Trajectory Prediction Model
Trajectory prediction models aim to forecast the future movements of agents (e.g., vehicles, pedestrians) in dynamic environments, primarily to enhance safety and efficiency in autonomous systems. Current research emphasizes improving model robustness and generalization across diverse scenarios, often employing deep learning architectures like transformers and graph convolutional networks, along with techniques such as multimodal learning and uncertainty quantification. These advancements are crucial for reliable autonomous navigation, human-robot interaction, and traffic simulation, impacting both the development of safer autonomous vehicles and a deeper understanding of agent behavior in complex systems.
Papers
December 19, 2024
December 10, 2024
November 26, 2024
November 25, 2024
November 21, 2024
October 22, 2024
October 21, 2024
October 14, 2024
September 25, 2024
September 24, 2024
September 23, 2024
September 17, 2024
August 1, 2024
July 18, 2024
June 17, 2024
June 2, 2024
May 29, 2024
May 3, 2024
May 2, 2024