RELATED CAPABILITIES:
Conduct Safety Risk Management Analysis on sUAS DAA Systems (A71_A11L.UAS.120)
This report presents research aimed at improving Safety Risk Management (SRM) analysis for small Unmanned Aircraft Systems (sUAS) Detect-and-Avoid (DAA) technologies operating within the United States National Airspace System (NAS). As unmanned aircraft operations expand—particularly Beyond Visual Line-of-Sight (BVLOS) operations—DAA systems are critical to replacing the pilot’s traditional see-and-avoid responsibility and enabling safe integration with existing aviation traffic.
The objective of this research was to develop analytical tools and risk-assessment methods capable of identifying hazards, quantifying operational risk, and evaluating DAA system performance under realistic operating conditions. Current SRM processes largely rely on qualitative risk matrices based on likelihood and severity. While effective for many traditional aviation systems, these approaches are limited when applied to highly automated and dynamic technologies such as DAA systems. Therefore, this research introduced probabilistic, data-driven methodologies to support more rigorous and scalable safety assessments.
The research was conducted in three phases. First, the current state of DAA technologies and SRM practices was evaluated to identify safety gaps and operational challenges. This analysis highlighted several key issues, including the absence of standardized reliability metrics for DAA systems, limited empirical performance data, and uncertainty surrounding the effects of environmental conditions on detection capability.
Second, the study developed two probabilistic risk assessment frameworks tailored to DAA systems. The first introduces a DAA timing-distribution method that evaluates the probability of late or missed detection of intruding aircraft prior to a potential near mid-air collision. The second expands the traditional risk model and explores a three-dimensional framework incorporating potentiality, severity, and exposure, allowing risk to be quantified within a defined safety boundary.
Finally, the proposed methodologies were validated through high-fidelity simulation using a ROS2-Gazebo environment combined with large-scale Monte Carlo analyses. These simulations modeled aircraft encounters, sensor performance, and degraded environmental conditions to evaluate detection capability and avoidance effectiveness.
Results indicated that environmental visibility, sensor capability, and detection timing are among the most influential factors affecting DAA safety performance. The proposed frameworks provide improved methods for quantifying operational risk and offer scalable tools for evaluating DAA systems across a wide range of operational scenarios.
Overall, this research contributed to the development of new analytical methods, probabilistic modeling techniques, and simulation-based evaluation tools that enhance the ability to assess DAA safety. These findings support the development of future regulatory guidance, operational standards, and risk-management practices necessary to enable safe and scalable BVLOS operations within the NAS.
FINAL REPORT | 
POC:
Tom Haritos, Ph.D.
Associate Director, Research | UAS Research Program Manager
Applied Aviation Research Center
Kansas State University Polytechnic
Email: tharitos@ksu.edu
Phone: 785.833.2152, Ext. 202
Participating Schools
