自動運転検証のための基盤モデル
Foundation Models for Rapid Autonomy Validation
マスク付きオートエンコーダで運転シナリオを再構成し、難易度スコアで重要度を重み付けすることで、衝突率と深刻度を迅速に推定する手法を提案。
著者: Alec Farid, Peter Schleede, Aaron Huang, Christoffer Heckman
分類: cs.RO, cs.LG
原文アブストラクト
We are motivated by the problem of autonomous vehicle performance validation. A key challenge is that an autonomous vehicle requires testing in every kind of driving scenario it could encounter, including rare events, to provide a strong case for safety and show there is no edge-case pathological behavior. Autonomous vehicle companies rely on potentially millions of miles driven in realistic simulation to expose the driving stack to enough miles to estimate rates and severity of collisions. To address scalability and coverage, we propose the use of a behavior foundation model, specifically a masked autoencoder (MAE), trained to reconstruct driving scenarios. We leverage the foundation model in two complementary ways: we (i) use the learned embedding space to group qualitatively similar scenarios together and (ii) fine-tune the model to label scenario difficulty based on the likelihood of a collision upon simulation. We use the difficulty scoring as importance weighting for the groups of scenarios. The result is an approach which can more rapidly estimate the rates and severity of collisions by prioritizing hard scenarios while ensuring exposure to every kind of driving scenario.
