贝叶斯网络
计算机科学
约束(计算机辅助设计)
发电机(电路理论)
集合(抽象数据类型)
测试用例
贝叶斯概率
场景测试
算法
概率逻辑
数学优化
数据挖掘
人工智能
机器学习
数学
功率(物理)
物理
量子力学
多样性(控制论)
回归分析
程序设计语言
几何学
作者
Xudong Hu,Bo Zhu,Dongkui Tan,Nong Zhang,Zexing Wang
标识
DOI:10.1177/09544070221125523
摘要
A test scenario generation method based on combinatorial testing (CT) and Bayesian Network for autonomous vehicles is proposed in this paper. Firstly, some parameters are selected to describe the test scenarios which are classified according to road types and driving tasks. Then, the constraint sets for the scenarios with forbidden tuples are established to avoid the generated cases do not conform to the reality, in which the construct constraint set (CCS) algorithm is utilized to compute implied constraints. Furthermore, the Bayesian networks is used as the probabilistic models of the scenarios, where the traffic participants are represented as object nodes and the relative relationships between the participants are converted into the network structures. Finally, an improved automatic efficient test case generator (AETG) is developed to generate test cases. By considering both probability and frequency, the select function is designed for determining the values of scenario parameters. And the generation mode can be changed by modifying the weight and target parameters. The effectiveness of the proposed method is evaluated by generating six typical test scenarios. Compared with other algorithms, the numbers of test cases in the sets generated by this method are less and the probability deviations are smaller.
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