Research on Methods for Assessing Bird-Drone Collision Probability in Urban Low-Altitude Airspace

As the use of low-altitude drones in urban environments continues to grow, the likelihood of near-miss events and collisions between birds and drones operating in shared airspace has drawn increasing attention. To address this issue, this paper proposes a collision probability assessment framework based on the Boids swarm behavior model and Monte Carlo statistical methods, using the Chengdu silky-tailed starling as a case study: simulating the relative motion between bird flocks and UAVs within typical flight segments, defining near-misses and physical collisions events based on minimum separation distance thresholds, and employing Wilson confidence intervals to quantify uncertainty and analyze convergence. The simulation results indicate that after introducing the detectionavoidance mechanism, the minimum distance distribution function curve shifts rightward overall, collision duration is significantly reduced, the probability of event occurrence is effectively lowered, and the cumulative estimate exhibits good convergence under large samples. Additionally, the probability of incidents exhibits a monotonically decreasing trend with enhanced detection capabilities, revealing a monotonically sensitive characteristic where higher detection capabilities reduce collision probability risks. This provides a quantitative basis for low-altitude planning and risk management.