Pedestrian Trajectory

Pedestrian trajectory prediction focuses on accurately forecasting the future movement paths of individuals, crucial for applications like autonomous vehicle navigation and urban planning. Current research heavily utilizes deep learning models, including transformers, graph neural networks, and variational autoencoders, often incorporating social interaction modeling and multimodal data (e.g., weather, time of day) to improve prediction accuracy and handle the inherent uncertainty in human movement. This field is significant because accurate trajectory prediction enhances safety in human-robot interaction, improves traffic flow management, and enables more efficient urban design and infrastructure development.