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.

翻译：在自动驾驶背景下预测道路使用者行为近年来已引起科学界的广泛关注。多数研究仅基于运动学信息进行行为预测，这在一定程度上简化了现实场景——因为道路使用者作为人类，其行为深受周围环境影响。此外，大量研究工作依赖强大的深度学习技术，这类方法在预测任务中虽然表现出高绩效指标，但可能缺乏充分理解和利用道路场景中上下文语义信息的能力，更无法提供人类可理解的可解释性预测。本研究提出了一种可解释的道路使用者行为预测系统，通过检索增强生成（RAG）技术，将知识图谱（KG）的推理能力与大语言模型（LLM）的表达能力相结合。为此，我们融合知识图谱嵌入（KGE）与贝叶斯推理，构建全归纳推理系统，可基于图谱中既有信息与车载传感器实时采集的当前证据生成预测。采用该方法实现了两个用例：1）行人过街行为预测；2）车道变更行为预测。在两项任务中，本方法在预测提前量与F1分数方面均超越现有技术水平，为未来该领域研究开辟了具有前景的方向。