Special Issue "Emerging Technologies in Autonomous Vehicles"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 31 July 2023 | Viewed by 548

Special Issue Editors

Department of Informatics and Telematics, Harokopio University of Athens, 17676 Kallithea, Greece
Interests: intelligent transportation systems; wireless communications; cognitive networks; autonomous systems
Special Issues, Collections and Topics in MDPI journals
Dr. Morten Larsen
E-Mail Website
Guest Editor
AnyWi Technologies BV, 2311 NR Leiden, The Netherlands
Interests: robustness and reliability through functional redundancy; human factors

Special Issue Information

Dear Colleagues,

The concept of creating machines (systems) that can work autonomously dates back to classical times. Today, autonomous systems are a rapidly growing branch of engineering that involves the conception, design, and operation of systems that overlap with principles of computer science, electronics, nanotechnology, Artificial Intelligence (AI), and bioengineering. The current maturity of autonomous systems is allowing them to leave the research lab and come to the consumer market in large numbers. This is especially valid in the Fifth Industrial Revolution era, the hallmark of which is bringing human cognition and AI-enabled autonomous systems together, working collaboratively to the benefit of both.

In the particular area of transport, Autonomous Transport Systems (ATS) include the full range of transport vehicles, including trucks, buses, trains, metros, ships, drones, helicopters, and airplanes. Automation in this area has the potential to make land, air, and marine operations safer, faster, more efficient, and greener, contributing to both the adapted Vision Zero, including work-related deaths as a new central element, and to the European Green Deal initiative. The European Green Deal defines four key elements for a sustainable mobility and automotive industry, namely: climate neutrality, a zero-pollution Europe, sustainable transport, and the transition to a circular economy.

The greatest challenge for the success of ATS is safety and the resulting trust in autonomy, which in turn represents the prominent driving force to reach a high user acceptance. Without addressing these issues across the entire Electronic Components and Systems (ECS) value chain, we will not be able to fully use the technological and commercial potentials of autonomous systems in general.

Aligned with the above, the goal of this Special Issue is to present key findings and challenges related to ECS and architectures for future mass market ATS.

Key technical topics can include (but are not limited to):

  • Fusion and perception of digital platforms for efficient and federated computing;
  • Efficient propulsion and energy modules;
  • Advanced connectivity for cooperative mobility applications;
  • Vehicle/edge/cloud computing integration concepts;
  • Intelligent components based on trustworthy AI techniques and methods;
  • Perception enhanced with Operational Design Domains (ODD) attributes;
  • ODD-aware decision making and planning for cross-domain applications;
  • Monitoring and control solutions for Electric, Connected, Autonomous and Shared (ECAS) vehicles;
  • ATS user acceptance.

Dr. George Dimitrakopoulos
Dr. Morten Larsen
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. Electronics 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 2000 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.


  • autonomous transport systems
  • highly automated vehicles
  • electronic components and systems
  • ODD
  • AI
  • acceptance

Published Papers (1 paper)

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Intelligent, In-Vehicle Autonomous Decision-Making Functionality for Driving Style Reconfigurations
Electronics 2023, 12(6), 1370; https://doi.org/10.3390/electronics12061370 - 13 Mar 2023
Viewed by 339
Intelligent connected vehicles (ICVs) constitute a transformative technology attracting immense research effort and holding great promise in providing road safety, transport efficiency, driving comfort, and eco-friendly mobility. As the driving environment becomes more and more “connected”, the manner in which an ICV is [...] Read more.
Intelligent connected vehicles (ICVs) constitute a transformative technology attracting immense research effort and holding great promise in providing road safety, transport efficiency, driving comfort, and eco-friendly mobility. As the driving environment becomes more and more “connected”, the manner in which an ICV is driven (driving style) can dynamically vary from time to time, due to the change in several parameters associated with personal traits and with the ICV’s surroundings. This necessitates fast and effective decisions to be made for a priori identifying the most appropriate driving style for an ICV. Accordingly, the main goal of this study is to present a novel, in-vehicle autonomous decision-making functionality, which enables ICVs to dynamically, transparently, and securely utilize the best available driving style (DS). The proposed functionality takes as input several parameters related to the driver’s personal characteristics and preferences, as well as the changing driving environment. A Naive Bayes learning classifier is applied for the cognitive nature of the presented functionality. Three scenarios, with regards to drivers with different personal preferences and to driving scenes with changing environment situations, are illustrated, showcasing the effectiveness of the proposed functionality. Full article
(This article belongs to the Special Issue Emerging Technologies in Autonomous Vehicles)
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