自动驾驶系统的集中测试生成

IF 6.6 2区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING ACM Transactions on Software Engineering and Methodology Pub Date : 2024-05-13 DOI:10.1145/3664605
Tahereh Zohdinasab, Vincenzo Riccio, Paolo Tonella
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引用次数: 0

摘要

测试自动驾驶系统(ADS)对于确保其在复杂环境中行驶时的可靠性至关重要。当自动驾驶系统在运行过程中遇到包含训练数据集中未充分体现的特征的驾驶场景时,可能会表现出意想不到的行为。为了解决这种从开发到运行的转变,开发人员必须利用新观察到的特征获取新数据。然后,可以利用这些数据对自动驾驶辅助系统进行微调,以便在执行驾驶任务时达到所需的可靠性水平。然而,自动驾驶辅助系统的测试需要大量资源,因此需要高效的方法来生成有针对性的多样化测试。在这项工作中,我们引入了一种新方法--DeepAtash-LR,它将代理模型纳入了重点测试生成流程。这种集成大大提高了集中测试的有效性和在资源密集型场景中的适用性。实验结果表明,代用模型的集成是 DeepAtash-LR 取得成功的基础。与基线方法相比,我们的方法平均能够生成多达 60 倍的有针对性的故障诱导输入。此外,DeepAtash-LR 生成的输入有助于通过微调显著提高原始 ADS 的质量。
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Focused Test Generation for Autonomous Driving Systems

Testing Autonomous Driving Systems (ADSs) is crucial to ensure their reliability when navigating complex environments. ADSs may exhibit unexpected behaviours when presented, during operation, with driving scenarios containing features inadequately represented in the training dataset. To address this shift from development to operation, developers must acquire new data with the newly observed features. This data can be then utilised to fine tune the ADS, so as to reach the desired level of reliability in performing driving tasks. However, the resource-intensive nature of testing ADSs requires efficient methodologies for generating targeted and diverse tests.

In this work, we introduce a novel approach, DeepAtash-LR, that incorporates a surrogate model into the focused test generation process. This integration significantly improves focused testing effectiveness and applicability in resource-intensive scenarios. Experimental results show that the integration of the surrogate model is fundamental to the success of DeepAtash-LR. Our approach was able to generate an average of up to 60 × more targeted, failure-inducing inputs compared to the baseline approach. Moreover, the inputs generated by DeepAtash-LR were useful to significantly improve the quality of the original ADS through fine tuning.

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来源期刊
ACM Transactions on Software Engineering and Methodology
ACM Transactions on Software Engineering and Methodology 工程技术-计算机:软件工程
CiteScore
6.30
自引率
4.50%
发文量
164
审稿时长
>12 weeks
期刊介绍: Designing and building a large, complex software system is a tremendous challenge. ACM Transactions on Software Engineering and Methodology (TOSEM) publishes papers on all aspects of that challenge: specification, design, development and maintenance. It covers tools and methodologies, languages, data structures, and algorithms. TOSEM also reports on successful efforts, noting practical lessons that can be scaled and transferred to other projects, and often looks at applications of innovative technologies. The tone is scholarly but readable; the content is worthy of study; the presentation is effective.
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