  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.
