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.

翻译：学习型模型预测控制在自动驾驶中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，同时伴随高度非线性和非凸特性。这些问题使得优化求解过于复杂，难以实现实时控制。为了解决这些问题，我们提出了一种分层残差学习模型，该模型利用随机森林和线性回归。所学习的模型由两个层级组成。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自行车动力学模型作为标称模型。将切换线性回归模型叠加到标称模型上，形成系统模型。这将学习型模型预测控制重新表述为二次规划问题，优化求解器能够有效求解。实验路径跟踪结果表明，与标称模型预测控制相比，驾驶车辆的预测精度和跟踪精度显著提升。与当前最先进的基于高斯过程的非线性模型预测控制相比，我们的方法在保持较低计算消耗的同时，在跟踪精度上取得了更优的性能。