Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving

@article{Williams2017InformationTM,
  title={Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving},
  author={Grady Williams and Paul Drews and Brian Goldfain and James M. Rehg and Evangelos Theodorou},
  journal={CoRR},
  year={2017},
  volume={abs/1707.02342}
}
We present an information theoretic approach to stochastic optimal control problems that can be used to derive general sampling based optimization schemes. This new mathematical method is used to develop a sampling based model predictive control algorithm. We apply this information theoretic model predictive control (IT-MPC) scheme to the task of aggressive autonomous driving around a dirt test track, and compare its performance to a model predictive control version of the cross-entropy method. 
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  • G. Williams, A. Aldrich, E. A. Theodorou
  • Journal of Guidance, Control, and Dynamics, vol…
  • 2017
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  • D. J. Fagnant, K. Kockelman
  • Transportation Research Part A: Policy and…
  • 2015
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