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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: 30 June 2025 | Viewed by 7494

Special Issue Editor

Prof. Dr. Ali Eydgahi

E-Mail Website
Guest Editor
College of Engineering & Technology, Eastern Michigan University (EMU), 201 Sill Hall, Ypsilanti, MI 48197, USA
Interests: renewable energy; green technologies; data analytics and optimization
Special Issues, Collections and Topics in MDPI journals

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Authors interested in the proposed Special Issue are invited to contribute by submiting their unpublished research results related, but not limited, to the following topics: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

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Published Papers (6 papers)

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Research

18 pages, 12033 KiB  
Article
Risk Assessment of Roundabout Scenarios in Virtual Testing Based on an Improved Driving Safety Field
by Wentao Chen, Aoxue Li and Haobin Jiang
Sensors 2024, 24(17), 5539; https://doi.org/10.3390/s24175539 - 27 Aug 2024
Viewed by 492
Abstract
With the advancement of autonomous driving technology, scenario-based testing has become the mainstream testing method for intelligent vehicles. However, traditional risk indicators often fail in roundabout scenarios and cannot accurately define dangerous situations. To accurately quantify driving risks in roundabout scenarios, an improved [...] Read more.
With the advancement of autonomous driving technology, scenario-based testing has become the mainstream testing method for intelligent vehicles. However, traditional risk indicators often fail in roundabout scenarios and cannot accurately define dangerous situations. To accurately quantify driving risks in roundabout scenarios, an improved driving safety field model is proposed in this paper. First, considering the unique traffic flow characteristics of roundabouts, the dynamic characteristics of vehicles during diverging or merging were taken into account, and the driving safety field model was improved to accurately quantify the driving risks in roundabout scenarios. Second, based on data from the rounD dataset, the model parameters were calibrated using the social force model. Finally, a DENCLUE-like method was used to extract collision systems, calculate vehicle risk degree, and analyze these risks for both the temporal and the spatial dimensions, providing guidance for virtual testing. The proposed method significantly improves detection efficiency, increasing the number of identified dangerous scenarios by 175% compared to the Time to Collision (TTC) method. Moreover, this method can more accurately quantify driving risks in roundabout scenarios and enhance the efficiency of generating dangerous scenarios, contributing to promoting the safety of autonomous vehicles. Full article
(This article belongs to the Special Issue Design, Communication, and Control of Autonomous Vehicle Systems)
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20 pages, 3550 KiB  
Article
Adaptive Frame Structure Design for Sensing-Assisted Downlink Communication in the Vehicle-to-Infrastructure Scenario
by Junliang Yao, Ze Wang, Chunli Zhang and Hui Hui
Sensors 2024, 24(15), 5061; https://doi.org/10.3390/s24155061 - 5 Aug 2024
Viewed by 576
Abstract
Vehicle-to-everything (V2X) is considered a key factor in driving the future development of intelligent transport, which requires high-quality communication and fast sensing of vehicle information in high-speed mobile scenarios. However, high-speed mobility makes the wireless channel change rapidly, which requires frequent channel estimation [...] Read more.
Vehicle-to-everything (V2X) is considered a key factor in driving the future development of intelligent transport, which requires high-quality communication and fast sensing of vehicle information in high-speed mobile scenarios. However, high-speed mobility makes the wireless channel change rapidly, which requires frequent channel estimation and channel feedback between a vehicle and the roadside unit (RSU), resulting in an increase in communication overhead. At the same time, the high maneuverability of vehicles leads to frequent switching and misalignment of communication beams, so the RSU must have better beam prediction and tracking capabilities. To address this problem, this paper proposes an adaptive frame structure design scheme for sensing-assisted downlink (DL) communication. The basic idea of the scheme involves analyzing the communication model during the vehicle’s movement. This analysis aims to establish a theoretical relationship between the Symbol Error Rate (SER) and the following two key factors: the vehicle’s starting position and the distance it travels across. Subsequently, the scheme leverages the vehicle’s position data, as detected by the RSU, to calculate the real-time SER for DL communication. The SER threshold is set based on the requirements of DL communication. If the real-time SER is below this threshold, channel estimation becomes unnecessary. This decreases the frequency of channel estimation and frees up time and frequency resources that would otherwise be occupied by channel estimation processes within the frame structure. The design of an adaptive frame structure, as detailed in the above scheme, is presented. Furthermore, the performance of the proposed method is analyzed and compared with that of the traditional communication protocol frame structure and the beam prediction-based frame structure. The simulation results indicate that the communication throughput of the proposed method can be improved by up to 6% compared with the traditional communication protocol frame structure while maintaining SER performance. Full article
