Aerodrome situational awareness of unmanned aircraft: an integrated self-learning approach with Bayesian network semantic segmentation

It is expected that soon there will be a significant number of unmanned aerial vehicles (UAVs) operating side-by-side with manned civil aircraft in national airspace systems. To be able to integrate UAVs safely with civil traffic, a number of challenges must be overcome first. This paper investigates situational awareness of UAVs’ autonomous taxiing in an aerodrome environment. The research work is based on a real outdoor experimental data collected at the Walney Island Airport, the United Kingdom. It aims to further develop and test UAVs’ autonomous taxiing in a challenging outdoor environment. To address various practical issues arising from the outdoor aerodrome such as camera vibration, taxiway feature extraction and unknown obstacles, we develop an integrated approach that combines the Bayesian-network based semantic segmentation with a self-learning method to enhance situational awareness of UAVs. Detailed analysis for the outdoor experimental data shows that the integrated method developed in this paper improves robustness of situational awareness for autonomous taxiing.