希少事象シミュレーションによるスケーラブルな自動運転車のエンドツーエンドテスト
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation
適応的重要度サンプリングを用いたシミュレーションで、深層学習ベースの知覚・制御を含む自動運転システム全体の事故確率を効率的に評価するフレームワークを実現した。
著者: Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, John Duchi, Russ Tedrake
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
While recent developments in autonomous vehicle (AV) technology highlight substantial progress, we lack tools for rigorous and scalable testing. Real-world testing, the $\textit{de facto}$ evaluation environment, places the public in danger, and, due to the rare nature of accidents, will require billions of miles in order to statistically validate performance claims. We implement a simulation framework that can test an entire modern autonomous driving system, including, in particular, systems that employ deep-learning perception and control algorithms. Using adaptive importance-sampling methods to accelerate rare-event probability evaluation, we estimate the probability of an accident under a base distribution governing standard traffic behavior. We demonstrate our framework on a highway scenario, accelerating system evaluation by $2$-$20$ times over naive Monte Carlo sampling methods and $10$-$300 \mathsf{P}$ times (where $\mathsf{P}$ is the number of processors) over real-world testing.
