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Electronics
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Advancements in Connected and Autonomous Vehicles
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Advancements in Connected and Autonomous Vehicles

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 8548

Special Issue Editor

Dr. Arman Sargolzaei

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of South Florida, Tampa, FL 33620, USA
Interests: security of networked control systems; safety and security of connected and autonomous vehicles; nonlinear control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Connected autonomous vehicles (CAVs) hold immense promise in revolutionizing transportation, offering benefits in energy efficiency, crash prevention, traffic management, etc. Through real-time data exchange, CAVs optimize routes and driving behavior, resulting in reduced fuel consumption and emissions, while their interconnectedness enables crash prediction and prevention, potentially saving countless lives. Furthermore, CAVs have the potential to alleviate traffic congestion by coordinating movement and streamlining traffic flow.

However, the realization of these advantages is not without its challenges. Ensuring the safety and security of CAVs is a top priority, as they are susceptible to cyberattacks, which could have catastrophic consequences. Rigorous testing and verification of autonomous driving algorithms are essential for guaranteeing their reliability and performance under diverse conditions. Moreover, the implementation of CAVs faces legal, regulatory, and ethical hurdles, necessitating the establishment of new standards and protocols for seamless integration into existing infrastructure. Another significant challenge lies in the readiness of the infrastructure to support CAVs effectively. Existing roads and transportation systems may require extensive upgrades and adaptations to accommodate the unique needs of CAVs. This includes the installation of smart sensors, communication networks, and the establishment of vehicle-to-infrastructure (V2I) communication capabilities. Additionally, ensuring interoperability among different CAV systems and infrastructure elements is crucial to achieving a cohesive and efficient transportation network.

In light of these challenges, this Special Issue welcomes researchers to contribute their valuable findings to address the multifaceted issues surrounding CAVs. Research focusing on security solutions, safety standards, testing methodologies, verification techniques, energy-efficient solutions, and infrastructure readiness will play a pivotal role in unlocking the full potential of CAVs. By surmounting these challenges, researchers can pave the way for a safer, more energy-efficient, and seamlessly integrated autonomous future in transportation.

Dr. Arman Sargolzaei
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. 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

  • testing and verification of CAVs
  • security of CAVs
  • energy efficiency of CAVs
  • safe and secure control design for cooperative driving algorithms
  • infrastructure readiness for CAVs
  • policies and standards
  • ethical challenges

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Published Papers (5 papers)

