With the level of automation increases in vehicles, such as conditional and highly automated vehicles (AVs), drivers are becoming increasingly out of the control loop, especially in unexpected driving scenarios. Although it might be not necessary to require the drivers to intervene on most occasions, it is still important to improve drivers' situation awareness (SA) in unexpected driving scenarios to improve their trust in and acceptance of AVs. In this study, we conceptualized SA at the levels of perception (SA L1), comprehension (SA L2), and projection (SA L3), and proposed an SA level-based explanation framework based on explainable AI. Then, we examined the effects of these explanations and their modalities on drivers' situational trust, cognitive workload, as well as explanation satisfaction. A three (SA levels: SA L1, SA L2 and SA L3) by two (explanation modalities: visual, visual + audio) between-subjects experiment was conducted with 340 participants recruited from Amazon Mechanical Turk. The results indicated that by designing the explanations using the proposed SA-based framework, participants could redirect their attention to the important objects in the traffic and understand their meaning for the AV system. This improved their SA and filled the gap of understanding the correspondence of AV's behavior in the particular situations which also increased their situational trust in AV. The results showed that participants reported the highest trust with SA L2 explanations, although the mental workload was assessed higher in this level. The results also provided insights into the relationship between the amount of information in explanations and modalities, showing that participants were more satisfied with visual-only explanations in the SA L1 and SA L2 conditions and were more satisfied with visual and auditory explanations in the SA L3 condition.
翻译：随着车辆自动化水平的提高,例如有条件和高度自动化车辆(AV)的自动化水平的提高,驾驶员越来越脱离控制圈,特别是在意外驾驶情况中。虽然可能没有必要要求驾驶员在多数情况下进行干预,但在意外驾驶情况下,提高驾驶员对AV的信任和接受程度,仍然有必要提高驾驶员对意外驾驶情况的认识(SA)。在这项研究中,我们根据认识水平(SA L1)、理解(SA L2)和投影(SA L3)对SA进行了概念化的SA概念化,并提出了基于可解释的AI的SA级解释框架。然后,我们审查了这些解释及其方式对驾驶员对形势直观信任、认知工作量以及解释满意度的影响。 三种(SA级别:SA L1、SA L2和SA L3),通过两种(解释方式:视觉、视觉+音频),提高驾驶员对AL的认知度。 通过使用拟议SA-框架设计解释,参加者可以将其注意力转向交通的重要目标,并理解其对AV系统的意义。 SA-A-L对A-L解释方式的理解也提高了,但A-A-A-A-A-L参与者对A-A-A-L解释结果的理解和A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-L-A-A-A-L-L-A-A-A-A-A-L-L-A-A-L-A-A-L-L-A-A-A-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L
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