Testing of Autonomous Vehicles Using Surrogate Models and Stochastic Optimization

Advancement in testing and verification methodologies
is one of the key requirements for the commercialization
and standardization of autonomous driving. Even though great
progress has been made, the main challenges encountered during
testing of autonomous vehicles, e.g., high number of test
scenarios, huge parameter space and long simulation runs, still
remain. In order to reduce current testing efforts, we propose
an innovative method based on surrogate models in combination
with stochastic optimization. The approach presents an iterative
zooming-in algorithm aiming to minimize a given cost function
and to identify faulty behavior regions within the parameter
space. The surrogate model is updated in each iteration and is
further used for intensive evaluation tasks, such as exploration
and optimization.