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Human-Machine Systems and Automated Driving–Involving the Human in the Journey
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Human-Machine Systems and Automated Driving–Involving the Human in the Journey

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 19560

Special Issue Editors

Prof. Dr. MariAnne Karlsson

E-Mail Website
Guest Editor
Division for Design & Human Factors, Department of Industrial and Materials Science, Chalmers University of Technology, Göteborg, Sweden
Interests: human-technology systems; user-centred design; adoption of innovation; methodology
Dr. Lars-Ola Bligård

E-Mail Website
Guest Editor
Researcher at Design & Human Factors, Department of Industrial and Materials Science, Chalmers University of Technology, Göteborg, Sweden
Interests: human-machine system; automation; user interface design
Dr. Helena Strömberg

E-Mail Website
Guest Editor
Senior lecturer at Design & Human Factors, Department of Industrial and Materials Science, Chalmers University of Technology, Göteborg, Sweden
Interests: adoption of innovation, user involvement, human - technology interaction

Special Issue Information

Dear Colleagues,

Progress towards fully autonomous vehicles is being made, but there are still a number of technological challenges as well as questions regarding preconditions for user acceptance and adoption. For some time, the human driver cannot be replaced in all driving situations, and different levels of automation will be available in different situations in the same vehicle. Hence, human and automation form a human–machine system. Further understanding of this human–machine system for automated driving relies on finding answers to questions such as how to design human–machine interfaces in automated vehicles, users’ understanding of the abilities and limitations of the automated systems, and what factors affect users’ trust in these systems. In addition, addressing these and related questions presents a number of methodological challenges associated with studying future human–machine systems and interaction with and use of technology that does not yet exist and involving users in order to proactively provide input into ongoing development.

This Special Issue on “Human–Machine Systems and Automated Driving—Involving the Human in the Journey“ welcomes work by academic and industrial researchers who have undertaken empirical research regarding these issues from a human-centered perspective.

Prof. MariAnne Karlsson
Dr. Lars-Ola Bligård
Dr. Helena Strömberg
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Human-Machine Interaction
  • Human-Machine Cooperation
  • Human-Machine Interface
  • Acceptance
  • Trust
  • Mental models
  • Methodology

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Published Papers (5 papers)

