Safety assessment for airborne intelligent avoidance system based on Bayesian optimization

To address the airworthiness safety challenges brought by the application of reinforcement learning in UAV intelligent avoidance systems, this paper proposes a safety assessment method for the intelligent avoidance system based on Bayesian optimization theory within the framework of the SAE ARP4761 standard. First, the intelligent avoidance system model is established based on the UAV kinematic model and the Proximal Policy Optimization (PPO) algorithm. Second, by integrating the system model verification task with Bayesian optimization theory, the iterative training of the Gaussian surrogate model is achieved through three acquisition functions: uncertainty exploration, boundary refinement, and failure region sampling. This enables safety verification, safety boundary determination, and functional failure probability analysis of the intelligent avoidance system with a small number of samples, supporting quantitative safety assessment at the whole aircraft/system level. Finally, taking a typical intelligent avoidance system architecture as a case, the proposed method is demonstrated to effectively support airworthiness safety assessment, providing essential airworthiness compliance methods and technical guarantees for the deployment of intelligent avoidance systems. Experimental results further validate that, under limited sample conditions, the Bayesian optimization-based method outperforms uniform sampling and Monte Carlo methods by offering more detailed failure boundary predictions, precise failure probability estimation, and higher confidence levels for the reinforcement learning module.