Accurate, computationally efficient, and adaptive vehicle models are essential for autonomous vehicle control. Hybrid models that combine a nominal model with a Gaussian Process (GP)-based residual model have emerged as a promising approach. However, the GP-based residual model suffers from the curse of dimensionality, high evaluation complexity, and the inefficiency of online learning, which impede the deployment in real-time vehicle controllers. To address these challenges, we propose SPLIT, a sparse incremental learning framework for control-oriented vehicle dynamics modeling. SPLIT integrates three key innovations: (i) Model Decomposition. We decompose the vehicle model into invariant elements calibrated by experiments, and variant elements compensated by the residual model to reduce feature dimensionality. (ii) Local Incremental Learning. We define the valid region in the feature space and partition it into subregions, enabling efficient online learning from streaming data. (iii) GP Sparsification. We use bayesian committee machine to ensure scalable online evaluation. Integrated into model-based controllers, SPLIT is evaluated in aggressive simulations and real-vehicle experiments. Results demonstrate that SPLIT improves model accuracy and control performance online. Moreover, it enables rapid adaptation to vehicle dynamics deviations and exhibits robust generalization to previously unseen scenarios.

翻译：精确、计算高效且自适应的车辆模型对于自动驾驶车辆控制至关重要。结合标称模型与基于高斯过程（GP）的残差模型的混合模型已成为一种有前景的方法。然而，基于GP的残差模型存在维度灾难、评估复杂度高以及在线学习效率低等问题，阻碍了其在实时车辆控制器中的部署。为应对这些挑战，我们提出了SPLIT，一种面向控制的车辆动力学建模的稀疏增量学习框架。SPLIT集成了三项关键创新：（i）模型分解。我们将车辆模型分解为通过实验标定的不变元素和由残差模型补偿的时变元素，以降低特征维度。（ii）局部增量学习。我们在特征空间中定义有效区域并将其划分为子区域，从而能够从流数据中进行高效的在线学习。（iii）GP稀疏化。我们使用贝叶斯委员会机来确保可扩展的在线评估。SPLIT集成到基于模型的控制器中，在激进仿真和实车实验中进行了评估。结果表明，SPLIT在线提升了模型精度和控制性能。此外，它能够快速适应车辆动力学偏差，并对先前未见场景展现出鲁棒的泛化能力。