FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866918291257163776 |
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| author | You, Junwei Gan, Rui Tang, Weizhe Huang, Zilin Liu, Jiaxi Jiang, Zhuoyu Shi, Haotian Wu, Keshu Long, Keke Fu, Sicheng Chen, Sikai Ran, Bin |
| author_facet | You, Junwei Gan, Rui Tang, Weizhe Huang, Zilin Liu, Jiaxi Jiang, Zhuoyu Shi, Haotian Wu, Keshu Long, Keke Fu, Sicheng Chen, Sikai Ran, Bin |
| contents | Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS). Although deep learning-based approaches - especially those utilizing transformer-based and generative models - have markedly improved prediction accuracy by capturing complex, non-linear patterns in vehicle dynamics and traffic interactions, they frequently overlook detailed car-following behaviors and the inter-vehicle interactions critical for real-world driving applications, particularly in fully autonomous or mixed traffic scenarios. To address the issue, this study introduces a scaled noise conditional diffusion model for car-following trajectory prediction, which integrates detailed inter-vehicular interactions and car-following dynamics into a generative framework, improving both the accuracy and plausibility of predicted trajectories. The model utilizes a novel pipeline to capture historical vehicle dynamics by scaling noise with encoded historical features within the diffusion process. Particularly, it employs a cross-attention-based transformer architecture to model intricate inter-vehicle dependencies, effectively guiding the denoising process and enhancing prediction accuracy. Experimental results on diverse real-world driving scenarios demonstrate the state-of-the-art performance and robustness of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16747 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction You, Junwei Gan, Rui Tang, Weizhe Huang, Zilin Liu, Jiaxi Jiang, Zhuoyu Shi, Haotian Wu, Keshu Long, Keke Fu, Sicheng Chen, Sikai Ran, Bin Computer Vision and Pattern Recognition Artificial Intelligence Emerging Technologies Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS). Although deep learning-based approaches - especially those utilizing transformer-based and generative models - have markedly improved prediction accuracy by capturing complex, non-linear patterns in vehicle dynamics and traffic interactions, they frequently overlook detailed car-following behaviors and the inter-vehicle interactions critical for real-world driving applications, particularly in fully autonomous or mixed traffic scenarios. To address the issue, this study introduces a scaled noise conditional diffusion model for car-following trajectory prediction, which integrates detailed inter-vehicular interactions and car-following dynamics into a generative framework, improving both the accuracy and plausibility of predicted trajectories. The model utilizes a novel pipeline to capture historical vehicle dynamics by scaling noise with encoded historical features within the diffusion process. Particularly, it employs a cross-attention-based transformer architecture to model intricate inter-vehicle dependencies, effectively guiding the denoising process and enhancing prediction accuracy. Experimental results on diverse real-world driving scenarios demonstrate the state-of-the-art performance and robustness of the proposed method. |
| title | FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Emerging Technologies |
| url | https://arxiv.org/abs/2411.16747 |