Drive My Way: 为个性化驾驶的视觉-语言-动作模型偏好对齐
Drive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving
AI总结：
本文提出Drive My Way框架，通过学习用户驾驶习惯和自然语言指令，实现个性化驾驶，提升对用户意图的适应能力。
摘要：
人类驾驶行为本质上具有个性特征，受长期习惯和短期意图影响。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，无法适应个体偏好或解读自然语言意图。为解决此问题，本文提出Drive My Way（DMW），一种个性化视觉-语言-动作（VLA）驾驶框架，通过学习用户嵌入向量并结合实时指令，实现驾驶策略的个性化调整。闭合环路评估表明，DMW提升了风格指令适应性，用户研究显示其生成行为可识别为各驾驶员的个人风格，凸显个性化在人本自动驾驶中的关键作用。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
