A Survey of Autonomous Driving Trajectory Prediction: Methodologies, Challenges, and Future Prospects

Trajectory prediction is a critical component of autonomous driving decision-making systems, directly impacting driving safety and traffic efficiency. Despite advancements, existing reviews exhibit limitations in timeliness, classification frameworks, and challenge analysis. This paper systematically reviews multi-agent trajectory prediction technologies, focusing on generating future position sequences from historical trajectories, high-precision maps, and scene context. We propose a multi-dimensional classification framework integrating input representation, output forms, method paradigms, and interaction modeling. The review comprehensively compares conventional methods and deep learning architectures, including diffusion models and large language models. We further analyze five core challenges: complex interactions, rule and map dependence, long-term prediction errors, extreme-scene generalization, and real-time constraints. Finally, interdisciplinary solutions are prospectively explored.