Pedestrian Trajectory Prediction

Pedestrian trajectory prediction aims to forecast the future movement of individuals, crucial for applications like autonomous driving and urban planning. Current research heavily utilizes deep learning, employing architectures such as transformers, recurrent neural networks (RNNs), and generative models (e.g., VAEs, diffusion models) to capture complex spatiotemporal patterns and social interactions. A key focus is improving prediction accuracy and robustness, particularly in challenging scenarios involving crowds, varying weather conditions, and limited sensor data, while also addressing issues like uncertainty quantification and model generalizability across different environments. These advancements have significant implications for enhancing safety and efficiency in various real-world applications.