Deep Kernel Learning for Uncertainty Estimation in Multiple Trajectory Prediction Networks

Abstract

Predicting future paths of vehicles or pedestrians is an essential task for automated vehicles to allow for planning the own trajectory. Using predicted paths, a planning algorithm can, e.g., react to anticipated manoeuvres of other traffic participants. For calculating risks of planned manoeuvres, it is essential that the predicted paths are generated with information about their uncertainty. Since today’s state of the art trajectory prediction algorithms are based on deep neural networks (DNNs), the estimation of uncertainty is left to the neural networks as well, which usually provide no means of assessing how the uncertainty estimation works. In this paper, we present a combination of DNNs with Gaussian processes via Deep Kernel Learning (DKL), which combines the ability of DNNs to perform the prediction task with the advantage of Gaussian processes of having more interpretable probabilistic outputs. We propose and evaluate two different variants for the task of multimodal trajectory prediction using Stochastic Variational Gaussian Processes (SVGPs) and the recently proposed regression method Deep Sigma Point Processes (DSPPs), respectively. We evaluate the predictive distributions of both approaches on the publicly available Argoverse Motion Forecasting dataset and compare them to other, purely neural network based methods for uncertainty estimation.