Scalable Generation of Statistical Evidence for the Safety of Automated Vehicles by the Use of Importance Sampling

Simulation-based validation methods exhibit properties which should allow them to contribute to the release of highly automated driving functions (HADF). Monte-Carlo (MC) simulations make it possible to generate statistical statements about a HADF. However, the fact that accidents happen very rarely in the real world can strongly enlarge the number of MC simulation runs which are needed to obtain statistically stable results. In the past, importance sampling (IS) was used to reduce these simulation costs. This paper considers some of the still existent limitations of IS: The definition of proper safety metrics needed to find good IS distributions and the application of IS to high dimensional parameter spaces. The approach in this paper was able to reduce an IS optimization of 2700 dimensions to 17 optimizations of 9 dimensions. The number of MC runs could be reduced by a factor of 350.