A New Markov Decision Process Based Behavioral Prediction System for Airborne Crews

• Main Authors: Yaozhong Zhang, Kangjia Yao, Jiandong Zhang, Frank Jiang, Matthew Warren
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Cockpit automation assistance; Markov decision process; behavioral prediction
• Online Access: https://ieeexplore.ieee.org/document/8937552/

In order to ensure the normal and stable flights in the aircraft, a variety of sensors and corresponding instrumentation systems have been applied on the aircraft to monitor/control the current flight status, and the resulted data ensure the flight safety with a heavy burden on the pilot. In views of this, nowadays, the aircraft cockpit automation assistance system has become a hot topic. This paper is based on the pilot's future operational behavior which can be predicted through different stages of flight operations after the automated assistance system is triggered, thus providing the pilot with assistance in accordance with his operating habits. We have established a MDP (Markov Decision Process) model via analyzing and modeling of pilot operational behavior and mission requirements for flight processes, and we also use value iterative algorithm to find the optimal prediction sequence, lastly, we verify the operability of the algorithm by flight operation simulation experiment. It provides a new solution for the safety of pilot operations and the intrusiveness of the cockpit adaptive automation assistance system.