TY - JOUR AU - Melo Castillo,A.N. AU - Salinas Maldonado,C. AU - Sotelo,Á. KW - autonomous driving KW - dataset selection KW - explainability KW - feature selection KW - interpretability KW - neuro-symbolic KW - pedestrian behavior prediction T1 - Towards Explainable Pedestrian Behavior Prediction: A Neuro-Symbolic Framework for Autonomous Driving LA - eng PY - 2025/06/01/ T2 - Applied Sciences (Switzerland) SN - 2076-3417 VL - 15 IS - 11 PB - Multidisciplinary Digital Publishing Institute (MDPI) AB - In the context of autonomous driving, predicting pedestrian behavior is a critical component for enhancing road safety. Currently, the focus of such predictions extends beyond accuracy and reliability, placing increasing emphasis on the explainability and interpretability of the models. This research presents a novel neuro-symbolic approach that integrates deep learning with fuzzy logic to develop a pedestrian behavior predictor. The proposed model leverages a set of explainable features and utilizes a fuzzy inference system to determine whether a pedestrian is likely to cross the street. The pipeline was trained and evaluated using both the Pedestrian Intention Estimation (PIE) and Joint Attention for Autonomous Driving (JAAD) datasets. The results provide experimental insights into achieving greater explainability in pedestrian behavior prediction. Additionally, the proposed method was applied to assess the data selection process through a series of experiments, leading to a set of guidelines and recommendations for data selection, feature engineering, and explainability. DO - 10.3390/APP15116283 UR - https://portalcientifico.uah.es/documentos/6857095a55e1544d97879dd4 DP - Dialnet - Portal de la Investigación ER -