# Drive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving arXiv: https://arxiv.org/abs/2603.25740 ยท HTML: https://mlsanity.com/paper/2603.25740/ ## 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 46 of its 54 references that are in the map. - First posted on arXiv 26 Mar 2026. ## 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, 30 of 46) - [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024) - [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018) - [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023) - [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017) - [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024) - [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021) - [How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites](https://mlsanity.com/paper/2404.16821/) (2024) - [DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models](https://mlsanity.com/paper/2402.12289/) (2024) - [GPT-Driver: Learning to Drive with GPT](https://mlsanity.com/paper/2310.01415/) (2023) - [AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning](https://mlsanity.com/paper/2506.13757/) (2025) - [Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving](https://mlsanity.com/paper/2406.03877/) (2024) - [ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation](https://mlsanity.com/paper/2503.19755/) (2025) - [Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving](https://mlsanity.com/paper/2305.06242/) (2023) - [FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving](https://mlsanity.com/paper/2505.17685/) (2025) - [VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction](https://mlsanity.com/paper/2505.20279/) (2025) - [SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment](https://mlsanity.com/paper/2503.09594/) (2025) - [DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving](https://mlsanity.com/paper/2308.00398/) (2023) - [AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning](https://mlsanity.com/paper/2503.07608/) (2025) - [A Survey on Vision-Language-Action Models for Autonomous Driving](https://mlsanity.com/paper/2506.24044/) (2025) - [Embodied Understanding of Driving Scenarios](https://mlsanity.com/paper/2403.04593/) (2024) - [Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Models](https://mlsanity.com/paper/2505.23757/) (2025) - [Personalized Autonomous Driving with Large Language Models: Field Experiments](https://mlsanity.com/paper/2312.09397/) (2023) - [LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programs](https://mlsanity.com/paper/2312.04372/) (2023) - [Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis](https://mlsanity.com/paper/2506.11526/) (2025) - [LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Planner](https://mlsanity.com/paper/2409.20560/) (2024) - [IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model](https://mlsanity.com/paper/2508.06571/) (2025) - [UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving](https://mlsanity.com/paper/2503.24381/) (2025) - [MAVERIC: A Data-Driven Approach to Personalized Autonomous Driving](https://mlsanity.com/paper/2301.08595/) (2023) - [StyleDrive: Towards Driving-Style Aware Benchmarking of End-To-End Autonomous Driving](https://mlsanity.com/paper/2506.23982/) (2025) - [On-Board Vision-Language Models for Personalized Autonomous Vehicle Motion Control: System Design and Real-World Validation](https://mlsanity.com/paper/2411.11913/) (2024) ## Built on by (most cited first, 5 of 5) - [Post-Training in End-to-End Autonomous Driving](https://mlsanity.com/paper/2607.08072/) (2026) - [PLAN-S: Bridging Planning with Latent Style Dynamics for Autonomous Driving World Models](https://mlsanity.com/paper/2606.06014/) (2026) - [PersonaDrive: Human-Style Retrieval-Augmented VLA Agents for Closed-Loop Driving Simulation](https://mlsanity.com/paper/2606.12616/) (2026) - [SPVC: Structured and Panoptic Video Fixing for Cross-Dataset Driving Scene Rendering](https://mlsanity.com/paper/2608.17420/) (2026) - [Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance](https://mlsanity.com/paper/2610.20607/) (2026) ## Abstract (arXiv) Human driving behavior is inherently personal, which is shaped by long-term habits and influenced by short-term intentions. Individuals differ in how they accelerate, brake, merge, yield, and overtake across diverse situations. However, existing end-to-end autonomous driving systems either optimize for generic objectives or rely on fixed driving modes, lacking the ability to adapt to individual preferences or interpret natural language intent. To address this gap, we propose Drive My Way (DMW), a personalized Vision-Language-Action (VLA) driving framework that aligns with users' long-term driving habits and adapts to real-time user instructions. DMW learns a user embedding from our personalized driving dataset collected across multiple real drivers and conditions the policy on this embedding during planning, while natural language instructions provide additional short-term guidance. Closed-loop evaluation on the Bench2Drive benchmark demonstrates that DMW improves style instruction adaptation, and user studies show that its generated behaviors are recognizable as each driver's own style, highlighting personalization as a key capability for human-centered autonomous driving. Our data and code are available at https://dmw-cvpr.github.io/.