This paper presents a novel Learning-based Model Predictive Contouring Control (L-MPCC) algorithm for evasive manoeuvres at the limit of handling. The algorithm uses the Student-t Process (STP) to minimise model mismatches and uncertainties online. The proposed STP captures the mismatches between the prediction model and the measured lateral tyre forces and yaw rate. The mismatches correspond to the posterior means provided to the prediction model to improve its accuracy. Simultaneously, the posterior covariances are propagated to the vehicle lateral velocity and yaw rate along the prediction horizon. The STP posterior covariance directly depends on the variance of observed data, so its variance is more significant when the online measurements differ from the recorded ones in the training set and smaller in the opposite case. Thus, these covariances can be utilised in the L-MPCC's cost function to minimise the vehicle state uncertainties. In a high-fidelity simulation environment, we demonstrate that the proposed L-MPCC can successfully avoid obstacles, keeping the vehicle stable while driving a double lane change manoeuvre at a higher velocity than an MPCC without STP. Furthermore, the proposed controller yields a significantly lower peak sideslip angle, improving the vehicle's manoeuvrability compared to an L-MPCC with a Gaussian Process.

翻译：本文提出了一种新颖的基于学习的模型预测轮廓控制算法，用于车辆在操控极限下的紧急避障。该算法利用学生t过程在线最小化模型失配与不确定性。所提出的STP捕获了预测模型与实测的轮胎侧向力及横摆角速度之间的失配。这些失配对应于提供给预测模型的后验均值，以提高其精度。同时，后验协方差沿预测时域传递至车辆侧向速度与横摆角速度。STP后验协方差直接取决于观测数据的方差，因此当在线测量数据与训练集中记录的数据存在差异时，其方差更为显著，反之则较小。因此，这些协方差可用于L-MPCC的成本函数中以最小化车辆状态的不确定性。在高保真仿真环境中，我们证明了所提出的L-MPCC能够成功避障，在比未使用STP的MPCC更高的速度下执行双移线操作时保持车辆稳定。此外，与采用高斯过程的L-MPCC相比，所提出的控制器产生了显著更低的峰值侧偏角，从而提升了车辆的操控性。