高级驾驶辅助系统（ADAS）日益普及，旨在支持驾驶员、降低工作负荷并提升交通安全。然而，许多系统因行为表现不自然且与个体驾驶习惯不符而使用率较低。大规模真实人类驾驶数据的可用性不断提升，使得开发更具拟人化及个性化的辅助功能成为可能。本文将自适应巡航控制（ACC）参数的适配问题建模为驾驶员个体的场景间传输问题，即从一个驾驶环境向另一个驾驶环境转换控制器参数。利用 Flow Matching 方法，我们训练了一个生成模型，仅通过源场景中的少量演示即可预测未见驾驶员在目标场景中的行为。我们将该方法与线性参数偏移基线进行比较，并评估了结合驾驶员一致性耦合、驾驶员嵌入以及基于变分自编码器从轨迹数据提取的驾驶配置文件进行无分类器引导的多种 Flow Matching 变体。

Advanced Driver Assistance Systems have become increasingly widespread, aiming to support drivers, reduce workload, and improve traffic safety. However, many systems are rarely used because their behavior feels unnatural and does not match individual driving behavior. The growing availability of large-scale, real-world human driving data enables more human-like and driver-individual assistance functions. This paper formulates the adaptation of Adaptive Cruise Control parameters as a driver-individual scenario-to-scenario transport problem, transforming controller parameters from one driving context to another. Using flow matching, we train a generative model to predict the target-scenario behavior of previously unseen drivers from only a few demonstrations in a source scenario. We compare the approach with a linear parameter-shift baseline and evaluate flow-matching variants incorporating driver-consistent coupling, driver embeddings, and classifier-free guidance based on driving profiles extracted from trajectory data using a variational autoencoder.