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Research

18 pages, 7185 KiB  
Article
Assessing Satellite-Augmented Connected Vehicle Technology for Security Credentials and Traveler Information Delivery
by Sisinnio Concas and Vishal C. Kummetha
Electronics 2024, 13(22), 4444; https://doi.org/10.3390/electronics13224444 - 13 Nov 2024
Viewed by 332
Abstract
Vehicle-to-Everything (V2X) technology has the capability to enhance road safety by enabling wireless exchange of telematics and spatiotemporal information between connected vehicles (CVs). Effective V2X communication depends on rapid information sharing between Roadside Units (RSUs), in-vehicle On-Board Units (OBUs), and other connected infrastructure. [...] Read more.
Vehicle-to-Everything (V2X) technology has the capability to enhance road safety by enabling wireless exchange of telematics and spatiotemporal information between connected vehicles (CVs). Effective V2X communication depends on rapid information sharing between Roadside Units (RSUs), in-vehicle On-Board Units (OBUs), and other connected infrastructure. However, there are increasing concerns with RSUs related to installation needs, reliability, and coverage, especially on rural roadways. This study aims to evaluate the benefits of augmenting CV infrastructure with satellite technology in situations where RSU access or coverage is limited while maintaining V2X security protocols and critical information exchange. The study utilizes data from over 400 personal, fleet, and commercial CVs collected during two real-world pilot deployments in the United States, one in an urban environment in Florida and one in a rural environment in Wyoming. The analysis performed shows that the delivery of critical security credential information and traveler information messages (TIMs) to CVs is dependent on a multitude of environmental and operational reliability factors. Overall, information delivery is faster with dense RSU infrastructure as compared to satellites. However, we show that by augmenting RSU infrastructure with satellite technology, the delivery of information is more robust, improving V2X system reliability, security, and overall road safety. Full article
(This article belongs to the Special Issue Advancements in Connected and Autonomous Vehicles)
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14 pages, 1073 KiB  
Article
Fast and Lightweight Vision-Language Model for Adversarial Traffic Sign Detection
by Furkan Mumcu and Yasin Yilmaz
Electronics 2024, 13(11), 2172; https://doi.org/10.3390/electronics13112172 - 3 Jun 2024
Viewed by 869
Abstract
Several attacks have been proposed against autonomous vehicles and their subsystems that are powered by machine learning (ML). Road sign recognition models are especially heavily tested under various adversarial ML attack settings, and they have proven to be vulnerable. Despite the increasing research [...] Read more.
Several attacks have been proposed against autonomous vehicles and their subsystems that are powered by machine learning (ML). Road sign recognition models are especially heavily tested under various adversarial ML attack settings, and they have proven to be vulnerable. Despite the increasing research on adversarial ML attacks against road sign recognition models, there is little to no focus on defending against these attacks. In this paper, we propose the first defense method specifically designed for autonomous vehicles to detect adversarial ML attacks targeting road sign recognition models, which is called ViLAS (Vision-Language Model for Adversarial Traffic Sign Detection). The proposed defense method is based on a custom, fast, lightweight, and salable vision-language model (VLM) and is compatible with any existing traffic sign recognition system. Thanks to the orthogonal information coming from the class label text data through the language model, ViLAS leverages image context in addition to visual data for highly effective attack detection performance. In our extensive experiments, we show that our method consistently detects various attacks against different target models with high true positive rates while satisfying very low false positive rates. When tested against four state-of-the-art attacks targeting four popular action recognition models, our proposed detector achieves an average AUC of 0.94. This result achieves a 25.3% improvement over a state-of-the-art defense method proposed for generic image attack detection, which attains an average AUC of 0.75. We also show that our custom VLM is more suitable for an autonomous vehicle compared to the popular off-the-shelf VLM and CLIP in terms of speed (4.4 vs. 9.3 milliseconds), space complexity (0.36 vs. 1.6 GB), and performance (0.94 vs. 0.43 average AUC). Full article
(This article belongs to the Special Issue Advancements in Connected and Autonomous Vehicles)
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16 pages, 3071 KiB  
Article
A Nonlinear Control Design for Cooperative Adaptive Cruise Control with Time-Varying Communication Delay
by Parisa Ansari Bonab and Arman Sargolzaei
Electronics 2024, 13(10), 1875; https://doi.org/10.3390/electronics13101875 - 10 May 2024
Cited by 1 | Viewed by 1297
Abstract
Cooperative adaptive cruise control (CACC) is one of the main features of connected and autonomous vehicles (CAVs), which uses connectivity to improve the efficiency of adaptive cruise control (ACC). The addition of reliable communication systems to ACC reduces fuel consumption, maximizes road capacity, [...] Read more.
Cooperative adaptive cruise control (CACC) is one of the main features of connected and autonomous vehicles (CAVs), which uses connectivity to improve the efficiency of adaptive cruise control (ACC). The addition of reliable communication systems to ACC reduces fuel consumption, maximizes road capacity, and ensures traffic safety. However, the performance, stability, and safety of CACC could be affected by the transmission of outdated data caused by communication delays. This paper proposes a Lyapunov-based nonlinear controller to mitigate the impact of time-varying delays in the communication channel of CACC. This paper uses Lyapunov–Krasovskii functionals in the stability analysis to ensure semi-global uniformly ultimately bounded tracking. The efficaciousness of the proposed CACC algorithm is demonstrated in simulation and through experimental implementation. Full article
