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Cooperative Intelligence in Automated Driving
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Cooperative Intelligence in Automated Driving

A special issue of Multimodal Technologies and Interaction (ISSN 2414-4088).

Deadline for manuscript submissions: closed (10 November 2022) | Viewed by 12508

Special Issue Editors

Prof. Dr. Andreas Riener

E-Mail Website
Guest Editor
Human-Computer Interaction Group, Technische Hochschule Ingolstadt, 85049 Ingolstadt, Germany
Interests: user experience design; automotive user interfaces; human-computer interaction; intelligent user interfaces; AR/VR applications
Special Issues, Collections and Topics in MDPI journals
Dr. Myounghoon Jeon (Philart)

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering; Department of Computer Science (by courtesy), Virginia Tech, Blacksburg, VA 24061, USA
Interests: auditory displays; affective computing; automotive user interfaces; assistive robotics; aesthetic computing
Special Issues, Collections and Topics in MDPI journals
Dr. Ronald Schroeter

E-Mail Website
Guest Editor
Centre for Accident Research and Road Safety – Queensland (CARRS-Q), Queensland University of Technology, Brisbane, QLD 4000, Australia
Interests: automotive user interfaces; autonomous driving; intelligent transport systems; road safety; games; augmented reality; user experience research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Although the research field of automated driving has experienced a major surge in development in recent years, major challenges remain unsolved. Up until recently, Europe did not have a legal framework in place for automated vehicles (AVs), and European automakers had yet to catch up technologically in order to put safe automated vehicles on the road. As of Feb. 24, 2022, the German parliament enacted a regulation to "regulate the operation of motor vehicles with automated and autonomous driving functions and to amend road traffic regulations" (Regulation 86/22). As a result, AV developments are expected to pick up speed in Europe, with targeted deployments, use cases and regular operation on public roads.

As legal challenges are being addressed, high usability and good user experience are the next frontier towards the public acceptance and uptake of automated vehicles. This calls for the User Experience Design (UXD) community to explore and propose human-centric solutions for automated driving to become a successful reality.

With this Special Issue and its scientific research papers, we would like to highlight research problems related to human interactions with automated vehicles and automated driving, drawing on fields such as human–computer interaction, human factors, and interaction design. We want to show how good system design, well-defined interfaces, aligned UI design principles, evaluation methods, and user experience metrics can help to engage the user and thus create enticing and successful automated driving products.

We encourage researchers and practitioners from academia and industry to submit novel (unpublished, according to the journal specifications/regulations) contributions. We are soliciting original research contributions on the following topics of interest:

  • HCXAI for automotive applications: e.g., “black boxes” representing artificial intelligence are starting to make safety-critical driving decisions, but how, when, and what need to be explained to the users to understand and trust these decisions (e.g., transparent displays)?
  • Engagement, situation/mode awareness: e.g., as drivers are free from the driving task, what level of engagement in the driving task is still required? What level of situation/mode awareness is needed? How can this be maintained, measured, etc.?
  • Trust in future mobility.
  • Design for marginal groups: e.g., how can we ensure marginal groups, such as users with disabilities, have user-friendly access to novel mobility technologies without being marginalized and/or patronized?
  • In-vehicle intelligent agents: what novel functions, modalities, and interactions with intelligent agents meet end-user needs and wants in the automated driving context?
  • Emotional experiences and well-being in automated driving.
  • Augmented perception and cognition (HUDs, ambient display, sonification, olfactory displays, etc.)
  • Forms of cooperation:
    1. AVs cooperating with other Avs;
    2. AVs cooperating (communicating) with external humans (VRUs/other drivers);
    3. AVs cooperating with their users (driver/passengers).
  • Novel methods/tools, in particular, those focusing on human interactions with AVs.

Facts & Figures:

  • Abstract/title submission deadline: 17 June 2022 (optional)
  • Manuscript deadline: 22 July 2022

All deadlines are AoE (anywhere on earth) on the date shown.

Prof. Dr. Andreas Riener
Dr. Myounghoon Jeon (Philart)
Dr. Ronald Schroeter
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. Multimodal Technologies and Interaction 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.

Published Papers (4 papers)

