Pedestrian Behavior

Pedestrian behavior research aims to understand and predict how people move and interact in various environments, primarily to improve safety and efficiency in shared spaces like roads and buildings. Current research focuses on developing accurate predictive models using diverse data sources (video, LiDAR, audio) and advanced algorithms such as deep learning (including transformers and convolutional neural networks), reinforcement learning, and Bayesian methods, often incorporating contextual factors like weather, time of day, and cultural influences. This work has significant implications for autonomous vehicle development, urban planning, and crowd management, enabling safer and more efficient systems through improved human-machine and human-environment interaction.