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.

Prediction of road users' behaviors in the context of autonomous driving has gained considerable attention by the scientific community in the last years. Most works focus on predicting behaviors based on kinematic information alone, a simplification of the reality since road users are humans, and as such they are highly influenced by their surrounding context. In addition, a large plethora of research works rely on powerful Deep Learning techniques, which exhibit high performance metrics in prediction tasks but may lack the ability to fully understand and exploit the contextual semantic information contained in the road scene, not to mention their inability to provide explainable predictions that can be understood by humans. In this work, we propose an explainable road users' behavior prediction system that integrates the reasoning abilities of Knowledge Graphs (KG) and the expressiveness capabilities of Large Language Models (LLM) by using Retrieval Augmented Generation (RAG) techniques. For that purpose, Knowledge Graph Embeddings (KGE) and Bayesian inference are combined to allow the deployment of a fully inductive reasoning system that enables the issuing of predictions that rely on legacy information contained in the graph as well as on current evidence gathered in real time by onboard sensors. Two use cases have been implemented following the proposed approach: 1) Prediction of pedestrians' crossing actions; 2) Prediction of lane change maneuvers. In both cases, the performance attained surpasses the current state of the art in terms of anticipation and F1-score, showing a promising avenue for future research in this field.