適応的メタ学習による行動予測の展開範囲の拡大
Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning
メタ学習（ベイズ回帰）を用いて行動予測モデルに適応層を追加し、新しい環境への効率的な適応を可能にした研究。
著者: Boris Ivanovic, James Harrison, Marco Pavone
分類: cs.LG, cs.CV, cs.RO
原文アブストラクト
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.
