Data-Driven Validation of DO-365 Detect-and-Avoid Well Clear Thresholds for UASs in Terminal Airspace Using One Million Encounters

The safe integration of unmanned aircraft systems (UASs) into the National Airspace System(NAS) requires robust Detect-and-Avoid (DAA) capabilities that maintain appropriateseparation from other aircraft. The RTCA DO-365Well Clear thresholds were developedprimarily for en-route operations, raising questions about their suitability for terminalairspace, where traffic density, maneuvering, and closure-rate characteristics differ substantially.This paper presents a large-scale evaluation of the RTCA DO-365 Well Clearthresholds using the MIT Lincoln Laboratory Terminal Encounter Model (LLTEM) V1.0dataset, which includes one million empirically sampled simulated terminal encountersgenerated from a Bayesian model fitted to FAA terminal radar data. Encounter severity wasdetermined based on the true closest point of approach, against the standard Near Mid-AirCollision (NMAC) definition. The default en-route Loss ofWell Clear (LoWC) criterion wasthen evaluated against those labels and compared jointly with the terminal-area thresholdsproposed by Vincent et al. Scored strictly against NMAC severity, the en-route defaultproduces a 2.8% false-alarm rate at a precision of 0.27, with no missed NMAC encounters.Of the three threshold parameters, only the predictive τmod term moves that rate. Vincentet al.’s full recommended range reduces it further at no detection cost, their lower boundreaching 0.9% at a precision of 0.54, representing a 67% relative reduction and the best ofthe configurations they propose. The predictive term’s constant-velocity extrapolation isrelatively accurate for the duration of most of the encounters in which it engages: 81% ofseverity labels are correct at the en-route default, rising to 86% at the tightest configuration tested. These findings provide an evidence-based assessment of the operational suitabilityof current Well Clear thresholds for terminal-area UAS operations and support futurerefinement of DAA standards.