Normalizing flows fit scenario parameter densities better than KDE on held-out data, but produce roughly 40 times lower collision risk estimates, with no ground truth to decide which is correct.
Risk quantification for automated driving systems in real-world driving scenarios,
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Comparing Normalizing Flows with Kernel Density Estimation in Estimating Risk of Automated Driving Systems
Normalizing flows fit scenario parameter densities better than KDE on held-out data, but produce roughly 40 times lower collision risk estimates, with no ground truth to decide which is correct.