Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning
This addresses the deployment challenge for autonomous systems like self-driving vehicles in new cities or countries, but it is incremental as it builds on existing meta-learning techniques.
The paper tackles the problem of behavior prediction models being specialized to specific geographic regions, complicating deployment to new environments, and presents a method using meta-learning to adapt these models efficiently, with experiments showing successful adaptation across multiple real-world datasets.
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.