The Unmanned Aircraft System Traffic Management (UTM) system is a set of services offering an automated management of the airspace and thus providing safe and secure Unmanned Aerial Vehicle (UAV) flights in both controlled and uncontrolled airspace. Controlled airspace refers to the portion of the airspace that is under the authority of Air Traffic Control (ATC) and where separation services are offered, while uncontrolled airspace refers to the portion of airspace where aircraft are not regulated by ATC. This article is a comprehensive survey of the existing UTMs development efforts with a focus on the different UTMs architectures, the provided services, the used communication technologies and the decision-making process within UTMs. We firstly review the different UTM architecture and propose a novel UTM taxonomy based on high-level qualitative criteria. Secondly, we detail the services provided by UTMs with an emphasis on the used technologies in the identification, the surveillance, the monitoring, and the deconfliction services. Effective decision-making is crucial, particularly in emergency scenarios such as Air-to-Ground (A2G) communication loss, battery or motor malfunction, or encountering aerial obstacles, among other potential hazards. Despite its significance, the UTM decision-making process is not enough considered in the literature and especially in UTM surveys. We analyze and compare in this article both the centralized and decentralized UTM decision-making. Centralized decision-making is not conducted in real-time and primarily relies on Air-to-Ground (A2G) communication. In the decentralized case, the decision-making process primarily relies on communication and collaboration among UAVs with varying degrees of autonomy. We show in this paper that centralized decision-making may encounter issues with packet loss and imperfect data, which can negatively impact the quality of decision-making. We also highlight that the decentralized decision-making may also face challenges related to security and scalability, which can hinder its effectiveness. Finally, evaluating the performance of UTMs on a real environment raises several challenges and the simulation is a cost-effective alternative. Hence, we provide a summary of the existing UTMs simulators and discuss their main features.

The anticipated proliferation of Unmanned Aerial Vehicles (UAVs) in the airspace in the coming years has raised concerns about how to manage their flights to avoid collisions and crashes at various stages of flight. To this end, many Unmanned Aircraft Traffic Management systems (UTM) have been designed. These systems use various methods for managing UAV conflicts. Several surveys have reviewed conflict resolution methods for UAVs. However, to the best of our knowledge, there is no survey specifically addressing conflict detection and resolution methods in UTM, particularly those using AI-based methods. Therefore, this article serves as a comprehensive survey of all UAVs conflicts detection and resolution methods proposed in the literature and their use in the UTM systems. This survey classifies the methods into two categories: classical (non-learning) methods and learning-based methods. Classical methods typically rely on pre-defined algorithms or rules for UAVs to avoid collisions, whereas Artificial Intelligence-based methods, including Machine Learning (ML) and especially Reinforcement Learning (RL), enable UAVs to adapt to their environment, autonomously resolve conflicts, and exhibit intelligent behavior based on their experiences. It also presents their application in the conflict resolution service for UTMs. Additionally, the challenges and issues associated with each type of methods are discussed. This article can serve as a foundational resource for researchers in guiding their selection of methods for conflict resolution, particularly those relevant to UTM systems.