Why Do I Have to Drive Now? Post Hoc Explanations of Takeover Requests

Körber, Moritz; Prasch, Lorenz; Bengler, Klaus

doi:10.1177/0018720817747730

Cited by 80 publications

(57 citation statements)

References 87 publications

(104 reference statements)

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“…We also found RtI frequency to lower usability, which was not expected but can probably be explained by the activation process of the implemented automation. As hypothesized (based on Körber et al [39]), RtI frequency showed no effect on trust. Furthermore, subjective time use was decreased by more RtIs.…”

Section: Transition Frequencysupporting

confidence: 51%

“…Based on findings on the effects of interruptions and the connection between acceptance and perceived usefulness, we hypothesize that with more RtIs workload is elevated and acceptance reduced. An impact on trust and usability is not expected, based on findings by Körber et al [39]. The predictive HMI is expected to have a positive effect on usability, workload, trust, and acceptance, like it was found in studies outlined above.…”

mentioning

confidence: 86%

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The Effects of a Predictive HMI and Different Transition Frequencies on Acceptance, Workload, Usability, and Gaze Behavior during Urban Automated Driving

2020

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Automated driving research as a key topic in the automotive industry is currently undergoing change. Research is shifting from unexpected and time-critical take-over situations to human machine interface (HMI) design for predictable transitions. Furthermore, new applications like automated city driving are getting more attention and the ability to engage in non-driving related activities (NDRA) starting from SAE Level 3 automation poses new questions to HMI design. Moreover, future introduction scenarios and automated capabilities are still unclear. Thus, we designed, executed, and assessed a driving simulator study focusing on the effect of different transition frequencies and a predictive HMI while freely engaging in naturalistic NDRA. In the study with 33 participants, we found transition frequency to have effects on workload and acceptance, as well as a small impact on the usability evaluation of the system. Trust, however, was not affected. The predictive HMI was used and accepted, as can be seen by eye-tracking data and the post-study questionnaire, but could not mitigate the above-mentioned negative effects induced by transition frequency. Most attractive activities were window gazing, chatting, phone use, and reading magazines. Descriptively, window gazing and chatting gained attractiveness when interrupted more often, while reading magazines and playing games were negatively affected by transition rate.

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Section: Transition Frequencysupporting

confidence: 51%

mentioning

confidence: 86%

The Effects of a Predictive HMI and Different Transition Frequencies on Acceptance, Workload, Usability, and Gaze Behavior during Urban Automated Driving

2020

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“…functions and monitor roadway conditions for an entire trip" (National Highway Traffic Safety Administration [NHTSA], 2013, p. 5). However, the AV automation level of the prior literature simulated level 2 and level 3 automation(Forster et al, 2017;Koo et al, 2015Koo et al, , 2016Körber et Du, N., Haspiel, J., Zhang, Q., Tilbury, D., Pradhan, A., Yang, X. J. and Robert, L. P. (Accepted May 21, 2019). Look Who's Talking Now: Implications of AV's Explanations on Driver's Trust, AV Preference, Anxiety and Mental Workload, Transportation Research Part C: Emerging Technologies, forthcoming.…”

mentioning

confidence: 99%

Look who’s talking now: Implications of AV’s explanations on driver’s trust, AV preference, anxiety and mental workload

Haspiel

Zhang

et al. 2019

Transportation Research Part C: Emerging Technologies

115

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Explanations given by automation are often used to promote automation adoption. However, it remains unclear whether explanations promote acceptance of automated vehicles (AVs). In this study, we conducted a within-subject experiment in a driving simulator with 32 participants, using four different conditions. The four conditions included: (1) no explanation, (2) explanation given before or (3) after the AV acted and (4) the option for the driver to approve or disapprove the AV's action after hearing the explanation. We examined four AV outcomes: trust, preference for AV, anxiety and mental workload. Results suggest that explanations provided before an AV acted were associated with higher trust in and preference for the AV, but there was no difference in anxiety and workload. These results have important implications for the adoption of AVs.

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“…Study results showed that the inclusion of system uncertainty information positively enhanced the interaction in terms of trust and acceptance. Körber et al [32] evaluated whether providing an additional explanation for a takeover request in automated driving influenced trust in automation and acceptance. Results revealed that an additional explanation for a takeover request did not influence trust or acceptance but increased the perceived understanding of the system.…”

Section: Introductionmentioning

confidence: 99%

“…The IC (3) was divided into three parts and displayed the current mode of the automated system, a bird's view of the current traffic situation, relevant navigational information, and planned system maneuvers. Additionally, the reason the automation stopped (e.g., construction site, end of motorway) was depicted to positively enhance participants' system understanding [32]. AR 4elements were used to highlight the surrounding relevant vehicles in takeover scenarios.…”

mentioning

confidence: 99%

Takeover Requests in Highly Automated Truck Driving: How Do the Amount and Type of Additional Information Influence the Driver–Automation Interaction?

Richardson

Flohr

Michel³

2018

MTI

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Vehicle automation is linked to various benefits, such as increase in fuel and transport efficiency as well as increase in driving comfort. However, automation also comes with a variety of possible downsides, e.g., loss of situational awareness, loss of skills, and inappropriate trust levels regarding system functionality. Drawbacks differ at different automation levels. As highly automated driving (HAD, level 3) requires the driver to take over the driving task in critical situations within a limited period of time, the need for an appropriate human–machine interface (HMI) arises. To foster adequate and efficient human–machine interaction, this contribution presents a user-centered, iterative approach for HMI evaluation of highly automated truck driving. For HMI evaluation, a driving simulator study [n = 32] using a dynamic truck driving simulator was conducted to let users experience the HMI in a semi-real driving context. Participants rated three HMI concepts, differing in their informational content for HAD regarding acceptance, workload, user experience, and controllability. Results showed that all three HMI concepts achieved good to very good results in these measures. Overall, HMI concepts offering more information to the driver about the HAD system showed significantly higher ratings, depicting the positive effect of additional information on the driver–automation interaction.

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Why Do I Have to Drive Now? Post Hoc Explanations of Takeover Requests

Abstract: 13Objective: It was investigated whether providing an explanation for a take-over request in automated 14 driving influences trust in automation and acceptance. 15 Background: Take-over requests will be recurring events in conditionally automated driving which 16

Cited by 80 publications

References 87 publications

The Effects of a Predictive HMI and Different Transition Frequencies on Acceptance, Workload, Usability, and Gaze Behavior during Urban Automated Driving

The Effects of a Predictive HMI and Different Transition Frequencies on Acceptance, Workload, Usability, and Gaze Behavior during Urban Automated Driving

Look who’s talking now: Implications of AV’s explanations on driver’s trust, AV preference, anxiety and mental workload

Takeover Requests in Highly Automated Truck Driving: How Do the Amount and Type of Additional Information Influence the Driver–Automation Interaction?

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