Research on conflict detection model for taxi‐in process on the apron based on aircraft wingtip keypoint detection

Abstract

Apron safety is critical to aviation operations and aprons are prone to wingtip scraping accidents. However, few studies have been conducted on wingtip conflicts. In this paper, a class of aircraft conflict detection models based on wingtip keypoint detection is devised to solve the problem of wingtip scraping that may occur during the aircraft taxi‐in process on the apron. Firstly, a concise camera calibration technique is designed for the conversion between the pixel coordinate and the airport actual coordinate, which allows for the acquisition of high accuracy activity posture within the apron. Secondly, the static and dynamic safety protection zone delineation methods for wingtip conflict detection are given, taking the apron standard operating procedures and aircraft kinematic model into consideration. Then, the corresponding conflict detection models and algorithms are designed for the possible scraping between the vehicle and aircraft wingtips and between the aircraft and aircraft wingtips during the aircraft taxi‐in process on the apron. Finally, HRNet is selected to detect the keypoint of the wingtip by comparison, and the corresponding models and algorithm validation are carried out based on the apron physical sandbox. The verification results indicate that the models have strong accuracy and real‐time performance.

SEHRNet: A lightweight, high‐resolution network for aircraft keypoint detection

“…It can be integrated into airport surveillance systems to monitor the status of aircraft in real time while taxiing. It includes detecting aircraft components that may cause collisions or damages, such as aircraft wingtips [51], engines etc. This will significantly enhance the safety of ground operations, reduce the risk of accidents, and contribute to the safeguarding of airport operations.…”

“…Zhang T et al have implemented keypoint detection for aircraft wingtips using HRNet. However, there remains room for enhancement in its network detection capabilities [39]. In addition, the European Space Agency (ESA) developed the Spacecraft PosE Estimation Dataset (SPEED) [40] and its extended dataset SPEED+ [41] for spacecraft attitude estimation needs.…”

Monte Carlo Simulation-Based Risk Assessment for Unmanned Ground Equipment Taxiing Guidance

“…The safety risk indicators for aircraft operations mainly include the following three categories: 1) Actual on-site video detection analysis. Tianxiong Zhang et al [7][8] implemented airfield aircraft video surveillance analysis through machine learning, evaluating aircraft encounter risks through wingtip distance. 2) Risk calculations considering the taxiing state of aircraft.…”