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Applied Sciences
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Human-Computer Interaction and Advanced Driver-Assistance Systems
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Human-Computer Interaction and Advanced Driver-Assistance Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 9034

Special Issue Editor

Dr. Lie Guo

E-Mail Website
Guest Editor
School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China
Interests: intelligent vehicle; human machine interaction; advanced driver assistance systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Sixth Asian Conference on Artificial Intelligence Technology will be held in Changzhou, China. The ACAIT 2022 conference invites the submission of substantial, original, and unpublished research papers regarding artificial intelligence (AI) applications in environment perception, intelligent vehicles, advanced driver-assistance systems, driver behavior analysis, and other recent advancements in and future trends of AI applications.

This Special Issue is dedicated to the application of human–machine interaction and advanced driver assistance systems in intelligent vehicles. In recent years, to reduce traffic accidents caused by human problems, advanced driving assistance technologies have been widely used to enhance the intelligence of vehicles. By reminding the driver or manipulating the car through actuators, advanced driving assistance technology greatly improves driving safety and comfort as well as frees the driver from heavy driving tasks.

However, during assisted driving, the driver may interfere with the assisted driving system and cause human–machine conflict. In some cases, due to system limitations, the driver may even need to regain control of the vehicle. Therefore, it is necessary to study human–machine interaction and co-driving technology.

For better design human–machine co-driving technology and advanced driving assistance systems, we need to comprehensively consider issues such as perception, control algorithms, human–machine interaction, human factors, and driving authority distribution, so as to reduce human–machine conflicts and improve vehicle safety and comfort.

This Special Issue includes but is not limited to the following topics:

  1. Vehicle states perception technologies;
  2. Intelligent decision technologies;
  3. Environment identification technologies;
  4. Chassis cooperative control technologies;
  5. Multisource information fusion technologies;
  6. Human factor;
  7. Human-machine interaction;
  8. Human-machine co-driving;
  9. Driving state detection and intention identification;
  10. Driver modeling.

Dr. Lie Guo
Guest Editor

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • vehicle states perception technologies
  • intelligent decision technologies
  • environment identification technologies
  • chassis cooperative control technologies
  • multi-source information fusion technologies
  • human factor
  • human-machine interaction
  • human-machine co-driving
  • driving state detection and intention identification
  • driver modeling

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Published Papers (2 papers)

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Research

25 pages, 3896 KiB  
Article
Take-Over Requests after Waking in Autonomous Vehicles
by Won Kim, Eunki Jeon, Gwangbin Kim, Dohyeon Yeo and SeungJun Kim
Appl. Sci. 2022, 12(3), 1438; https://doi.org/10.3390/app12031438 - 28 Jan 2022
Cited by 10 | Viewed by 4600
Abstract
Autonomous vehicles (AVs) enable drivers to devote their primary attention to non-driving-related tasks (NDRTs). Consequently, AVs must provide intelligibility services appropriate to drivers’ in-situ states and in-car activities to ensure driver safety, and accounting for the type of NDRT being performed can result [...] Read more.
Autonomous vehicles (AVs) enable drivers to devote their primary attention to non-driving-related tasks (NDRTs). Consequently, AVs must provide intelligibility services appropriate to drivers’ in-situ states and in-car activities to ensure driver safety, and accounting for the type of NDRT being performed can result in higher intelligibility. We discovered that sleeping is drivers’ most preferred NDRT, and this could also result in a critical scenario when a take-over request (TOR) occurs. In this study, we designed TOR situations where drivers are woken from sleep in a high-fidelity AV simulator with motion systems, aiming to examine how drivers react to a TOR provided with our experimental conditions. We investigated how driving performance, perceived task workload, AV acceptance, and physiological responses in a TOR vary according to two factors: (1) feedforward timings and (2) presentation modalities. The results showed that when awakened by a TOR alert delivered >10 s prior to an event, drivers were more focused on the driving context and were unlikely to be influenced by TOR modality, whereas TOR alerts delivered <5 s prior needed a visual accompaniment to quickly inform drivers of on-road situations. This study furthers understanding of how a driver’s cognitive and physical demands interact with TOR situations at the moment of waking from sleep and designs effective interventions for intelligibility services to best comply with safety and driver experience in AVs. Full article
(This article belongs to the Special Issue Human-Computer Interaction and Advanced Driver-Assistance Systems)
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18 pages, 2109 KiB  
Article
Pose Estimation of Driver’s Head Panning Based on Interpolation and Motion Vectors under a Boosting Framework
by Syed Farooq Ali, Ahmed Sohail Aslam, Mazhar Javed Awan, Awais Yasin and Robertas Damaševičius
Appl. Sci. 2021, 11(24), 11600; https://doi.org/10.3390/app112411600 - 7 Dec 2021
Cited by 18 | Viewed by 3459
Abstract
Over the last decade, a driver’s distraction has gained popularity due to its increased significance and high impact on road accidents. Various factors, such as mood disorder, anxiety, nervousness, illness, loud music, and driver’s head rotation, contribute significantly to causing a distraction. Many [...] Read more.
Over the last decade, a driver’s distraction has gained popularity due to its increased significance and high impact on road accidents. Various factors, such as mood disorder, anxiety, nervousness, illness, loud music, and driver’s head rotation, contribute significantly to causing a distraction. Many solutions have been proposed to address this problem; however, various aspects of it are still unresolved. The study proposes novel geometric and spatial scale-invariant features under a boosting framework for detecting a driver’s distraction due to the driver’s head panning. These features are calculated using facial landmark detection algorithms, including the Active Shape Model (ASM) and Boosted Regression with Markov Networks (BoRMaN). The proposed approach is compared with six existing state-of-the-art approaches using four benchmark datasets, including DrivFace dataset, Boston University (BU) dataset, FT-UMT dataset, and Pointing’04 dataset. The proposed approach outperforms the existing approaches achieving an accuracy of 94.43%, 92.08%, 96.63%, and 83.25% on standard datasets. Full article
(This article belongs to the Special Issue Human-Computer Interaction and Advanced Driver-Assistance Systems)
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