@article{You2025, 
author = {Junwei You and Rui Gan and Weizhe Tang and Zilin Huang and Jiaxi Liu and Zhuoyu Jiang and Haotian Shi and Keshu Wu and Keke Long and Sicheng Fu and Sikai Chen and Bin Ran},
title = {FollowGen: A scaled noise conditional diffusion model for car-following trajectory prediction},
year = {2025},
journal = {Communications in Transportation Research},
volume = {5},
number = {4},
pages = {100215},
keywords = {Generative (AI), Autonomous driving, Conditional diffusion model, Scaled noise, Vehicle trajectory prediction, Car-following dynamics},
url = {https://www.sciopen.com/article/10.1016/j.commtr.2025.100215},
doi = {10.1016/j.commtr.2025.100215},
abstract = {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. Experimental results in various real-world driving scenarios demonstrate the state-of-the-art performance and robustness of the proposed method.}
}