TY - CONF AU - Melo,A.N. AU - Salinas,C. AU - Sotelo,M.A. KW - Autonomous driving KW - Bayesian inference KW - CARLA simulator KW - Collision avoidance KW - Knowledge graph KW - Knowledge graph embeddings KW - Occluded pedestrians T1 - Anticipating the Invisible: A Knowledge-Based Agent for Occluded Pedestrian Collision Avoidance in Virtual Scenarios LA - eng PY - 2025/// SP - 1330 EP - 1336 T2 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC SN - 2153-0017 SN - 9798331524180 PB - Institute of Electrical and Electronics Engineers Inc. AB - Poor pedestrian visibility is recognized as one of the common risk factors contributing to pedestrian crashes. Despite advances in perception algorithms and collision avoidance systems, there remains a significant gap in handling scenarios involving fully occluded pedestrians. In this work, we present a custom behavior agent that leverages a knowledge-based occluded pedestrian predictor to influence the behavior of an Autonomous vehicles (AV). We designed and implemented 72 scenarios in Virtual Reality (VR) to evaluate the performance of both the agent and the predictor, using metrics such as collision avoidance rate, Time To Collision (TTC), and Pedestrian Detection Anticipation Time (PDAT). We compared the results against traditional agents that rely on standard detection algorithms. Preliminary findings demonstrate the effectiveness of incorporating contextual information to predict fully occluded pedestrians: Our agent achieved an average pedestrian detection anticipation time of 4.27 seconds and a collision avoidance rate of 87.5% across all experiments. DO - 10.1109/ITSC60802.2025.11423427 UR - https://portalcientifico.uah.es/documentos/69f8dc8d579ceb0ec2bf47b7 DP - Dialnet - Portal de la Investigación ER -