Pedestrian Crossing Prediction

Pedestrian crossing prediction aims to anticipate when and where pedestrians will cross roads, a crucial task for autonomous driving and improving road safety. Current research emphasizes improving prediction accuracy and robustness through various approaches, including the use of deep learning models (like transformers and neural networks), synthetic data augmentation to address data limitations, and the incorporation of both trajectory and appearance data for more comprehensive understanding of pedestrian behavior. This research area is significant because reliable pedestrian crossing prediction is essential for safe and efficient autonomous vehicle navigation, impacting both the development of advanced driver-assistance systems and the broader field of computer vision.