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Recent Advances in Autonomous Vehicle Solutions

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 4372

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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 (4 papers)

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Research

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22 pages, 3497 KiB

Open AccessArticle

Two-Phase Fuzzy Real-Time Approach for Fuzzy Demand Electric Vehicle Routing Problem with Soft Time Windows

by Mohamed A. Wahby Shalaby and Sally S. Kassem

Computers 2024, 13(6), 135; https://doi.org/10.3390/computers13060135 - 27 May 2024

Viewed by 146

Abstract

Environmental concerns have called for several measures to be taken within the logistics and transportation fields. Among these measures is the adoption of electric vehicles instead of diesel-operated vehicles for personal and commercial-delivery use. The optimized routing of electric vehicles for the commercial delivery of products is the focus of this paper. We study the effect of several practical challenges that are faced when routing electric vehicles. Electric vehicle routing faces the additional challenge of the potential need for recharging while en route, leading to more travel time, and hence cost. Therefore, in this work, we address the issue of electric vehicle routing problem, allowing for partial recharging while en route. In addition, the practical mandate of the time windows set by customers is also considered, where electric vehicle routing problems with soft time windows are studied. Real-life experience shows that the delivery of customers’ demands might be uncertain. In addition, real-time traffic conditions are usually uncertain due to congestion. Therefore, in this work, uncertainties in customers’ demands and traffic conditions are modeled and solved using fuzzy methods. The problems of fuzzy real-time, fuzzy demand, and electric vehicle routing problems with soft time windows are addressed. A mixed-integer programming mathematical model to represent the problem is developed. A novel two-phase solution approach is proposed to solve the problem. In phase I, the classical genetic algorithm (GA) is utilized to obtain an optimum/near-optimum solution for the fuzzy demand electric vehicle routing problem with soft time windows (FD-EVRPSTW). In phase II, a novel fuzzy real-time-adaptive optimizer (FRTAO) is developed to overcome the challenges of recharging and real-time traffic conditions facing FD-EVRPSTW. The proposed solution approach is tested on several modified benchmark instances, and the results show the significance of recharging and congestion challenges for routing costs. In addition, the results show the efficiency of the proposed two-phase approach in overcoming the challenges and reducing the total costs. Full article

(This article belongs to the Special Issue Recent Advances in Autonomous Vehicle Solutions)

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20 pages, 5333 KiB

Open AccessArticle

Indoor Scene Classification through Dual-Stream Deep Learning: A Framework for Improved Scene Understanding in Robotics

by Sultan Daud Khan and Kamal M. Othman

Computers 2024, 13(5), 121; https://doi.org/10.3390/computers13050121 - 14 May 2024

Viewed by 480

Abstract

Indoor scene classification plays a pivotal role in enabling social robots to seamlessly adapt to their environments, facilitating effective navigation and interaction within diverse indoor scenes. By accurately characterizing indoor scenes, robots can autonomously tailor their behaviors, making informed decisions to accomplish specific tasks. Traditional methods relying on manually crafted features encounter difficulties when characterizing complex indoor scenes. On the other hand, deep learning models address the shortcomings of traditional methods by autonomously learning hierarchical features from raw images. Despite the success of deep learning models, existing models still struggle to effectively characterize complex indoor scenes. This is because there is high degree of intra-class variability and inter-class similarity within indoor environments. To address this problem, we propose a dual-stream framework that harnesses both global contextual information and local features for enhanced recognition. The global stream captures high-level features and relationships across the scene. The local stream employs a fully convolutional network to extract fine-grained local information. The proposed dual-stream architecture effectively distinguishes scenes that share similar global contexts but contain different localized objects. We evaluate the performance of the proposed framework on a publicly available benchmark indoor scene dataset. From the experimental results, we demonstrate the effectiveness of the proposed framework. Full article

(This article belongs to the Special Issue Recent Advances in Autonomous Vehicle Solutions)

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14 pages, 2801 KiB

Open AccessArticle

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 1424

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 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

Open AccessReview

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 1540

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 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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