データ駆動型自動運転テストにおける評価の不確実性
Evaluation Uncertainty in Data-Driven Self-Driving Testing
実世界データから構築した入力モデルの推定誤差が、モンテカルロ評価による安全指標の確率推定に与える影響を、ブートストラップ法と尤度比法を組み合わせて効率的に評価する手法を提案した。
著者: Zhiyuan Huang, Mansur Arief, Henry Lam, Ding Zhao
分類: stat.ME, cs.RO, stat.ML
原文アブストラクト
Safety evaluation of self-driving technologies has been extensively studied. One recent approach uses Monte Carlo based evaluation to estimate the occurrence probabilities of safety-critical events as safety measures. These Monte Carlo samples are generated from stochastic input models constructed based on real-world data. In this paper, we propose an approach to assess the impact on the probability estimates from the evaluation procedures due to the estimation error caused by data variability. Our proposed method merges the classical bootstrap method for estimating input uncertainty with a likelihood ratio based scheme to reuse experiment outputs. This approach is economical and efficient in terms of implementation costs in assessing input uncertainty for the evaluation of self-driving technology. We use an example in autonomous vehicle (AV) safety evaluation to demonstrate the proposed approach as a diagnostic tool for the quality of the fitted input model.
