Pedestrian Prediction Model

Pedestrian prediction models aim to forecast the future movement of pedestrians, a crucial task for autonomous vehicles and robotics to ensure safe navigation. Current research emphasizes improving prediction accuracy and efficiency through advanced deep learning architectures like Transformers and diffusion models, often incorporating physics-based constraints or interaction modeling (e.g., using collision risk calculations) to enhance realism and robustness. These advancements are vital for enhancing the safety and reliability of autonomous systems operating in shared spaces, particularly in complex urban environments.