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Safety Assessment of Mid-Air Collision Avoidance for Unmanned Aircraft Systems Using Aircraft Encounter Models

机译：使用飞机遇到模型的无人机系统中空碰撞安全评估

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Seamlessly integration of unmanned aerial vehicles (UAVs) into the U.S. National Airspace Systems (NAS) requires that UAVs have the ability to automatically "sense and avoid" other aircraft. To prove the robustness of the collision avoidance system, a safety assessment simulation using aircraft encounter model is proposed in this paper. A 6-week dataset of manned aircraft flight trajectories includes GPS coordinates, heading, and velocities for each aircraft equipped with an Automatic Dependent Surveillance-Broadcast (ADS-B) transceiver or a Mode S transponder flying in Grand Forks, North Dakota, is logged by an ADS-B based ground surveillance station at a rate of 1Hz. By using Bayesian Network, the conditional probabilities of the aircraft state vector, including altitude, horizontal velocity, vertical velocity, horizontal acceleration and turn rate are calculated and estimated and statistics tables representing the regional airspace characters are established. From the statistics tables, a UAV ownship determinative flight path and an intruder vehicle flight path with randomness are generated with a potential mid-air collision scenario. The separation between two vehicles is monitored throughout the simulation. When intruder vehicle penetrates predefined the UAV ownship safety cylinder during the simulation, the collision avoidance algorithm will automatically find the best trajectory and perform necessary maneuvers for UAV ownship. After a massive number of simulations, the ratio of the number of failure cases to the total number of simulations reflects the safety level of the UAV flying among simulated cooperative manned aircraft.
机译：无人驾驶飞行器（无人机）将无人驾驶飞行器（UAV）与美国国家领空系统（NAS）无缝集成，要求无人机能够自动“感知和避免”其他飞机。为了证明碰撞避免系统的稳健性，本文提出了使用飞机遇到模型的安全评估仿真。载人飞机飞行轨迹的6周数据集包括全部依赖监视广播（ADS-B）收发器的每架飞机的GPS坐标，标题和速度，或者在北达科他州的大叉子中飞行的模式的转发器飞行由基于ADS-B的地面监视站以1Hz的速率。通过使用贝叶斯网络，计算了飞机状态向量的条件概率，包括高度，水平速度，垂直速度，水平加速度和横向速率，并建立了代表区域空域字符的统计表。从统计表中，通过潜在的中空冲突场景产生无人机自己的飞行路径和入侵车辆飞行路径。在整个模拟中监测两个车辆之间的分离。当入侵者车辆穿透预定义的仿真过程中，碰撞避免算法将自动找到最佳轨迹，并为UAV自己执行必要的操纵。在大量的模拟之后，故障情况的数量与模拟总数的比率反映了模拟协同载人飞机之间的无人机飞行的安全水平。

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