用大模型理解用户隐含驾驶偏好，生成三种风格的变道行为。
A Large-Language-Model Supported Personalized Driving Framework for Lane Change in Highway Scenarios
- 基于大模型解析自然语言指令，映射到自动驾驶规划参数。
- 三种风格参数集可生成明显区分的激进/正常/保守变道行为。
- 检索增强生成提升隐式指令理解，适合个性化自动驾驶研究者。
个性化驾驶能提升自动驾驶系统的用户接受度。然而，现有方法对以自然语言表达的驾驶偏好（尤其是隐含表达）支持有限，难以转化为可执行且可区分的驾驶行为。本文提出一种基于大语言模型（LLM）的高速公路变道个性化驾驶框架。该框架将自然语言驾驶指令映射至开源Apollo自动驾驶系统中的可执行规划参数，对应激进、正常、保守三种驾驶风格。通过评估候选参数生成的变道行为，结合聚类与风格强度排序构建风格专属参数集。在指令解析方面，构建检索数据集以支持检索增强生成（RAG），实现对隐式用户指令的基于大模型的理解。实验表明，所获参数集能生成明显区分的个性化变道行为，且RAG显著提升偏好解析效果，尤其在处理隐式指令时表现更优。结果表明，将基于大模型的自然语言交互与Apollo系统结合，具备支持个性化变道行为生成的潜力。源代码与相关数据集已公开：https://github.com/ftgTUGraz/LLM-Personalized-Driving。
原文摘要 · Abstract (English)
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.
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