Proactive Pedestrian Safety System by Fusing Trajectory Prediction With Semantic Ground Region Classification

This paper presents a proactive pedestrian risk assessment system for traffic environments that integrates trajectory prediction with situation classification. Unlike end-to-end approaches that function as black boxes when predicting pedestrians' crossing intentions, our system employs trajectory forecasting combined with ground region classification of predicted paths. The proposed methodology first predicts future pedestrian trajectories using an attention-based recurrent neural network, then classifies the predicted situation using accumulated segmentation maps to assess potential pedestrian risk. Experimental evaluations demonstrate that our system outperforms black box approaches across multiple evaluation metrics. We also present validation results using real-world surveillance footage captured in urban environments, demonstrating the system’s real-time capability and practical applicability for integration into smart city infrastructure.