Hazy Pedestrian Trajectory Prediction via Physical Priors and Graph-Mamba
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908563653263360 |
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| author | Chen, Jian Zheng, Zhuoran Hu, Han Zhang, Guijuan Lu, Dianjie Li, Liang Lyu, Chen |
| author_facet | Chen, Jian Zheng, Zhuoran Hu, Han Zhang, Guijuan Lu, Dianjie Li, Liang Lyu, Chen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24020 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hazy Pedestrian Trajectory Prediction via Physical Priors and Graph-Mamba Chen, Jian Zheng, Zhuoran Hu, Han Zhang, Guijuan Lu, Dianjie Li, Liang Lyu, Chen Computer Vision and Pattern Recognition 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. |
| title | Hazy Pedestrian Trajectory Prediction via Physical Priors and Graph-Mamba |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.24020 |