Open AccessAerodrome situational awareness of unmanned aircraft: an integrated self‐learning approach with Bayesian network semantic segmentation
Author(s) -
Lu Bowen,
Coombes Matthew,
Li Baibing,
Chen WenHua
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2017.0101
Subject(s) - situation awareness , computer science , robustness (evolution) , bayesian network , segmentation , situation analysis , artificial intelligence , situational ethics , aeronautics , engineering , aerospace engineering , biochemistry , chemistry , marketing , law , political science , business , gene
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 study 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 UK. 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, the authors 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 of the outdoor experimental data shows that the integrated method developed in this study improves the robustness of situational awareness for autonomous taxiing.