Targeting Requirements Violations of Autonomous Driving Systems by Dynamic Evolutionary Search
Targeting Requirements Violations of Autonomous Driving Systems by Dynamic Evolutionary Search
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DOI：
10.1109/ase51524.2021.9678883
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Yixing Luo;Xiaoyi Zhang;Paolo Arcaini;Zhi Jin;Haiyan Zhao;F. Ishikawa;Rongxin Wu;Tao Xie
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文献类型：
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作者：
Yixing Luo;Xiaoyi Zhang;Paolo Arcaini;Zhi Jin;Haiyan Zhao;F. Ishikawa;Rongxin Wu;Tao Xie
自主驾驶系统（ADSS）是必须满足多个要求的复杂系统，例如安全性，遵守交通规则和舒适性。但是，由于新兴的环境条件，满足所有这些要求可能并不总是可能。因此，在正在进行的操作过程中，ADS可能必须在多个要求之间进行权衡，从而违反一项或多项要求。对于广告工程师而言，知道可能发生哪些要求违规的组合非常重要，因为不同的组合可以暴露出不同类型的故障。但是，目前尚无测试方法可以生成场景以暴露不同要求违规的组合。为了解决这个问题，在本文中，我们介绍了违反要求模式的概念，以表征违反要求的特定组合。基于这个概念，我们提出了一种名为EMOOD的测试方法，该方法可以有效地生成测试场景，以暴露尽可能多的要求违规模式。 Emood使用优先级技术来对所有可能的模式进行分类，从最重要的模式到最不关键的模式。然后，Emood迭代包含一种进化的多目标优化算法，以查找违反需求的不同组合。在每次迭代中，靶向模式都由动态优先级技术确定，以赋予那些具有较高关键性和更高可能发生可能性的模式的偏好。我们将EMOOD应用于两个常见的交通情况下的工业广告。评估结果表明，EMOOD通过发现更多要求违规模式来超过三种基线方法来生成测试场景。
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.