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:在不同路面条件下自动驾驶是具有挑战性的,特别是对于自主驾驶车辆来说,它必须实时适应环境的变化,例如雨天、雪天等。在这些时变的情况下,应用离线学习方法是困难的,因为控制器应该在数据集上进行训练,代表它未来可能遇到的所有条件。虽然在线学习可以从实时数据中适应模型,但它的收敛速度往往太慢,无法适应快速变化的路面条件。我们在自主赛车中研究这个问题,其中在不同路面条件下驾驶需要赛车驾驶员在极限条件下驾驶。我们提出了一种计算效率高的方法,它利用一组高斯过程 (GPs) 的集成来推广和适应预训练的 GPs 来应对看似陌生的情况。每个 GPs 都是基于不同的路面摩擦力训练的。在模型预测控制 (MPC) 框架内使用这些 GPs 的时变凸组合,在基于实时数据的当前路面情况下在线适应模型权重。集成高斯过程 (EGP) 模型的预测方差允许控制器考虑预测不确定性,从而实现安全自主驾驶。完整的自动驾驶汽车的广泛模拟证明了我们提出的 EGP-MPC 方法的有效性,可在各种路面条件下提供良好的跟踪性能,并能够推广到未知地图。