Pedestrian Crossing Intention

Predicting pedestrian crossing intentions is crucial for safe and efficient autonomous vehicle navigation, focusing on accurately anticipating whether a pedestrian will cross a path. Current research emphasizes the development of robust deep learning models, often incorporating recurrent neural networks and attention mechanisms, that leverage diverse data sources including pedestrian kinematics, contextual scene features (e.g., traffic signals, road markings), and multi-camera perspectives to improve prediction accuracy and interpretability. This research area is vital for enhancing the safety and reliability of autonomous vehicles, driving advancements in computer vision, machine learning, and human-robot interaction.