  Automated vehicles have been under heavy developments in major auto and tech
companies and are expected to release into market in the foreseeable future.
However, the road safety of these vehicles remains a concern. One approach to
evaluate their safety is via on-track experimentation, but this requires
gigantic costs and time investments. This paper discusses a sequential learning
approach based on kriging models to reduce the experimental runs and economize
on-track experimentation. The approach relies on a heuristic simulation-based
gradient descent procedure to search for the best next test scenario. We
demonstrate our approach with some numerical test cases.
