Staff spotlight: Using A.I to predict a pilot’s maneuver

The School of Engineering and Built Environment recently welcomed a new academic staff member to the Aviation team, Dr Xiaoyu Wu, who is originally from China but has travelled from The United States where he completed his Master’s and PhD.


Dr Wu brings to the team a wealth of knowledge within the aviation industry, both as an educator and as a researcher. Wu will be conducting further research at Griffith University, examining how artificial intelligence can predict a pilot’s maneuvers. Read more of Wu’s story below.




Tell us about your career background?

Originally I am from southwest China. I did my undergraduate with the Civil Aviation Flight University of China, one of the country’s best aviation schools. After graduation, I was going to be an air traffic controller in Shanghai; however, I got admission to further my study in the United States during my initial training.


I did my Master’s degree in Aviation Operation with Embry-Riddle Aeronautical University, the world’s best aviation school. I then finished my PhD with Saint Louis University which has a very high reputation in the United States’ aviation industry. Of course, I ended up as an educator and researcher in the aviation field. I taught two years in the United States and helped the program establishing many industry connections.




What have been some of your career highlights?

The first highlight of my research career was actually related to Brisbane and Australia. In 2012, I worked with Lockheed Martin and many other providers to demonstrate a flight data object exchange experiment crossing the Pacific Ocean for the Federal Aviation Administration (FAA). The partner included Tokyo Centre and Brisbane Centre.


Later on, I moved to use machine learning to understand safety reports better. The team that I worked with was the first team to use natural language processing techniques to analyse the Aviation Safety Reporting System (ASRS) reports and have made a significant impact.




What courses will you be teaching at Griffith?

For now, I will be teaching Flight Procedure courses, and advising students on a project course in Trimester 3.

What are you most looking forward to at Griffith?

I believe the aviation team at Griffith is one of the best in the world. I am looking forward to working with my colleagues together to discover the future of aviation. Of course, I am excited about students here too. I want to grow with the students together. I help them, and they help me as well.


I have a very diverse background in aviation. I know the practice and development trends in both China and the United States. I believe I can provide students different perspectives from other countries.

Will you be undertaking any research at Griffith?

Yes, I will undertake research here at Griffith. My current project is to make artificial intelligence (A.I.) predict the pilot’s maneuvers. By doing that, I want to create an assistant to pilots instead of using automation to replace the pilot. The current safety challenge is automation cuts pilots out of the decision loop. I want to create a personalised flight assistant for each pilot. Put in analogy, I want to breed a border collie to help a shepherd.


I believe, just like driving a car, each person or a group of people may share similar flying characteristics. How can we understand them? I think we can use their control inputs to tell a pilot’s intentions and aircraft status. In the current stage, I think first we need to teach computers about what does a good pilot’s control inputs looks like. I asked 12 senior flight instructors to perform a very simple flight pattern which includes take-off, climbing, turning, descending, and landing. Then I run the “good profiles” against student pilot profiles; I can get some suggestions from the computer, something like, looks like you are not stable during the descending, a “good pilot” might choose to go around.

What would be your number one advice for students?

Commitment. Commitment to the subject you really like.


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