TY - CONF AU - Manzour,M. AU - Ballardini,A. AU - Izquierdo,R. AU - Sotelo,M.A. KW - Bayesian Inference KW - CARLA KW - Knowledge Graph Embeddings KW - Near-Crash KW - Retrieval Augmented Generation KW - Risky Lane Change Prediction T1 - Explainable Lane Change Prediction for Near-Crash Scenarios Using Knowledge Graph Embeddings and Retrieval Augmented Generation LA - eng PY - 2025/// SP - 1538 EP - 1545 T2 - IEEE Intelligent Vehicles Symposium, Proceedings SN - 2642-7214 SN - 9798331538033 PB - Institute of Electrical and Electronics Engineers Inc. AB - Lane-changing maneuvers, particularly those executed abruptly or in risky situations, are a significant cause of road traffic accidents. However, current research mainly focuses on predicting safe lane changes. Furthermore, existing accident datasets are often based on images only and lack comprehensive sensory data. In this work, we focus on predicting risky lane changes using the CARLA Risky-lane-change Anticipation in Simulated Highways (CRASH) dataset (our own collected dataset specifically for risky lane changes), and safe lane changes (using the HighD dataset). Then, we leverage Knowledge Graphs (KGs) and Bayesian inference to predict these maneuvers using linguistic contextual information, enhancing the model's interpretability and transparency. The model achieved a 91.5% f1-score with anticipation time extending to four seconds for risky lane changes, and a 90.0% f1-score for predicting safe lane changes with the same anticipation time. We validate our model by integrating it into a vehicle within the CARLA simulator in scenarios that involve risky lane changes. The model managed to anticipate sudden lane changes, thus providing automated vehicles with further time to plan and execute appropriate safe reactions. Finally, to enhance the explainability of our model, we utilize Retrieval Augmented Generation (RAG) to provide clear and natural language explanations for the given prediction. DO - 10.1109/IV64158.2025.11097512 UR - https://portalcientifico.uah.es/documentos/68c5b6f68237274acbcc16f2 DP - Dialnet - Portal de la Investigación ER -