During the use of advanced driver assistance systems, drivers frequently intervene into the active driving function and adjust the system's behavior to their personal wishes. These active driver-initiated takeovers contain feedback about deviations in the driving function's behavior from the drivers' personal preferences. This feedback should be utilized to optimize and personalize the driving function's behavior. In this work, the adjustment of the speed profile of a Predictive Longitudinal Driving Function (PLDF) on a pre-defined route is highlighted. An algorithm is introduced which iteratively adjusts the PLDF's speed profile by taking into account both the original speed profile of the PLDF and the driver demonstration. This approach allows for personalization in a traded control scenario during active use of the PLDF. The applicability of the proposed algorithm is tested in a driving simulator-based test group study with 43 participants. The study finds a significant increase in driver satisfaction and a significant reduction in the intervention frequency when using the proposed adaptive PLDF. Additionally, feedback by the participants was gathered to identify further optimization potentials of the proposed system.

翻译：在使用高级驾驶辅助系统时，驾驶员经常介入主动驾驶功能，并根据个人意愿调整系统行为。这些由驾驶员主动发起的接管行为包含了驾驶功能行为与驾驶员个人偏好之间偏差的反馈信息。此类反馈应当被用于优化和个性化驾驶功能的行为。本工作重点研究了预定义路线上预测性纵向驾驶功能速度曲线的调整方法。本文提出了一种算法，该算法通过综合考虑PLDF的原始速度曲线与驾驶员示范操作，迭代调整PLDF的速度曲线。这种方法使得在PLDF主动运行时的交易控制场景中实现个性化成为可能。所提算法的适用性在一项基于驾驶模拟器的测试组研究中得到验证，该研究共有43名参与者参与。研究发现，使用所提出的自适应PLDF能显著提升驾驶员满意度，并显著降低干预频率。此外，研究还收集了参与者的反馈意见，以识别所提系统的进一步优化潜力。