作者
Shuo Feng, Yiheng Feng, Haowei Sun, Yi Zhang, Henry X Liu
发表日期
2020/3/8
期刊
IEEE Transactions on Intelligent Transportation Systems
卷号
23
期号
2
页码范围
1213 - 1222
简介
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 article, 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 for the performance dissimilarities and leverage each test of the CAV, Bayesian optimization techniques are applied with classification-based Gaussian Process Regression and a newly designed acquisition function. Comparing with a pre …
学术搜索中的文章
S Feng, Y Feng, H Sun, Y Zhang, HX Liu - IEEE Transactions on Intelligent Transportation …, 2020
S Feng, Y Feng, H Sun, Y Zhang, HX Liu - arXiv preprint arXiv:2003.03712, 2020