A Review of Pedestrian Trajectory Prediction Methods Based on Deep Learning Technology

Pedestrian trajectory prediction is a critical component of autonomous driving and intelligent urban systems, with deep learning now dominating the field by overcoming the limitations of traditional models in handling multi-modal behaviors and complex social interactions. This survey provides a systematic review and critical analysis of deep learning-based approaches, offering a structured examination of four key model families: RNNs, GANs, GCNs, and Transformer. Unlike previous reviews, we introduce a comparative analytical framework that evaluates each method's strengths and limitations across standardized criteria. The review also presents a comprehensive taxonomy of datasets and evaluation metrics, highlighting both established practices and emerging trends. Finally, we derive future research directions directly from our critical assessment, focusing on semantic scene understanding, model transferability, and the precision-efficiency trade-off. Our work provides both a historical perspective on methodological evolution and a forward-looking analysis to guide future research development.