Applications of Human–Computer Interaction in Driving

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 791

Special Issue Editor


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Guest Editor
Department of Information Engineering, Università di Padova, via Gradenigo 6/B, 35131 Padova, Italy
Interests: optimal control; automotive; MPC; driving simulators

Special Issue Information

Dear Colleagues,

The advent of advanced driver assistance systems (ADASs), advanced rider assistance systems (ARASs) and autonomous vehicles (AVs) has led to significant interest in leveraging human–computer interaction (HCI) principles to optimize the interaction between drivers or riders and their vehicles. In particular, (semi-)autonomous vehicles need to interact with humans in different situations and by different means, ranging from communicating with the human who is driving to coordinating with other human-driven vehicles on the road.

This Special Issue on applications of human–computer interaction in driving aims to collect cutting-edge research and innovative applications that explore the intersection of HCI and automotive technology. It focuses on highlighting the potential for enhancing safety, comfort, and overall driver experience through the application of modern methodologies, such as machine learning, artificial intelligence, predictive control, optimal control, etc.

This Special Issue brings together researchers from both academia and industry, across diverse disciplines, to present their latest findings, methodologies, and case studies in this rapidly evolving field.

The issue covers of a wide range of topics related to applications of HCI in driving, including, but not limited to:

  1. Driver assistance systems and transition between autonomous and manual driving;
  2. Application of autonomous vehicle control strategies focusing on human acceptance;
  3. Driver behavior analysis and intentions modeling;
  4. Human-in-the-loop driving simulations;
  5. Driver monitoring through biological and vision-based signals;
  6. In-vehicle infotainment systems and ADAS interfaces;
  7. Ethical considerations and user experience.

Dr. Bruschetta Mattia
Guest Editor

Manuscript Submission Information

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Keywords

  • human–computer interaction
  • cooperative driving
  • driving assistance systems
  • riding assistance systems
  • human driving behavior
  • driver monitoring
  • vehicle infotainment systems

Published Papers (1 paper)

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Research

17 pages, 2499 KiB  
Article
The Impact of Transparency on Driver Trust and Reliance in Highly Automated Driving: Presenting Appropriate Transparency in Automotive HMI
by Jue Li, Jiawen Liu, Xiaoshan Wang and Long Liu
Appl. Sci. 2024, 14(8), 3203; https://doi.org/10.3390/app14083203 - 11 Apr 2024
Viewed by 493
Abstract
Automation transparency offers a promising way for users to understand the uncertainty of automated driving systems (ADS) and to calibrate their trust in them. However, not all levels of information may be necessary to achieve transparency. In this study, we conceptualized the transparency [...] Read more.
Automation transparency offers a promising way for users to understand the uncertainty of automated driving systems (ADS) and to calibrate their trust in them. However, not all levels of information may be necessary to achieve transparency. In this study, we conceptualized the transparency of the automotive human–machine interfaces (HMIs) in three levels, using driving scenarios comprised of two degrees of urgency to evaluate drivers’ trust and reliance on a highly automated driving system. The dependent measures included non-driving related task (NDRT) performance and visual attention, before and after viewing the interface, along with the drivers’ takeover performance, subjective trust, and workload. The results of the simulated experiment indicated that participants interacting with an SAT level 1 + 3 (system’s action and projection) and level 1 + 2 + 3 (system’s action, reasoning, and projection) HMI trusted and relied on the ADS more than did those using the baseline SAT level 1 (system’s action) HMI. The low-urgency scenario was associated with higher trust and reliance, and the drivers’ visual attention and NDRT performance improved after viewing the HMI, but not statistically significantly. The findings verified the positive role of the SAT model regarding human trust in the ADS, especially in regards to projection information in time-sensitive situations, and these results have implications for the design of automotive HMIs based on the SAT model to facilitate the human–ADS relationship. Full article
(This article belongs to the Special Issue Applications of Human–Computer Interaction in Driving)
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