# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 4 Application of UAV-Aided Non-Terrestrial Networks in UAIS
### Connected and Automated Vehicles in UAV-Aided NTNs
#### 4.2.2 6G NTN-Enabled CAV Capabilities  
The autonomous driving of CAVs is a major application of 6G NTN communication and will facilitate fully automated CAV applications in a variety of ways. In the future, with ubiquitous 6G communication coverage, CAVs will be capable of interconnecting with nearby vehicles, RSUs, pedestrians, and other elements. This capability is essential for achieving fully automated self-driving and enhancing the safety of intelligent transportation systems. NTN-enabled V2X communication holds potential in various scenarios for fully automated CAVs, including vehicle localization, navigation, environment perception, platoon formation, and real-time control. For example, UAVs can function as flying base stations or RSUs, capable of sending accident broadcasts and early warnings to vehicles on highways through reliable line-of-sight links, thereby enhancing road safety. From the perspective of upcoming 6G technology, edge computing plays an increasingly important role in the context of CAVs. Due to limited computing and storage resources, external resources are necessary to provide data storage and computing offloading services [141]. In dynamic road intersections, vehicular edge computing systems need to be much more flexible, intelligent, and adaptive than stationary systems [142]. To tackle this problem, in [141], the potential for implementing vehicular edge computing using NTN was explored. This approach would allow connected autonomous vehicles to transfer large computing tasks to UAVs or HAPS. In the past few years, UAV and HAPS platforms have been widely used to enhance the communication and computation capabilities of ground vehicles in applications such as traffic monitoring in complex environments. The authors in [143] proposed a dynamic topology optimization method for transmitting traffic monitoring information using UAVs. In [144], a systematic approach was implemented to optimize the computation offloading for traffic data collected by UAVs. The aforemen  
Figure 5: The illustration of UAV-assisted V2X scenario. tioned approach dealt with the the issues of UAV computation task offloading utilizing a game-based model. The autonomous navigation of CAVs is based on precise position and orientation information relative to high-definition maps, which is crucial to autonomous driving. The emergence of NTN V2X communication technology presents new opportunities for improved vehicular localization and navigation. In remote areas such as mountains and deserts, terrestrial cellular networks are not always available. Therefore non-terrestrial networks, such as UAVs and satellites, can be utilized to achieve the localization of CAVs [145; 146; 147]. Researchers in [145] proposed a relative localization method for CAVs using UAVs. This method involves making time delay measurements with multiple nodes to determine the relative position of CAVs. Furthermore [146], addressed the optimal UAV swarm configuration problem in vehicle localization They utilized reinforcement deep Q-learning was utilized to optimize localization based on signal strength. Platoon formation coordination is widely discussed in applications of CAVs to improve control robustness and reduce fuel consumption. In a UAV V2X scenario, the dynamic motion of vehicles and changing road situation brings about great challenges for continuous vehicle-to-vehicle communication. In [148], UAV platforms were utilized as mobile communication relays to assist with inter-vehicle communication. To avoid the failure of multi-user detection caused by obstacles, the power allocation in non-orthogonal multiple access (NOMA) system was optimized with a low signal overhead strategy. Taking into account the maneuverability of CAVs [149; 150], used comprehensive joint methods considering mobility, communication, and energy consumption problems to establish V2X connections for vehicle platoon tasks.