(This article belongs to the Special Issue Advancements in Connected and Autonomous Vehicles)
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20 pages, 11395 KiB  
Article
Autonomous Driving System Architecture with Integrated ROS2 and Adaptive AUTOSAR
by Dongwon Hong and Changjoo Moon
Electronics 2024, 13(7), 1303; https://doi.org/10.3390/electronics13071303 - 30 Mar 2024
Cited by 1 | Viewed by 3682
Abstract
In the automotive industry, research is now underway to apply Adaptive Automotive Open System Architecture (AUTOSAR) to the development of next-generation mobility, such as autonomous driving and connected cars. However, research on autonomous driving is being predominantly conducted on the robotics platform ROS2 [...] Read more.
In the automotive industry, research is now underway to apply Adaptive Automotive Open System Architecture (AUTOSAR) to the development of next-generation mobility, such as autonomous driving and connected cars. However, research on autonomous driving is being predominantly conducted on the robotics platform ROS2 (Robot Operating System 2). This demonstrates a considerable distance between autonomous driving research and its application in actual vehicles. To bridge this gap, interoperability that leverages the strengths of the Adaptive AUTOSAR and ROS2 platforms and compensates for their weaknesses is required. Therefore, this study proposes an architecture for interoperability between the two platforms, named Autonomous Driving System with Integrated ROS2 and Adaptive AUTOSAR (ASIRA). The proposed architecture enables communication between each of the two platforms through the ROS2 SOME/IP Bridge and allows for the necessary data exchange. It validates them in autonomous driving scenarios and goes beyond vehicle development, testing, and prototyping to exploit the advantages of each platform. Additionally, the simulation of autonomous vehicles within the ASIRA architecture is demonstrated by interoperating the ROS2 representative open-source autonomous driving project, Autoware, with the Adaptive AUTOSAR simulator. This study contributes to the assimilation of ROS2 into the automotive industry and its application in real vehicles by linking ROS2 and Adaptive AUTOSAR. Full article
(This article belongs to the Special Issue Advancements in Connected and Autonomous Vehicles)
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14 pages, 4720 KiB  
Article
LiDAR Localization by Removing Moveable Objects
by Seonghark Jeong, Minseok Ko and Jungha Kim
Electronics 2023, 12(22), 4659; https://doi.org/10.3390/electronics12224659 - 15 Nov 2023
Cited by 3 | Viewed by 1781
Abstract
In this study, we propose reliable Light Detection and Ranging (LiDAR) mapping and localization via the removal of moveable objects, which can cause noise for autonomous driving vehicles based on the Normal Distributions Transform (NDT). LiDAR measures the distances to objects such as [...] Read more.
In this study, we propose reliable Light Detection and Ranging (LiDAR) mapping and localization via the removal of moveable objects, which can cause noise for autonomous driving vehicles based on the Normal Distributions Transform (NDT). LiDAR measures the distances to objects such as parked and moving cars and objects on the road, calculating the time of flight required for the sensor’s beam to reflect off an object and return to the system. The proposed localization system uses LiDAR to implement mapping and matching for the surroundings of an autonomous vehicle. This localization is applied to an autonomous vehicle, a mid-size Sports Utility Vehicle (SUV) that has a 64-channel Velodyne sensor, detecting moveable objects via modified DeepLabV3 and semantic segmentation. LiDAR and vision sensors are popular perception sensors, but vision sensors have a weakness that does not allow for an object to be detected accurately under special circumstances, such as at night or when there is a backlight in daylight. Even if LiDAR is more expensive than other detecting sensors, LiDAR can more reliably and accurately sense an object with the right depth because a LiDAR sensor estimates an object’s distance using the time of flight required for the LiDAR sensor’s beam to detect the object and return to the system. The cost of a LiDAR product will decrease dramatically in the case of skyrocketing demand for LiDAR in the industrial areas of autonomous vehicles, humanoid robots, service robots, and unmanned drones. As a result, this study develops a precise application of LiDAR localization for a mid-size SUV, which gives the best performance with respect to acquiring an object’s information and contributing to the appropriate, timely control of the mid-size SUV. We suggest mapping and localization using only LiDAR, without support from any other sensors, such as a Global Positioning System (GPS) or an Inertial Measurement Unit (IMU) sensor; using only a LiDAR sensor will be beneficial for cost competitiveness and reliability. With the powerful modified DeepLabV3, which is faster and more accurate, we identify and remove a moveable object through semantic segmentation. The improvement rate of the mapping and matching performance of our proposed NDT, by removing the moveable objects, was approximately 12% in terms of the Root-Mean-Square Error (RMSE) for the first fifth of the test course, where there were fewer parked cars and more moving cars. Full article
(This article belongs to the Special Issue Advancements in Connected and Autonomous Vehicles)
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