(This article belongs to the Special Issue Design, Communication, and Control of Autonomous Vehicle Systems)
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23 pages, 6222 KiB  
Article
The Effectiveness of eHMI Displays on Pedestrian–Autonomous Vehicle Interaction in Mixed-Traffic Environments
by Ali Alhawiti, Valerian Kwigizile, Jun-Seok Oh, Zachary D. Asher, Obaidullah Hakimi, Saad Aljohani and Sherif Ayantayo
Sensors 2024, 24(15), 5018; https://doi.org/10.3390/s24155018 - 2 Aug 2024
Viewed by 680
Abstract
External human–machine interfaces (eHMIs) serve as communication bridges between autonomous vehicles (AVs) and road users, ensuring that vehicles convey information clearly to those around them. While their potential has been explored in one-to-one contexts, the effectiveness of eHMIs in complex, real-world scenarios with [...] Read more.
External human–machine interfaces (eHMIs) serve as communication bridges between autonomous vehicles (AVs) and road users, ensuring that vehicles convey information clearly to those around them. While their potential has been explored in one-to-one contexts, the effectiveness of eHMIs in complex, real-world scenarios with multiple pedestrians remains relatively unexplored. Addressing this gap, our study provides an in-depth evaluation of how various eHMI displays affect pedestrian behavior. The research aimed to identify eHMI configurations that most effectively convey an AV’s information, thereby enhancing pedestrian safety. Incorporating a mixed-methods approach, our study combined controlled outdoor experiments, involving 31 participants initially and 14 in a follow-up session, supplemented by an intercept survey involving 171 additional individuals. The participants were exposed to various eHMI displays in crossing scenarios to measure their impact on pedestrian perception and crossing behavior. Our findings reveal that the integration of a flashing green LED, robotic sign, and countdown timer constitutes the most effective eHMI display. This configuration notably increased pedestrians’ willingness to cross and decreased their response times, indicating a strong preference and enhanced concept understanding. These findings lay the groundwork for future developments in AV technology and traffic safety, potentially guiding policymakers and manufacturers in creating safer urban environments. Full article
(This article belongs to the Special Issue Design, Communication, and Control of Autonomous Vehicle Systems)
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21 pages, 3803 KiB  
Article
Combining Optimization and Simulation for Next-Generation Off-Road Vehicle E/E Architectural Design
by Cristian Bianchi, Rosario Merlino and Roberto Passerone
Sensors 2024, 24(15), 4889; https://doi.org/10.3390/s24154889 - 27 Jul 2024
Viewed by 726
Abstract
The automotive industry, with particular reference to the off-road sector, is facing several challenges, including the integration of Advanced Driver Assistance Systems (ADASs), the introduction of autonomous driving capabilities, and system-specific requirements that are different from the traditional car market. Current vehicular electrical–electronic [...] Read more.
The automotive industry, with particular reference to the off-road sector, is facing several challenges, including the integration of Advanced Driver Assistance Systems (ADASs), the introduction of autonomous driving capabilities, and system-specific requirements that are different from the traditional car market. Current vehicular electrical–electronic (E/E) architectures are unable to support the amount of data for new vehicle functionalities, requiring the transition to zonal architectures, new communication standards, and the adoption of Drive-by-Wire technologies. In this work, we propose an automated methodology for next-generation off-road vehicle E/E architectural design. Starting from the regulatory requirements, we use a MILP-based optimizer to find candidate solutions, a discrete event simulator to validate their feasibility, and an ascent-based gradient method to reformulate the constraints for the optimizer in order to converge to the final architectural solution. We evaluate the results in terms of latency, jitter, and network load, as well as provide a Pareto analysis that includes power consumption, cost, and system weight. Full article
(This article belongs to the Special Issue Design, Communication, and Control of Autonomous Vehicle Systems)
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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
Cited by 3 | Viewed by 1901
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 - 6 Nov 2023
Cited by 3 | Viewed by 1739
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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