JS: And you are seeing that some of what they do is instinctive?
CG: That’s what the brainwaves suggest. There are certain patterns that are really suggestive of cognitive workload, and there are other times we can see that signal is absent, and that would suggest to us that it really is a reflexive motion. ‘Car going sideways? Sure – I’ll correct that – just one more day at the office.’
JS: How does what you’re learning from human drivers feed back into how you program a robotic car to do the same task?
CG: We are developing algorithms based on what the humans do. For example, by looking at what they are doing with the steering and the brakes and the throttle. A normal thought is ‘I want to turn the car – I’ll use the steering wheel’. But once you run out of friction at the front tyres, anyone who has done racing knows that doesn’t work anymore. In fact they’re actually steering the car with the brakes and the throttle. So there are some counterintuitive behaviors that racecar drivers are very good at that, that we are learning to encode.
JS: How does this research feed back into the development of autonomous vehicles that we might one day drive – or have drive us – on the roads?
CG: One of the nice things about vehicle dynamics is that it is a scalable problem. At the end of the day, you are limited by how much friction you have between the tyres and the road. In our case we are running out of friction because we are going really fast on a racetrack. But you could run out of friction simply because you are driving on a road that has become icy. So what we are learning on the racetrack translates exactly into the sorts of situations you might get simply on a wet road. Mathematically it’s really the same problem. So we can take something that is visually very exciting, that gets students charged up, we can develop the mathematics behind that, and the control systems, and then port it over into situations that you would encounter in an everyday drive.