Modeling, Design, Analysis and Management of Embedded Control Systems for Automated Driving

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 2401

Special Issue Editors

Department of Machine Design, KTH Royal Institute of Technology, SE 100 44 Stockholm, Sweden
Interests: systems theory; architecture design; ontological engineering; domain-specific language; logic programming; anomaly detection; safety engineering
Special Issues, Collections and Topics in MDPI journals
Department of Electrification and Reliability, RISE Research Institutes of Sweden, SE 501 15 Borås, Sweden
Interests: dependability engineering; safety and security assurance; automated systems
Dr. Anders Thorsén
E-Mail Website
Co-Guest Editor
Department of Electrification and Reliability, RISE Research Institutes of Sweden, SE 501 15 Borås, Sweden
Interests: system architecture; automated systems; safety and security assurance; verification and validation of automated driving systems; product-line management
Dr. Anders Cassel
E-Mail Website
Co-Guest Editor
Functional Safety Expert, Qamcom Research and Technology AB, SE 583 30 Linköping, Sweden
Interests: systems engineering; contract theory; safety engineering; methodology, automotive perception systems

Special Issue Information

Dear Colleagues,

Embedded Control Systems (ECS) constitute the key enabling technology for autonomous agents and multi-agent systems. The aim is to allow a wide range of intelligent features, relating to the operation perception, situation reasoning, action planning and actuation of physical processes and human behaviors, through the integration of advanced functions, embedded software and hardware. Many of these intelligent features are, however, safety or mission critical, as the errors can result in system failures with unreasonable risks. This calls on the one hand for effective engineering methods and tools for correct by construction, verification and validation; and, on the other hand, for advanced technologies for observing and analyzing various actual operational feedbacks. 

This Special Issue focuses on the modeling, design, analysis and management of ECS for Automated Driving (AD) vehicles. We solicit high-quality articles presenting original and unpublished results of conceptual, theoretical, and empirical research relating to system specifications, quality management, safety and reliability engineering. We also especially welcome industrial insights, experiences and practices regarding post-deployment-time data collection and analysis for safety certification, product-line and lifecycle management.

Dr. Dejiu Chen
Dr. Fredrik Warg
Dr. Anders Thorsén
Dr. Anders Cassel
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

  • knowledge engineering
  • ontological engineering
  • system modeling and analysis
  • architecture design and optimization
  • simulation, formal methods
  • variability modeling and analysis
  • operational and functional safety engineering
  • reliability engineering
  • contract theory and component-based engineering
  • self-adaptation
  • XAI and data-driven analysis
  • lifecycle management

Published Papers (2 papers)

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Research

14 pages, 26592 KiB  
Article
Deep Learning-Based Multiple Droplet Contamination Detector for Vision Systems Using a You Only Look Once Algorithm
Information 2024, 15(3), 134; https://doi.org/10.3390/info15030134 - 28 Feb 2024
Abstract
This paper presents a practical contamination detection system for camera lenses using image analysis with deep learning. The proposed system can detect contamination in camera digital images through contamination learning utilizing deep learning, and it aims to prevent performance degradation of intelligent vision [...] Read more.
This paper presents a practical contamination detection system for camera lenses using image analysis with deep learning. The proposed system can detect contamination in camera digital images through contamination learning utilizing deep learning, and it aims to prevent performance degradation of intelligent vision systems due to lens contamination in cameras. This system is based on the object detection algorithm YOLO (v5n, v5s, v5m, v5l, and v5x), which is trained with 4000 images captured under different lighting and background conditions. The trained models showed that the average precision improves as the algorithm size increases, especially for YOLOv5x, which showed excellent efficiency in detecting droplet contamination within 23 ms. They also achieved an average precision (mAP@0.5) of 87.46%, recall (mAP@0.5:0.95) of 51.90%, precision of 90.28%, recall of 81.47%, and F1 score of 85.64%. As a proof of concept, we demonstrated the identification and removal of contamination on camera lenses by integrating a contamination detection system and a transparent heater-based cleaning system. The proposed system is anticipated to be applied to autonomous driving systems, public safety surveillance cameras, environmental monitoring drones, etc., to increase operational safety and reliability. Full article
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15 pages, 2642 KiB  
Article
Why Does the Automation Say One Thing but Does Something Else? Effect of the Feedback Consistency and the Timing of Error on Trust in Automated Driving
Information 2022, 13(10), 480; https://doi.org/10.3390/info13100480 - 06 Oct 2022
Cited by 1 | Viewed by 1227
Abstract
Driving automation deeply modifies the role of the human operator behind the steering wheel. Trust is required for drivers to engage in such automation, and this trust also seems to be a determinant of drivers’ behaviors during automated drives. On the one hand, [...] Read more.
Driving automation deeply modifies the role of the human operator behind the steering wheel. Trust is required for drivers to engage in such automation, and this trust also seems to be a determinant of drivers’ behaviors during automated drives. On the one hand, first experiences with automation, either positive or not, are essential for drivers to calibrate their level of trust. On the other hand, an automation that provides feedback about its own level of capability to handle a specific driving situation may also help drivers to calibrate their level of trust. The reported experiment was undertaken to examine how the combination of these two effects will impact the driver trust calibration process. Four groups of drivers were randomly created. Each experienced either an early (i.e., directly after the beginning of the drive) or a late (i.e., directly before the end of it) critical situation that was poorly handled by the automation. In addition, they experienced either a consistent continuous feedback (i.e., that always correctly informed them about the situation), or an inconsistent one (i.e., that sometimes indicated dangers when there were none) during an automated drive in a driving simulator. Results showed the early- and poorly-handled critical situation had an enduring negative effect on drivers’ trust development compared to drivers who did not experience it. While being correctly understood, inconsistent feedback did not have an effect on trust during properly managed situations. These results suggest that the performance of the automation has the most severe influence on trust, and the automation’s feedback does not necessarily have the ability to influence drivers’ trust calibration during automated driving. Full article
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