School of Transportation Science and Engineering, Beihang University, Beijing, 100191, China; Beijing Advanced Innovation Center for Big Data and Brain Computing, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beijing, 100191, China.
School of Transportation Science and Engineering, Beihang University, Beijing, 100191, China; Beijing Advanced Innovation Center for Big Data and Brain Computing, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beijing, 100191, China.
Accid Anal Prev. 2021 Jul;157:106156. doi: 10.1016/j.aap.2021.106156. Epub 2021 May 3.
The takeover process in level 3 automated driving determines the controllability of the functions of automated vehicles and thereby traffic safety. In this study, we attempted to explain drivers' takeover performance variation in a level 3 automated vehicle in consideration of the effects of trust, system characteristics, environmental characteristics, and driver characteristics with a structural equation model. The model was built by incorporating drivers' takeover time and quality as endogenous variables. A theoretical framework of the model was hypothesized on the basis of the ACT-R cognitive architecture and relevant research results. The validity of the model was confirmed using data collected from 136 driving simulator samples under the condition of voluntary non-driving-related tasks. Results revealed that takeover time budget was the most critical factor in promoting the safety and stability of takeover process, which, together with traffic density, drivers' age and manual driving experience, determined drivers' takeover quality directly. In addition, the pre-existing experience with an automated system or a similar technology and self-confidence of the driver, as well as takeover time budget, strongly influenced the takeover time directly. Apart from the direct effects mentioned above, trust, as an intermediary variable, explained a major portion of the variance in takeover time. Theoretically, these findings suggest that takeover behavior could be comprehensively evaluated from the two dimensions of takeover time and quality through the combination of trust, driver characteristics, environmental characteristics, and vehicle characteristics. The influence mechanism of the above factors is complex and multidimensional. In addition to the form of direct influence, trust, as an intermediary variable, could reflect the internal mechanism of the takeover behavior variation. Practically, the findings emphasize the crucial role of trust in the change in takeover behavior through the dimensions of subjective trust level and monitoring strategy, which may provide new insights into the function design of takeover process.
三级自动驾驶接管过程决定了自动驾驶汽车功能的可控性，从而影响交通安全。本研究试图在考虑信任、系统特性、环境特性和驾驶员特性的基础上，用结构方程模型解释三级自动驾驶车辆驾驶员接管性能的变化。该模型将驾驶员的接管时间和质量作为内生变量构建而成。在 ACT-R 认知架构和相关研究结果的基础上，对模型的理论框架进行了假设。通过在非驾驶相关任务的自愿驾驶模拟器样本中收集的 136 个样本数据，验证了模型的有效性。结果表明，接管时间预算是促进接管过程安全稳定的最关键因素，它与交通密度、驾驶员年龄和手动驾驶经验一起直接决定了驾驶员的接管质量。此外，驾驶员对自动化系统或类似技术的预先经验和自信心，以及接管时间预算，直接强烈影响接管时间。除了上述直接影响外，信任作为中介变量，直接解释了接管时间变化的大部分差异。从理论上讲，这些发现表明可以通过信任、驾驶员特征、环境特征和车辆特征的组合，从接管时间和质量两个维度来综合评估接管行为。上述因素的影响机制是复杂和多维的。除了直接影响的形式外，信任作为中介变量，还可以反映接管行为变化的内在机制。从实际应用的角度来看，研究结果强调了信任在接管行为变化中的关键作用，通过主观信任水平和监测策略这两个维度，这可能为接管过程的功能设计提供新的思路。
