Knowledge-based explainable pedestrian behavior predictor

In the context of autonomous driving, pedestrian behavior prediction is a key component for improving road safety. Presently, many existing prediction models prioritize achieving reliable results, however, they often lack insights into the explainability of each prediction. In this work, we propose a novel approach to pedestrian behavior prediction using knowledge graphs (KG), knowledge graph embeddings (KGE), and a Bayesian Inference process, enabling fully inductive reasoning on KGEs. Our approach aims to consolidate knowledge from annotated datasets through explainable pedestrian features and fuzzy rules, evaluating the importance of these two components within the KG. The entire pipeline has been trained and tested using two datasets: Joint Attention for Autonomous Driving (JAAD) and Pedestrian Situated Intent (PSI). Preliminary results demonstrate the effectiveness of this system in providing explainable clues for pedestrian behavior predictions, even improving results by up to 15% compared to other models. Our approach achieves an F1 score of 0.84 for PSI and 0.82 for JAAD.