会話で操るモジュラー自動運転：LLMによる意思決定フレームワーク
Modular Autonomy with Conversational Interaction: An LLM-driven Framework for Decision Making in Autonomous Driving
LLMを対話インターフェースとしてAutowareに統合し、乗客の高レベルな指示を安全に走行行動へ変換する枠組みを提案・評価した。
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著者: Marvin Seegert, Korbinian Moller, Johannes Betz
分類: cs.RO
原文アブストラクト
Recent advancements in Large Language Models (LLMs) offer new opportunities to create natural language interfaces for Autonomous Driving Systems (ADSs), moving beyond rigid inputs. This paper addresses the challenge of mapping the complexity of human language to the structured action space of modular ADS software. We propose a framework that integrates an LLM-based interaction layer with Autoware, a widely used open-source software. This system enables passengers to issue high-level commands, from querying status information to modifying driving behavior. Our methodology is grounded in three key components: a taxonomization of interaction categories, an application-centric Domain Specific Language (DSL) for command translation, and a safety-preserving validation layer. A two-stage LLM architecture ensures high transparency by providing feedback based on the definitive execution status. Evaluation confirms the system's timing efficiency and translation robustness. Simulation successfully validated command execution across all five interaction categories. This work provides a foundation for extensible, DSL-assisted interaction in modular and safety-conscious autonomy stacks.
