Sequential Experimentation to Efficiently Test Automated Vehicles
For developers of automated vehicles, it offers a method to economize on-track safety testing, though the results are preliminary.
The paper proposes a sequential learning approach using kriging models to reduce the number of on-track experiments needed to test automated vehicles, demonstrated with numerical test cases.
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.