1. [Publications](/publications) 2. Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning # Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning ![](/sites/default/files/styles/wide/public/publications/featured_7.png?itok=sZ9Qslmf) 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. ## Authors [Boris Ivanovic](/person/boris-ivanovic) James Harrison (Google Brain) [Marco Pavone](/person/marco-pavone) ## Publication Date Monday, May 29, 2023 ## Published in [IEEE International Conference on Robotics and Automation (ICRA) 2023](https://www.icra2023.org/) ## Research Area [Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence) [Autonomous Vehicles](/research-area/autonomous-vehicles) [Generative AI](/research-area/generative-ai) ## External Links [Paper](https://arxiv.org/abs/2209.11820) [Code](https://github.com/NVlabs/adaptive-prediction) ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .