Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs), and how to generate testing scenario library is a major challenge. 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 library. In this work, an adaptive testing scenario library generation method is proposed to solve this problem based on Bayesian optimization. A customized testing scenario library for a specific CAV model will be generated as the result of the adaptive process. Compared 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 and a highway exit case are studied for safety and functionality evaluation respectively. For both two cases, the proposed method can further accelerate the evaluation process by a few orders of magnitudes.

Shuo Feng is currently a postdoctoral researcher in the Department of Civil and Environmental Engineering at the University of Michigan.