Louw Tyron, Markkula Gustav, Boer Erwin, Madigan Ruth, Carsten Oliver, Merat Natasha
Institute for Transport Studies, University of Leeds, LS2 9JT Leeds, United Kingdom.
Institute for Transport Studies, University of Leeds, LS2 9JT Leeds, United Kingdom.
Accid Anal Prev. 2017 Nov;108:9-18. doi: 10.1016/j.aap.2017.08.011. Epub 2017 Sep 6.
This driving simulator study, conducted as part of the EU AdaptIVe project, investigated drivers' performance in critical traffic events, during the resumption of control from an automated driving system. Prior to the critical events, using a between-participant design, 75 drivers were exposed to various screen manipulations that varied the amount of available visual information from the road environment and automation state, which aimed to take them progressively further 'out-of-the-loop' (OoTL). The current paper presents an analysis of the timing, type, and rate of drivers' collision avoidance response, also investigating how these were influenced by the criticality of the unfolding situation. Results showed that the amount of visual information available to drivers during automation impacted on how quickly they resumed manual control, with less information associated with slower take-over times, however, this did not influence the timing of when drivers began a collision avoidance manoeuvre. Instead, the observed behaviour is in line with recent accounts emphasising the role of scenario kinematics in the timing of driver avoidance response. When considering collision incidents in particular, avoidance manoeuvres were initiated when the situation criticality exceeded an Inverse Time To Collision value of ≈0.3s. Our results suggest that take-over time and timing and quality of avoidance response appear to be largely independent, and while long take-over time did not predict collision outcome, kinematically late initiation of avoidance did. Hence, system design should focus on achieving kinematically early avoidance initiation, rather than short take-over times.
这项作为欧盟“自适应驾驶（AdaptIVe）”项目一部分开展的驾驶模拟器研究，调查了驾驶员在从自动驾驶系统恢复控制期间，应对关键交通事件时的表现。在关键事件发生前，采用组间设计，让75名驾驶员接触各种屏幕操作，这些操作改变了来自道路环境和自动化状态的可用视觉信息量，目的是使他们逐渐进一步“脱离驾驶循环”（OoTL）。本文对驾驶员碰撞避免反应的时间、类型和速率进行了分析，还研究了这些因素如何受到当前展开情况的危急程度的影响。结果表明，自动化过程中驾驶员可获得的视觉信息量会影响他们恢复手动控制的速度，信息量越少，接管时间越慢，然而，这并不影响驾驶员开始碰撞避免操作的时间。相反，观察到的行为与最近强调情景运动学在驾驶员避免反应时间中的作用的观点一致。特别是在考虑碰撞事故时，当情况危急程度超过约0.3秒的倒数碰撞时间值时，就会启动避免操作。我们的结果表明，接管时间与避免反应的时间和质量似乎在很大程度上是独立的，虽然长接管时间并不能预测碰撞结果，但运动学上避免操作启动较晚则会导致碰撞。因此，系统设计应侧重于在运动学上尽早启动避免操作，而不是缩短接管时间。
