Learning Residual Model of Model Predictive Control via Random Forests
for Autonomous Driving
- URL: http://arxiv.org/abs/2304.04366v1
- Date: Mon, 10 Apr 2023 03:32:09 GMT
- Title: Learning Residual Model of Model Predictive Control via Random Forests
for Autonomous Driving
- Authors: Kang Zhao, Jianru Xue, Xiangning Meng, Gengxin Li, and Mengsen Wu
- Abstract summary: One major issue in predictive control (MPC) for autonomous driving is the contradiction between the system model's prediction and computation.
This paper reformulates the MPC tracking accuracy as a program (QP) problem optimization as a program (QP) can effectively solve it.
- Score: 13.865293598486492
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: One major issue in learning-based model predictive control (MPC) for
autonomous driving is the contradiction between the system model's prediction
accuracy and computation efficiency. The more situations a system model covers,
the more complex it is, along with highly nonlinear and nonconvex properties.
These issues make the optimization too complicated to solve and render
real-time control impractical.To address these issues, we propose a
hierarchical learning residual model which leverages random forests and linear
regression.The learned model consists of two levels. The low level uses linear
regression to fit the residues, and the high level uses random forests to
switch different linear models. Meanwhile, we adopt the linear dynamic bicycle
model with error states as the nominal model.The switched linear regression
model is added to the nominal model to form the system model. It reformulates
the learning-based MPC as a quadratic program (QP) problem and optimization
solvers can effectively solve it. Experimental path tracking results show that
the driving vehicle's prediction accuracy and tracking accuracy are
significantly improved compared with the nominal MPC.Compared with the
state-of-the-art Gaussian process-based nonlinear model predictive control
(GP-NMPC), our method gets better performance on tracking accuracy while
maintaining a lower computation consumption.
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