用大模型生成逻辑规则，让自动驾驶更像人一样灵活应对复杂路况。
Human-like Semantic Navigation for Autonomous Driving using Knowledge Representation and Large Language Models
- 用大模型将口语化导航指令转为逻辑规则，实现动态推理。
- 在真实城市场景中验证，系统能适应突发路况变化且决策可解释。
- 适合关注自动驾驶可解释性与人类行为对齐的研究者。
实现完全自动化的自动驾驶仍面临挑战，尤其在动态城市环境中，导航需实时适应变化。现有系统因过度依赖预设地图，在道路布局突变、临时绕行或地图缺失时难以应对。本文探索利用大语言模型（LLM）将非正式导航指令转化为答案集编程（ASP）规则，通过非单调逻辑推理实现无需预设地图的动态适应。实验表明，LLM生成的ASP约束可编码真实城市驾驶逻辑，形成形式化知识表示。该方法实现了从自然语言到逻辑规则的自动化转换，显著提升自动驾驶系统的适应性与可解释性，支持基于语义的决策，其规划逻辑与人类沟通导航意图高度一致。
原文摘要 · Abstract (English)
Achieving full automation in self-driving vehicles remains a challenge, especially in dynamic urban environments where navigation requires real-time adaptability. Existing systems struggle to handle navigation plans when faced with unpredictable changes in road layouts, spontaneous detours, or missing map data, due to their heavy reliance on predefined cartographic information. In this work, we explore the use of Large Language Models to generate Answer Set Programming rules by translating informal navigation instructions into structured, logic-based reasoning. ASP provides non-monotonic reasoning, allowing autonomous vehicles to adapt to evolving scenarios without relying on predefined maps. We present an experimental evaluation in which LLMs generate ASP constraints that encode real-world urban driving logic into a formal knowledge representation. By automating the translation of informal navigation instructions into logical rules, our method improves adaptability and explainability in autonomous navigation. Results show that LLM-driven ASP rule generation supports semantic-based decision-making, offering an explainable framework for dynamic navigation planning that aligns closely with how humans communicate navigational intent.
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