论文标题
使用高斯流程增强状态空间模型学习驾驶员行为
Learning Driver Behaviors Using A Gaussian Process Augmented State-Space Model
论文作者
论文摘要
提出了高斯过程增强状态空间模型的推理方法。这类灰色框模型可以使域知识合并到推理过程中，以确保最低的性能，但它们仍然足够灵活，可以学习部分未知的模型动态和输入。为了促进模型的在线推断（递归），提出了基于诱导点的高斯过程的稀疏近似。为了说明模型的应用和推理方法，提出了一个示例，用于跟踪位置并了解一组穿过交叉路口的汽车的行为。与仅使用状态空间模型的情况相比，增强状态空间模型的使用既可以减少估计误差和偏差。
An inference method for Gaussian process augmented state-space models are presented. This class of grey-box models enables domain knowledge to be incorporated in the inference process to guarantee a minimum of performance, still they are flexible enough to permit learning of partially unknown model dynamics and inputs. To facilitate online (recursive) inference of the model a sparse approximation of the Gaussian process based upon inducing points is presented. To illustrate the application of the model and the inference method, an example where it is used to track the position and learn the behavior of a set of cars passing through an intersection, is presented. Compared to the case when only the state-space model is used, the use of the augmented state-space model gives both a reduced estimation error and bias.
