Measuring trust in automated driving using a multi-level approach to human factors

Clement, Philipp; Danzinger, Herbert; Veledar, Omar; Könczöl, Clemens; Macher, Georg; Eichberger, Arno

doi:10.1109/dsd53832.2021.00069

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…Moreover, Pearson correlations were calculated to examine the correlations between the different factors. The method used to process the gathered results is already made available in Clement et al [44].…”

Section: Resultsmentioning

confidence: 99%

“…Each participant followed a standardised experimental procedure also described in Clement et al [44]. Figure 3 offers a top-level visualization of this procedure, starting with the introduction phase and continuing on to the post-testing phase.…”

Section: Methodsmentioning

confidence: 99%

“…All components of the environment are synchronised by the co-simulation framework Model.Connect [55]. The simulator is capable of performing all movements of normal driving conditions up to the aforementioned 6 m/s 2 accelerations [44].…”

Section: Equipment and Techniquesmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%