Advancements in technology are steering attention toward creating comfortable and acceptable driving characteristics in autonomous vehicles. Ensuring a safe and comfortable ride experience is vital for the widespread adoption of autonomous vehicles, as mismatches in driving styles between humans and autonomous systems can impact passenger confidence. Current driving functions have fixed parameters, and there is no universally agreed-upon driving style for autonomous vehicles. Integrating driving style preferences into automated vehicles may enhance acceptance and reduce uncertainty, expediting their adoption. A controlled vehicle study (N = 62) was conducted with a variety of German participants to identify the individual lateral driving behavior of human drivers, specifically emphasizing rural roads. We introduce novel indicators for assessing stationary and transient curve negotiation, directly applicable in developing personalized lateral driving functions. To assess the predictability of these indicators using self-reports, we introduce the MDSI-DE, the German version of the Multidimensional Driving Style Inventory. The correlation analysis between MDSI factor scores and proposed indicators showed modest but significant associations, primarily with acceleration and jerk statistics while the in-depth lateral driving behavior turned out to be highly driver-heterogeneous. The dataset including the anonymized socio-demographics and questionnaire responses, the raw vehicle measurements including labels, and the derived driving behavior indicators are publicly available at https://www.kaggle.com/datasets/jhaselberger/spodb-subject-study-of-lateral-vehicle-guidance.

翻译：技术发展正引导人们关注如何在自动驾驶车辆中创造舒适且可接受的驾驶特性。确保安全舒适的乘坐体验对于自动驾驶车辆的广泛应用至关重要，因为人类与自动驾驶系统之间驾驶风格的差异会影响乘客的信任度。当前驾驶功能采用固定参数，且尚无普遍认同的自动驾驶车辆驾驶风格。将驾驶风格偏好整合到自动驾驶车辆中，可提升接受度并减少不确定性，从而加速其应用推广。我们开展了一项包含62名德国参与者的受控车辆研究，旨在识别人类驾驶员的个体横向驾驶行为，重点关注乡村道路。我们引入了用于评估稳态和瞬态弯道通过的新指标，这些指标可直接应用于个性化横向驾驶功能的开发。为评估这些指标通过自我报告的可预测性，我们引入了MDSI-DE（多维驾驶风格量表的德语版）。MDSI因子得分与所提出指标之间的相关性分析显示，两者存在微弱但显著的相关性，主要涉及加速度和冲击度统计量，而深层横向驾驶行为则表现出高度的驾驶员异质性。包含匿名化社会人口统计数据和问卷回应的数据集、包含标签的原始车辆测量数据以及导出的驾驶行为指标，已在 https://www.kaggle.com/datasets/jhaselberger/spodb-subject-study-of-lateral-vehicle-guidance 公开提供。