Multi Agent Motion Prediction

Multi-agent motion prediction aims to forecast the future movements of multiple interacting agents, such as vehicles and pedestrians, a crucial task for autonomous driving and robotics. Current research emphasizes developing efficient and accurate models, focusing on architectures like transformers and diffusion models, often incorporating scene context and agent interactions through various methods such as message passing, graph neural networks, and game-theoretic approaches. These advancements are driving improvements in prediction accuracy and speed, enabling real-time applications in autonomous systems and contributing to safer and more efficient navigation in complex environments.