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Modeling take-over performance in level 3 conditionally automated vehicles

机译:对3级有条件自动驾驶汽车的接管性能进行建模

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摘要

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-tocollision, 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 timebudget, traffic density and the repetition strongly influenced the take-over performance, while the non-drivingrelated tasks, the lane and drivers' age explained a minor portion of the variance in the take-over performances.
机译:从级别3有条件的自动车辆接管车辆控制对于驾驶员而言可能是一项艰巨的任务。接管决定了自动车辆功能的可控制性,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接管时间,最小碰撞时间,制动应用和碰撞概率的模型。这些变量是与时间和驾驶员相关因素有关的,这些因素包括时间预算,交通密度,非驾驶相关任务,重复次数,当前车道和驾驶员年龄。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。该模型已通过其他五个驾驶模拟器实验的数据进行了验证,这些实验都是由大多数无关的作者进行的,另外还有729个接管情况。这些模型准确地记录了接管时间,碰撞时间和碰撞概率,并适度地预测了制动器的应用。特别是时间预算,交通密度和重复次数严重影响了接管性能,而与非驾驶相关的任务,车道和驾驶员的年龄则解释了接管性能差异的一小部分。

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