How we designed a SELF DRIVING RACECAR path planning algorithm | MIT Driverless | Roborace Event 1.1
Behind the scenes of MIT Driverless preparing for Roborace Event 1.1. We developed an optimal algorithm to collect rewards and avoid obstacles! Roborace is a race where a self-driving racecar races on a real track somewhere in the world, but navigates a virtual metaverse.
In this video, I talk about my experience on MIT Driverless's path planning team, and how we came up with our algorithm to avoid the virtual obstacles and collect the virtual rewards in the metaverse.
On the path planning team we primarily answer two questions:
1) Given a track and a car model, what is the optimal path to take?
2) Given the obstacles and collectables, what is the optimal way to collect/avoid them, if at all?
These are both hard questions. LOL
In the few days leading up to Event 1.1, we developed what we consider is the optimal algorithm for obstacle avoidance and reward collection. We named this SPLINE RACER. I give some details about how spline racer works using cost functions and assigning costs to the obstacles/rewards.
HOWEVER, at the end of the day, we were not able to test our code on the actual racecar, and so we were forced to deploy our MVP - minimum viable product. :') This MVP was supposed to be guaranteed to at least finish the race... but unfortunately during race 1, there was a localization error on the car, which forced the car off the track :( This was out of our control. Race 2 this was a lot better though!
This is the essence of software engineering - implement, test, and pray it works until it breaks and then panic. (Just kidding) (Not really)
Feel free to leave any questions.
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Links:
Roborace stream: / roborace
MIT Driverless: http://driverless.mit.edu/
Bon Parfumeur: http://bit.ly/KylieYing
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