FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction
| Autor: | You, Junwei, Gan, Rui, Tang, Weizhe, Huang, Zilin, Liu, Jiaxi, Jiang, Zhuoyu, Shi, Haotian, Wu, Keshu, Long, Keke, Fu, Sicheng, Chen, Sikai, Ran, Bin |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: arXiv admin note: text overlap with arXiv:2406.11941 |
| Databáze: | arXiv |
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