Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving
This work addresses control challenges in autonomous driving, presenting a novel method that is incremental as it builds on existing MPC frameworks.
The authors tackled stochastic optimal control by developing an information theoretic model predictive control (IT-MPC) algorithm, which they applied to aggressive autonomous driving on a dirt track and compared to a cross-entropy method-based MPC, showing performance improvements.
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.