What Do You Do? An Analysis of Non-driving Related Activities During a 60 Minutes Conditionally Automated Highway Drive

Hecht, Tobias; Feldhütter, Anna; Draeger, Kathrin; Bengler, Klaus

doi:10.1007/978-3-030-25629-6_5

Cited by 36 publications

(20 citation statements)

References 9 publications

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“…The possibility of engaging in a NDRA was found to be one of the most valued benefits of AVs [3]. Studies by [29,30] have shown that passengers driving in automated mode watch videos, observe the traffic environment, use smartphones, read, write, talk to passengers or sleep. Because resources for visual input are limited, there is a significant interference between two activities that use the same resource, which could lead to lower performance in tasks using the same resource in a parallel manner.…”

Section: Ndrasmentioning

confidence: 99%

“…This NDRA has the advantages of being easy to interrupt and being able to be carried out successfully with little practice. Therefore, it can be assumed that S4: Passing static bottleneck due to roadworks By way of an NDRA, half of the participants were offered the option of watching a movie on a 9.6 inch tablet mounted in front of the central information display, as this is a frequently requested and performed NDRA during automated driving [29,30]. This cognitive, visual NDRA has already been used in previous studies [43][44][45].…”

Section: Descriptionmentioning

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: Ndrasmentioning

confidence: 99%

Section: Descriptionmentioning

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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“…The iHMI is intended to enable performing NDRA beyond the classic infotainment offering (radio and telephone) and to guarantee its safe performance. Studies by [44,45] have shown that passengers want to be engaged in auditory NDRA such as talking to passengers, listening to music, calling, as well as visual NDRA such as watching the surrounding environment, watching videos on a tablet, and using their phone (e.g., reading or writing) during automated driving. This selection of visual NDRAs is not only offered via the display in the center console, but portable devices such as smartphones and tablets would inevitably have to be introduced as interaction elements.…”

Section: Infotainment Hmi (Ihmi)mentioning

confidence: 99%

From HMI to HMIs: Towards an HMI Framework for Automated Driving

et al. 2020

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During automated driving, there is a need for interaction between the automated vehicle (AV) and the passengers inside the vehicle and between the AV and the surrounding road users outside of the car. For this purpose, different types of human machine interfaces (HMIs) are implemented. This paper introduces an HMI framework and describes the different HMI types and the factors influencing their selection and content. The relationship between these HMI types and their influencing factors is also presented in the framework. Moreover, the interrelations of the HMI types are analyzed. Furthermore, we describe how the framework can be used in academia and industry to coordinate research and development activities. With the help of the HMI framework, we identify research gaps in the field of HMI for automated driving to be explored in the future.

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“…These studies suggest activity engagement during automated driving to be more like activity engagement on train rides than during today's manual driving. However, many researchers highlight large individual differences in the choice of activities and their respective durations [21,22]. Hecht et al [23] thus researched factors influencing the choice of activities and found that factors involving privacy, comfort, travel purpose, trip duration, and age have an effect on the attractiveness of several activities in an online survey with 200 participants.…”

Section: Introductionmentioning

confidence: 99%

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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What Do You Do? An Analysis of Non-driving Related Activities During a 60 Minutes Conditionally Automated Highway Drive

Cited by 36 publications

References 9 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

From HMI to HMIs: Towards an HMI Framework for Automated Driving

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

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