On February 15, Udacity selected the group of 18 talented engineers (out of hundreds of applicants) to form the Self-Racing Cars team. Our team was composed of individuals with largely varying backgrounds from all over the world, with the commonalities that we were all enrolled in the Udacity Self-Driving Car Nanodegree program, and extremely passionate about autonomous vehicles. The team was given six weeks to develop the software to drive an autonomous vehicle around the track at Thunderhill Raceway for the Self-Racing Cars event. We were partnered for the event with the awesome team at PolySync who provided us with a Kia Soul vehicle outfitted with their Open Source Car Controls kit (OSCC). Our team would not have access to the car until 2 days before the event, so the six-week lead-up was all about getting familiar with the PolySync software and building our own autonomous models/system that would communicate to the OSCC to control the vehicle.
The PolySync vehicle had a single forward-facing Point Grey Black Fly camera and a Swift Navigation GPS. The car was also outfitted with Radar and Lidar, but we did not use these systems for the event. Our team decided early on that we wanted to develop an end-to-end deep learning-type autonomous system, and rely on GPS as little as possible. GPS waypoint followers are fairly commonplace, but to develop a vision-based deep learning approach using the single forward-facing camera as the only input is at the cutting edge of autonomous vehicle development.