Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.
Traffic Inj Prev. 2023;24(7):599-608. doi: 10.1080/15389588.2023.2224910. Epub 2023 Jun 22.
Safety has become one of the primary concerns of level 3 automated driving, especially during the takeover process. Since most studies have focused on impacts of various factors on takeover performance of drivers, there seems to be a gap between the causes of crashes and the desired means to mitigate their occurrence and consequences. Hence, the main objective of this study is to extract from crash data during takeovers drivers' patterns of gaze behaviors and maneuvers and then utilize them to extract some guidance on human-machine-interface design to enhance safety and acceptability of automated driving.
A study involving 27 subjects was conducted on a high-fidelity driving simulator with a Steward motion platform of six degrees of freedom. Each subject participated in 6 takeover scenarios with a lead time of 5 s and different duration of monitoring (DoM), with their maneuvers recorded by the system and eye gazes recorded by the Smart Eye Pro and Smart Recorder. Crash data collected during the takeover process were then utilized for the analysis.
From 132 valid takeovers collected from 23 out of the 27 participants, 15 crashes were recorded. Based on which, five typical patterns of unsafe behaviors were recognized that may have caused the crashes, denoted as Type I to Type V, respectively. Besides, it appears that even if drivers were given more time to observe the surroundings, i.e., longer DoM, the number of crashes has not decreased as anticipated. Therefore, what is more important seems to be drivers' gaze behaviors and maneuvers shortly after TOR.
For takeovers to be safe, good cooperations between drivers' gaze behaviors and maneuvers are essential. Overall, it seems that in emergent situations that require takeovers, some drivers have difficulty in allocating attentions reasonably, which appears to have less to do with the time left for drivers to observe the surroundings. While designing HMIs, we may as well consider providing enough information to guide drivers according to drivers' states and maneuvers at the time to improve safety of takeovers in emergent situations, and more importantly, to provide the information timely and effectively.
安全性已成为 3 级自动驾驶的主要关注点之一，尤其是在接管过程中。由于大多数研究都集中在各种因素对驾驶员接管性能的影响上，因此在事故的原因和减轻事故发生及其后果的理想手段之间似乎存在差距。因此，本研究的主要目的是从接管过程中的事故数据中提取驾驶员注视行为和操作模式，然后利用这些数据为人机界面设计提供一些指导，以提高自动驾驶的安全性和可接受性。
在具有六自由度 Stewart 运动平台的高保真驾驶模拟器上进行了一项涉及 27 名受试者的研究。每个受试者参与了 6 个接管场景，前置时间为 5s，监控时间（DoM）不同，其操作由系统记录，注视由 Smart Eye Pro 和 Smart Recorder 记录。然后利用接管过程中收集的碰撞数据进行分析。
从 27 名参与者中的 23 名收集了 132 个有效的接管操作，记录了 15 次碰撞。在此基础上，识别出了可能导致碰撞的 5 种不安全行为模式，分别表示为 Type I 到 Type V。此外，即使驾驶员有更多的时间观察周围环境，即较长的 DoM，也没有像预期的那样减少碰撞的数量。因此，似乎更重要的是驾驶员在 TOR 之后不久的注视行为和操作。
为了确保接管安全，驾驶员的注视行为和操作之间的良好配合至关重要。总的来说，在需要接管的紧急情况下，一些驾驶员似乎难以合理分配注意力，这似乎与驾驶员观察周围环境的剩余时间关系不大。在设计 HMIs 时，我们不妨考虑根据驾驶员的状态和当时的操作提供足够的信息来指导驾驶员，以提高紧急情况下接管的安全性，更重要的是，要及时有效地提供信息。
