  We introduce Ignition: an end-to-end neural network architecture for training
unconstrained self-driving vehicles in simulated environments. The model is a
ResNet-18 variant, which is fed in images from the front of a simulated F1 car,
and outputs optimal labels for steering, throttle, braking. Importantly, we
never explicitly train the model to detect road features like the outline of a
track or distance to other cars; instead, we illustrate that these latent
features can be automatically encapsulated by the network.
