Title: Aggressive Deep Driving: Model Predictive Control with a CNN Cost Model,
Abstract: We present a framework for vision-based model predictive control (MPC) for
the task of aggressive, high-speed autonomous driving. Our approach uses deep
convolutional neural networks to predict cost functions from input video which
are directly suitable for online trajectory optimization with MPC. We
demonstrate the method in a high speed autonomous driving scenario, where we
use a single monocular camera and a deep convolutional neural network to
predict a cost map of the track in front of the vehicle. Results are
demonstrated on a 1:5 scale autonomous vehicle given the task of high speed,
aggressive driving.