Pedestrian Behavior Prediction

Pedestrian behavior prediction aims to accurately forecast pedestrian actions, crucial for safe autonomous vehicle navigation. Current research emphasizes improving prediction accuracy and explainability using various deep learning architectures, including transformers and reinforcement learning models, often incorporating multimodal data (e.g., images, 3D keypoints, behavioral features) and focusing on addressing challenges like cross-dataset generalization and the need for robust handling of edge cases. This field is vital for advancing autonomous driving safety and efficiency, impacting both the development of more reliable self-driving systems and the broader understanding of human-robot interaction in dynamic environments.