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.
ASJC Scopus subject areas
- !!Industrial and Manufacturing Engineering
- !!Information Systems and Management