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

Bengler, Klaus; Rettenmaier, Michael; Fritz, Nicole; Feierle, Alexander

doi:10.3390/info11020061

Cited by 141 publications

(80 citation statements)

References 68 publications

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“…Smart HMIs can communicate with each other, adjust to changing conditions in real time, learn from that experience, and operate autonomously without human intervention. They will take over tasks that people are not particularly good at, for instance, repetitive processes and dangerous or data-intensive tasks, or those tasks that are either too large or too small to be efficiently performed by human beings (Bengler et al 2020).…”

Section: • Smart Hmismentioning

confidence: 99%

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How Humans and Machines Interact

Heinke

2021

Managing Work in the Digital Economy

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On May 1, 2030, International Labor Day, the evening news worldwide has only one top news item: China's new prime minister is not a person, but an avatar. The pictures of the meeting of the Standing Committee of the Politburo of the Communist Party show, indeed, a lifelike figure on the right side of President Xi, who bears considerable resemblance to the first head of the government of the People's Republic of Zhou Enlai, also bearing the name of his son Sun Yang, but in reality is a hologram. China is celebrating the appointment as a great success on the way to fulfilling the Chinese dream and a vital stage victory just a few years before the centenary of the founding of the People's Republic. President Xi says that the wisdom and courage of the founding generation will now be preserved forever. Xi says, "No power in the world can make better decisions if we are protected by our ancestors. They guide my hand and give us their wisdom. China, under the leadership of its party, has become invincible once and for all." Only insiders know that the top secret Tianhe-10 (Milky Way) supercomputer, which controls the Avatar's utterances and movements, has been instrumental in preparing the Chinese leadership for decision-making for 2 years now. According to the findings of the American secret services, the rate of agreement between the recommendations of the supercomputer and the actual decisions of the Politburo was initially only 30 percent. In the meantime, however, it has reached 97 percent. All attempts by the Western powers to gain access to the supercomputer have so far been in vain. Since the Trump administration, the open conflict between the United States and China had led to an increasingly aggressive rupture, turning an initial "decoupling" into a fundamentally hostile separation. As a result, for the past 5 years, all components and algorithms in Chinese products and applications have been domestically produced. All Chinese companies and government institutions were forbidden to use hardware or software from non-Chinese sources under threat of sanction. The standards have already diverged so far apart that there is no longer any compatibility. So much for a possible vision from the year 2030: Of course nobody can know the future; at best plausible assumptions are possible. But as science fiction author William Gibson says, "The future is already there, it is just unequally distributed."

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Section: • Smart Hmismentioning

confidence: 99%

“…Adapting to rapid changes in the influencing factors is essential. Relevant HMI interrelations are the concurrence for space, content transitions, practicability, consistency, and chronological coordination (Bengler et al 2020).…”

Section: Systematics Of Human-machine Interactionmentioning

confidence: 99%

How Humans and Machines Interact

Heinke

2021

Managing Work in the Digital Economy

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“…What defines the user-friendly design of automated systems has been the subject of scientific discussion for decades [ 1 , 2 ]. Especially in the upcoming years, when automated vehicles of SAE (society of automotive engineers) automation levels 3 and 4 will emerge, the demands on the driver’s cognitive system will alter radically, as the role of humans as continuously active decision-makers in vehicles is replaced by automated systems [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Westdrive X LoopAR: An Open-Access Virtual Reality Project in Unity for Evaluating User Interaction Methods during Takeover Requests

Nezami

Wächter

Maleki

et al. 2021

Sensors

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With the further development of highly automated vehicles, drivers will engage in non-related tasks while being driven. Still, drivers have to take over control when requested by the car. Here, the question arises, how potentially distracted drivers get back into the control-loop quickly and safely when the car requests a takeover. To investigate effective human–machine interactions, a mobile, versatile, and cost-efficient setup is needed. Here, we describe a virtual reality toolkit for the Unity 3D game engine containing all the necessary code and assets to enable fast adaptations to various human–machine interaction experiments, including closely monitoring the subject. The presented project contains all the needed functionalities for realistic traffic behavior, cars, pedestrians, and a large, open-source, scriptable, and modular VR environment. It covers roughly 25 km2, a package of 125 animated pedestrians, and numerous vehicles, including motorbikes, trucks, and cars. It also contains all the needed nature assets to make it both highly dynamic and realistic. The presented repository contains a C++ library made for LoopAR that enables force feedback for gaming steering wheels as a fully supported component. It also includes all necessary scripts for eye-tracking in the used devices. All the main functions are integrated into the graphical user interface of the Unity® editor or are available as prefab variants to ease the use of the embedded functionalities. This project’s primary purpose is to serve as an open-access, cost-efficient toolkit that enables interested researchers to conduct realistic virtual reality research studies without costly and immobile simulators. To ensure the accessibility and usability of the mentioned toolkit, we performed a user experience report, also included in this paper.

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“…This difference in activities may be accompanied by a change in position inside the Fully Autonomous Vehicle (FAV), for example, reclining the seat to use a laptop would position the driver farther from the dashboard than during normal driving [ 8 , 9 ]. Yet drivers engaged in NDRTs still need to interact with the vehicle [ 10 ] (e.g., adjust the infotainment system) or intervene to assume control (e.g., instruct the vehicle to pass a slower car ahead) [ 11 , 12 , 13 ]. Further, different NDRTs demand of drivers different physical and cognitive loads [ 9 , 14 , 15 ], which are also influenced by differences between maneuver- and nonmaneuver-based intervention scenarios [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…While developments in interior design [ 5 , 9 , 18 ] were studied to better accommodate drivers, their interaction needs inside FAVs have yet to be fully addressed [ 10 , 19 , 20 ]. Researchers should consider the conditions of individual drivers and what they might need to do.…”

Section: Introductionmentioning

confidence: 99%

How to Interact with a Fully Autonomous Vehicle: Naturalistic Ways for Drivers to Intervene in the Vehicle System While Performing Non-Driving Related Tasks

Ataya

Kim

Elsharkawy

et al. 2021

Sensors

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Autonomous vehicle technology increasingly allows drivers to turn their primary attention to secondary tasks (e.g., eating or working). This dramatic behavior change thus requires new input modalities to support driver–vehicle interaction, which must match the driver’s in-vehicle activities and the interaction situation. Prior studies that addressed this question did not consider how acceptance for inputs was affected by the physical and cognitive levels experienced by drivers engaged in Non-driving Related Tasks (NDRTs) or how their acceptance varies according to the interaction situation. This study investigates naturalistic interactions with a fully autonomous vehicle system in different intervention scenarios while drivers perform NDRTs. We presented an online methodology to 360 participants showing four NDRTs with different physical and cognitive engagement levels, and tested the six most common intervention scenarios (24 cases). Participants evaluated our proposed seven natural input interactions for each case: touch, voice, hand gesture, and their combinations. Results show that NDRTs influence the driver’s input interaction more than intervention scenario categories. In contrast, variation of physical load has more influence on input selection than variation of cognitive load. We also present a decision-making model of driver preferences to determine the most natural inputs and help User Experience designers better meet drivers’ needs.

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

Cited by 141 publications

References 68 publications

How Humans and Machines Interact

How Humans and Machines Interact

Westdrive X LoopAR: An Open-Access Virtual Reality Project in Unity for Evaluating User Interaction Methods during Takeover Requests

How to Interact with a Fully Autonomous Vehicle: Naturalistic Ways for Drivers to Intervene in the Vehicle System While Performing Non-Driving Related Tasks

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