# Learning Driver Behaviors Using A Gaussian Process Augmented State-Space Model

arXiv: https://arxiv.org/abs/2003.07312 · HTML: https://mlsanity.com/paper/2003.07312/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [Recursive Gaussian Process State Space Model](https://mlsanity.com/paper/2411.14679/) (2024)

## Abstract (arXiv)

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.