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：在多变道路条件下驾驶充满挑战，尤其对需实时适应环境变化（如雨、雪等）的自动驾驶车辆而言。离线学习方法难以适用于这些时变场景，因为控制器需基于涵盖未来可能遇到的所有条件的训练数据集进行训练。尽管在线学习可通过实时数据调整模型，但其收敛速度往往难以适应快速变化的道路条件。我们以自动驾驶赛车为研究场景，此类场景要求在多变路面条件下以操控极限行驶以赢得比赛。本文提出一种高效计算方法，利用高斯过程集成（GPs）将预训练的高斯过程泛化并适应未知条件。每个高斯过程基于不同路面摩擦系数的驾驶数据训练。在模型预测控制（MPC）框架中，采用这些高斯过程的时变凸组合，其模型权重根据实时数据在线自适应调整当前道路条件。集成高斯过程（EGP）模型的预测方差使控制器能够考虑预测不确定性，从而实现安全自动驾驶。全尺寸自动驾驶车辆的大规模仿真实验表明，我们提出的EGP-MPC方法能在多变道路条件下实现良好跟踪性能，并具备对未知地图的泛化能力。