In automated driving, an appropriate level of driver trust is essential to improve safety and ensure zero fatalities. Drivers must have a sufficient level of trust to intervene correctly in safety-critical situations: very low levels may lead to either continuous and excessive monitoring of the functions, reducing the attention paid to the environment or switching off these functions, whereas extreme trust in automated driving functions can result in dangerous driving situations because the environment is either insufficiently monitored or not monitored at all. A deeper understanding of trust in automated driving is challenging and requires a triangulated study in which the type of driver, vehicle usage, and environmental data are varied. However, many previous studies were based on a rather limited set of data sources, often relying on qualitative means such as pre-and-post interviews or trust questionnaires to evaluate trust in autonomous driving functions. Although data gathered through empirical research, such as conducting quantitative surveys or qualitative interviews, are simple to store and analyze, the collection and integration of vehicle and sensor data from different data sources usually pose important technical challenges in practice. Hence, a suitable data collection and integration strategy is required to address these challenges. In this context, we propose a general framework for collecting and integrating data from different sources with diverse capabilities and requirements to determine a driver’s trust in automated driving. Our proposed framework facilitates the integration of empirical and measurement data, allowing a triangulated investigation to provide a road map for the automotive industry.

在自动驾驶中，适当的驾驶员信任度对于提高安全性和确保零死亡至关重要。驾驶员必须具有足够的信任度，才能在对安全至关重要的情况下正确进行干预：非常低的级别可能会导致对功能进行连续和过度的监控，从而减少了对环境的关注或关闭了这些功能，而对自动化的高度信任驾驶功能可能会导致危险的驾驶情况，因为对环境的监控不足或根本没有受到监控。对自动驾驶信任的更深入了解具有挑战性，需要进行三角研究，其中驾驶员类型，车辆使用情况和环境数据会有所不同。但是，以前的许多研究都是基于相当有限的数据源，通常依靠定性手段（例如前后访谈或信任问卷）来评估对自动驾驶功能的信任。尽管通过实证研究（例如进行定量调查或定性访谈）收集的数据易于存储和分析，但从不同数据源收集和集成车辆和传感器数据通常在实践中会带来重要的技术挑战。因此，需要一种合适的数据收集和集成策略来应对这些挑战。在这种情况下，我们提出了一个通用框架，用于收集和集成来自具有不同功能和要求的不同来源的数据，以确定驾驶员对自动驾驶的信任。我们提出的框架有助于整合经验数据和衡量数据，