Adaptive Human-Machine Interface

A special issue of Safety (ISSN 2313-576X).

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 12162

Special Issue Editors


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Guest Editor
Department of Industrial Engineering and Mathematical Science (DIISM), Università Politecnica delle Marche, Piazza Roma, 22, 60121 Ancona, Italy
Interests: design theory; extended reality technologies; user experience; affective computing; human–machine interaction
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Guest Editor
Interaction Design, Suor Orsola Benincasa University, Suor Orsola, 10, 80135 Napoli, Italy
Interests: human factors; vehicle technologies; human–machine interface; interaction engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, a wide range of Advanced Driver Assistance Systems (ADAS) are being developed in order to enhance drivers’ perceptions of hazards and/or to partly automate the driving task. Most ADAS consist of sensorized systems aimed at enhancing vehicle awareness, improving occupants’ experience and increasing driving safety. Effective communication between ADAS and drivers, mainly deployed by vehicular Human-Machine Interfaces (HMI), is a challenging design task for practitioners and scholars in the automotive field.

The effectiveness of these HMIs partly depends on the ADAS sensors’ reliability and their real-time monitoring, as well as the interaction quality in terms of drivers’ feedback, compliancy with driving task, usability, consistency with human factor guidelines, norms and, more widely, design lessons learned. Of course, the general aims of the HMI are to decrease drivers’ distraction and workload and to improve attention while driving.

In their development, HMIs are exploiting novel concepts for driver-vehicle interaction, namely improvements in new and already existing sensory modalities, such as visual, tactile and auditory, and even more in their merging and integration. Meanwhile, sensing technologies for driver state monitoring and understanding are growing significantly, becoming more reliable, unobtrusive and integrated into the vehicle’s architecture. Thanks to improvements in the domain of artificial intelligence, data derived from these sensors are promising in detecting even more accurately the factors involved in drivers’ cognitive processes, such as attention and workload, in correctly detecting their emotional levels and, finally, in identifying potential predictions of drivers’ behavior.

A pivotal challenge in future HMIs for ADAS, therefore, is the appropriate combination of the newest ADAS sensors’ capabilities, multimodal HMIs and driver status. If this chain is operated successfully, it could promote long-term changes in driver behavior in a move towards safer driving.

This Special Issue is focused on the design, technological and human challenges behind this chain. It also considers the “Adaptive HMI” domain, where interaction is modulated taking into account driver behavioral intention and adaptation, emotional regulation, cognitive status, etc. New theories, design methodologies and enabling technological solutions for innovative, integrated and adaptive HMI are within the scope of this Special Issue. The final aim is to maximize the positive effects of these strategies in promoting drivers’ awareness, as well as drivers’ cognitive and emotional readiness to cope with riskier driving situations and to generally improve driving quality, performance, comfort and safety.

We invite you to share your real-world experience with interaction engineering, human factors, AI and HMI that can help UX designers, automotive producers, system integrators and, in general, those organizations contributing to improving human-machine interaction in complex systems to conceive and realize safer vehicles.

Prof. Dr. Maura Mengoni
Prof. Dr. Roberto Montanari
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. Safety is an international peer-reviewed open access quarterly 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 1800 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 Interface
  • Machine Learning
  • Human Factors
  • Vehicle Technology
  • Multisensory Interaction

Published Papers (2 papers)

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Research

16 pages, 2226 KiB  
Article
Nudges-Based Design Method for Adaptive HMI to Improve Driving Safety
by Andrea Generosi, Silvia Ceccacci, Buse Tezçi, Roberto Montanari and Maura Mengoni
Safety 2022, 8(3), 63; https://doi.org/10.3390/safety8030063 - 5 Sep 2022
Viewed by 2562
Abstract
This study introduces a new operational tool based on the AEIOU observational framework to support the design of adaptive human machine interfaces (HMIs) that aim to modify people’s behavior and support people’s choices, to improve safety using emotional regulation techniques, through the management [...] Read more.
This study introduces a new operational tool based on the AEIOU observational framework to support the design of adaptive human machine interfaces (HMIs) that aim to modify people’s behavior and support people’s choices, to improve safety using emotional regulation techniques, through the management of environmental characteristics (e.g., temperature and illumination), according to an approach based on the nudging concept within a design thinking process. The proposed approach focuses on research in the field of behavioral psychology that has studied the correlations between human emotions and driving behavior, pushing towards the elicitation of those emotions judged to be most suitable for safe driving. The main objective is to support the ideation of scenarios and/or design features for adaptive HMIs to implement a nudging strategy to increase driving safety. At the end, the results from a collaborative workshop, organized as a case study to collect concept ideas in the context of sports cars, will be shown and evaluated to highlight the validity of the proposed methodology, but also the limitations due to the requirement of prototypes to evaluate the actual effectiveness of the presented nudging strategies. Full article
(This article belongs to the Special Issue Adaptive Human-Machine Interface)
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17 pages, 3202 KiB  
Article
How to Keep Drivers Attentive during Level 2 Automation? Development and Evaluation of an HMI Concept Using Affective Elements and Message Framing
by Tobias Hecht, Weisi Zhou and Klaus Bengler
Safety 2022, 8(3), 47; https://doi.org/10.3390/safety8030047 - 28 Jun 2022
Cited by 2 | Viewed by 2807
Abstract
With Level 3 and 4 automated driving activated, users will be allowed to engage in a wide range of non-driving related activities (NDRAs). Although Level 2 automation can appear very similar to L3 and L4, drivers are required to always monitor the system. [...] Read more.
With Level 3 and 4 automated driving activated, users will be allowed to engage in a wide range of non-driving related activities (NDRAs). Although Level 2 automation can appear very similar to L3 and L4, drivers are required to always monitor the system. However, past research has found drivers neglect this obligation at least partly and instead engage in NDRAs. Since this behavior can have negative impacts on traffic safety, the goal of this work was to develop a human–machine interface (HMI) concept to motivate users to continue their supervision task. This work’s concept used message framing in connection with affective elements. Every three minutes, messages were displayed on the head-up display. To evaluate the affective message concept’s (AMC) effectiveness, we conducted a between-subject driving simulator study (baseline vs. advanced HMI) with 32 participants and 45 min of driving time with both L2 and L4 phases and a silent system malfunction. Results show the road attention ratio decreases and the NDRA engagement ratio increases over time only for baseline participants. Participants supported by the AMC did not show a change over time in monitoring behavior and NDRA engagement. However, no effect on the drivers’ reaction to the system failure became apparent. No effects on subjective workload and user experience were found. Additional research is needed to further investigate the safety implications and long-term effectiveness of the concept, as well as a driver-state-dependent design. Full article
(This article belongs to the Special Issue Adaptive Human-Machine Interface)
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