Joint Trajectory Prediction

Joint trajectory prediction aims to forecast the future movements of multiple interacting agents, such as vehicles and pedestrians, within a shared environment, crucial for safe autonomous systems. Current research heavily focuses on improving the accuracy and efficiency of these predictions, employing diverse approaches including transformer networks, diffusion models, and game-theoretic formulations, often incorporating scene context (e.g., lane occupancy) and explicit interaction modeling between agents. These advancements are vital for enhancing the reliability and safety of autonomous driving and robotics applications, enabling more robust and predictable decision-making in complex dynamic scenarios.