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Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning
Boris Ivanovic · James Harrison · Marco Pavone

@
in NeurIPS 2022 Workshop on Meta-Learning »
Event URL: https://openreview.net/forum?id=_9Br06CLJB »

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.

Author Information

Boris Ivanovic (Stanford University)
James Harrison (Google)
Marco Pavone (Stanford University)

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