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-oflateral-vehicle-guidance.

翻译：技术进步的持续发展正促使人们关注为自动驾驶汽车创建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汽车的广泛普及至关重要，因为人类与自主系统之间驾驶风格的不匹配会影响乘客的信心。当前的驾驶功能具有固定参数，且针对自动驾驶汽车尚无普遍认可的驾驶风格。将驾驶风格偏好整合到自动驾驶汽车中，可能会提升接受度并减少不确定性，从而加速其普及。本研究针对德国参与者（N = 62）开展了一项受控车辆实验，以识别人类驾驶员的个体横向驾驶行为，特别侧重于乡村道路。我们提出了用于评估稳态和瞬态曲线行驶的新指标，这些指标可直接应用于开发个性化横向驾驶功能。为了评估通过自我报告预测这些指标的可行性，我们引入了MDSI-DE，即多维度驾驶风格量表的德语版本。MDSI因子得分与所提出指标之间的相关性分析显示出微弱但显著的关联，主要体现在加速度和急动度统计数据上，而深入的横向驾驶行为则表现出高度驾驶员异质性。包含匿名化社会人口学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，均可公开获取于https://www.kaggle.com/datasets/jhaselberger/spodb-subject-study-oflateral-vehicle-guidance。