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

• URL: http://arxiv.org/abs/2606.31483v2
• Date: Wed, 01 Jul 2026 07:57:12 GMT
• Title: A Large-Language-Model Supported Personalized Driving Framework for Lane Change in Highway Scenarios
• Abstract summary: This paper proposes a large language model (LLM)-supported personalized driving framework for highway lane-change scenarios.<n>The framework maps natural-language driving commands to executable planning parameters in the open-source Apollo automated driving stack.<n>Experiments show that the derived parameter sets generate distinguishable personalized lane-change behaviors, while RAG consistently improves preference interpretation.
• Score: 1.6314656571060535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• DriveJudge: Rethinking Autonomous Driving Evaluation with Vision-Language Models [53.73898294218455]
  DriveJudge is a driving evaluation agent that combines rule-grounded evaluation with Vision-Language Model (VLM) reasoning.<n>We introduce two benchmark tasks: Driving Quality Classification and Trajectory Preference Selection.<n>DriveJudge outperformsS for driving quality classification by 21.23 AUC, and the recent VLM-based DriveCritic for trajectory preference selection by 6.5%.
  arXiv  Detail & Related papers  (2026-06-15T23:39:36Z)
• Language-Driven Cost Optimization for Autonomous Driving [15.884773782381346]
  This work presents a language-driven framework for adaptive cost design in autonomous driving.<n>A Large Language Model (LLM) interprets structured scenario descriptions and natural language user queries.<n>The system incorporates a human-in-the-loop validation stage in which the proposed behavioral changes are described in non-technical language.
  arXiv  Detail & Related papers  (2026-06-09T15:13:30Z)
• ICR-Drive: Instruction Counterfactual Robustness for End-to-End Language-Driven Autonomous Driving [1.6961250145588112]
  We introduce ICR-Drive, a diagnostic framework for instruction counterfactual robustness in language-conditioned autonomous driving.<n>We replay identical CARLA routes under matched simulator configurations and seeds to isolate performance changes attributable to instruction language.<n>Experiments on LMDrive and BEVDriver show that minor instruction changes can induce substantial performance drops and distinct failure modes.
  arXiv  Detail & Related papers  (2026-04-07T03:24:22Z)
• Vega: Learning to Drive with Natural Language Instructions [93.49357278917223]
  We propose a unified Vision-Language-World-Action model, Vega, for instruction-based generation and planning.<n>We employ the autoregressive paradigm to process visual inputs (vision) and language instructions (language) and the diffusion paradigm to generate future predictions.<n>Our method achieves superior planning performance and exhibits strong instruction-following abilities.
  arXiv  Detail & Related papers  (2026-03-26T17:59:56Z)
• Drive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving [18.914134572789703]
  Drive My Way (DMW) is a personalized driving framework that aligns with users' long-term driving habits and adapts to real-time user instructions.<n>DMW learns a user embedding from our personalized driving dataset collected across multiple real drivers and conditions the policy on this embedding during planning.<n>Our data and code are available at https://dmw-cvpr.io/.
  arXiv  Detail & Related papers  (2026-03-26T17:59:54Z)
• DRIV-EX: Counterfactual Explanations for Driving LLMs [5.315914253152163]
  Large language models (LLMs) are increasingly used as reasoning engines in autonomous driving.<n>We propose to study their decision process through counterfactual explanations.<n>We introduce DRIV-EX, a method that leverages gradient-based optimization on continuous embeddings.
  arXiv  Detail & Related papers  (2026-02-28T15:12:51Z)
• ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving [49.07731497951963]
  ReCogDrive is a novel Reinforced Cognitive framework for end-to-end autonomous driving.<n>We introduce a hierarchical data pipeline that mimics the sequential cognitive process of human drivers.<n>We then address the language-action mismatch by injecting the VLM's learned driving priors into a diffusion planner.
  arXiv  Detail & Related papers  (2025-06-09T03:14:04Z)
• Human-like Semantic Navigation for Autonomous Driving using Knowledge Representation and Large Language Models [11.045662497663514]
  Large Language Models are used to generate Answer Set Programming rules.<n> ASP provides non-monotonic reasoning, allowing autonomous vehicles to adapt to evolving scenarios.<n>Results show that LLM-driven ASP rule generation supports semantic-based decision-making.
  arXiv  Detail & Related papers  (2025-05-22T10:32:43Z)
• DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving [69.82743399946371]
  DriveMLM is a framework that can perform close-loop autonomous driving in realistic simulators. We employ a multi-modal LLM (MLLM) to model the behavior planning module of a module AD system. This model can plug-and-play in existing AD systems such as Apollo for close-loop driving.
  arXiv  Detail & Related papers  (2023-12-14T18:59:05Z)
• DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model [84.29836263441136]
  This study introduces DriveGPT4, a novel interpretable end-to-end autonomous driving system based on multimodal large language models (MLLMs) DriveGPT4 facilitates the interpretation of vehicle actions, offers pertinent reasoning, and effectively addresses a diverse range of questions posed by users.
  arXiv  Detail & Related papers  (2023-10-02T17:59:52Z)
• Guiding Large Language Models via Directional Stimulus Prompting [114.84930073977672]
  We introduce Directional Stimulus Prompting, a novel framework for guiding black-box large language models (LLMs) toward specific desired outputs. Instead of directly adjusting LLMs, our method employs a small tunable policy model to generate an auxiliary directional stimulus prompt for each input instance.
  arXiv  Detail & Related papers  (2023-02-22T17:44:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.