Vehicle Trajectory Prediction

Vehicle trajectory prediction estimates the future paths and possible maneuvers of vehicles to support planning, collision avoidance, and traffic simulation. Methods combine vehicle histories with road maps and surrounding-agent context, using approaches such as graph and Transformer networks, goal-conditioned or motion-primitive decoders, and probabilistic or diffusion models to represent multiple possible futures. Physics-based constraints, lane topology, and explicit uncertainty estimates can improve feasibility and safety, while streaming inference and closed-loop training address real-time use. A central challenge is balancing accurate, diverse long-horizon forecasts with calibrated uncertainty, robustness to noisy or unfamiliar scenes, and efficient deployment.