{"ID":5438805,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-03T10:35:20.036867845Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31483","arxiv_id":"2606.31483","title":"A Large-Language-Model Supported Personalized Driving Framework for Lane Change in Highway Scenarios","abstract":"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.","short_abstract":"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 pr...","url_abs":"https://arxiv.org/abs/2606.31483","url_pdf":"https://arxiv.org/pdf/2606.31483v1","authors":"[\"Dong Bi\",\"Yongqi Zhao\",\"Paul Kovacevic\",\"Tomislav Mihalj\",\"Ji Zhou\",\"Jiayuan Gong\",\"Arno Eichberger\"]","published":"2026-06-30T10:58:21Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"RAG\",\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":613781,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-01T01:17:58.482524686Z","DeletedAt":null,"paper_id":5438805,"paper_url":"https://arxiv.org/abs/2606.31483","paper_title":"A Large-Language-Model Supported Personalized Driving Framework for Lane Change in Highway Scenarios","repo_url":"https://github.com/ftgTUGraz/LLM-Personalized-Driving","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}