Happee Riender, Gold Christian, Radlmayr Jonas, Hergeth Sebastian, Bengler Klaus
Technical University of Munich, Chair of Ergonomics, Boltzmannstraße 15, D-85747 Garching, Germany; Delft University of Technology, BioMechanical Engineering, Mekelweg 2, 2628 CD, Delft, The Netherlands.
Technical University of Munich, Chair of Ergonomics, Boltzmannstraße 15, D-85747 Garching, Germany.
Accid Anal Prev. 2017 Sep;106:211-222. doi: 10.1016/j.aap.2017.04.017. Epub 2017 Jun 20.
We investigated after effects of automation in take-over scenarios in a high-end moving-base driving simulator. Drivers performed evasive manoeuvres encountering a blocked lane in highway driving. We compared the performance of drivers 1) during manual driving, 2) after automated driving with eyes on the road while performing the cognitively demanding n-back task, and 3) after automated driving with eyes off the road performing the visually demanding SuRT task. Both minimum time to collision (TTC) and minimum clearance towards the obstacle disclosed a substantial number of near miss events and are regarded as valuable surrogate safety metrics in evasive manoeuvres. TTC proved highly sensitive to the applied definition of colliding paths, and we prefer robust solutions using lane position while disregarding heading. The extended time to collision (ETTC) which takes into account acceleration was close to the more robust conventional TTC. In line with other publications, the initial steering or braking intervention was delayed after using automation compared to manual driving. This resulted in lower TTC values and stronger steering and braking actions. Using automation, effects of cognitive distraction were similar to visual distraction for the intervention time with effects on the surrogate safety metric TTC being larger with visual distraction. However the precision of the evasive manoeuvres was hardly affected with a similar clearance towards the obstacle, similar overshoots and similar excursions to the hard shoulder. Further research is needed to validate and complement the current simulator based results with human behaviour in real world driving conditions. Experiments with real vehicles can disclose possible systematic differences in behaviour, and naturalistic data can serve to validate surrogate safety measures like TTC and obstacle clearance in evasive manoeuvres.
我们在高端运动基座驾驶模拟器中研究了接管场景下自动化的后效应。驾驶员在高速公路驾驶中遇到车道堵塞时进行规避操作。我们比较了驾驶员在以下三种情况下的表现：1）手动驾驶时；2）自动驾驶且眼睛注视道路同时执行认知要求较高的n-back任务后；3）自动驾驶且眼睛离开道路执行视觉要求较高的SuRT任务后。碰撞前最短时间（TTC）和与障碍物的最小净空距离都揭示了大量险些相撞事件，并且在规避操作中被视为有价值的替代安全指标。事实证明，TTC对碰撞路径的应用定义高度敏感，我们更倾向于使用车道位置而忽略航向的稳健解决方案。考虑加速度的扩展碰撞时间（ETTC）与更稳健的传统TTC接近。与其他出版物一致，与手动驾驶相比，使用自动化后初始转向或制动干预有所延迟。这导致TTC值更低，转向和制动动作更强。使用自动化时，认知分心和视觉分心对干预时间的影响相似，对替代安全指标TTC的影响在视觉分心时更大。然而，规避操作的精度几乎没有受到影响，与障碍物的净空距离、过冲和驶向硬路肩的偏移量相似。需要进一步研究以在现实世界驾驶条件下用人类行为验证和补充当前基于模拟器的结果。真实车辆实验可以揭示行为中可能存在的系统差异，自然主义数据可用于验证规避操作中TTC和障碍物净空等替代安全措施。
