基于随机森林的自动驾驶模型预测控制残差模型学习
Learning Residual Model of Model Predictive Control via Random Forests for Autonomous Driving
AI总结：
针对自动驾驶中基于学习的MPC预测精度与计算效率矛盾，提出利用随机森林和线性回归的分层残差模型，将问题转化为QP求解，显著提升跟踪精度且计算消耗低于GP-NMPC。
摘要：
基于学习的自动驾驶模型预测控制（MPC）的一个主要问题是系统模型的预测精度与计算效率之间的矛盾。系统模型覆盖的场景越多，其复杂度就越高，并伴随高度非线性和非凸特性。这些问题使得优化过于复杂而难以求解，导致实时控制不切实际。为了解决这些问题，我们提出了一种利用随机森林和线性回归的分层学习残差模型。学习到的模型分为两个层级：低层使用线性回归拟合残差，高层使用随机森林切换不同的线性模型。同时，我们采用带有误差状态的线性动态自行车模型作为标称模型。切换线性回归模型被加入到标称模型中，共同构成系统模型。该方法将基于学习的MPC重新表述为二次规划（QP）问题，优化求解器可以有效地对其进行求解。实验路径跟踪结果表明，与标称MPC相比，驾驶车辆的预测精度和跟踪精度均显著提高。与最先进的基于高斯过程的非线性模型预测控制（GP-NMPC）相比，我们的方法在保持较低计算消耗的同时，获得了更好的跟踪精度性能。
英文摘要：
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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