Enhancing Acceptance and Trust in Automated Driving trough Virtual Experience on a Driving Simulator

Philipp Clement, Omar Veledar, Clemens Könczöl, Herbert Danzinger, Markus Posch, Arno Eichberger and Georg Macher
Additional contact information
Philipp Clement: Faculty of Mechanical Engineering and Economic Sciences, Institute of Automotive Engineering, Graz University of Technology, 8010 Graz, Austria
Omar Veledar: AVL List GmbH, Hans-List-Platz 1, 8020 Graz, Austria
Clemens Könczöl: Faculty of Natural Sciences, Institute of Psychology, University of Graz, 8010 Graz, Austria
Herbert Danzinger: AVL List GmbH, Hans-List-Platz 1, 8020 Graz, Austria
Markus Posch: AVL List GmbH, Hans-List-Platz 1, 8020 Graz, Austria
Arno Eichberger: Faculty of Mechanical Engineering and Economic Sciences, Institute of Automotive Engineering, Graz University of Technology, 8010 Graz, Austria
Georg Macher: Faculty of Electrical and Information Engineering, Institute of Technical Informatics, Graz University of Technology, 8010 Graz, Austria

Energies, 2022, vol. 15, issue 3, 1-22

Abstract: As vehicle driving evolves from human-controlled to autonomous, human–machine interaction ensures intuitive usage as well as the feedback from vehicle occupants to the machine for optimising controls. The feedback also improves understanding of the user satisfaction with the system behaviour, which is crucial for determining user trust and, hence, the acceptance of the new functionalities that aim to improve mobility solutions and increase road safety. Trust and acceptance are potentially the crucial parameters for determining the success of autonomous driving deployment in wider society. Hence, there is a need to define appropriate and measurable parameters to be able to quantify trust and acceptance in a physically safe environment using dependable methods. This study seeks to support technical developments and data gathering with psychology to determine the degree to which humans trust automated driving functionalities. The primary aim is to define if the usage of an advanced driving simulator can improve consumer trust and acceptance of driving automation through tailor-made studies. We also seek to measure significant differences in responses from different demographic groups. The study employs tailor-made driving scenarios to gather feedback on trust, usability and user workload of 55 participants monitoring the vehicle behaviour and environment during the automated drive. Participants’ subjective ratings are gathered before and after the simulator session. Results show a significant increase in trust ensuing the exposure to the driving automation functionalities. We quantify this increase resulting from the usage of the driving simulator. Those less experienced with driving automation show a higher increase in trust and, therefore, profit more from the exercise. This appears to be linked to the demanded participant workload, as we establish a link between workload and trust. The findings provide a noteworthy contribution to quantifying the method of evaluating and ensuring user acceptance of driving automation. It is only through the increase of trust and consequent improvement of user acceptance that the introduction of the driving automation into wider society will be a guaranteed success.

Keywords: automated driving (AD); driving simulator; expression of trust; acceptance; simulator case study; NASA TLX; advanced driver assistant systems (ADAS); system usability scale; driving school (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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