Wintersberger, PhilippRiener, AndreasZiegler, Jürgen2017-11-202017-11-202016https://dl.gi.de/handle/20.500.12116/6124Trust in technology is an important factor to be considered for safety-critical systems. Of particular interest today is the transport domain, as more and more complex information and assistance systems find their way into vehicles. Research in driving automation / automated driving systems is in the focus of many research institutes worldwide. On the operational side, active safety systems employed to save lives are frequently used by non-professional drivers that neither know system boundaries nor the underlying functional principle. This is a serious safety issue, as systems are activated under false circumstances and with wrong expectations. At least some of the recent incidents with advanced driving assistance systems (ADAS) or automated driving systems (ADS; SAE J3016) could have been prevented with a full understanding of the driver about system functionality and limitations (instead of overreliance). Drivers have to be trained to accept and use these systems in a way, that subjective trust matches objective trustworthiness (cf. “appropriate trust”) to prevent disuse and / or misuse. In this article, we present an interaction model for trust calibration that issues personalized messages in real time. On the showcase of automated driving we report the results of two user studies related to trust in ADS and driving ethics. In the first experiment (N = 48), mental and emotional states of front-seat passengers were compared to get deeper insight into the dispositional trust of potential users of automated vehicles. Using quantitative and qualitative methods, we found that subjects accept and trust ADSs almost similarly as male / female drivers. In another study (N = 40), moral decisions of drivers were investigated in a systematic way. Our results indicate that the willingness of drivers to risk even severe accidents increases with the number and age of pedestrians that would otherwise be sacrificed. Based on our initial findings, we further discuss related aspects of trust in driving automation. Effective shared vehicle control and expected advantages of fully / highly automated driving (SAE levels 3 or higher) can only be achieved when trust issues are demonstrated and resolved.Automated VehiclesTrust CalibrationUser AcceptanceEthical Decision MakingSAE J3016Text/Conference Paper2196-6826