Vehicle Trajectory Prediction

Vehicle trajectory prediction aims to forecast the future movement of vehicles, crucial for autonomous driving and intelligent transportation systems. Current research heavily utilizes deep learning, employing transformer networks, graph neural networks, and diffusion models to capture complex spatiotemporal interactions and incorporate factors like lane structure, traffic signals, and driver behavior (including cognitive and emotional states). These advancements improve prediction accuracy and robustness, particularly for long-term predictions and challenging scenarios like intersections, but challenges remain in generalizing models across diverse environments and ensuring reliable uncertainty quantification for safety-critical applications. The ultimate goal is to enhance road safety, traffic efficiency, and the development of reliable autonomous driving systems.