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 3281

Special Issue Editor


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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
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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

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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

Published Papers (3 papers)

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Research

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
Viewed by 491
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
Viewed by 1253
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 1 | Viewed by 1183
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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