## 6 AI-Based Approaches for Autonomous UAV Swarm Systems
### AI-Enhanced Data Processing for Swarm UAV Applications
#### 6.1.1 Intelligent Traffic Monitoring and Analysis Using AI-Based UAV Swarms  
Recently, intelligent transportation techniques and intelligent transportation services have gained increasing attention and development support from government agencies. The need to optimize traffic efficiency, reduce energy costs, increase safety, and comply with traffic laws is increasingly urgent. An AI-integrated video analytics framework (TAU) has been developed to meet the needs of automated transportation [128]. The study used high-resolution images to detect and track traffic objects automatically, with six main contributions. First, a preprocessing algorithm to adjust the high-resolution image as input to the object detector. Second, an algorithm to calibrate vehicle coordinates for tracking on multiple cuts of a frame. Third, a speed calculation algorithm based on the accumulated information from the frames. The fourth is the number of vehicles in each traffic area based on the Ray Tracing algorithm. The traffic regulation at the intersection is based on the observed images in the surrounding areas. Finally, the algorithm extracts twenty-four types of detailed information from the collected raw data. This study is typical for the analysis of traffic situations using curves, charts, heat maps, and animations.  
Ground traffic management in the context of urban streets and highways requires a large amount of data. In addition, access to real-time traffic information is necessary in emergency situations. This requires the traffic control center to constantly monitor the flow of vehicles and take appropriate actions to reduce traffic congestion. A method that reduces the transmission bandwidth while maintaining acceptable video quality for on-server image processing is presented in [129]. The authors have developed a framework formonitoring and controlling highway traffic using UAVs to collect data, which are then sent to the server. An object detection algorithm supported by artificial intelligence deployed on the server is capable of classifying objects and different vehicles.  
An advanced machine learning algorithm called TSR-YOLO has been designed for traffic sign recognition for traffic monitoring [130]. A system capable of performing global feature extraction improves accuracy when identifying many smaller traffic signs. When compared to other machine learning methods for traffic monitoring, YOLO performs better because it scans the entire scene in a single pass, significantly improving speed without sacrificing accuracy. When compared to traditional machine learning methods, YOLO's integration of real-time detection and classification is a major advantage [131].  
Researchers are currently using a variety of datasets for traffic monitoring models. All systems have certain challenges and scope for development. Despite its potential benefits, traffic monitoring using machine learning currently has limitations, as discussed in [132]. These algorithms may give some false results when counting vehicles because they are only partially accurate [133]. The complexity of the environment increases the possibility that monitoring may give false results in extreme weather conditions. Researchers are turning their attention to generative AI in UAVs for traffic surveillance. This technology revolutionizes aerial surveillance by accurately predicting traffic patterns and congestion. It enables drones to improve real-time routes and enhance traffic management and emergency response, thereby enhancing the safety and efficiency of urban transportation using sophisticated predictive analytics derived from aerial data [134].