一句话：提出需求违规模式概念和 EMOOD 测试方法，用动态演化多目标搜索生成暴露更多需求违规组合的自动驾驶测试场景。
自动驾驶系统需同时满足安全、交通规则合规和舒适性等多重要求，但环境变化可能导致要求间权衡，从而产生一个或多个需求违规。现有测试方法无法生成暴露不同违规组合的场景。本文提出“需求违规模式”来刻画需求违规的具体组合，并设计测试方法 EMOOD：先按关键性对候选模式排序，再迭代使用演化多目标优化算法搜索违规组合；每轮通过动态优先级优先选择关键性高且更可能发生的模式。作者在工业自动驾驶系统的两种常见交通场景中评估 EMOOD，并与三个基线方法比较，结果表明 EMOOD 能发现更多需求违规模式，测试场景生成效果更优。
Autonomous Driving Systems (ADSs) are complex systems that must satisfy multiple requirements such as safety, compliance to traffic rules, and comfortableness. However, satisfying all these requirements may not always be possible due to emerging environmental conditions. Therefore, the ADSs may have to make trade-offs among multiple requirements during the ongoing operation, resulting in one or more requirements violations. For ADS engineers, it is highly important to know which combinations of requirements violations may occur, as different combinations can expose different types of failures. However, there is currently no testing approach that can generate scenarios to expose different combinations of requirements violations. To address this issue, in this paper, we introduce the notion of requirements violation pattern to characterize a specific combination of requirements violations. Based on this notion, we propose a testing approach named EMOOD that can effectively generate test scenarios to expose as many requirements violation patterns as possible. EMOOD uses a prioritization technique to sort all possible patterns to search for, from the most to the least critical ones. Then, EMOOD iteratively includes an evolutionary many-objective optimization algorithm to find different combinations of requirements violations. In each iteration, the targeted pattern is determined by a dynamic prioritization technique to give preferences to those patterns with higher criticality and higher likelihood to occur. We apply EMOOD to an industrial ADS under two common traffic situations. Evaluation results show that EMOOD outperforms three baseline approaches in generating test scenarios by discovering more requirements violation patterns.
DOI 原文 ·
@inproceedings{paperbot6588,
title = {Targeting Requirements Violations of Autonomous Driving Systems by Dynamic Evolutionary Search},
author = {Yixing Luo and Xiao-Yi Zhang and Paolo Arcaini and Zhi Jin and Haiyan Zhao and Fuyuki Ishikawa and Rongxin Wu and Tao Xie},
booktitle = {ASE},
year = {2021},
doi = {10.1109/ase51524.2021.9678883}
}