This paper proposes a nonplanar model predictive control (MPC) framework for autonomous vehicles operating on nonplanar terrain. To approximate complex vehicle dynamics in such environments, we develop a geometry-aware modeling approach that learns a residual Gaussian Process (GP). By utilizing a recursive sparse GP, the framework enables real-time adaptation to varying terrain geometry. The effectiveness of the learned model is demonstrated in a reference-tracking task using a Model Predictive Path Integral (MPPI) controller. Validation within a custom Isaac Sim environment confirms the framework's capability to maintain high tracking accuracy on challenging 3D surfaces.

翻译：本文提出了一种适用于非平面地形行驶的自动驾驶车辆非平面模型预测控制（MPC）框架。为近似此类环境中的复杂车辆动力学，我们开发了一种几何感知建模方法，用于学习残差高斯过程（GP）。通过采用递归稀疏GP，该框架能够实时适应变化的道路几何形态。学习模型的有效性在一个使用模型预测路径积分（MPPI）控制器的参考轨迹跟踪任务中得到验证。在定制Isaac Sim环境中的测试结果证实了该框架在具有挑战性的三维表面上保持高跟踪精度的能力。