Human-like Semantic Navigation for Autonomous Driving using Knowledge Representation and Large Language Models

TL;DR

This paper introduces a novel approach using Large Language Models to generate logic-based rules for autonomous vehicle navigation, enhancing adaptability and explainability in dynamic urban environments.

Contribution

It presents a method to translate informal navigation instructions into ASP rules via LLMs, enabling real-time, adaptable, and explainable autonomous driving decisions.

Findings

LLMs can effectively generate ASP constraints from natural language instructions.

The approach improves navigation adaptability in unpredictable urban scenarios.

The framework enhances explainability in autonomous decision-making.

Abstract

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.