Unmanned Aircraft Systems Traffic Management (UTM) and its European version, U-Space, are regulatory frameworks designed to ensure safe, efficient, and secure integration of Unmanned Aerial Vehicles (UAVs) into urban airspace by providing services such as monitoring, conflict resolution, and traffic management. To ensure the safety of UAVs' operations, a comprehensive safety assessment framework is crucial. To build such a framework, it is necessary to identify appropriate safety metrics and develop an approach to measure them, enabling the measurement and management of associated safety risks. In this work, we identify and analyze two categories of safety metrics: collision metrics and surveillance performance metrics. We present an approach grounded in U-space regulatory framework concepts to design and conduct a comprehensive experimental study investigating the impact of several factors that can affect UAV safety, including GPS and IMU failures of varying duration at different UAV speeds, update intervals, traffic densities, and weather conditions, through quantitative assessment of the identified safety metrics. The results reveal key insights into: 1) Identifying the metrics most affected by variations in factors in the presence of GPS or IMU failures, 2) Determination of metrics most correlated to safety risk level under varying conditions, 3) Establishment of risk thresholds for selected metrics under erroneous or varying conditions, contributing to the identification of reliable risk indicators, and 4) Evaluation of the performance and limitations of preventive mechanisms, such as the fail-safe system, under erroneous behavior of GPS and IMU and across different operational and environmental conditions.

In recent years, UAVs have been increasingly used in urban environments due to agility in movement, simplicity in mechanics, low price, and ability to access locations that are difficult or impossible to reach by humans. A significant number of drones are expected to fly in the urban sky shortly. The profitable nature of commercial UAVs/drone applications in urban space will imply a high density of drones; therefore, avoiding mid-air collisions will be critical for the safe operation of the UAVs. In Europe, U-space services are being created to guarantee the safe operations of UAVs in urban Very Low Level (VLL) airspace. To avoid collisions, U-space considers a separation minima (i.e., the minimum safe distance between UAVs) surrounding each UAV. Thus, violating the separation minima, which might be caused by abnormal conditions (e.g., bad weather conditions), failure conditions (e.g., GPS failure in UAVs), or unreliable behavior of the system (e.g., inaccurate GPS positioning data or erratic position estimation by flight controller), could potentially result in conflicts that require immediate mitigation measures to avoid mid-air collisions. Failure prediction is a promising method for preventing separation minima violations in U-space services. However, in order to have effective failure prediction, the lead time, which is the time between the activation of a fault and its manifestation in a system as a failure, must account for both the prediction step and the subsequent mitigation actions. This paper aims to evaluate the lead time in UAV systems in the presence of positioning-related issues (as being critical for the safe operation of UAVs) from a U-space perspective. We used fault injection to inject 18 different types of faults (or emulating failures) in 28 different UAV missions. The results show that the lead time for 17 types of faults injected is at least 14 seconds (in some cases, no failure occurred). Thus, U-space has at least 14 seconds to predict and mitigate such faults. In the case of GPS failure (i.e., GPS signal is entirely missing), lead time is about 5 seconds, requiring faster strategies for failure prediction and mitigation plans.