  How to generate testing scenario libraries for connected and automated
vehicles (CAVs) is a major challenge faced by the industry. In previous
studies, to evaluate maneuver challenge of a scenario, surrogate models (SMs)
are often used without explicit knowledge of the CAV under test. However,
performance dissimilarities between the SM and the CAV under test usually
exist, and it can lead to the generation of suboptimal scenario libraries. In
this paper, an adaptive testing scenario library generation (ATSLG) method is
proposed to solve this problem. A customized testing scenario library for a
specific CAV model is generated through an adaptive process. To compensate the
performance dissimilarities and leverage each test of the CAV, Bayesian
optimization techniques are applied with classification-based Gaussian Process
Regression and a new-designed acquisition function. Comparing with a
pre-determined library, a CAV can be tested and evaluated in a more efficient
manner with the customized library. To validate the proposed method, a cut-in
case study was performed and the results demonstrate that the proposed method
can further accelerate the evaluation process by a few orders of magnitude.
