This study presents a comprehensive analysis of integrating Remote Identification (Remote ID) and Detect-and-Avoid (DAA) systems within Unmanned Aerial Systems (UAS) to enhance airspace safety by modeling the maximum safe operating speeds. Through mathematical modeling and extensive simulations, this research investigates the operational constraints imposed by factors such as the effective range of Remote ID broadcasts, signal latency, and the UAS's deceleration capabilities. Results provide insights into how safety margins can be affected by technological and environmental factors.

The rapid expansion of Unmanned Aircraft (UA) usage has increased the need for reliable collision avoidance systems. This paper presents a trust-based, sensor-free collision avoidance system for UAs, leveraging the Drone Remote Identification Protocol (DRIP) to establish trust between aircraft. The proposed system uses a geometric-based cooperative avoidance method to optimize efficiency and a fail-safe repulsion avoidance mechanism for enhanced safety. The proposed system's effectiveness is evaluated through a series of simulations and real-world tests, focusing on metrics such as safety, flight distance, flight time, and acceleration requirements. The results are promising, indicating that the trust-based approach may successfully balance efficiency and safety, providing insights into potential use cases for DRIP.