Automated Vehicle (AV) validation based on simulated testing requires unbiased evaluation and high efficiency. One effective solution is to increase the exposure to risky rare events while reweighting the probability measure. However, characterizing the distribution of risky events is particularly challenging due to the paucity of samples and the temporality of continuous scenario variables. To solve it, we devise a method to represent, generate, and reweight the distribution of risky rare events. We decompose the temporal evolution of continuous variables into distribution components based on conditional probability. By introducing the Risk Indicator Function, the distribution of risky rare events is theoretically precipitated out of naturalistic driving distribution. This targeted distribution is practically generated via Normalizing Flow, which achieves exact and tractable probability evaluation of intricate distribution. The rare event distribution is then demonstrated as the advantageous Importance Sampling distribution. We also promote the technique of temporal Importance Sampling. The combined method, named as TrimFlow, is executed to estimate the collision rate of Car-following scenarios as a tentative practice. The results showed that sampling background vehicle maneuvers from rare event distribution could evolve testing scenarios to hazardous states. TrimFlow reduced 86.1% of tests compared to generating testing scenarios according to their exposure in the naturalistic driving environment. In addition, the TrimFlow method is not limited to one specific type of functional scenario.

翻译：基于仿真测试的自动驾驶车辆验证需要无偏评估与高效率。一种有效的解决方案是在重新加权概率测度的同时增加对高风险罕见事件的暴露。然而，由于样本稀缺以及连续场景变量的时序特性，刻画高风险事件的分布尤为困难。为解决此问题，我们设计了一种表征、生成并重新加权高风险罕见事件分布的方法。我们基于条件概率将连续变量的时序演化分解为分布分量。通过引入风险指示函数，高风险罕见事件分布从理论上从自然驾驶分布中析出。该目标分布通过归一化流实际生成，实现了对复杂分布的精确且可处理的概率评估。随后，罕见事件分布被证明是优势的重要性采样分布。我们还推广了时序重要性采样技术。将上述方法结合并命名为TrimFlow，以跟车场景的碰撞率估计作为初步实践进行验证。结果表明，从罕见事件分布中采样背景车辆行为可使测试场景演化至危险状态。与根据自然驾驶环境中的暴露程度生成测试场景相比，TrimFlow减少了86.1%的测试量。此外，TrimFlow方法不限于特定类型的功能场景。