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Research

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18 pages, 2216 KiB  
Article
The Decline of User Experience in Transition from Automated Driving to Manual Driving
by Mikael Johansson, Mattias Mullaart Söderholm, Fjollë Novakazi and Annie Rydström
Information 2021, 12(3), 126; https://doi.org/10.3390/info12030126 - 16 Mar 2021
Cited by 5 | Viewed by 3369
Abstract
Automated driving technologies are rapidly being developed. However, until vehicles are fully automated, the control of the dynamic driving task will be shifted between the driver and automated driving system. This paper aims to explore how transitions from automated driving to manual driving [...] Read more.
Automated driving technologies are rapidly being developed. However, until vehicles are fully automated, the control of the dynamic driving task will be shifted between the driver and automated driving system. This paper aims to explore how transitions from automated driving to manual driving affect user experience and how that experience correlates to take-over performance. In the study 20 participants experienced using an automated driving system during rush-hour traffic in the San Francisco Bay Area, CA, USA. The automated driving system was available in congested traffic situations and when active, the participants could engage in non-driving related activities. The participants were interviewed afterwards regarding their experience of the transitions. The findings show that most of the participants experienced the transition from automated driving to manual driving as negative. Their user experience seems to be shaped by several reasons that differ in temporality and are derived from different phases during the transition process. The results regarding correlation between participants’ experience and take-over performance are inconclusive, but some trends were identified. The study highlights the need for new design solutions that do not only improve drivers’ take-over performance, but also enhance user experience during take-over requests from automated to manual driving. Full article
(This article belongs to the Special Issue Human-Machine Systems and Automated Driving–Involving the Human in the Journey)
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14 pages, 501 KiB  
Article
Concept of an Ontology for Automated Vehicle Behavior in the Context of Human-Centered Research on Automated Driving Styles
by Johannes Ossig, Stephanie Cramer and Klaus Bengler
Information 2021, 12(1), 21; https://doi.org/10.3390/info12010021 - 8 Jan 2021
Cited by 8 | Viewed by 3730
Abstract
In the human-centered research on automated driving, it is common practice to describe the vehicle behavior by means of terms and definitions related to non-automated driving. However, some of these definitions are not suitable for this purpose. This paper presents an ontology for [...] Read more.
In the human-centered research on automated driving, it is common practice to describe the vehicle behavior by means of terms and definitions related to non-automated driving. However, some of these definitions are not suitable for this purpose. This paper presents an ontology for automated vehicle behavior which takes into account a large number of existing definitions and previous studies. This ontology is characterized by an applicability for various levels of automated driving and a clear conceptual distinction between characteristics of vehicle occupants, the automation system, and the conventional characteristics of a vehicle. In this context, the terms ‘driveability’, ‘driving behavior’, ‘driving experience’, and especially ‘driving style’, which are commonly associated with non-automated driving, play an important role. In order to clarify the relationships between these terms, the ontology is integrated into a driver-vehicle system. Finally, the ontology developed here is used to derive recommendations for the future design of automated driving styles and in general for further human-centered research on automated driving. Full article
(This article belongs to the Special Issue Human-Machine Systems and Automated Driving–Involving the Human in the Journey)
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18 pages, 6855 KiB  
Article
Human-Centered AI to Support an Adaptive Management of Human-Machine Transitions with Vehicle Automation
by Thierry Bellet, Aurélie Banet, Marie Petiot, Bertrand Richard and Joshua Quick
Information 2021, 12(1), 13; https://doi.org/10.3390/info12010013 - 31 Dec 2020
Cited by 5 | Viewed by 3966
Abstract
This article is about the Human-Centered Design (HCD), development and evaluation of an Artificial Intelligence (AI) algorithm aiming to support an adaptive management of Human-Machine Transition (HMT) between car drivers and vehicle automation. The general principle of this algorithm is to monitor (1) [...] Read more.
This article is about the Human-Centered Design (HCD), development and evaluation of an Artificial Intelligence (AI) algorithm aiming to support an adaptive management of Human-Machine Transition (HMT) between car drivers and vehicle automation. The general principle of this algorithm is to monitor (1) the drivers’ behaviors and (2) the situational criticality to manage in real time the Human-Machine Interactions (HMI). This Human-Centered AI (HCAI) approach was designed from real drivers’ needs, difficulties and errors observed at the wheel of an instrumented car. Then, the HCAI algorithm was integrated into demonstrators of Advanced Driving Aid Systems (ADAS) implemented on a driving simulator (dedicated to highway driving or to urban intersection crossing). Finally, user tests were carried out to support their evaluation from the end-users point of view. Thirty participants were invited to practically experience these ADAS supported by the HCAI algorithm. To increase the scope of this evaluation, driving simulator experiments were implemented among three groups of 10 participants, corresponding to three highly contrasted profiles of end-users, having respectively a positive, neutral or reluctant attitude towards vehicle automation. After having introduced the research context and presented the HCAI algorithm designed to contextually manage HMT with vehicle automation, the main results collected among these three profiles of future potential end users are presented. In brief, main findings confirm the efficiency and the effectiveness of the HCAI algorithm, its benefits regarding drivers’ satisfaction, and the high levels of acceptance, perceived utility, usability and attractiveness of this new type of “adaptive vehicle automation”. Full article
(This article belongs to the Special Issue Human-Machine Systems and Automated Driving–Involving the Human in the Journey)
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15 pages, 1012 KiB  
Article
Cyclists’ Crossing Intentions When Interacting with Automated Vehicles: A Virtual Reality Study
by Juan Pablo Nuñez Velasco, Anouk de Vries, Haneen Farah, Bart van Arem and Marjan P. Hagenzieker
Information 2021, 12(1), 7; https://doi.org/10.3390/info12010007 - 24 Dec 2020
Cited by 16 | Viewed by 3275
Abstract
Most of cyclists’ fatalities originate from collisions with motorized vehicles. It is expected that automated vehicles (AV) will be safer than human-driven vehicles, but this depends on the nature of interactions between non-automated road users, among them cyclists. Little research on the interactions [...] Read more.
Most of cyclists’ fatalities originate from collisions with motorized vehicles. It is expected that automated vehicles (AV) will be safer than human-driven vehicles, but this depends on the nature of interactions between non-automated road users, among them cyclists. Little research on the interactions between cyclists and AVs exists. This study aims to determine the main factors influencing cyclists’ crossing intentions when interacting with an automated vehicle as compared to a conventional vehicle (CV) using a 360° video-based virtual reality (VR) method. The considered factors in this study included vehicle type, gap size between cyclist and vehicle, vehicle speed, and right of way. Each factor had two levels. In addition, cyclist’s self-reported behavior and trust in automated vehicles were also measured. Forty-seven participants experienced 16 different crossing scenarios in a repeated measures study using VR. These scenarios are the result of combinations of the studied factors at different levels. In total, the experiment lasted 60 min. The results show that the gap size and the right of way were the primary factors affecting the crossing intentions of the individuals. The vehicle type and vehicle speed did not have a significant effect on the crossing intentions. Finally, the 360° video-based VR method scored relatively high as a research method and comparable with the results of a previous study investigating pedestrians’ crossing intentions confirming its suitability as a research methodology to study cyclists’ crossing intentions. Full article
(This article belongs to the Special Issue Human-Machine Systems and Automated Driving–Involving the Human in the Journey)
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Review

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16 pages, 575 KiB  
Review
Effects of User Interfaces on Take-Over Performance: A Review of the Empirical Evidence
by Soyeon Kim, René van Egmond and Riender Happee
Information 2021, 12(4), 162; https://doi.org/10.3390/info12040162 - 10 Apr 2021
Cited by 28 | Viewed by 4114
Abstract
In automated driving, the user interface plays an essential role in guiding transitions between automated and manual driving. This literature review identified 25 studies that explicitly studied the effectiveness of user interfaces in automated driving. Our main selection criterion was how the user [...] Read more.
In automated driving, the user interface plays an essential role in guiding transitions between automated and manual driving. This literature review identified 25 studies that explicitly studied the effectiveness of user interfaces in automated driving. Our main selection criterion was how the user interface (UI) affected take-over performance in higher automation levels allowing drivers to take their eyes off the road (SAE3 and SAE4). We categorized user interface (UI) factors from an automated vehicle-related information perspective. Short take-over times are consistently associated with take-over requests (TORs) initiated by the auditory modality with high urgency levels. On the other hand, take-over requests directly displayed on non-driving-related task devices and augmented reality do not affect take-over time. Additional explanations of take-over situation, surrounding and vehicle information while driving, and take-over guiding information were found to improve situational awareness. Hence, we conclude that advanced user interfaces can enhance the safety and acceptance of automated driving. Most studies showed positive effects of advanced UI, but a number of studies showed no significant benefits, and a few studies showed negative effects of advanced UI, which may be associated with information overload. The occurrence of positive and negative results of similar UI concepts in different studies highlights the need for systematic UI testing across driving conditions and driver characteristics. Our findings propose future UI studies of automated vehicle focusing on trust calibration and enhancing situation awareness in various scenarios. Full article
(This article belongs to the Special Issue Human-Machine Systems and Automated Driving–Involving the Human in the Journey)
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