自动驾驶模拟器的场景驱动变形测试

Yifan Zhang, Dave Towey, Matthew Pike, Jia Cheng Han, Zhi Quan Zhou, Chenghao Yin, Qian Wang, Chen Xie
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引用次数: 0

摘要

随着汽车驾驶辅助功能的普及,公众对完全自动驾驶系统(ADS)的兴趣与日俱增。然而,这些复杂系统的软硬件集成带来了巨大的测试挑战,尤其是在确保乘客安全方面。为应对这些挑战,模拟已成为自动驾驶系统测试的关键步骤。本文针对自动驾驶辅助系统测试中面临的挑战提出了一种解决方案,重点关注自动驾驶辅助系统模拟器的验证。所提出的方法包括利用模拟和变形测试(MT)生成多个具体的变形关系(MR),用于测试 ADS 模拟器。为了实现这一目标,我们引入了三种元变形关系模式(MRP)。每个 MRP 都配有一个元变形关系输入模式 (MRIP),以帮助生成详细的 MR。这些 MR 旨在识别 ADS 模拟器中的潜在问题。为了简化测试过程并方便测试人员进行 MT 测试,还提出了一个自适应场景测试框架。该框架允许测试人员反复改进测试用例和 MR,直到发现的问题得到确认。该框架的优点和局限性通过一个行业案例研究得以展示。总之,本研究为测试 ADS 所面临的挑战提供了实用的解决方案,并为该领域的研究人员和从业人员提高测试效率提供了有用的见解。
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Scenario‐Driven Metamorphic Testing for Autonomous Driving Simulators
The proliferation of driver‐assistance features in vehicles has resulted in a growing interest among the public in fully autonomous driving systems (ADSs). However, the integration of software and hardware in these complex systems presents significant testing challenges, particularly with respect to ensuring passenger safety. To address these challenges, simulation has emerged as a crucial step in the testing of ADSs. This paper presents a solution to the challenges faced in testing ADSs, with a focus on the validation of ADS simulators. The proposed approach involves using simulations and metamorphic testing (MT) to generate multiple concrete metamorphic relations (MRs) for testing ADS simulators. In order to accomplish this goal, we introduce three metamorphic relation patterns (MRPs). Each MRP is accompanied by a metamorphic relation input pattern (MRIP) that aids in generating detailed MRs. These MRs are designed to identify potential issues within the ADS simulator. To simplify the testing process and facilitate MT for testers, a self‐evolving scenario‐testing framework is also presented. The framework allows testers to improve test cases and MRs iteratively until issues detected are confirmed. The benefits and limitations of the framework are demonstrated using an industry case study. Overall, this study offers a practical solution to the challenges in testing ADSs and provides useful insights into improving testing efficiency for researchers and practitioners in the field.
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