System and method for predicting ownship clearance and timing by modelling controller pilot conversations

A computer-implemented method in an aerial vehicle for predicting the timing for clearance instructions, the method comprising:

• collecting radio communications between air traffic control (ATC) and other aerial vehicles located in a configurable range and for a configurable duration;
• grouping the collected radio communications by aerial vehicle;
• deriving an average time, from the collected radio communications, between successive instructions from ATC to an aerial vehicle;
• deriving an average time, from the collected radio communications, for a following instruction in a specific time interval;
• generating a clearance instruction prediction model using machine learning techniques that is configured to predict a time by which an ownship will receive a subsequence clearance instruction, based on the average time between successive instructions from ATC to an aerial vehicle, the average time for a following instruction in a specific time interval, and the clearances provided by ATC to other aerial vehicles;
• predicting, using the generated clearance instruction prediction model, an expected time for a next clearance instruction from ATC to the ownship aerial vehicle;
• and
• displaying the predicted time for the next clearance instruction on a display device.

Non-transitory computer readable media encoded with programming instructions configurable to cause one or more processors to perform a method in an aerial vehicle, the method comprising:

• collecting radio communications between air traffic control (ATC) and other aerial vehicles located in a configurable range and for a configurable duration;
• grouping the collected radio communications by aerial vehicle;
• deriving an average time, from the collected radio communications, between successive instructions from ATC to an aerial vehicle;
• deriving an average time, from the collected radio communications, for a following instruction in a specific time interval;
• generating a clearance instruction prediction model using machine learning techniques, based on the average time between successive instructions from ATC to an aerial vehicle, the average time for a following instruction in a specific time interval, any anomalies detected in aerodrome operations, and the clearances provided by ATC to other aerial vehicles, that is configured to predict a time by which an ownship will receive a subsequence clearance instruction and a reliability factor for the prediction;
• predicting, using the generated clearance instruction prediction model, an expected time for a next clearance instruction from ATC to the ownship aerial vehicle and an associated reliability factor for the predicted expected time;
• and
• displaying the predicted time for the next clearance instruction and the associated reliability factor on a display device.