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.

翻译：先进的机器学习方法可以将先前的知识纳入学习系统。这可以提高准确性和稳健性,或减少数据需求。然而,现有方法往往会承担严格的限制知识,这不需要用观察来交换先前的知识,而是可以直接减少问题空间。其他方法则使用具体的建筑变化来代表先前的知识,限制适用性。我们提出基于持续学习的知情的机器学习方法。这可以将可能来自多种来源的、可能来自多种来源的、可能不需要特定结构的任意的、先前的知识纳入学习系统。此外,我们的方法有助于预测性和多模式的预测,这可以提高预测的准确性和稳健性。我们通过将这种方法应用到最先进的轨迹预测器上来举例说明我们的方法,这特别取决于知情的学习方法,因为它取决于极其广泛的可能的环境和非常罕见的事件,同时需要强有力和准确的预测。我们用一个常用的基准数据集来评估我们的模型,但只能使用常规数据集中已有的数据。我们证明我们的方法超越了不知情和知情的多式预测方法,可以提高预测性和稳健性。我们用的方法,我们甚至可以将它作为半个常规的基线,我们经常使用。