SPLIT: 基于误差动态的稀疏增量学习用于自动驾驶车辆的控制导向建模
SPLIT: Sparse Incremental Learning of Error Dynamics for Control-Oriented Modeling in Autonomous Vehicles
AI总结：
SPLIT通过稀疏增量学习框架提升自动驾驶车辆的控制导向建模精度与适应性。
摘要：
准确、计算高效且适应性强的车辆模型对于自动驾驶车辆控制至关重要。混合模型将名义模型与基于高斯过程（GP）的残差模型相结合，已成为一种有前景的方法。然而，基于GP的残差模型面临维度灾难、评估复杂性和在线学习效率低下的问题，阻碍了其在实时车辆控制器中的应用。为了解决这些挑战，我们提出SPLIT，一种用于控制导向车辆动力学建模的稀疏增量学习框架。SPLIT集成了三个关键创新：（i）模型分解。我们将车辆模型分解为通过实验校准的不变元素和通过残差模型补偿的可变元素，以减少特征维度。（ii）局部增量学习。我们定义特征空间中的有效区域，并将其划分为子区域，从而能够从流数据中高效地进行在线学习。（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。将SPLIT集成到基于模型的控制器中，在激进的仿真和真实车辆实验中进行了评估。结果表明，SPLIT提高了模型精度和控制性能在线。此外，它能够快速适应车辆动力学偏差，并在之前未见过的场景中表现出鲁棒的泛化能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
