Chair of Ergonomics, Technical University of Munich, Munich, Germany.
Department Intelligent Vehicles, Delft University of Technology, Delft, The Netherlands.
Accid Anal Prev. 2018 Jul;116:3-13. doi: 10.1016/j.aap.2017.11.009. Epub 2017 Nov 29.
Taking over vehicle control from a Level 3 conditionally automated vehicle can be a demanding task for a driver. The take-over determines the controllability of automated vehicle functions and thereby also traffic safety. This paper presents models predicting the main take-over performance variables take-over time, minimum time-to-collision, brake application and crash probability. These variables are considered in relation to the situational and driver-related factors time-budget, traffic density, non-driving-related task, repetition, the current lane and driver's age. Regression models were developed using 753 take-over situations recorded in a series of driving simulator experiments. The models were validated with data from five other driving simulator experiments of mostly unrelated authors with another 729 take-over situations. The models accurately captured take-over time, time-to-collision and crash probability, and moderately predicted the brake application. Especially the time-budget, traffic density and the repetition strongly influenced the take-over performance, while the non-driving-related tasks, the lane and drivers' age explained a minor portion of the variance in the take-over performances.
从处于三级有条件自动驾驶状态的车辆接管车辆控制，对于驾驶员来说可能是一项艰巨的任务。接管决定了自动驾驶功能的可控性，从而也决定了交通安全。本文提出了预测主要接管性能变量的模型，包括接管时间、最小碰撞时间、制动应用和碰撞概率。这些变量与情境和驾驶员相关因素（时间预算、交通密度、非驾驶相关任务、重复、当前车道和驾驶员年龄）有关。使用在一系列驾驶模拟器实验中记录的 753 个接管情况开发了回归模型。使用来自五个其他驾驶模拟器实验的数据对模型进行了验证，这些实验来自与原始实验无关联的作者，涉及 729 个接管情况。模型准确地捕捉了接管时间、最小碰撞时间和碰撞概率，并且适度地预测了制动应用。特别是时间预算、交通密度和重复强烈影响了接管性能，而非驾驶相关任务、车道和驾驶员年龄仅解释了接管性能变化的一小部分。
