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.
Scenario-based safety assessment of automated driving systems,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
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.