Trajectory Prediction
Trajectory prediction focuses on forecasting the future movement of objects, particularly crucial for autonomous systems like self-driving cars and robots. Current research emphasizes improving prediction accuracy and robustness, especially in complex, uncertain environments, using diverse model architectures such as transformers, graph neural networks, and diffusion models, often incorporating multimodal data (e.g., images, LiDAR, maps) and addressing challenges like uncertainty quantification and out-of-distribution generalization. This field is vital for enhancing the safety and efficiency of autonomous systems and has significant implications for various applications, including robotics, traffic management, and assistive technologies.
Papers
TrajLearn: Trajectory Prediction Learning using Deep Generative Models
Amirhossein Nadiri, Jing Li, Ali Faraji, Ghadeer Abuoda, Manos Papagelis
DEMO: A Dynamics-Enhanced Learning Model for Multi-Horizon Trajectory Prediction in Autonomous Vehicles
Chengyue Wang, Haicheng Liao, Kaiqun Zhu, Guohui Zhang, Zhenning Li
THÖR-MAGNI Act: Actions for Human Motion Modeling in Robot-Shared Industrial Spaces
Tiago Rodrigues de Almeida, Tim Schreiter, Andrey Rudenko, Luigi Palmieiri, Johannes A. Stork, Achim J. Lilienthal
Exploring Transformer-Augmented LSTM for Temporal and Spatial Feature Learning in Trajectory Prediction
Chandra Raskoti, Weizi Li
Landing Trajectory Prediction for UAS Based on Generative Adversarial Network
Jun Xiang, Drake Essick, Luiz Gonzalez Bautista, Junfei Xie, Jun Chen
Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning
Jiange Yang, Haoyi Zhu, Yating Wang, Gangshan Wu, Tong He, Limin Wang