Vehicle Trajectory
Vehicle trajectory research focuses on understanding and predicting the movement of vehicles, primarily to improve safety and efficiency in transportation systems and autonomous driving. Current research emphasizes developing accurate and interpretable predictive models using diverse data sources (e.g., GPS, cameras, sensors) and advanced architectures like transformers, graph neural networks, and generative models (e.g., diffusion models). These advancements are crucial for applications ranging from traffic management and accident prevention to the development of robust and safe autonomous driving systems.
Papers
PTrajM: Efficient and Semantic-rich Trajectory Learning with Pretrained Trajectory-Mamba
Yan Lin, Yichen Liu, Zeyu Zhou, Haomin Wen, Erwen Zheng, Shengnan Guo, Youfang Lin, Huaiyu Wan
TrajFM: A Vehicle Trajectory Foundation Model for Region and Task Transferability
Yan Lin, Tonglong Wei, Zeyu Zhou, Haomin Wen, Jilin Hu, Shengnan Guo, Youfang Lin, Huaiyu Wan