State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, China.
Institute for Transport Studies, University of Leeds, UK.
Accid Anal Prev. 2023 Feb;180:106905. doi: 10.1016/j.aap.2022.106905. Epub 2022 Dec 9.
The removal of drivers' active engagement in driving tasks can lead to erratic gaze patterns in SAE Level 2 (L2) and Level 3 (L3) automation, which has been linked to their subsequential degraded take-over performance. To further address how changes in gaze patterns evolve during the take-over phase, and whether they are influenced by the take-over urgency and the location of the human-machine interface, this driving simulator study used a head-up display (HUD) to relay information about the automation status and conducted take-over driving experiments where the ego car was about to exit the highway with variations in the automation level (L2, L3) and time budget (2 s, 6 s). In L2 automation, drivers were required to monitor the environment, while in L3, they were engaged with a visual non-driving related task. Manual driving was also embodied in the experiments as the baseline. Results showed that, compared to manual driving, drivers in L2 automation focused more on the HUD and Far Road (roadway beyond 2 s time headway ahead), and less on the Near Road (roadway within 2 s time headway ahead); while in L3, drivers' attention was predominantly allocated on the non-driving related task. After receiving take-over requests (TORs), there was a gradual diversion of attention from the Far Road to the Near Road in L2 take-overs. This trend changed nearly in proportion to the time within the time budget and it exaggerated given a shorter time budget of 2 s. While in L3, drivers' gaze distribution was similar in the early stage of take-overs for both time budget conditions (2 s vs. 6 s), where they prioritized their early glances to Near Road with a gradual increase in attention towards Far Road. The HUD used in the present study showed the potential to maintain drivers' attention around the road center during automation and to encourage drivers to glance the road earlier after TORs by reducing glances to the instrument cluster, which might be of significance to take-over safety. These findings were discussed based on an extended conceptual gaze control model, which advances our understanding of gaze patterns around control transitions and their underlying gaze control causations. Implications can be contributed to the design of autonomous vehicles to facilitate the transition of control by guiding drivers' attention appropriately according to drivers' attentional state and the take-over urgency.
驾驶员主动参与驾驶任务的去除会导致 SAE 级别 2（L2）和级别 3（L3）自动化中的不稳定注视模式，这与随后的接管性能下降有关。为了进一步研究在接管阶段注视模式如何演变，以及它们是否受到接管紧迫性和人机界面位置的影响，本驾驶模拟器研究使用抬头显示器（HUD）传递有关自动化状态的信息，并进行接管驾驶实验，其中自动驾驶汽车即将离开高速公路，自动化水平（L2、L3）和时间预算（2s、6s）发生变化。在 L2 自动化中，驾驶员需要监控环境，而在 L3 中，他们参与视觉非驾驶相关任务。手动驾驶也作为基线体现在实验中。结果表明，与手动驾驶相比，L2 自动化中的驾驶员更关注 HUD 和远路（距离 2s 时距前方的道路），而对近路（距离 2s 时距前方的道路）关注较少；而在 L3 中，驾驶员的注意力主要分配在非驾驶相关任务上。在收到接管请求（TOR）后，L2 接管中驾驶员的注意力逐渐从远路转移到近路。这种趋势与时间预算内的时间几乎成比例变化，并且在时间预算较短的 2s 时更为夸张。而在 L3 中，对于两种时间预算条件（2s 与 6s），在接管的早期阶段，驾驶员的注视分布相似，他们优先关注近路，随着对远路的注意力逐渐增加。本研究中使用的 HUD 有可能在自动化期间保持驾驶员对道路中心的注意力，并通过减少对仪器组的注视，鼓励驾驶员在 TOR 后更早地注视道路，这可能对接管安全具有重要意义。这些发现基于扩展的概念性注视控制模型进行了讨论，该模型增进了我们对控制转换周围注视模式及其潜在注视控制原因的理解。可以为自动驾驶汽车的设计提供启示，通过根据驾驶员的注意力状态和接管紧迫性适当地引导驾驶员的注意力，促进控制的过渡。
