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.

在自动驾驶的背景下，通过结合知识图谱和大型语言模型的推理能力与表达能力，提出了一种可解释的道路用户行为预测系统，该系统使用检索增强生成技术，通过知识图谱嵌入和贝叶斯推理结合在一起，实现对行为的预测，并在行人过马路行为和变道操作的两个应用场景中表现出超越现有技术的性能和 F1 得分，展示了未来研究的有希望的方向。