Diffusion Models for Intelligent Transportation Systems: A Survey
<|reference_start|>Diffusion Models for Intelligent Transportation Systems: A Survey: Intelligent Transportation Systems (ITS) are vital in modern traffic management and optimization, significantly enhancing traffic efficiency and safety. Recently, diffusion models have emerged as transformative tools for addressing complex challenges within ITS. In this paper, we present a comprehensive survey of diffusion models for ITS, covering both theoretical and practical aspects. First, we introduce the theoretical foundations of diffusion models and their key variants, including conditional diffusion models and latent diffusion models, highlighting their suitability for modeling complex, multi-modal traffic data and enabling controllable generation. Second, we outline the primary challenges in ITS and the corresponding advantages of diffusion models, providing readers with a deeper understanding of the intersection between ITS and diffusion models. Third, we offer a multi-perspective investigation of current applications of diffusion models in ITS domains, including autonomous driving, traffic simulation, trajectory prediction, and traffic safety. Finally, we discuss state-of-the-art diffusion model techniques and highlight key ITS research directions that warrant further investigation. Through this structured overview, we aim to provide researchers with a comprehensive understanding of diffusion models for ITS, thereby advancing their future applications in the transportation domain.<|reference_end|>
@article{peng2024diffusion,
    title={Diffusion Models for Intelligent Transportation Systems: A Survey},
    author={Mingxing Peng, Kehua Chen, Xusen Guo, Qiming Zhang, Hongliang Lu, Hui
  Zhong, Di Chen, Meixin Zhu, and Hai Yang},
    journal={arXiv preprint arXiv:2409.15816},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15816},
    primaryClass={eess.SY cs.SY}
}