Informed Priors for Knowledge Integration in Trajectory Prediction

TL;DR

This paper introduces a flexible, continual learning-based method for integrating prior knowledge into trajectory prediction models, enhancing accuracy and robustness without architectural constraints, demonstrated on autonomous driving data.

Contribution

The proposed approach allows arbitrary prior knowledge integration via continual learning, enabling probabilistic, multi-modal predictions without requiring specific model architectures.

Findings

Outperforms existing non-informed and informed methods on benchmark datasets.

Achieves comparable accuracy with half the observational data.

Enhances robustness and predictive quality in autonomous driving scenarios.

Abstract

Informed machine learning methods allow the integration of prior knowledge into learning systems. This can increase accuracy and robustness or reduce data needs. However, existing methods often assume hard constraining knowledge, that does not require to trade-off prior knowledge with observations, but can be used to directly reduce the problem space. Other approaches use specific, architectural changes as representation of prior knowledge, limiting applicability. We propose an informed machine learning method, based on continual learning. This allows the integration of arbitrary, prior knowledge, potentially from multiple sources, and does not require specific architectures. Furthermore, our approach enables probabilistic and multi-modal predictions, that can improve predictive accuracy and robustness. We exemplify our approach by applying it to a state-of-the-art trajectory predictor for autonomous driving. This domain is especially dependent on informed learning approaches, as it is subject to an overwhelming large variety of possible environments and very rare events, while requiring robust and accurate predictions. We evaluate our model on a commonly used benchmark dataset, only using data already available in a conventional setup. We show that our method outperforms both non-informed and informed learning methods, that are often used in the literature. Furthermore, we are able to compete with a conventional baseline, even using half as many observation examples.