mlsanityData to 7 Oct 2026

Driving Style Alignment for LLM-powered Driver Agent

arXiv 2403.11368 ↗First posted 17 Mar 2024cs.RO cs.AIcited by 15

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

Recently, LLM-powered driver agents have demonstrated considerable potential in the field of autonomous driving, showcasing human-like reasoning and decision-making abilities.However, current research on aligning driver agent behaviors with human driving styles remains limited, partly due to the scarcity of high-quality natural language data from human driving behaviors.To address this research gap, we propose a multi-alignment framework designed to align driver agents with human driving styles through demonstrations and feedback. Notably, we construct a natural language dataset of human driver behaviors through naturalistic driving experiments and post-driving interviews, offering high-quality human demonstrations for LLM alignment. The framework's effectiveness is validated through simulation experiments in the CARLA urban traffic simulator and further corroborated by human evaluations. Our research offers valuable insights into designing driving agents with diverse driving styles.The implementation of the framework and details of the dataset can be found at the link.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 25 of its 35 references that are in the map. First posted on arXiv 17 Mar 2024.

Builds on

25 of 35 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models8 Dec

Other references · 24

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  5. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  6. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  7. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  8. 2023ExpeL: LLM Agents Are Experiential Learnerscited by 814
  9. 2023GAIA-1: A Generative World Model for Autonomous Drivingcited by 618
  10. 2023DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Modelcited by 455
  11. 2023GPT-Driver: Learning to Drive with GPTcited by 344
  12. 2023LMDrive: Closed-Loop End-to-End Driving with Large Language Modelscited by 301
12 more
  1. 2023DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Drivingcited by 233
  2. 2019INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Mapscited by 231
  3. 2023Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Drivingcited by 231
  4. 2023DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Modelscited by 204
  5. 2020A2D2: Audi Autonomous Driving Datasetcited by 189
  6. 2023Drive Like a Human: Rethinking Autonomous Driving with Large Language Modelscited by 158
  7. 2022DRAMA: Joint Risk Localization and Captioning in Drivingcited by 125
  8. 2023Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous Drivingcited by 122
  9. 2023Retroformer: Retrospective Large Language Agents with Policy Gradient Optimizationcited by 110
  10. 2023Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehiclescited by 100
  11. 2023Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulationcited by 30
  12. 2019Learning Accurate, Comfortable and Human-like Drivingcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 8; 2026: 3.

15 papers.

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  1. 2025Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmapcited by 26
  2. 2024AD-H: Language-guided Autonomous Driving with Hierarchical Agentscited by 15
  3. 2025Large Language Models and Their Applications in Roadway Safety and Mobility Enhancement: A Comprehensive Reviewcited by 8
  4. 2024XLM for Autonomous Driving Systems: A Comprehensive Reviewcited by 5
  5. 2024From Words to Wheels: Automated Style-Customized Policy Generation for Autonomous Drivingcited by 5
  6. 2025A Comprehensive LLM-powered Framework for Driving Intelligence Evaluationcited by 5
  7. 2025Multi-Objective Reinforcement Learning for Adaptable Personalized Autonomous Drivingcited by 3
  8. 2025Urban Computing in the Era of Large Language Modelscited by 2
  9. 2026Can Users Specify Driving Speed? Bench2Drive-Speed: Benchmark and Baselines for Desired-Speed Conditioned Autonomous Drivingcited by 2
  10. 2025DISC: Dataset for Analyzing Driving Styles In Simulated Crashes for Mixed Autonomycited by 1
  11. 2024A Novel MLLM-based Approach for Autonomous Driving in Different Weather Conditionscited by 0
  12. 2025Driving Style Recognition Like an Expert Using Semantic Privileged Information from Large Language Modelscited by 0
3 more
  1. 2025LLM-based Human-like Traffic Simulation for Self-driving Testscited by 0
  2. 2026Reason--Imagine--Act: Closed-Loop LLM Decision Making with World Models for Autonomous Drivingcited by 0
  3. 2026PersonaDrive: Human-Style Retrieval-Augmented VLA Agents for Closed-Loop Driving Simulationcited by 0

Similar, not cited

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  1. 2023SurrealDriver: Designing LLM-powered Generative Driver Agent Framework based on Human Drivers' Driving-thinking Data
  2. 2025Align2Act: Instruction-Tuned Models for Human-Aligned Autonomous Driving
  3. 2026Drive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving
  4. 2024Text-to-Drive: Diverse Driving Behavior Synthesis via Large Language Models
  5. 2026STAGE: STyle-controllable Action GEneration for personalized autonomous driving
  6. 2025Do LLM Modules Generalize? A Study on Motion Generation for Autonomous Driving
  7. 2023A Language Agent for Autonomous Driving
  8. 2023Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles