Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-LearningDownload PDF

04 Oct 2022, 23:09 (modified: 26 Nov 2022, 09:49)NeurIPS 2022 Workshop MetaLearn PosterReaders: Everyone
Keywords: Adaptive Meta-Learning, Behavior Prediction, Bayesian Regression
TL;DR: We present a method for adaptive behavior prediction based on Bayesian last-layer adaptation that enables recurrent prediction models to efficiently adapt to new environments.
Abstract: 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.
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