Keenan Burnett is the Technical Team Lead for aUToronto. He is also a Master of Applied Science at the University of Toronto Institute for Aerospace Studies under the supervision of Professors Angela Schoellig and Tim Barfoot. His research interests are in control systems design, computer vision, and self-driving cars.
Q: What is your motivation to be the Technical Team Lead of aUToronto?
I like working on difficult problems. The leadership experience that I had during my undergrad was generally positive. Given that experience, I see leading teams of researchers and engineers as possibly being a big part of my career.
Q: What experience do you bring to this team?
I have been a part of numerous student design teams during my undergrad. The most notable of which was my involvement in the University of Toronto Aerospace Team's Space Systems division from 2014 to 2016. During that time, we designed and built a 3U CubeSat for the Canadian Satellite Design Challenge. Aside from that, I just finished my undergrad in Engineering Science during which I tried my best to enroll in courses relevant to robotics. My fourth year capstone project was to build a mobile robot that would have the capability to play a game of laser tag. I also have experience as a software engineering intern at IBM.
Q: How do you think autonomous driving will shape North America in the future?
I think one of the interesting things is that fewer people are going to own cars because ride-share services are becoming so good. When Uber’s fleet becomes robotic, you’ll be able to call them anytime 24/7. This car will then show up and take wherever you need to go. So I think more people will ask themselves why they need a car anymore. It doesn’t make sense. So I think car ownership is going to change a lot in North America.
Q: What is one of the most important technical aspects of the self-driving car development?
Perception is the most difficult component. By perception, I mean taking the visual information from your cameras, and the depth information from your Lidar, radar, and other depth sensors and turning that into a cohesive (and accurate!) representation of the environment. That includes creating a map of the environment that you can localize against, as well detecting and tracking the obstacles in your environment.
Q: What are some potential obstacles aUToronto might face this year? What are your biggest fears about what could go wrong? How do we plan to solve it?
I think testing is going to be really important. You know, having a good simulation environment and doing enough testing with your vehicle is pretty critical to get a system that’s high enough in reliability. So it’s going to be very important for us to make sure that we were conducting and following safety procedures. The last thing we want is for someone to get hurt. This is a big heavy robot, and we do not want it to run into things and hurt the drivers or other people around it. This is the number one priority, to keep everybody safe.
Q: What kind of team atmosphere do you wish to create within aUToronto?
I want it to be an amicable, positive working environment. However the main thing is that we want to win. Winning the competition means that everybody who was a part of this project has their hard work validated, and that’s something they can carry with them for the rest of their lives. So we're looking to strike a balance between that amicable atmosphere, and a competitive and driven one.
Q: What are your hobbies outside of work?
I rock climb pretty regularly, as in bouldering and indoor rock climbing. I do a bit of outdoor rock climbing as well. That’s my main hobby outside of work.