Hazy Pedestrian Trajectory Prediction via Physical Priors and Graph-Mamba

• URL: http://arxiv.org/abs/2509.24020v1
• Date: Sun, 28 Sep 2025 18:29:43 GMT
• Title: Hazy Pedestrian Trajectory Prediction via Physical Priors and Graph-Mamba
• Authors: Jian Chen, Zhuoran Zheng, Han Hu, Guijuan Zhang, Dianjie Lu, Liang Li, Chen Lyu,
• Abstract summary: We propose a deep learning model that combines physical priors of atmospheric scattering with topological modeling of pedestrian relationships.<n>Our method reduces minADE / minFDE metrics by 37.2% and 41.5%, respectively, compared to the SOTA models in dense haze scenarios.
• Score: 23.886173346851123
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To address the issues of physical information degradation and ineffective pedestrian interaction modeling in pedestrian trajectory prediction under hazy weather conditions, we propose a deep learning model that combines physical priors of atmospheric scattering with topological modeling of pedestrian relationships. Specifically, we first construct a differentiable atmospheric scattering model that decouples haze concentration from light degradation through a network with physical parameter estimation, enabling the learning of haze-mitigated feature representations. Second, we design an adaptive scanning state space model for feature extraction. Our adaptive Mamba variant achieves a 78% inference speed increase over native Mamba while preserving long-range dependency modeling. Finally, to efficiently model pedestrian relationships, we develop a heterogeneous graph attention network, using graph matrices to model multi-granularity interactions between pedestrians and groups, combined with a spatio-temporal fusion module to capture the collaborative evolution patterns of pedestrian movements. Furthermore, we constructed a new pedestrian trajectory prediction dataset based on ETH/UCY to evaluate the effectiveness of the proposed method. Experiments show that our method reduces the minADE / minFDE metrics by 37.2% and 41.5%, respectively, compared to the SOTA models in dense haze scenarios (visibility < 30m), providing a new modeling paradigm for reliable perception in intelligent transportation systems in adverse environments.

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