This paper proposes a novel framework for addressing the challenge of autonomous overtaking and obstacle avoidance, which incorporates the overtaking path planning into Gaussian Process-based model predictive control (GPMPC). Compared with conventional control strategies, this approach has two main advantages. Firstly, combining Gaussian Process (GP) regression with a nominal model allows for learning from model mismatch and unmodeled dynamics, which enhances a simple model and delivers significantly better results. Due to the approximation for propagating uncertainties, we can furthermore satisfy the constraints and thereby the safety of the vehicle is ensured. Secondly, we convert the geometric relationship between the ego vehicle and other obstacle vehicles into the constraints. Without relying on a higher-level path planner, this approach substantially reduces the computational burden. In addition, we transform the state constraints under the model predictive control (MPC) framework into a soft constraint and incorporate it as relaxed barrier function into the cost function, which makes the optimizer more efficient. Simulation results indicate that the proposed method can not only fulfill the overtaking tasks but also maintain safety at all times.

中文翻译：

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基于高斯过程的自动驾驶超车模型预测控制。

本文提出了一种解决自主超车和避障挑战的新框架，将超车路径规划纳入基于高斯过程的模型预测控制（GPMPC）。与传统的控制策略相比，这种方法有两个主要优点。首先，将高斯过程 (GP) 回归与标称模型相结合，可以从模型不匹配和未建模的动态中学习，这增强了简单模型并提供了明显更好的结果。由于传播不确定性的近似性，我们可以进一步满足约束条件，从而确保车辆的安全。其次，我们将自我车辆与其他障碍车辆之间的几何关系转换为约束。无需依赖更高级别的路径规划器，这种方法大大减少了计算负担。此外，我们将模型预测控制 (MPC) 框架下的状态约束转换为软约束，并将其作为松弛障碍函数纳入成本函数，从而使优化器更加高效。仿真结果表明，所提出的方法不仅可以完成超车任务，而且可以始终保持安全。