Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning

Boris Ivanovic; James Harrison; Marco Pavone

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arXiv · 2209.11820

Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning

Boris Ivanovic·James Harrison·Marco Pavone

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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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Boris Ivanovic, James Harrison, Marco Pavone. 2022-09-23. Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning. https://arxiv.org/abs/2209.11820

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