提出新方法精准估算自动驾驶系统罕见故障概率。
Failure Probability Estimation for Black-Box Autonomous Systems using State-Dependent Importance Sampling Proposals
- 基于状态依赖的采样策略，自适应优化重要性采样分布。
- 在4个序列决策系统上，故障概率估计精度显著优于传统方法。
- 适合需要高可靠性验证的自动驾驶与复杂决策系统研究者。
估计故障概率是开发安全关键型自主系统的关键步骤。直接采用蒙特卡洛采样等方法常因故障罕见而不可行。现有重要性采样方法难以扩展至具有大状态空间和长时序的序列决策系统。本文提出一种自适应重要性采样算法，通过最小化前向Kullback-Leibler散度来逼近最优采样分布，并利用马尔可夫得分上升法进行目标估计。我们在四个序列系统上评估该方法，结果表明其故障概率估计精度显著优于基准蒙特卡洛和重要性采样技术。相关代码已开源。
原文摘要 · Abstract (English)
Estimating the probability of failure is a critical step in developing safety-critical autonomous systems. Direct estimation methods such as Monte Carlo sampling are often impractical due to the rarity of failures in these systems. Existing importance sampling approaches do not scale to sequential decision-making systems with large state spaces and long horizons. We propose an adaptive importance sampling algorithm to address these limitations. Our method minimizes the forward Kullback-Leibler divergence between a state-dependent proposal distribution and a relaxed form of the optimal importance sampling distribution. Our method uses Markov score ascent methods to estimate this objective. We evaluate our approach on four sequential systems and show that it provides more accurate failure probability estimates than baseline Monte Carlo and importance sampling techniques. This work is open sourced.
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