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Computers
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Recent Advances in Autonomous Vehicle Solutions
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Recent Advances in Autonomous Vehicle Solutions

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2674

Special Issue Editors

Dr. Kh Tohidul Islam

E-Mail Website
Guest Editor
Monash Biomedical Imaging, Monash University, Clayton, VIC 3800, Australia
Interests: image processing (classification, registration, and segmentation); machine learning and deep learning; AI
Prof. Dr. Subhas Mukhopadhyay

E-Mail Website
Guest Editor
School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
Interests: smart sensors; sensing technology; WSN; IoT; ICT; smart grid; energy harvesting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Esteemed Colleagues,

As our world continues to embrace the digital age, autonomous vehicles are progressively becoming integral components of various industries such as transportation, agriculture, and the development of smart cities. Their role in decision-making processes is not only transformative but also pivotal. A key driver propelling this evolution is the omnipresence of artificial intelligence (AI), with a particular emphasis on deep-learning-based AI systems, a field witnessing rapid advancements and growing adoption in optimizing autonomous vehicle navigation.

It is against this backdrop that we announce a Special Issue on "Recent Advances in Autonomous Vehicle Solutions" in the journal Computers. We cordially invite scholars, researchers, and industry professionals from diverse disciplines to contribute their insights, enhancing our collective understanding of the current landscape of deep-learning-based AI systems for autonomous vehicles.

This Special Issue aspires to foster collaborative discussions and knowledge exchange, shedding light on a myriad of topics related to the development and application of autonomous vehicles. Your invaluable contributions could address, but are certainly not limited to, the most recent advancements in the field, pioneering research, and the exploration of state and international initiatives related to autonomous vehicle technology. A special emphasis will be placed on ethical considerations, a critical aspect of autonomous vehicle development and implementation.

By bringing together a wealth of expertise and diverse perspectives, we aim to push the boundaries of existing knowledge and stimulate innovative approaches towards autonomous vehicle solutions.

We look forward to your contributions and to embarking on this intellectual journey together.

Dr. Kh Tohidul Islam
Prof. Dr. Subhas Mukhopadhyay
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. Computers 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 1800 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

  • deep learning for autonomous vehicles
  • autonomous vehicle ethics
  • autonomous navigation
  • artificial intelligence
  • multi-sensor fusion techniques
  • object detection, segmentation, and classification for autonomous vehicles
  • autonomous vehicles simulation

Published Papers (2 papers)

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Research

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14 pages, 2801 KiB  
Article
Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques
by Dragos Alexandru Andrioaia, Vasile Gheorghita Gaitan, George Culea and Ioan Viorel Banu
Computers 2024, 13(3), 64; https://doi.org/10.3390/computers13030064 - 29 Feb 2024
Viewed by 1042
Abstract
Over the past decade, Unmanned Aerial Vehicles (UAVs) have begun to be increasingly used due to their untapped potential. Li-ion batteries are the most used to power electrically operated UAVs for their advantages, such as high energy density and the high number of [...] Read more.
Over the past decade, Unmanned Aerial Vehicles (UAVs) have begun to be increasingly used due to their untapped potential. Li-ion batteries are the most used to power electrically operated UAVs for their advantages, such as high energy density and the high number of operating cycles. Therefore, it is necessary to estimate the Remaining Useful Life (RUL) and the prediction of the Li-ion batteries’ capacity to prevent the UAVs’ loss of autonomy, which can cause accidents or material losses. In this paper, the authors propose a method of prediction of the RUL for Li-ion batteries using a data-driven approach. To maximize the performance of the process, the performance of three machine learning models, Support Vector Machine for Regression (SVMR), Multiple Linear Regression (MLR), and Random Forest (RF), were compared to estimate the RUL of Li-ion batteries. The method can be implemented within UAVs’ Predictive Maintenance (PdM) systems. Full article
(This article belongs to the Special Issue Recent Advances in Autonomous Vehicle Solutions)
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Review

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20 pages, 359 KiB  
Review
Pedestrian Collision Avoidance in Autonomous Vehicles: A Review
by Timothé Verstraete and Naveed Muhammad
Computers 2024, 13(3), 78; https://doi.org/10.3390/computers13030078 - 16 Mar 2024
Viewed by 925
Abstract
Pedestrian collision avoidance is a crucial task in the development and democratization of autonomous vehicles. The aim of this review is to provide an accessible overview of the pedestrian collision avoidance systems in autonomous vehicles that have been proposed by the scientific community [...] Read more.
Pedestrian collision avoidance is a crucial task in the development and democratization of autonomous vehicles. The aim of this review is to provide an accessible overview of the pedestrian collision avoidance systems in autonomous vehicles that have been proposed by the scientific community over the last ten years. For this purpose, we propose a classification of studies in the literature in terms of the following: (i) pedestrian detection methods, (ii) collision avoidance approaches, (iii) actions, (iv) computing methods, and (v) test methods. Full article
(This article belongs to the Special Issue Recent Advances in Autonomous Vehicle Solutions)
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