With the rise of intelligent transportation systems, accurate pedestrian trajectory prediction is critical for autonomous driving safety. Traditional methods struggle with complex, dynamic environments and bird's-eye view datasets, limiting their applicability to vehicle-centric scenarios. This paper proposes the Multi-Feature Fusion YNet (MFF-YNet), an egocentric pedestrian trajectory prediction model that integrates multimodal features—head orientation, pedestrian behavior, status, and vehicle distance—using a novel fusion mechanism within the YNet architecture. Trained on a self-annotated Taiwan Pedestrian Road Use Dataset capturing localized hazardous scenarios, MFF-YNet significantly outperforms baselines in average displacement error (ADE) and final displacement error (FDE), achieving robust predictions for safety-critical applications.
WANG et al. (Mon,) studied this question.