YOUDrive：驾驶风格作为端到端自动驾驶的可组合方向
YOUDrive: Driving Style as a Composable Direction for End-to-End Autonomous Driving
AI总结：
YOUDrive是一种视觉-语言-动作规划器，通过提炼教师行为生成可组合的驾驶风格方向，结合流匹配解码器生成可行运动，在NAVSIM测试中实现了风格控制与高PDMS指标，相关资源将公开。
摘要：
端到端驾驶策略可实现出色的任务性能，但对驾驶风格的控制有限。驾驶风格描述车辆如何加速、制动和保持车距；未经过明确偏好条件训练的策略，在推理阶段缺乏调整这些行为的直接机制。我们提出YOUDrive，一种用于连续且可组合驾驶风格控制的视觉-语言-动作规划器。每个教师的行为被提炼为任务向量，这是一种学习到的参数更新，可在规划器的表示中诱导出风格方向；这些方向可在推理阶段进行缩放和组合，无需重新训练骨干网络。基于这些方向，流匹配解码器对多模态轨迹分布进行建模，并生成运动学上可行的运动备选方案。我们引入风格对齐分数（SAS）来描述经过安全和任务门控后的人类参考运动学行为。在NAVSIM navtest上，单个系数可单调控制来自6个教师角色的自信程度，而PDMS在测试的单个及混合配置下均保持在0.90以上，最高达0.954。进一步分析显示，在控制场景上下文后存在系统的策略特定运动学，而评估的离散风格标签提供的额外解释力有限。代码、检查点和该指标将被发布。
英文摘要：
End-to-end driving policies can achieve strong task performance while offering limited control over their driving style. Driving style describes how a vehicle accelerates, brakes, and maintains distance. Policies trained without explicit preference conditioning lack a direct mechanism for adjusting these behaviors at inference. We propose YOUDrive, a vision--language--action planner for continuous and composable driving-style control. Each teacher's behavior is distilled into a task vector, a learned parameter update that induces a style direction in the planner's representation. These directions can be scaled and combined at inference without retraining the backbone. Conditioned on these directions, a flow-matching decoder models a multimodal trajectory distribution and generates kinematically feasible motion alternatives. We introduce the Style Alignment Score (SAS) to describe human-referenced kinematic behavior after safety and task gating. On NAVSIM navtest, a single coefficient monotonically controls assertiveness across six teacher-derived personas, while PDMS remains above 0.90 across the tested individual and blended configurations, reaching 0.954. Further analyses reveal systematic policy-specific kinematics after controlling for scene context, whereas the evaluated discrete style labels provide limited additional explanatory power. Code, checkpoints, and the metric will be released.
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。
