  One approach to designing decision making logic for an aircraft collision
avoidance system frames the problem as a Markov decision process and optimizes
the system using dynamic programming. The resulting collision avoidance
strategy can be represented as a numeric table. This methodology has been used
in the development of the Airborne Collision Avoidance System X (ACAS X) family
of collision avoidance systems for manned and unmanned aircraft, but the high
dimensionality of the state space leads to very large tables. To improve
storage efficiency, a deep neural network is used to approximate the table.
With the use of an asymmetric loss function and a gradient descent algorithm,
the parameters for this network can be trained to provide accurate estimates of
table values while preserving the relative preferences of the possible
advisories for each state. By training multiple networks to represent
subtables, the network also decreases the required runtime for computing the
collision avoidance advisory. Simulation studies show that the network improves
the safety and efficiency of the collision avoidance system. Because only the
network parameters need to be stored, the required storage space is reduced by
a factor of 1000, enabling the collision avoidance system to operate using
current avionics systems.
