Driving under varying road conditions is challenging, especially for autonomous vehicles that must adapt in real-time to changes in the environment, e.g., rain, snow, etc. It is difficult to apply offline learning-based methods in these time-varying settings, as the controller should be trained on datasets representing all conditions it might encounter in the future. While online learning may adapt a model from real-time data, its convergence is often too slow for fast varying road conditions. We study this problem in autonomous racing, where driving at the limits of handling under varying road conditions is required for winning races. We propose a computationally-efficient approach that leverages an ensemble of Gaussian processes (GPs) to generalize and adapt pre-trained GPs to unseen conditions. Each GP is trained on driving data with a different road surface friction. A time-varying convex combination of these GPs is used within a model predictive control (MPC) framework, where the model weights are adapted online to the current road condition based on real-time data. The predictive variance of the ensemble Gaussian process (EGP) model allows the controller to account for prediction uncertainty and enables safe autonomous driving. Extensive simulations of a full scale autonomous car demonstrated the effectiveness of our proposed EGP-MPC method for providing good tracking performance in varying road conditions and the ability to generalize to unknown maps.

翻译：在变化的道路条件下驾驶具有挑战性，尤其是对于必须实时适应环境变化（如雨、雪等）的自动驾驶汽车而言。在时变环境下，基于离线学习的方法难以应用，因为控制器需要在涵盖所有未来可能遇到情况的数据集上进行训练。尽管在线学习能够从实时数据中自适应模型，但其收敛速度往往难以应对快速变化的道路条件。本研究在自动驾驶赛车场景中探讨此问题——在该场景中，需在变化路面条件下以车辆操控极限行驶方能获胜。我们提出一种计算高效的方法，利用高斯过程集成（GPs）对预训练的GP进行泛化与自适应调整，使其适应未知工况。每个GP基于不同道路摩擦系数的驾驶数据独立训练，并在模型预测控制（MPC）框架中采用时变凸组合策略，根据实时数据在线调整各GP权重以匹配当前道路条件。集成高斯过程（EGP）模型的预测方差使控制器能够量化预测不确定性，从而保障安全自主驾驶。基于全尺寸自动驾驶车辆的仿真实验表明，所提出的EGP-MPC方法在多变道路条件下具有优异的跟踪性能，并能有效泛化至未知地图场景。