Advanced Aerial Mobility: Innovative Urban Air Transportation System

Abstract

Recent advancements in computer vision, deep learning, and GPS technologies have enabled the creation of sophisticated flying drone transportation systems. These systems use advanced algorithms and sensors to navigate safely and efficiently, potentially transforming transportation through autonomous delivery, monitoring, and emergency response. This paper reviews the application of YOLO (You Only Look Once) and R-CNN (Region-based Convolutional Neural Network) algorithms for object detection and tracking in drones, providing them with enhanced awareness and responsiveness to dynamic environments. Furthermore, Deep Neural Networks (DNNs) support autonomous navigation and decision-making, enabling drones to maneuver complex routes and make real-time adjustments in response to unforeseen obstacles.

The literature review offers an in-depth analysis of these technologies, examining how they collectively enhance drone capabilities. The paper also discusses the barriers that must be addressed to facilitate the integration of flying drones into current transportation systems. These challenges include regulatory considerations, airspace management, safety protocols, and public concerns regarding privacy and security. Addressing these issues is essential for drones' safe, ethical, and sustainable adoption in urban settings. Overall, this review underscores the transformative potential of flying drone technology while highlighting the importance of a coordinated approach to ensure responsible deployment.