Closed-loop driving simulators typically populate their environments with non-ego traffic agents that behave largely the same way, produced either by rule-based traffic managers or by learned models trained toward a single behavioral mode. Recent work introduces style variation through post-hoc labels on observational data or LLM-inferred reward weights, but these signals act as proxies for what a style should reward rather than demonstrations of humans explicitly asked to drive in that style. We introduce PersonaDrive, a pipeline that conditions a vision-language-action (VLA) driving agent on retrieved demonstrations from a style-instructed human driving dataset, in which participants drive CARLA leaderboard routes under aggressive, neutral, and conservative instructions on a driver-in-the-loop rig. The pipeline has three stages: (i) offline triplet mining over per-style human driving data using a combined image-text similarity score; (ii) training a lightweight retrieval head that fuses frozen visual features with a small control encoder over per-style databases; and (iii) fine-tuning a single VLA backbone to treat retrieved context points as in-context behavioral demonstrations during waypoint prediction. At inference, the same backbone is conditioned on any style by swapping which per-style database the retrieval head queries, so selecting a style requires no per-style retraining while enabling human-style, style-diverse non-ego agents for closed-loop simulation. On Bench2Drive, PersonaDrive (no style) improves the driving score by 4.6% over SimLingo and 2.5% over HiP-AD, and under style conditioning attains the highest driving score in every style within a roughly 2% band (its weakest style surpassing the strongest baseline, DMW, by 5.4%), while average speed and acceleration rise by 18% and 25% from the conservative to the aggressive instruction.

翻译：闭环驾驶仿真器通常以近乎一致的行为模式填充非自车交通智能体，这些行为要么由基于规则的交通管理器生成，要么由面向单一行为模式训练的学习模型产生。近期研究通过事后标注的观测数据或大语言模型推断的奖励权重引入风格差异，但这些信号仅作为风格应奖励指标的代理，而非基于人类明确要求按特定风格驾驶的演示数据。本文提出PersonaDrive流水线，该系统基于从受风格指令约束的人类驾驶数据集中检索到的演示，对视觉-语言-动作（VLA）驾驶智能体进行条件化建模。该数据集由参与者在驾驶员在环驾驶平台上，按照激进、中立、保守三种指令完成CARLA排行榜路线驾驶任务。流水线包含三个阶段：（i）基于图像-文本联合相似度分数，对按风格分类的人类驾驶数据进行离线三元组挖掘；（ii）训练轻量级检索头，通过冻结视觉特征与小型控制编码器的融合，建立按风格划分的数据库；（iii）微调单一VLA骨干网络，将检索到的上下文点作为路径点预测过程中的上下文行为演示。推理时，通过切换检索头所查询的按风格划分的数据库，同一骨干网络可依据任意风格进行条件化建模——选择风格无需针对每种风格重新训练，从而为闭环仿真提供具备人类风格且风格多样化的非自车智能体。在Bench2Drive基准上，PersonaDrive（无风格）的驾驶评分较SimLingo提升4.6%，较HiP-AD提升2.5%；在风格条件化建模下，每种风格均实现最高驾驶评分且波动幅度约2%（其最弱风格仍超越最强基线模型DMW 5.4%），从保守指令到激进指令的工况下，平均车速与加速度分别提升18%和25%。