FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction

• URL: http://arxiv.org/abs/2411.16747v1
• Date: Sat, 23 Nov 2024 23:13:45 GMT
• Title: FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction
• Authors: Junwei You, Rui Gan, Weizhe Tang, Zilin Huang, Jiaxi Liu, Zhuoyu Jiang, Haotian Shi, Keshu Wu, Keke Long, Sicheng Fu, Sikai Chen, Bin Ran,
• Abstract summary: This study introduces a scaled noise conditional diffusion model for car-following trajectory prediction. It integrates detailed inter-vehicular interactions and car-following dynamics into a generative framework, improving the accuracy and plausibility of predicted trajectories. Experimental results on diverse real-world driving scenarios demonstrate the state-of-the-art performance and robustness of the proposed method.
• Score: 9.2729178775419
• License:
• Abstract: 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.

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