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/.

翻译：人类驾驶行为天然具有个性化特征，这种特征由长期习惯塑造并受短期意图影响。不同驾驶员在加速、制动、并道、让行和超车等场景行为各异。然而，现有端到端自动驾驶系统或优化通用目标，或依赖固定驾驶模式，缺乏适应个体偏好或理解自然语言意图的能力。为填补这一空白，我们提出Drive My Way（DMW）——一种与用户长期驾驶习惯对齐、并能适配实时用户指令的个性化视觉-语言-行动（VLA）驾驶框架。DMW从我们收集的多真实驾驶员个性化驾驶数据集中学习用户嵌入，在规划阶段将策略条件建立在该嵌入之上，同时通过自然语言指令提供额外短期引导。在Bench2Drive基准上的闭环评估表明，DMW提升了驾驶风格指令自适应能力，用户研究显示其生成行为可被识别为各驾驶员自身风格，凸显了个性化作为以人为中心自动驾驶的关键能力。我们的数据和代码已开源在https://dmw-cvpr.github.io/。