FollowGen: A scaled noise conditional diffusion model for car-following trajectory prediction

作者：Junwei You, Rui Gan, Wenhao Tang, Zilin Huang, Jiaxi Liu, Zhuoyu Jiang, Haotian Shi, Keshu Wu, Keke Long, Sicheng Fu, Sikai Chen, Bin Ran · 发表于：Communications in Transportation Research · 年份：2025 · DOI：10.1016/j.commtr.2025.100215 · 被引用次数：7 · 研究领域：Autonomous Vehicle Technology and Safety、Traffic Prediction and Management Techniques、Traffic control and management

Vehicle trajectory prediction is critical for advancing autonomous driving and advanced driver assistance systems (ADASs). Deep learning-based approaches, especially those using transformer-based and generative models, have significantly improved prediction accuracy by capturing complex, non-linear patterns in vehicle dynamics and traffic interactions. However, they often overlook detailed car-following behaviors and the inter-vehicle interactions essential for real-world driving, particularly in fully autonomous or mixed traffic scenarios. Moreover, existing generative approaches in trajectory prediction are inefficient at conditioning predictions on relevant constraints. To address these issues, this study proposes FollowGen, a novel scaled noise conditional diffusion model for car-following trajectory prediction. FollowGen incorporates detailed inter-vehicular interactions and car-following dynamics within a generative framework, enhancing both the accuracy and realism of the predicted trajectories. The model uses a novel pipeline to capture historical vehicle behaviors. It leverages a noise scaling conditioning strategy to scale the noise with encoded historical features within the forward diffusion process to ensure history-constrained noise transformation. A cross-attention-based transformer architecture is employed in the reverse process to model intricate inter-vehicle dependencies, effectively guiding the denoising process and enhancing prediction accuracy. Experimen...