Risk Assessment Method for UAV’s Sense and Avoid System Based on Multi-Parameter Quantification and Monte Carlo Simulation

Abstract

The rise in Unmanned Aerial Vehicle (UAV) usage has opened exciting possibilities but has also introduced risks, particularly in aviation, with instances of UAVs flying dangerously close to commercial airplanes. The potential for accidents underscores the urgent need for effective measures to mitigate mid-air collision risks. This research aims to assess the effectiveness of the Sense and Avoid (SAA) system during operation by providing a rating system to quantify its parameters and operational risk, ultimately enabling authorities, developers, and operators to make informed decisions to reach a certain level of safety. Seven parameters are quantified in this research: the SAA’s detection range, field of view, sensor accuracy, measurement rate, system integration, and the intruder’s range and closing speed. While prior studies have addressed these parameter quantifications separately, this research’s main contribution is the comprehensive method that integrates them all within a simple five-level risk rating system. This quantification is complemented by a risk assessment simulator capable of testing a UAV’s risk rating within a large sample of arbitrary flight traffic in a Monte Carlo simulation setup, which ultimately derives its maximum risk rating. The simulation results demonstrated safety improvements using the SAA system, shown by the combined maximum risk rating value. Among the contributing factors, the detection range and sensor accuracy of the SAA system stand out as the primary drivers of this improvement. This conclusion is consistent even in more regulated air traffic imposed with five or three mandatory routes. Interestingly, increasing the number of intruders to 50 does not alter the results, as the intruders’ probability of being detected remains almost the same. On the other hand, improving SAA radar capability has a more significant effect on risk rating than enforcing regulations or limiting intruders.

Keywords

• UAV
• Sense and Avoid systems
• risk assessment
• risk rating system
• Monte Carlo simulation