Extended Traffic Alert Information to Improve TCAS Performance by means of Causal Models

Abstract

Near-midair collisions (NMACs) between aircraft have long been a primary safety concern and have incessantly motivated the development of ingenious onboard collision avoidance (CA) systems to reduce collision risk. The Traffic Alert and Collision Avoidance System (TCAS) acts as a proverbially accepted last-resort means to resolve encounters, while it also has been proved to potentially induce a collision in the hectic and congested traffic. This paper aims to improve the TCAS collision avoidance performance by enriching traffic alert information, which strictly fits with present TCAS technological requirements and extends the threat detection considering induced collisions and probabilistic pilot response. The proposed model is specified in coloured Petri net (CPN) formalism, to generate by simulation all the future possible downstream reachable states to enhance the follow-up decision making of pilots via synthesising relevant information related to collision states. With the complete state space, the potential collision scenarios can be identified together with those manoeuvres that may transform a conflict into a collision. The causal TCAS model is demonstrated to work effectively for complex multiaircraft scenarios and to identify the feasible manoeuvres that contribute to reduce the nonzero TCAS-induced collision risk.