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17 pages, 375 KiB  
Article
When Self-Driving Fails: Evaluating Social Media Posts Regarding Problems and Misconceptions about Tesla’s FSD Mode
by Anne Linja, Tauseef Ibne Mamun and Shane T. Mueller
Multimodal Technol. Interact. 2022, 6(10), 86; https://doi.org/10.3390/mti6100086 - 23 Sep 2022
Cited by 2 | Viewed by 3849
Abstract
With the recent deployment of the latest generation of Tesla’s Full Self-Driving (FSD) mode, consumers are using semi-autonomous vehicles in both highway and residential driving for the first time. As a result, drivers are facing complex and unanticipated situations with an unproven technology, [...] Read more.
With the recent deployment of the latest generation of Tesla’s Full Self-Driving (FSD) mode, consumers are using semi-autonomous vehicles in both highway and residential driving for the first time. As a result, drivers are facing complex and unanticipated situations with an unproven technology, which is a central challenge for cooperative cognition. One way to support cooperative cognition in such situations is to inform and educate the user about potential limitations. Because these limitations are not always easily discovered, users have turned to the internet and social media to document their experiences, seek answers to questions they have, provide advice on features to others, and assist other drivers with less FSD experience. In this paper, we explore a novel approach to supporting cooperative cognition: Using social media posts can help characterize the limitations of the automation in order to get information about the limitations of the system and explanations and workarounds for how to deal with these limitations. Ultimately, our goal is to determine the kinds of problems being reported via social media that might be useful in helping users anticipate and develop a better mental model of an AI system that they rely on. To do so, we examine a corpus of social media posts about FSD problems to identify (1) the typical problems reported, (2) the kinds of explanations or answers provided by users, and (3) the feasibility of using such user-generated information to provide training and assistance for new drivers. The results reveal a number of limitations of the FSD system (e.g., lane-keeping and phantom braking) that may be anticipated by drivers, enabling them to predict and avoid the problems, thus allowing better mental models of the system and supporting cooperative cognition of the human-AI system in more situations. Full article
(This article belongs to the Special Issue Cooperative Intelligence in Automated Driving)
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12 pages, 2808 KiB  
Article
Developing a Multimodal HMI Design Framework for Automotive Wellness in Autonomous Vehicles
by Yaqi Zheng and Xipei Ren
Multimodal Technol. Interact. 2022, 6(9), 84; https://doi.org/10.3390/mti6090084 - 18 Sep 2022
Cited by 2 | Viewed by 2782
Abstract
With the development of autonomous technology, the research into multimodal human-machine interaction (HMI) for autonomous vehicles (AVs) has attracted extensive attention, especially in automotive wellness. To support the design of HMIs for automotive wellness in AVs, this paper proposes a multimodal design framework. [...] Read more.
With the development of autonomous technology, the research into multimodal human-machine interaction (HMI) for autonomous vehicles (AVs) has attracted extensive attention, especially in automotive wellness. To support the design of HMIs for automotive wellness in AVs, this paper proposes a multimodal design framework. First, three elements of the framework were envisioned based on the typical composition of an interactive system. Second, a five-step process for utilizing the proposed framework was suggested. Third, the framework was applied in a design education course for exemplification. Finally, the AttrakDiff questionnaire was used to evaluate these interactive prototypes with 20 participants who had an affinity for HMI design. The questionnaire responses showed that the overall impression was positive and this framework can help design students to effectively identify research gaps and expand design concepts in a systematic way. The proposed framework offers a design approach for the development of multimodal HMIs for autonomous wellness in AVs. Full article
(This article belongs to the Special Issue Cooperative Intelligence in Automated Driving)
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19 pages, 2080 KiB  
Article
The Effects of Transparency and Reliability of In-Vehicle Intelligent Agents on Driver Perception, Takeover Performance, Workload and Situation Awareness in Conditionally Automated Vehicles
by Jing Zang and Myounghoon Jeon
Multimodal Technol. Interact. 2022, 6(9), 82; https://doi.org/10.3390/mti6090082 - 14 Sep 2022
Cited by 5 | Viewed by 2531
Abstract
In the context of automated vehicles, transparency of in-vehicle intelligent agents (IVIAs) is an important contributor to driver perception, situation awareness (SA), and driving performance. However, the effects of agent transparency on driver performance when the agent is unreliable have not been fully [...] Read more.
In the context of automated vehicles, transparency of in-vehicle intelligent agents (IVIAs) is an important contributor to driver perception, situation awareness (SA), and driving performance. However, the effects of agent transparency on driver performance when the agent is unreliable have not been fully examined yet. This paper examined how transparency and reliability of the IVIAs affect drivers’ perception of the agent, takeover performance, workload and SA. A 2 × 2 mixed factorial design was used in this study, with transparency (Push: proactive vs. Pull: on-demand) as a within-subjects variable and reliability (high vs. low) as a between-subjects variable. In a driving simulator, 27 young drivers drove with two types of in-vehicle agents during the conditionally automated driving. Results suggest that transparency influenced participants’ perception on the agent and perceived workload. High reliability agent was associated with higher situation awareness and less effort, compared to low reliability agent. There was an interaction effect between transparency and reliability on takeover performance. These findings could have important implications for the continued design and development of IVIAs of the automated vehicle system. Full article
(This article belongs to the Special Issue Cooperative Intelligence in Automated Driving)
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16 pages, 7945 KiB  
Article
Ambient Light Conveying Reliability Improves Drivers’ Takeover Performance without Increasing Mental Workload
by Nikol Figalová, Lewis L. Chuang, Jürgen Pichen, Martin Baumann and Olga Pollatos
Multimodal Technol. Interact. 2022, 6(9), 73; https://doi.org/10.3390/mti6090073 - 26 Aug 2022
Cited by 6 | Viewed by 1983
Abstract
Drivers of L3 automated vehicles (AVs) are not required to continuously monitor the AV system. However, they must be prepared to take over when requested. Therefore, it is necessary to design an in-vehicle environment that allows drivers to adapt their levels of preparedness [...] Read more.
Drivers of L3 automated vehicles (AVs) are not required to continuously monitor the AV system. However, they must be prepared to take over when requested. Therefore, it is necessary to design an in-vehicle environment that allows drivers to adapt their levels of preparedness to the likelihood of control transition. This study evaluates ambient in-vehicle lighting that continuously communicates the current level of AV reliability, specifically on how it could influence drivers’ take-over performance and mental workload (MW). We conducted an experiment in a driving simulator with 42 participants who experienced 10 take-over requests (TORs). The experimental group experienced a four-stage ambient light display that communicated the current level of AV reliability, which was not provided to the control group. The experimental group demonstrated better take-over performance, based on lower vehicle jerks. Notably, perceived MW did not differ between the groups, and the EEG indices of MW (frontal theta power, parietal alpha power, Task–Load Index) did not differ between the groups. These findings suggest that communicating the current level of reliability using ambient light might help drivers be better prepared for TORs and perform better without increasing their MW. Full article
(This article belongs to the Special Issue Cooperative Intelligence in Automated Driving)
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