  We present a set of CNN-based end-to-end models for controls of a Formula SAE
racecar, along with various benchmarking and visualization tools to understand
model performance. We tackled three main problems in the context of
cone-delineated racetrack driving: (1) discretized steering, which translates a
first-person frame along to the track to a predicted steering direction. (2)
real-value steering, which translates a frame view to a real-value steering
angle, and (3) a network design for predicting brake and throttle. We
demonstrate high accuracy on our discretization task, low theoretical testing
errors with our model for real-value steering, and a starting point for future
work regarding a controller for our vehicle's brake and throttle. Timing
benchmarks suggests that the networks we propose have the latency and
throughput required for real-time controllers, when run on GPU-enabled
hardware.
