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Design, Communication, and Control of Autonomous Vehicle Systems
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Design, Communication, and Control of Autonomous Vehicle Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 3297

Special Issue Editor

Dr. Ali Eydgahi

E-Mail Website
Guest Editor
School of Engineering, Eastern Michigan University, Ypsilanti, MI 48197, USA
Interests: unmanned vehicle design; sensor fusion; digital signal processing; control systems; data and communication technology; robotics and systems automation

Special Issue Information

Dear Colleagues,

Autonomous vehicles have become an important research area in recent years. Autonomous vehicle development and its design challenges such as localization, communication, perception, prediction, and safety are emerging topics. It is important to further discuss how to design and develop autonomous vehicle systems and to address its challenges, limits, and disadvantages.

Sensors are the backbone of autonomous vehicles, making them safer, more reliable, and more intelligent.

This Special Issue aims to publish high-quality papers that address the challenges involved in the design, communication, and control of autonomous vehicle systems for applications in different domains such as intelligent transportation, smart manufacturing, military, etc.

Authors interested in the proposed Special Issue are invited to contribute by submiting their unpublished research results related, but not limited, to the following topics:

  • Unmanned/Autonomous Vehicles
  • Unmanned/Autonomous Driving
  • Sensor Fusion for Autonomous Vehicles
  • Control Systems for Autonomous Vehicles
  • Intelligent Transportation using Autonomous Vehicles
  • Manufacturing Automation based on Autonomous Vehicles

Dr. Ali Eydgahi
Guest Editor

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. Sensors 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 2600 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.

Published Papers (2 papers)

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Research

19 pages, 22636 KiB  
Article
Analyzing Performance of YOLOx for Detecting Vehicles in Bad Weather Conditions
by Imran Ashraf, Soojung Hur, Gunzung Kim and Yongwan Park
Sensors 2024, 24(2), 522; https://doi.org/10.3390/s24020522 - 14 Jan 2024
Viewed by 1190
Abstract
Recent advancements in computer vision technology, developments in sensors and sensor-collecting approaches, and the use of deep and transfer learning approaches have excelled in the development of autonomous vehicles. On-road vehicle detection has become a task of significant importance, especially due to exponentially [...] Read more.
Recent advancements in computer vision technology, developments in sensors and sensor-collecting approaches, and the use of deep and transfer learning approaches have excelled in the development of autonomous vehicles. On-road vehicle detection has become a task of significant importance, especially due to exponentially increasing research on autonomous vehicles during the past few years. With high-end computing resources, a large number of deep learning models have been trained and tested for on-road vehicle detection recently. Vehicle detection may become a challenging process especially due to varying light and weather conditions like night, snow, sand, rain, foggy conditions, etc. In addition, vehicle detection should be fast enough to work in real time. This study investigates the use of the recent YOLO version, YOLOx, to detect vehicles in bad weather conditions including rain, fog, snow, and sandstorms. The model is tested on the publicly available benchmark dataset DAWN containing images containing four bad weather conditions, different illuminations, background, and number of vehicles in a frame. The efficacy of the model is evaluated in terms of precision, recall, and mAP. The results exhibit the better performance of YOLOx-s over YOLOx-m and YOLOx-l variants. YOLOx-s has 0.8983 and 0.8656 mAP for snow and sandstorms, respectively, while its mAP for rain and fog is 0.9509 and 0.9524, respectively. The performance of models is better for snow and foggy weather than rainy weather sandstorms. Further experiments indicate that enhancing image quality using multiscale retinex improves YOLOx performance. Full article
(This article belongs to the Special Issue Design, Communication, and Control of Autonomous Vehicle Systems)
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25 pages, 5498 KiB  
Article
Reinforcement Learning Algorithms for Autonomous Mission Accomplishment by Unmanned Aerial Vehicles: A Comparative View with DQN, SARSA and A2C
by Gonzalo Aguilar Jiménez, Arturo de la Escalera Hueso and Maria J. Gómez-Silva
Sensors 2023, 23(21), 9013; https://doi.org/10.3390/s23219013 - 06 Nov 2023
Viewed by 1123
Abstract
Unmanned aerial vehicles (UAV) can be controlled in diverse ways. One of the most common is through artificial intelligence (AI), which comprises different methods, such as reinforcement learning (RL). The article aims to provide a comparison of three RL algorithms—DQN as the benchmark, [...] Read more.
Unmanned aerial vehicles (UAV) can be controlled in diverse ways. One of the most common is through artificial intelligence (AI), which comprises different methods, such as reinforcement learning (RL). The article aims to provide a comparison of three RL algorithms—DQN as the benchmark, SARSA as a same-family algorithm, and A2C as a different-structure one—to address the problem of a UAV navigating from departure point A to endpoint B while avoiding obstacles and, simultaneously, using the least possible time and flying the shortest distance. Under fixed premises, this investigation provides the results of the performances obtained for this activity. A neighborhood environment was selected because it is likely one of the most common areas of use for commercial drones. Taking DQN as the benchmark and not having previous knowledge of the behavior of SARSA or A2C in the employed environment, the comparison outcomes showed that DQN was the only one achieving the target. At the same time, SARSA and A2C did not. However, a deeper analysis of the results led to the conclusion that a fine-tuning of A2C could overcome the performance of DQN under certain conditions, demonstrating a greater speed at maximum finding with a more straightforward structure. Full article
(This article belongs to the Special Issue Design, Communication, and Control of Autonomous Vehicle Systems)
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