nobuhiko Nishio .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Johns Hopkins University, Baltimore, MD, USA

    Vishal Patel

  3. Chinese University of Hong Kong, Shatin, Hong Kong

    Jinwei Gu

  4. University of California, Davis, CA, USA

    Julian Panetta

  5. George Mason University, Fairfax, VA, USA

    Yotam Gingold

  6. University of Georgia, Athens, GA, USA

    Kyle Johnsen

  7. Colorado State University, Fort Collins, CO, USA

    Mohammed Safayet Arefin

  8. Indian Institute of Technology, Kanpur, Uttar Pradesh, India

    Soumya Dutta

  9. Los Alamos National Lab., Los Alamos, NM, USA

    Ayan Biswas

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Bao, Y., Saito, Y., Nishio, N. (2025). PIEPredict++: An Improved Pedestrian Intention Estimation Model Incorporating Comprehensive Environment Information. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2024. Lecture Notes in Computer Science, vol 15046. Springer, Cham. https://doi.org/10.1007/978-3-031-77392-1_11

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  • DOI: https://doi.org/10.1007/978-3-031-77392-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-77391-4

  • Online ISBN: 978-3-031-77392-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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