Autonomous vehicles often make complex decisions via machine learning-based predictive models applied to collected sensor data. While this combination of methods provides a foundation for real-time actions, self-driving behavior primarily remains opaque to end users. In this sense, explainability of real-time decisions is a crucial and natural requirement for building trust in autonomous vehicles. Moreover, as autonomous vehicles still cause serious traffic accidents for various reasons, timely conveyance of upcoming hazards to road users can help improve scene understanding and prevent potential risks. Hence, there is also a need to supply autonomous vehicles with user-friendly interfaces for effective human-machine teaming. Motivated by this problem, we study the role of explainable AI and human-machine interface jointly in building trust in vehicle autonomy. We first present a broad context of the explanatory human-machine systems with the "3W1H" (what, whom, when, how) approach. Based on these findings, we present a situation awareness framework for calibrating users' trust in self-driving behavior. Finally, we perform an experiment on our framework, conduct a user study on it, and validate the empirical findings with hypothesis testing.

翻译：自动驾驶汽车通常通过将机器学习预测模型应用于收集的传感器数据来做出复杂决策。尽管这种方法的组合为实时行动提供了基础，但自动驾驶行为对终端用户而言仍然主要是不透明的。从这个意义上说，实时决策的可解释性是建立对自动驾驶汽车信任的关键且自然的要求。此外，由于自动驾驶汽车仍因各种原因导致严重交通事故，及时向道路使用者传达即将发生的危险有助于改善场景理解并预防潜在风险。因此，还需要为自动驾驶汽车提供用户友好界面，以实现有效的人机协作。受此问题启发，我们联合研究了可解释人工智能与人机界面在建立对车辆自动化信任中的作用。我们首先通过“3W1H”（什么、谁、何时、如何）方法阐述了可解释人机系统的广泛背景。基于这些发现，我们提出了一种用于校准用户对自动驾驶行为信任的态势感知框架。最后，我们在该框架上进行实验，开展用户研究，并通过假设检验验证实证结果。