Trust in automated vehicles:constructs, psychological processes and assessment

There is a growing body of research on trust in driving automation systems. In this paper, we seek to clarify the way trust is conceptualized, calibrated and measured taking into account issues related to specific levels of driving automation. We find that: (1) experience plays a vital role in trust calibration; (2) experience should be measured not just in terms of distance traveled, but in terms of the range of situations encountered; (3) system malfunctions and recovery from such malfunctions is a fundamental part of this experience. We summarize our findings in a framework describing the dynamics of trust calibration. We observe that methods used to quantify trust often lack objectivity, reliability, and validity, and propose a set of recommendations for researchers seeking to select suitable trust measures for their studies. In conclusion, we argue that the safe deployment of current and future automated vehicles depends on drivers developing appropriate levels of trust. Given the potentially severe consequences of miscalibrated trust, it is essential that drivers incorporate the possibility of new and unexpected driving situations in their mental models of system capabilities. It is vitally important that we develop methods that contribute to this goal

Human’s Overtrust in and Overreliance on Advanced Driver Assistance Systems: A Theoretical Framework

This paper gives a theoretical framework to describe, analyze, and evaluate the driver’s overtrust in and overreliance on ADAS. Although “overtrust” and “overreliance” are often used as if they are synonyms, this paper differentiates the two notions rigorously. To this end, two aspects, (1) situation diagnostic aspect and (2) action selection aspect, are introduced. The first aspect is to describe overtrust, and it has three axes: (1-1) dimension of trust, (1-2) target object, and (1-3) chances of observation. The second aspect, (2), is to describe overreliance on the ADAS, and it has other three axes: (2-1) type of action selected, (2-2) benefits expected, and (2-3) time allowance for human intervention. Document type: Articl

Real-time Personalized Driver Support System for Pilot Assist Promotion in Different Traffic Conditions

The complexity of advanced in-vehicle systems and level of automation provided is currently increasing, making the understanding of smart systems design and limitations challenging to a driver. As a result, misinterpretation of the system\u27s capabilities can be detrimental to perceived usefulness and the system\u27s usage. The personalized real-time driver support concept presented in this paper is designed to improve the driver\u27s understanding of Pilot Assist (PA) and increase PA usage effectiveness in various traffic contexts. The designed communication informs drivers about PA capabilities in various traffic conditions, helping drivers recognize the appropriate context for PA activation and reflect on their own PA use strategy