Secondary task engagement and disengagement in the context of highly automated driving

Wandtner, Bernhard; Schömig, Nadja; Schmidt, G.

doi:10.1016/j.trf.2018.06.001

Cited by 63 publications

(41 citation statements)

References 18 publications

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“…References [4,11,13,16,18] report a degree of certainty (uncertainty information) with which the automation can cope with the current scenario as relevant. Additionally, it has been reported that the remaining automated travel distance/time is important [11,[19][20][21]. These information requirements have already been incorporated into the HMI design for partially and conditionally automated driving in numerous studies (e.g., [12,14,[22][23][24]).…”

Section: Information Needs In Automated Drivingmentioning

confidence: 99%

“…Therefore, participants wanted to have an overview of this intersection. In the context of the navigation data, a display of the remaining automated travel time/distance was mentioned only three times, and, in contrast to the literature [11,[19][20][21], had only a subordinate role. On the one hand, this may be due to the fact that there were no interruptions due to requests to intervene, and, on the other hand, no free NDRAs were offered which might have required better planning via the remaining time.…”

Section: Need For Informationmentioning

confidence: 99%

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Information Needs and Visual Attention during Urban, Highly Automated Driving—An Investigation of Potential Influencing Factors

et al. 2020

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During highly automated driving, the passenger is allowed to conduct non-driving related activities (NDRA) and no longer has to act as a fallback at the functional limits of the driving automation system. Previous research has shown that at lower levels of automation, passengers still wish to be informed about automated vehicle behavior to a certain extent. Due to the aim of the introduction of urban automated driving, which is characterized by high complexity, we investigated the information needs and visual attention of the passenger during urban, highly automated driving. Additionally, there was an investigation into the influence of the experience of automated driving and of NDRAs on these results. Forty participants took part in a driving simulator study. As well as the information presented on the human–machine interface (system status, navigation information, speed and speed limit), participants requested information about maneuvers, reasons for maneuvers, environmental settings and additional navigation data. Visual attention was significantly affected by the NDRA, while the experience of automated driving had no effect. Experience and NDRA showed no significant effect on the need for information. Differences in information needs seem to be due to the requirements of the individual passenger, rather than the investigated factors.

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Section: Information Needs In Automated Drivingmentioning

confidence: 99%

Section: Need For Informationmentioning

confidence: 99%

Information Needs and Visual Attention during Urban, Highly Automated Driving—An Investigation of Potential Influencing Factors

et al. 2020

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“…Closely linked to interruptions is the process of activity planning. According to the three-level model of planning by Schömig and Metz [36] and Wandtner et al [15], appropriate self-regulatory behavior includes limiting task engagements to sections of automated driving (planning level), considering estimated task duration and predicted system availability (decision level), and maintenance of take-over readiness (control level). Planning one's activity engagement in automated driving can be supported by predictive HMI elements that display current and upcoming periods of automated driving, thus theoretically allowing the user to adapt NDRA engagement and in turn reduce workload and discomfort due to interruption events.…”

Section: Introductionmentioning

confidence: 99%

“…Studies actually investigating design and effects of predictive HMI elements were conducted by Richardson et al [14], Wandtner et al [15], and Holländer et al [12]. All three studies found positive effects; Richardson et al [14] discovered time-and distance-based predictive HMIs for conditionally automated trucks to lower workload, and increase acceptance and usability compared to a baseline HMI with no predictive elements.…”

Section: Introductionmentioning

confidence: 99%

“…Not displaying planned RtIs leads to significantly lower usability ratings than visual feedback with a dynamic bar. Also dealing with a distance-based predictive HMI, Wandtner et al [15] assessed its impact on NDRA engagement. In a driving simulator study with several requests to intervene in a Level 3 highway scenario, a predictive HMI was supplemented by an overview of the automated driving sections of the entire track.…”

Section: Introductionmentioning

confidence: 99%

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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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Investigating User Needs for Trip Planning with Limited Availability of Automated Driving Functions

Hecht

Sievers

Bengler

2020

Communications in Computer and Information Science

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Secondary task engagement and disengagement in the context of highly automated driving

Cited by 63 publications

References 18 publications

Information Needs and Visual Attention during Urban, Highly Automated Driving—An Investigation of Potential Influencing Factors

Information Needs and Visual Attention during Urban, Highly Automated Driving—An Investigation of Potential Influencing Factors

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

Investigating User Needs for Trip Planning with Limited Availability of Automated Driving Functions

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