Estimating the Process Noise Variance for Vehicle Motion Models

Abstract

Vehicle motion models are employed in driver assistance systems for tracking and prediction tasks. For probabilistic decision making and uncertainty propagation, the prediction's inaccuracy is taken into account in the form of process noise. This work estimates Gaussian process noise models from measured vehicle trajectories using the expectation maximisation (EM) algorithm. The method is exemplified and the results evaluated for three commonly used motion models based on a large-scale dataset. A novel closed-form adaptation of the algorithm to a covariance matrix with Kronecker product structure, as in models for translational motion, is presented. The findings suggest that the longitudinal prediction errors feature a non-Gaussian distribution but a reasonable approximation is given by the estimated model.