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.