A safe integration of drones into the airspace is fundamental to unblock the potential of drone applications. U-space is the drone traffic management solution for Europe, intended to handle a large number of drones in the airspace, especially at very low level (VLL). This paper presents the procedures we have designed and tested in real flights in the SAFEDRONE European project to pave the way for a safe integration of drones into the airspace using U-space services. We include three important aspects: Design of procedures related to no-fly zones, ensure separation with manned aircraft, and autonomous non-cooperative detect-and-avoid (DAA) technologies. A specific U-space architecture has been designed and implemented for flight campaigns with up to eight drones with different configurations and a manned aircraft. From this experience, specific recommendations about procedures to exit and avoiding no-fly zones are presented. Additionally, it has been concluded that the use of surveillance information of manned aircraft will allow a more efficient use of the airspace while maintaining a proper safety level, avoiding the creation of large geofence areas.

Today, aircraft demand is exceeding the capacity of the Air Traffic Control (ATC) system. As a result, airspace is becoming a very complex environment to control. The complexity of airspace is thus closely related to the workload of controllers and is a topic of great interest. The major concern is that variables that are related to complexity are currently recognised, but there is still a debate about how to define complexity. This paper attempts to define which variables determine airspace complexity. To do so, a novel methodology based on the use of machine learning models is used. In this way, it tries to overcome one of the main disadvantages of the current complexity models: the subjectivity of the models based on expert opinion. This study has determined that the main indicator that defines complexity is the number of aircraft in the sector, together with the occupancy of the traffic flows and the vertical distribution of aircraft. This research can help numerous studies on both air traffic complexity assessment and Air Traffic Controller (ATCO) workload studies. This model can also help to study the behaviour of air traffic and to verify that there is symmetry in structure and the origin of the complexity in the different ATC sectors. This would have a great benefit on ATM, as it would allow progress to be made in solving the existing capacity problem.

Many civilian applications of commercial unmanned aircraft are being planned to operate in the years ahead. Several countries have developed their own framework to design the operation of unmanned aircraft and the different services that demand safe operation. This paper focuses on the European framework denoted as U-space which concludes with the joint integration of manned and unmanned aircraft in the airspace. U-space is a set of novel services and specific procedures designed to provide safe and efficient access into the airspace to the airspace users. U-space constitutes a management system to organise unmanned operations and provides relevant information to drone operators as well as manned aircraft, air navigation service providers and authorities. The understanding of associated hazards and risks to unmanned aircraft is a critical issue for their operation in complex and non-segregated airspaces. The safety assessment developed herein is crucial to identify safety indicators for U-space. In addition, the identification of safety indicators was used to identify gaps in U-spaces services that are not correctly covered by the U-space framework. Particularly, several safety indicators are identified that currently U-space services do not consider and can imply an increase in the operational risk of unmanned operations.