Informed Priors for Knowledge Integration in Trajectory Prediction

Abstract

Informed learning approaches explicitly integrate prior knowledge into learning systems, which can reduce data needs and increase robustness. However, existing work typically aims to integrate formal scientific knowledge by directly pruning the problem space, which is infeasible for more intuitive world and expert knowledge, or requires specific architecture changes and knowledge representations. We propose a probabilistic informed learning approach to integrate prior world and expert knowledge without these requirements. Our approach repurposes continual learning methods to operationalize Baye’s rule for informed learning and to enable probabilistic and multi-modal predictions. We exemplify our proposal in an application to two state-of-the-art trajectory predictors for autonomous driving. This safety-critical domain is subject to an overwhelming variety of rare scenarios requiring robust and accurate predictions. We evaluate our models on a public benchmark dataset and demonstrate that our approach outperforms non-informed and informed learning baselines. Notably, we can compete with a conventional baseline, even using only half as many observations of the training dataset.