Probabilistic model checking of the next-generation airborne collision avoidance system

We present a probabilistic model checking approach for evaluating the safety and operational suitability of the Airborne Collision Avoidance System X (ACAS X). This system issues advisories to pilots when the risk of mid-air collision is imminent, and is expected to be equipped on all large, piloted aircraft in the future. We developed an approach to efficiently compute the probabilities of generically specified events and the most likely sequences of states leading to those events within a discrete-time Markov chain model of aircraft flight and ACAS X. The probabilities and sequences are computed for all states in the model. Events of interest include near mid-air collisions (NMACs) and undesirable sequences of advisories that affect operational suitability. We have validated numerous observations of the model with higher-fidelity simulations of the full system. This analysis has revealed several characteristics of ACAS X's behavior.