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Electronics
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Automated Driving Systems: Latest Advances and Prospects
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Automated Driving Systems: Latest Advances and Prospects

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

Deadline for manuscript submissions: 15 April 2026 | Viewed by 480

Special Issue Editors

Dr. Eva Michelaraki

E-Mail Website
Guest Editor
Department of Transportation Planning and Engineering, National Technical University of Athens, 15773 Athens, Greece
Interests: traffic engineering; driver behaviour; road safety; crash analysis; statistical modelling; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. George Yannis

E-Mail Website
Guest Editor
Department of Transportation Planning and Engineering, National Technical University of Athens, 15773 Athens, Greece
Interests: road safety; transportation planning and management; urban mobility; intelligent transportation systems; automation; data management and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced vehicle technologies hold the promise not only to change the way we drive but also to save lives. The continuing evolution of automotive technology aims to deliver even greater safety benefits than earlier innovations. One day, automated driving systems may be able to handle the entire driving task when we choose not to or are unable to do so. These advancements are transforming mobility, improving traffic efficiency, and reducing human error-related crashes, marking a significant step toward safer, smarter, and more efficient transportation.

This Special Issue on "Automated Driving Systems: Latest Advances and Prospects" aims to bring together cutting-edge research on the latest developments, emerging trends, and future prospects in automated driving. We encourage researchers from academia and industry to explore novel methodologies and contribute their latest findings, addressing the challenges and opportunities in the deployment of fully automated driving systems.

Topics of interest include, but are not limited to, the following:

  • Perception and sensing technologies in automated driving systems;
  • Advanced AI and machine learning applications for decision-making in autonomous vehicles;
  • Human–machine interaction and shared control in automated driving;
  • Challenges in deploying automated driving systems;
  • Vehicle performance guidance for automated vehicles;
  • Autonomous vehicles in smart cities and the role of intelligent transportation systems (ITSs);
  • Adaptive and predictive navigation in complex traffic environments.

Dr. Eva Michelaraki
Prof. Dr. George Yannis
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 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

  • automated driving systems
  • autonomous vehicles
  • artificial intelligence
  • road safety
  • travel behaviour
  • intelligent transportation systems (ITSs)
  • advanced driver assistance systems (ADASs)

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Published Papers (2 papers)

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24 pages, 3990 KB  
Article
An Adaptive PID Controller for Longitudinal Velocity and Yaw Rate Tracking of Autonomous Mobility Based on RLS with Multiple Constraints
by Jeongwoo Lee and Kwangseok Oh
Electronics 2025, 14(20), 4111; https://doi.org/10.3390/electronics14204111 - 20 Oct 2025
Viewed by 142
Abstract
Recently, various forms and purposes of autonomous mobility have been widely developed and commercialized. To control the various iterations of shaped and purposeful mobility, control technology that can adapt to the dynamic characteristics of the mobility and environmental changes is essential. This study [...] Read more.
Recently, various forms and purposes of autonomous mobility have been widely developed and commercialized. To control the various iterations of shaped and purposeful mobility, control technology that can adapt to the dynamic characteristics of the mobility and environmental changes is essential. This study presents an adaptive proportional–integral–derivative (PID) controller for longitudinal velocity and yaw rate tracking in autonomous mobility, addressing the aforementioned issue. To design the adaptive PID controller, error dynamics have been designed using error and control input with two coefficients. It is designed that the two coefficients are estimated in real time by recursive least squares with multiple constraints and forgetting factors. The estimated coefficients are used to compute PI gains based on the Lyapunov direct method with constant derivative gain. Multiple constraints, such as value and rate limits, have been incorporated into the RLS algorithm to enhance the control stability. The performance evaluation is conducted through the co-simulation of MATLAB/Simulink and CarMaker under integrated control scenarios, such as longitudinal velocity and yaw rate tracking, for mobility. Full article
(This article belongs to the Special Issue Automated Driving Systems: Latest Advances and Prospects)
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Review

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27 pages, 879 KB  
Review
A Literature Review of Automated Roadside Parking Monitoring Using Artificial Intelligence Algorithms
by Christina Georgopoulou and Panagiotis Papantoniou
Electronics 2025, 14(20), 4119; https://doi.org/10.3390/electronics14204119 - 21 Oct 2025
Abstract
The issue of parking has been a major concern in urban centers, primarily due to the increasing demand and daily traffic congestion. This paper endeavors to explore, process, and evaluate the existing literature on parking space detection methodologies, integrating photogrammetric techniques with deep [...] Read more.
The issue of parking has been a major concern in urban centers, primarily due to the increasing demand and daily traffic congestion. This paper endeavors to explore, process, and evaluate the existing literature on parking space detection methodologies, integrating photogrammetric techniques with deep learning models. Towards that end, various existing systems, applications, and models that have been studied were evaluated, and their impact on different test cases was assessed. The literature review was based on the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). Results indicated that smart parking systems significantly enhance dynamic parking management by leveraging deep learning techniques, particularly convolutional neural networks (CNNs). These systems process visual data from monitoring sources to generate statistics, diagrams, and maps that highlight occupied and available parking spaces, allowing for more efficient parking management and improved traffic flow. These methods contributed to improved urban mobility by providing real-time information to drivers about parking conditions along their routes. This not only enhanced convenience but also supported the development of smarter and more sustainable urban transportation solutions. Full article
(This article belongs to the Special Issue Automated Driving Systems: Latest Advances and Prospects)
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