Deep Learning Techniques for Manned and Unmanned Ground, Aerial and Marine Vehicles

Dear Colleagues,

Manned and unmanned ground, aerial, and marine vehicles enable many promising and revolutionary civilian and military applications that will change our lives in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture, and transmission line inspection, to name just a few. These vehicles will benefit from advances in deep learning as a subfield of machine learning able to endow these vehicles with different capabilities such as perception, situation awareness, planning, and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets.

In recent years, deep learning research has received increasing attention from researchers in academia, government laboratories, and industry. These research activities have borne some fruit in tackling some of the remaining challenging problems of manned and unmanned ground, aerial, and marine vehicles. Moreover, deep learning methods have recently been actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard deep learning methods such as RNN (recent neural network) and CNN (coevolutionary neural networks). 

The purpose of this Special Issue is to report recent applications of deep learning approaches in manned and unmanned ground, aerial, and marine vehicles. Topics include but are not limited to:

• Cognitive data collection;
• Data cleansing;
• Data compression;
• Multisensor data fusion;
• Vehicle localization;
• Perception systems;
• AI for automation systems;
• Object detection, localization, and tracking;
• Situation awareness;
• Vehicle control;
• Autonomous vehicles;
• Connected vehicles;
• Self-driving cars;
• Generative adversarial networks (GANs);
• Collective intelligence;
• Multiagent systems;
• Platooning, flocking, and self-organization;
• Applications: unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), unmanned underwater vehicles (UUVs), and unmanned surface vehicles (USVs), self-driving cars, delivery robots, search and rescue, reconnaissance, surveillance, swarm robotics, etc.

Prof. Dr. Ahmad Taher Azar
Prof. Dr. Anis Koubaa
Prof. Dr. Alaa Khamis
Prof. Dr. Ibrahim A. Hameed
Dr. Gabriella Casalino
Guest Editors

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