高速道路の車線変更における大規模言語モデル支援型パーソナライズ運転フレームワーク
A Large-Language-Model Supported Personalized Driving Framework for Lane Change in Highway Scenarios
大規模言語モデルを用いて、自然言語の運転好みを実行可能な運転パラメータに変換し、高速道路の車線変更を個別化するフレームワークを提案した。
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著者: Dong Bi, Yongqi Zhao, Paul Kovacevic, Tomislav Mihalj, Ji Zhou, Jiayuan Gong, Arno Eichberger
分類: cs.RO
原文アブストラクト
Personalized driving can improve the user acceptance of automated driving systems. However, existing methods still provide limited support for translating natural-language driving preferences, especially when such preferences are expressed implicitly, into executable and distinguishable driving behaviors. This paper proposes a large language model (LLM)-supported personalized driving framework for highway lane-change scenarios. The framework maps natural-language driving commands to executable planning parameters in the open-source Apollo automated driving stack according to three driving styles: aggressive, normal, and conservative. To establish this mapping, candidate planning parameters are evaluated based on the resulting lane-change behaviors, and style-specific parameter sets are constructed through clustering and style-intensity ranking. For command interpretation, a retrieval dataset is constructed to support retrieval-augmented generation (RAG), enabling LLM-based interpretation of implicit user commands. Experimental results show that the derived parameter sets generate distinguishable personalized lane-change behaviors, while RAG consistently improves preference interpretation, particularly for implicit commands. These results indicate the potential of integrating LLM-based natural-language interaction with Apollo to support personalized lane-change behavior generation. The source code and the relevant datasets are available at: https://github.com/ftgTUGraz/LLM-Personalized-Driving.
