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.
@article{arxiv.1806.11349,
title = {Ignition: An End-to-End Supervised Model for Training Simulated Self-Driving Vehicles},
author = {Rooz Mahdavian and Richard Diehl Martinez},
journal= {arXiv preprint arXiv:1806.11349},
year = {2018}
}