Probabilistic context integration‐based aircraft behaviour intention classification at airport ramps

Abstract

Self‐driving baggage tractors driving on airport ramps/aprons present new trends that promote better airport operation procedures and proliferate the aviation market. Airport ramps have unique mobility requirements when it comes to layout, population, demand, and patterns. Estimating aircraft movement is highly crucial and must be required because of safety reasons and airport operations rules. The movement of aircraft at the airport ramp is not dynamic but relatively static and slow. However, it is more complicated in estimating attributes. Even though the aircraft is parked and stationary, all operational vehicles should be cautious on aircraft pushing in or out from the parking stand. However, this is never dealt with in any research since aircraft detection observed at the airport ramp is not suggested in previous research. In this paper, a context‐aware fusion‐based method for aircraft intention detection at airport ramps is proposed. A parallel extraction of behavioural features from the aircraft and situational context detection from other adjacent objects are suggested. Using the proposed method, aircraft can be detected and aircraft movement context can be estimated. The feasibility of this algorithm is demonstrated based on the fluent dataset obtained at Cincinnati Airport.

Validating Synthetic Data for Perception in Autonomous Airport Navigation Tasks

“…Suder et al [5] further improved automating taxi operations by enhancing the reliability of lane detection across various environmental conditions, utilizing light photometry systems to detect multi-colored lines and navigational aids. The integration of additional sensors, like LiDAR and camera sen-sors, also improves object detection and classification, thereby facilitating safer operations of autonomous baggage tractors on airport ramps [6]. However, adding extra sensors to flying vehicles could increase weight, power consumption, and certification challenges, which is why Coombes et al [7] advocated for a machine vision-only approach as the most practical solution for enabling automated taxiing.…”

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

“…Zonglei et al [20] proposed an object detection algorithm for the apron, which correlates the context information on the apron and introduces an attention mechanism to improve the accuracy of apron object detection. Soomok Lee et al [21] used the YOLO algorithm to detect objects on the apron, and by recognizing the intent of the aircraft to solve the potential problems that may arise from the autopilot of vehicles on the apron. Also, Gauci et al [22] provided a new idea for aircraft collision avoidance on the apron by installing cameras on the wingtip.…”

“…Soomok Lee et al. [21] used the YOLO algorithm to detect objects on the apron, and by recognizing the intent of the aircraft to solve the potential problems that may arise from the autopilot of vehicles on the apron. Also, Gauci et al.…”