Pilot Support System : A Machine Learning Approach

Abstract

Pilots can be one of the factors in many air traffic accidents. When one or both pilots are impaired (e.g. fatigue, drunk or distracted), one or both pilots are disabled, one or both pilots are capable but wrong-headed, both pilots don’t have sufficient training, both pilots are fully capable but distracted, both pilots miscommunicate with the air traffic controller, or both pilots follow wrong instructions from the air traffic controller, the risk of accident will increase dramatically. In some of these cases, the risk can be mitigated by using big data and machine learning. The system will collect and analyze large amount of data about the state of the aircraft, e.g., the flight path, the immediate environment around the aircraft, the weather and terrain information, and the pilots’ input to control the aircraft. Additional sensors such as eye tracking devices and biological monitor can also be added to determine the condition of the pilots. If the pilots’ input does not match proper reaction to the situation or the pilots are impaired, the learning machine will first provide an advisory to the pilot. When the situation becomes more urgent, the advisory will be elevated to warning. If there is at least one capable pilot, these advisories and warnings may help the pilot take proper actions. If both pilots are impaired or incapable, a warning will be sent to the air traffic controllers so that they can take appropriate actions.