用自适应贝叶斯多保真采样高效发现自动驾驶隐患并精准估算事故率
Rate-Informed Discovery via Bayesian Adaptive Multifidelity Sampling
- 基于贝叶斯自适应策略，动态选择低性能区域进行采样
- 发现的问题数量是传统方法的10倍，事故率估计方差分别降低15倍和6倍
- 适合自动驾驶安全验证，尤其需高效率发现罕见故障场景的团队
确保自动驾驶车辆（AV）的安全性需要准确评估其性能并高效发现潜在故障案例。本文提出贝叶斯自适应多保真采样（BAMS），利用自适应贝叶斯采样能力，在高效发现异常情况的同时，同步估计不良事件的发生率。BAMS优先探索可能表现较差的区域，从而识别出传统方法可能遗漏的新颖且关键的场景。基于真实世界自动驾驶数据的实验表明，BAMS发现的问题数量是蒙特卡洛（MC）和重要性采样（IS）基线的10倍；同时，其事故率估计的方差分别比MC和IS基线窄15倍和6倍。
原文摘要 · Abstract (English)
Ensuring the safety of autonomous vehicles (AVs) requires both accurate estimation of their performance and efficient discovery of potential failure cases. This paper introduces Bayesian adaptive multifidelity sampling (BAMS), which leverages the power of adaptive Bayesian sampling to achieve efficient discovery while simultaneously estimating the rate of adverse events. BAMS prioritizes exploration of regions with potentially low performance, leading to the identification of novel and critical scenarios that traditional methods might miss. Using real-world AV data we demonstrate that BAMS discovers 10 times as many issues as Monte Carlo (MC) and importance sampling (IS) baselines, while at the same time generating rate estimates with variances 15 and 6 times narrower than MC and IS baselines respectively.
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