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Special Issue Editors

Prof. Dr. Claude Baron

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Guest Editor

Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), Institute of Higher Education and Research (INSA), Université de Toulouse, 31031 Toulouse, France
Interests: systems engineering; model-based engineering; safety critical embedded software and systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jean-Yves Choley

E-Mail Website
Guest Editor

ISAE-Supméca, Quartz Laboratory, 93400 Saint-Ouen, France
Interests: model-based systems engineering; model-based safety assessment; mechatronics; cyber-physical systems; Industry 4.0; multiphysics; 3D modeling and tolerancing

Special Issue Information

Dear Colleagues,

We invite you to contribute to a Special Issue of the journal Applied Sciences, entitled “Autonomous Intelligent Systems and Their Safety”, which aims to present recent developments in the field of autonomous systems, with a focus on design and safety-related issues.

Autonomous Intelligent Systems act independently of direct human supervision, e.g., self-driving cars, UAVs, smart manufacturing robots, care robots for the elderly and virtual agents for training or support. They usually embed AI software components. Such systems need to be able to make safe, rational and human value-compatible decisions in unforeseen circumstances. Their decision making should be understandable by human users and collaborators to ensure the necessary trust on behalf of the human users. There are multiple challenges in this area, related to their design, that require consistent modeling and simulation encompassing systems engineering, their safety, human-centered AI, ethics and law, multi-physics and 3D interactions, or knowledge representation and reasoning, to name a few.

Topics include but are not limited to:

Assurance levels for decision making;
Autonomy of autonomous systems;
Applications (in transportation, Industry 4.0, medicine, etc.);
Life-cycle management;
Obsolescence of autonomous systems and impact on their safety;
Risk assessment of artificial intelligence;
Reliability and traceability of decision making;
Autonomous systems design and architecture;
Ethical framework for designing autonomous systems;
Safety- and security-related issues;
Model-based safety assessment;
Autonomous vehicle control systems;
Decision algorithms and biases;
Model-based systems and software engineering;
Human-centric design of autonomous systems;
Multi-physics and 3D interaction issues for safety;
Mechatronics and cyber-physical systems;
Energy- and eco-design-related issues;
Agility in autonomous systems development;
Robust design and multi-domain tolerancing;
Optimization strategies;
Regulation and standards;
Dedicated technologies;
Interests and risks of AI-based control;
Simulation-based verification and validation;
Knowledge representation and reasoning.

Prof. Dr. Claude Baron
Prof. Dr. Jean-Yves Choley
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. 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

assurance
safety assessment
model-based engineering
obsolescence
risk assessment
autonomous driving
energy
decision algorithms
life-cycle management
optimization
supervision
security
autonomy
decision
reliability
traceability
human-centric
agility
robustness

Published Papers (2 papers)

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Research

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30 pages, 1803 KiB

Open AccessArticle

Category Theory Framework for System Engineering and Safety Assessment Model Synchronization Methodologies

by Julien Vidalie, Michel Batteux, Faïda Mhenni and Jean-Yves Choley

Appl. Sci. 2022, 12(12), 5880; https://0-doi-org.brum.beds.ac.uk/10.3390/app12125880 - 09 Jun 2022

Cited by 3 | Viewed by 1728

Abstract

In recent decades, there has been a significant increase in systems’ complexity, leading to a rise in the need for more and more models. Models created with different intents are written using different formalisms and give diverse system representations. This work focuses on the system engineering domain and its models. It is crucial to assert a critical system’s compliance with its requirements. Thus, multiple models dedicated to these assertions are designed, such as safety or multi-physics models. As those models are independent of the architecture model, we need to provide means to assert and maintain consistency between them if we want the analyses to be relevant. The model synchronization methodologies give means to work on the consistency between the models through steps of abstraction to a common formalism, comparison, and concretization of the comparison results in the original models. This paper proposes a mathematical framework that allows for a formal definition of such a consistency relation and a mathematical description of the models. We use the context of category theory, as this is a mathematical theory providing great tools for taking into account different abstraction levels and composition of relations. Finally, we show how this mathematical framework can be applied to a specific synchronization methodology with a realistic study case. Full article

(This article belongs to the Special Issue Autonomous Intelligent Systems and Their Safety)

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Review

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51 pages, 2272 KiB

Open AccessReview

Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

by Monirul Islam Pavel, Siok Yee Tan and Azizi Abdullah

Appl. Sci. 2022, 12(14), 6831; https://0-doi-org.brum.beds.ac.uk/10.3390/app12146831 - 06 Jul 2022

Cited by 18 | Viewed by 4973

Abstract

In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. However, the AVS is still far away from mass production because of the high cost of sensor fusion and a lack of combination of top-tier solutions to tackle uncertainty on roads. To reduce sensor dependency and to increase manufacturing along with enhancing research, deep learning-based approaches could be the best alternative for developing practical AVS. With this vision, in this systematic review paper, we broadly discussed the literature of deep learning for AVS from the past decade for real-life implementation in core fields. The systematic review on AVS implementing deep learning is categorized into several modules that cover activities including perception analysis (vehicle detection, traffic signs and light identification, pedestrian detection, lane and curve detection, road object localization, traffic scene analysis), decision making, end-to-end controlling and prediction, path and motion planning and augmented reality-based HUD, analyzing research works from 2011 to 2021 that focus on RGB camera vision. The literature is also analyzed for final representative outcomes as visualization in augmented reality-based head-up display (AR-HUD) with categories such as early warning, road markings for improved navigation and enhanced safety with overlapping on vehicles and pedestrians in extreme visual conditions to reduce collisions. The contribution of the literature review includes detailed analysis of current state-of-the-art deep learning methods that only rely on RGB camera vision rather than complex sensor fusion. It is expected to offer a pathway for the rapid development of cost-efficient and more secure practical autonomous vehicle systems. Full article

(This article belongs to the Special Issue Autonomous Intelligent Systems and Their Safety)

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