让自动驾驶理解个人驾驶习惯和实时指令，实现真正个性化行驶。
Drive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving
- 用用户驾驶数据生成嵌入向量，动态调节驾驶策略
- 在Bench2Drive上提升指令适应能力，用户辨识率达85%
- 适合注重驾驶风格一致性的智能汽车研发团队
人类驾驶行为具有显著个人特征，由长期习惯与短期意图共同塑造。不同个体在加速、刹车、变道、让行和超车等场景中表现各异。现有端到端自动驾驶系统或追求通用目标，或依赖固定驾驶模式，难以适应个体偏好或理解自然语言指令。为此，我们提出个性化视觉-语言-动作框架Drive My Way（DMW），通过收集多驾驶员、多场景的真实驾驶数据，学习用户嵌入向量，并在规划阶段以该向量条件化策略，同时结合自然语言指令提供短期引导。闭环评估在Bench2Drive基准上表明，DMW显著提升对驾驶风格指令的适应能力；用户研究显示，其生成行为可被识别为特定驾驶员的风格，凸显个性化在以人为本自动驾驶中的关键作用。数据与代码已公开于https://dmw-cvpr.github.io/。
原文摘要 · Abstract (English)
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/.
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