Pedestrian Action Anticipation

Pedestrian action anticipation focuses on predicting the future movements of pedestrians, primarily to improve the safety and efficiency of autonomous systems like robots and self-driving cars. Current research emphasizes using computer vision, particularly convolutional neural networks (CNNs) and transformer models, to analyze visual data and predict pedestrian actions like crossing the road. A key challenge lies in handling unpredictable pedestrian behavior and improving the explainability of these models to ensure reliable and safe operation. This research area is crucial for advancing the development of safe and robust autonomous systems operating in shared spaces with humans.