用知识图谱和检索增强生成预测危险变道，提升自动驾驶可解释性。
Explainable Lane Change Prediction for Near-Crash Scenarios Using Knowledge Graph Embeddings and Retrieval Augmented Generation
- 结合知识图谱与贝叶斯推理，利用语言上下文预测变道行为。
- 危险变道预测F1达91.5%，安全变道达90.0%，提前4秒预警。
- 通过RAG生成自然语言解释，适合需高透明度的自动驾驶系统。
突然或高风险情境下的变道是交通事故的主要原因，但现有研究多聚焦于安全变道。本文基于自建的CRASH数据集（专用于危险变道）和HighD数据集（用于安全变道），采用知识图谱（KG）与贝叶斯推断，结合语言上下文信息预测变道行为，提升模型可解释性。模型在危险变道预测上达到91.5% F1分数，安全变道为90.0%，且具备4秒的预判能力。在CARLA模拟器中集成验证，成功提前识别突发变道，为自动驾驶车辆争取反应时间。为进一步增强可解释性，引入检索增强生成（RAG），输出清晰自然语言解释。
原文摘要 · Abstract (English)
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 CRASH dataset (our own collected dataset specifically for risky lane changes), and safe lane changes (using the HighD dataset). Then, we leverage KG 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 RAG to provide clear and natural language explanations for the given prediction.
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