Probabilistic Safety Assessment of Unmanned Aerial System Operations

U NMANNED aircraft systems (UASs) are becoming increasingly popular, encompassing awide variety ofmissions ranging from military reconnaissance to wildfire monitoring. However, there are inherent safety concerns with UAS due to the lack of an onboard human pilot. Currently, to operate UAS in the National Airspace System, the operators must obtain either a Certificate of Authorization or Waiver or a special airworthiness certificate from the Federal Aviation Administration (FAA) [1]. One of the important steps in obtaining the approval is a proof that the UAS operation can be conducted at an acceptable level of safety [1]. Many past studies assessing the safety of UAS operations used uniform traffic densities. Anno [2] investigated midair collision risk using a random collision theory and compared the results with historic collision data from 1969 to 1978. McGeer et al. [3,4] performed hazard estimation studies of the Aerosonde UAS. In these studies, two different constant densities were used for the UAS and the background traffic. A comprehensive system-wide study performed byWeibel and Hansman [5] used a ratio of the volume swept by the background aircraft to the total airspace volume. Lum and Waggoner [6] conducted a study on both midair collision and ground impact based on the collision model of gas molecules. These approaches are adequate for obtaining a general idea of the risk around a given region but do not consider traffic patterns that are specific to the region of interest. Lum et al. [7] used actual UAS trajectories for a ground impact analysis. In this study, a realistic distribution of average glide angle was used to calculate the expected value of ground fatalities. Sheridan [8] proposed a model to estimate the relative collision probability between two aircraft at the closest point of approach based on Gaussian density functions. Maki et al. [9] created a method to efficiently estimate the probability of near midair collision using Gaussian probability distributions of proposed UAS trajectories and historic track data. In thiswork, the nearmidair collision probabilities are expressed as confidence intervals. Some of the work is related to quantitatively establishing the boundary of “well clear” for sense-and-avoid systems. Weibel et al. [10] used conditional probability to develop a separation standard model based on uncorrelated encounter model [11]. Asmat et al. [12] developed a UAS-specific collision-avoidance system that can communicate with the existing traffic alert collision and avoidance system. In this work, a distributed traffic model similar to Maki et al. [9] is constructed using actual traffic data collected over a one-year period to enable a probabilistic approach to risk assessment. The radar data provided by the U.S. Air Force contains not only the cooperative traffic data but also the noncooperative traffic data with altitude information. Inclusion of noncooperative traffic, mostly general aviation (GA) traffic, is important because they tend to fly at lower altitudes where the UAS are likely to operate, and it is harder to implement collision mitigation measures with them. The current study computes the collision rates, which are defined by the number of collisions per unit time of UAS operation, based on UAS tracks flying through the continuous background traffic model. The procedures and results are explained in detail throughout the following sections. Following the introduction, the area around the Grand Forks Air Force Base where the U.S. Air Force is planning to operate UAS is described in Sec. II. Then, the description of the continuous-traffic model is presented in Sec. III. In Sec. IV, mathematical formulations for the continuous-traffic model and for the computation of conflict and collision probabilities are presented. Section V reviews the air traffic characteristics of the given area in terms of average aircraft counts and their spatial distributions, and Sec. VI presents the collision risk computed for a potential mission scenario. Finally, the results and recommendations are summarized in Sec. VII.