Deep Neural Network Compression for Aircraft Collision Avoidance Systems

TL;DR

This paper presents a deep neural network approach to compress aircraft collision avoidance tables, significantly reducing storage requirements while maintaining safety and efficiency in the ACAS X system.

Contribution

It introduces a neural network-based compression method for collision avoidance tables, enabling efficient storage and faster computation in aircraft systems.

Findings

Storage space reduced by a factor of 1000

Simulation shows improved safety and efficiency

Network approximates advisory tables accurately

Abstract

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.