人間の不確実性を考慮した確率的安全性保証の限界
Limits of Probabilistic Safety Guarantees when Considering Human Uncertainty
自動運転などで必要なδ<10⁻⁸の安全保証に対し、既存の学習ベース手法は分布仮定の不正確さやデータ量の非現実性から信頼できる信頼区間を計算できないことを示した論文。
著者: Richard Cheng, Richard M. Murray, Joel W. Burdick
分類: cs.RO, cs.MA, cs.SY, eess.SY
原文アブストラクト
When autonomous robots interact with humans, such as during autonomous driving, explicit safety guarantees are crucial in order to avoid potentially life-threatening accidents. Many data-driven methods have explored learning probabilistic bounds over human agents' trajectories (i.e. confidence tubes that contain trajectories with probability $\delta$), which can then be used to guarantee safety with probability $1-\delta$. However, almost all existing works consider $\delta \geq 0.001$. The purpose of this paper is to argue that (1) in safety-critical applications, it is necessary to provide safety guarantees with $\delta < 10^{-8}$, and (2) current learning-based methods are ill-equipped to compute accurate confidence bounds at such low $\delta$. Using human driving data (from the highD dataset), as well as synthetically generated data, we show that current uncertainty models use inaccurate distributional assumptions to describe human behavior and/or require infeasible amounts of data to accurately learn confidence bounds for $\delta \leq 10^{-8}$. These two issues result in unreliable confidence bounds, which can have dangerous implications if deployed on safety-critical systems.
