Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control

Paul Drews; Grady Williams; Brian Goldfain; Evangelos A. Theodorou; James M. Rehg

CoRL 20170 citations

Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control

Paul Drews, Grady Williams, Brian Goldfain, Evangelos A. Theodorou, James M. Rehg

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.

BibTeX

@inproceedings{corl2017_aggressivedeepdr,
  title = {Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control},
  author = {Paul Drews and Grady Williams and Brian Goldfain and Evangelos A. Theodorou and James M. Rehg},
  booktitle = {CoRL 2017},
  year = {2017}
}