Test Scenarios Construction Based on Combinatorial Testing Strategy for Automated Vehicles

计算机科学 场景测试 成对比较 测试用例 考试(生物学) 聚类分析 任务(项目管理) 代码覆盖率 可靠性工程 数据挖掘 模拟 机器学习 人工智能 工程类 系统工程 古生物学 回归分析 多样性(控制论) 程序设计语言 软件 生物
作者
Hong Shu,Haoran Lv,Kang Liu,Yuan Kang,Xiaolin Tang
出处
期刊:IEEE Access [Institute of Electrical and Electronics Engineers]
卷期号:9: 115019-115029 被引量:9
标识
DOI:10.1109/access.2021.3103912
摘要

Scenario-based testing is an important verification and certification measure to evaluate the safety of automated vehicles. In view of the existing test scenario composition methods, which may miss some critical scenario problems that have low occurrence probability, we fully combined the ego-vehicle with the possible relative positions and movement directions of surrounding traffic participants based on a complex scenario group. We applied scenario-screening rules to obtain the functional test scenarios with different traffic environments and driving task complexities, which ensured the coverage of the test scenarios and reduced the number of test scenarios. The problem arose that the amount of test cases was too large after the discretized combination of test scenario parameters, so we adopted a three-way combinatorial testing strategy to greatly reduce the number of test cases. Taking the complicated lane changing scenario of the ego-vehicle as an example, the simulation method was adopted, and the critical test cases were obtained by screening through safety indicators. Finally, the K-medoids clustering method was used to further reduce the number of critical test cases, and a pairwise combinatorial test strategy was used to combine dynamic scenario and static scenario elements to obtain critical test cases for closed-road testing.
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