# From Words to Wheels: Automated Style-Customized Policy Generation for Autonomous Driving arXiv: https://arxiv.org/abs/2409.11694 ยท HTML: https://mlsanity.com/paper/2409.11694/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving. - Builds on 29 of its 58 references that are in the map. - First posted on arXiv 18 Sep 2024. ## Placed in - End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/) ## Builds on (most cited first, 29 of 29) - [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020) - [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017) - [DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model](https://mlsanity.com/paper/2310.01412/) (2023) - [A Survey on Multimodal Large Language Models for Autonomous Driving](https://mlsanity.com/paper/2311.12320/) (2023) - [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018) - [DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving](https://mlsanity.com/paper/2312.09245/) (2023) - [Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving](https://mlsanity.com/paper/2310.01957/) (2023) - [DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2309.16292/) (2023) - [Vision Language Models in Autonomous Driving: A Survey and Outlook](https://mlsanity.com/paper/2310.14414/) (2023) - [Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles](https://mlsanity.com/paper/2310.08034/) (2023) - [Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1902.00089/) (2019) - [Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning](https://mlsanity.com/paper/1901.00569/) (2019) - [Personalized Autonomous Driving with Large Language Models: Field Experiments](https://mlsanity.com/paper/2312.09397/) (2023) - [Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning](https://mlsanity.com/paper/2010.03118/) (2020) - [LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programs](https://mlsanity.com/paper/2312.04372/) (2023) - [Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving](https://mlsanity.com/paper/1905.00229/) (2019) - [DriVLMe: Enhancing LLM-based Autonomous Driving Agents with Embodied and Social Experiences](https://mlsanity.com/paper/2406.03008/) (2024) - [Human-Centric Autonomous Systems With LLMs for User Command Reasoning](https://mlsanity.com/paper/2311.08206/) (2023) - [FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling](https://mlsanity.com/paper/2306.05381/) (2023) - [A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving](https://mlsanity.com/paper/2405.01440/) (2024) - [Driving Style Alignment for LLM-powered Driver Agent](https://mlsanity.com/paper/2403.11368/) (2024) - [Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches](https://mlsanity.com/paper/1708.08986/) (2017) - [Generating and Evolving Reward Functions for Highway Driving with Large Language Models](https://mlsanity.com/paper/2406.10540/) (2024) - [EnsembleFollower: A Hybrid Car-Following Framework Based On Reinforcement Learning and Hierarchical Planning](https://mlsanity.com/paper/2308.16008/) (2023) - [EditFollower: Tunable Car Following Models for Customizable Adaptive Cruise Control Systems](https://mlsanity.com/paper/2407.02516/) (2024) - [A Study on Learning and Simulating Personalized Car-Following Driving Style](https://mlsanity.com/paper/2208.08002/) (2022) - [A Rapid Pattern-Recognition Method for Driving Types Using Clustering-Based Support Vector Machines](https://mlsanity.com/paper/1605.06742/) (2016) ## Built on by (most cited first, 5 of 5) - [Generative AI for Autonomous Driving: Frontiers and Opportunities](https://mlsanity.com/paper/2505.08854/) (2025) - [Drive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving](https://mlsanity.com/paper/2603.25740/) (2026) - [Multi-Objective Reinforcement Learning for Adaptable Personalized Autonomous Driving](https://mlsanity.com/paper/2505.05223/) (2025) - [Can Users Specify Driving Speed? Bench2Drive-Speed: Benchmark and Baselines for Desired-Speed Conditioned Autonomous Driving](https://mlsanity.com/paper/2603.25672/) (2026) - [Language-Driven Cost Optimization for Autonomous Driving](https://mlsanity.com/paper/2606.10974/) (2026) ## Abstract (arXiv) Autonomous driving technology has witnessed rapid advancements, with foundation models improving interactivity and user experiences. However, current autonomous vehicles (AVs) face significant limitations in delivering command-based driving styles. Most existing methods either rely on predefined driving styles that require expert input or use data-driven techniques like Inverse Reinforcement Learning to extract styles from driving data. These approaches, though effective in some cases, face challenges: difficulty obtaining specific driving data for style matching (e.g., in Robotaxis), inability to align driving style metrics with user preferences, and limitations to pre-existing styles, restricting customization and generalization to new commands. This paper introduces Words2Wheels, a framework that automatically generates customized driving policies based on natural language user commands. Words2Wheels employs a Style-Customized Reward Function to generate a Style-Customized Driving Policy without relying on prior driving data. By leveraging large language models and a Driving Style Database, the framework efficiently retrieves, adapts, and generalizes driving styles. A Statistical Evaluation module ensures alignment with user preferences. Experimental results demonstrate that Words2Wheels outperforms existing methods in accuracy, generalization, and adaptability, offering a novel solution for customized AV driving behavior. Code and demo available at https://yokhon.github.io/Words2Wheels/.