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

  ![Publication image](/sites/default/files/styles/wide/public/default_images/default.jpeg?itok=qUFsuJCP "Publication image")

 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 Research)

Marco Pavone (NVIDIA)

 

 

 ## Publication Date



Friday, September 23, 2022

 

 ## Published in



[Arxiv](https://arxiv.org/abs/2209.11820)

 

 ## Research Area



[Autonomous Vehicles](/research-area/autonomous-vehicles)