Learning-based behavior prediction methods are increasingly being deployed in real-world autonomous systems, e.g., in fleets of self-driving vehicles, which are beginning to commercially operate in major cities across the world. Despite their advancements, however, the vast majority of prediction systems are specialized to a set of well-explored geographic regions or operational design domains, complicating deployment to additional cities, countries, or continents. Towards this end, we present a novel method for efficiently adapting behavior prediction models to new environments. Our approach leverages recent advances in meta-learning, specifically Bayesian regression, to augment existing behavior prediction models with an adaptive layer that enables efficient domain transfer via offline fine-tuning, online adaptation, or both. Experiments across multiple real-world datasets demonstrate that our method can efficiently adapt to a variety of unseen environments.

翻译：基于学习的行为预测方法正越来越多地部署在现实世界的自主系统中，例如在开始在全球主要城市进行商业运营的自动驾驶车队中。然而，尽管取得了进展，绝大多数的预测系统仍局限于一组经过充分探索的地理区域或运行设计领域，这使得向其他城市、国家或大洲的部署变得复杂。为此，我们提出了一种新颖的方法，用于高效地将行为预测模型适应新环境。我们的方法利用了元学习（尤其是贝叶斯回归）的最新进展，通过一个自适应层来增强现有的行为预测模型，该层能够通过离线微调、在线自适应或两者结合来实现高效的领域迁移。在多个真实世界数据集上的实验表明，我们的方法能够高效地适应各种未见过的环境。