通过司机干预反馈优化自动驾驶速度策略，提升驾驶满意度。
Improving Driver Satisfaction with a Driving Function Learning from Implicit Human Feedback -- a Test Group Study
- 基于司机主动干预行为迭代调整车辆纵向控制速度曲线。
- 43人测试显示满意度显著提高，干预频率下降。
- 适合自动驾驶系统个性化优化与人机协同研究者参考。
在高级驾驶辅助系统使用过程中，驾驶员常主动接管并调整系统行为以符合个人偏好。这些主动干预行为隐含了系统行为与驾驶员期望之间的偏差信息，应被用于优化和个性化驾驶功能。本文聚焦于预设路线下的预测性纵向驾驶功能（PLDF）速度曲线的调整。提出一种算法，通过结合原始速度曲线与驾驶员示范，迭代优化PLDF的速度设定，在动态使用中实现个性化控制。该方法在基于驾驶模拟器的43人测试组研究中验证。结果表明，采用自适应PLDF后，驾驶员满意度显著提升，干预频率明显降低。同时收集了参与者反馈，进一步挖掘系统优化潜力。
原文摘要 · Abstract (English)
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.
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