Prediction of Occluded Pedestrians in Road Scenes Using Human-Like Reasoning: Insights from the OccluRoads Dataset

Pedestrian detection is a critical task in autonomous driving, aimed at improving safety and reducing risks on the road. In recent years, significant advancements have been made in detection performance. However, these achievements still fall short of human perception, particularly in cases involving occluded pedestrians, especially those entirely invisible. In this work, we present the Occlusion-Rich Road Scenes with Pedestrians (OccluRoads) Dataset, a diverse collection of road scenes with partially and fully occluded pedestrians in both real-world and virtual environments. All scenes are meticulously labeled and enriched with contextual information that encapsulates human perception in such scenarios. Leveraging this Dataset, we developed a pipeline to predict the presence of occluded pedestrians using Knowledge Graph (KG), Knowledge Graph Embedding (KGE), and a Bayesian inference process. Our approach achieves an F1 score of 0.91, representing an improvement of up to 42% compared to traditional machine learning models.