  Recent works in end-to-end control for autonomous driving have investigated
the use of vision-based exteroceptive perception. Inspired by such results, we
propose a new end-to-end memory-based neural architecture for robot steering
and throttle control. We describe and compare this architecture with previous
approaches using fundamental error metrics (MAE, MSE) and several external
metrics based on their performance on simulated test circuits. The presented
work demonstrates the advantages of using internal memory for better
generalization capabilities of the model and allowing it to drive in a broader
amount of circuits/situations. We analyze the algorithm in a wide range of
environments and conclude that the proposed pipeline is robust to varying
camera configurations. All the present work, including datasets, network models
architectures, weights, simulator, and comparison software, is open source and
easy to replicate and extend.
