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Guest editorial

Guest editorial Intelligent edge analytics and coordinated control for autonomous vehicles Autonomous vehicles are widely researched topic by many leading industries with the main focus on safety, reliability and performance. Autonomous vehicle technologies find applications in self-driving cars, drones, underwater vehicles and security applications. Autonomous vehicles need to provide superior performance and they have to react to the real- world conditions very fast to ensure safety. Reliability and safety of autonomous systems are improved recently by deploying data analytics at the edge rather than cloud. Autonomous vehicle framework needs to have machine learning and deep learning-based intelligent edge analytics to deal with the data generated from sensors and cameras. To enhance reliability and safety of autonomous vehicles, numerous sensors and cameras are required in the system design. Intelligent sensors and camera of autonomous vehicles require deep learning algorithms to process signal, image and video in a better way than existing computational solutions. This special issue focused on applying artificial intelligence and edge computing for autonomous vehicles. The first paper titled “An improved rank criterion-based NLOS node detection mechanism in VANETs” focuses on reliable warning message delivery in vehicular ad hoc networks. The second paper “Camouflage detection with texture statistical characterization in http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Intelligent Unmanned Systems Emerald Publishing

Guest editorial

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2049-6427
DOI
10.1108/ijius-01-2021-078
Publisher site
See Article on Publisher Site

Abstract

Intelligent edge analytics and coordinated control for autonomous vehicles Autonomous vehicles are widely researched topic by many leading industries with the main focus on safety, reliability and performance. Autonomous vehicle technologies find applications in self-driving cars, drones, underwater vehicles and security applications. Autonomous vehicles need to provide superior performance and they have to react to the real- world conditions very fast to ensure safety. Reliability and safety of autonomous systems are improved recently by deploying data analytics at the edge rather than cloud. Autonomous vehicle framework needs to have machine learning and deep learning-based intelligent edge analytics to deal with the data generated from sensors and cameras. To enhance reliability and safety of autonomous vehicles, numerous sensors and cameras are required in the system design. Intelligent sensors and camera of autonomous vehicles require deep learning algorithms to process signal, image and video in a better way than existing computational solutions. This special issue focused on applying artificial intelligence and edge computing for autonomous vehicles. The first paper titled “An improved rank criterion-based NLOS node detection mechanism in VANETs” focuses on reliable warning message delivery in vehicular ad hoc networks. The second paper “Camouflage detection with texture statistical characterization in

Journal

International Journal of Intelligent Unmanned Systems – Emerald Publishing

Published: Dec 31, 2020

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