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

Paul Drews, Grady Williams, Brian Goldfain, Evangelos A. Theodorou, James M. Rehg
Proceedings of the 1st Annual Conference on Robot Learning, PMLR 78:133-142, 2017.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v78-drews17a, title = {Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control}, author = {Drews, Paul and Williams, Grady and Goldfain, Brian and Theodorou, Evangelos A. and Rehg, James M.}, booktitle = {Proceedings of the 1st Annual Conference on Robot Learning}, pages = {133--142}, year = {2017}, editor = {Levine, Sergey and Vanhoucke, Vincent and Goldberg, Ken}, volume = {78}, series = {Proceedings of Machine Learning Research}, month = {13--15 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v78/drews17a/drews17a.pdf}, url = {https://proceedings.mlr.press/v78/drews17a.html}, 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.} }
Endnote
%0 Conference Paper %T Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control %A Paul Drews %A Grady Williams %A Brian Goldfain %A Evangelos A. Theodorou %A James M. Rehg %B Proceedings of the 1st Annual Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2017 %E Sergey Levine %E Vincent Vanhoucke %E Ken Goldberg %F pmlr-v78-drews17a %I PMLR %P 133--142 %U https://proceedings.mlr.press/v78/drews17a.html %V 78 %X 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.
APA
Drews, P., Williams, G., Goldfain, B., Theodorou, E.A. & Rehg, J.M.. (2017). Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control. Proceedings of the 1st Annual Conference on Robot Learning, in Proceedings of Machine Learning Research 78:133-142 Available from https://proceedings.mlr.press/v78/drews17a.html.

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