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UAV-assisted and deep learning-driven object detection and tracking for autonomous driving

Recent attention has been devoted to non-terrestrial networks (NTNs), as an enabling technology, in response to the stringent requirements of 5G cellular networks. Due to their global and ubiquitous connectivity, NTNs are considered complementary to terrestrial networks. From unmanned aerial vehicles (UAVs) to high altitude platform systems (HAPS), and satellites in higher earth orbits, NTNs provide a 3D aerial space supporting communications in different layers. As a result of their mobility, proximity to the earth, and better applications, such as object detection, UAVs are more appealing in this situation. In the future, autonomous driving will rely heavily on UAVs. However, processing images and videos captured by UAVs is not an easy task due to several problems they present. A great deal of progress has been made in solving such problems with deep learning (DL) models, e.g., federated learning (FL) in recent years. In this paper, we study the state-of-the-art UAV-assisted and DL-driven object detection in autonomous driving. Furthermore, we review the most useful datasets for object detection from UAV images and videos. The detection methods and datasets are classified, analyzed, and compared accordingly. Next, we consider an architecture for autonomous driving utilizing UAVs and several DL methods (including Convolutional neural networks (CNN), recurrent neural networks (RNN), Long short-term memory (LSTM), and FL) for object detection and model updating while ensuring privacy and security. Finally, we outline several open research problems for future investigation.