# A Large-Language-Model Supported Personalized Driving Framework for Lane Change in Highway Scenarios

arXiv: https://arxiv.org/abs/2606.31483 · HTML: https://mlsanity.com/paper/2606.31483/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Large language models for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 23 of its 46 references that are in the map.
- First posted on arXiv 30 Jun 2026.

## Placed in

- End-to-end autonomous driving / [Large language models for driving](https://mlsanity.com/driving/large-language-models-for-driving/)

## Builds on (most cited first, 23 of 23)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [LMDrive: Closed-Loop End-to-End Driving with Large Language Models](https://mlsanity.com/paper/2312.07488/) (2023)
- [DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2309.16292/) (2023)
- [Talk2Car: Taking Control of Your Self-Driving Car](https://mlsanity.com/paper/1909.10838/) (2019)
- [LLM4Drive: A Survey of Large Language Models for Autonomous Driving](https://mlsanity.com/paper/2311.01043/) (2023)
- [Vision Language Models in Autonomous Driving: A Survey and Outlook](https://mlsanity.com/paper/2310.14414/) (2023)
- [Language Prompt for Autonomous Driving](https://mlsanity.com/paper/2309.04379/) (2023)
- [Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles](https://mlsanity.com/paper/2309.10228/) (2023)
- [Personalized Autonomous Driving with Large Language Models: Field Experiments](https://mlsanity.com/paper/2312.09397/) (2023)
- [GPT-4 Enhanced Multimodal Grounding for Autonomous Driving: Leveraging Cross-Modal Attention with Large Language Models](https://mlsanity.com/paper/2312.03543/) (2023)
- [MAVERIC: A Data-Driven Approach to Personalized Autonomous Driving](https://mlsanity.com/paper/2301.08595/) (2023)
- [Human-Centric Autonomous Systems With LLMs for User Command Reasoning](https://mlsanity.com/paper/2311.08206/) (2023)
- [doScenes: An Autonomous Driving Dataset with Natural Language Instruction for Human Interaction and Vision-Language Navigation](https://mlsanity.com/paper/2412.05893/) (2024)
- [Chat2Scenario: Scenario Extraction From Dataset Through Utilization of Large Language Model](https://mlsanity.com/paper/2404.16147/) (2024)
- [StyleVLA: Driving Style-Aware Vision Language Action Model for Autonomous Driving](https://mlsanity.com/paper/2603.09482/) (2026)
- [PADriver: Towards Personalized Autonomous Driving](https://mlsanity.com/paper/2505.05240/) (2025)
- [Autoware.Flex: Human-Instructed Dynamically Reconfigurable Autonomous Driving Systems](https://mlsanity.com/paper/2412.16265/) (2024)
- [A Survey on the Application of Large Language Models in Scenario-Based Testing of Automated Driving Systems](https://mlsanity.com/paper/2505.16587/) (2025)
- [User-Driven Adaptation: Tailoring Autonomous Driving Systems with Dynamic Preferences](https://mlsanity.com/paper/2403.02928/) (2024)
- [Accelerating the Evolution of Personalized Automated Lane Change through Lesson Learning](https://mlsanity.com/paper/2405.07543/) (2024)
- [A Communication-Latency-Aware Co-Simulation Platform for Safety and Comfort Evaluation of Cloud-Controlled ICVs](https://mlsanity.com/paper/2506.07696/) (2025)
- [Toward a Full-Stack Co-Simulation Platform for Testing of Automated Driving Systems](https://mlsanity.com/paper/2507.06884/) (2025)

## Abstract (arXiv)

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.