Automated scenario generation and iterative regression testing method for autonomous driving systems

Jianli Duan, Rui Wang, Shulian Zhao, Mengxia Hu, Hua Chen, Chuzhao Li, Hao Wu, Pengfei Bai, Yi Wang, Keqiang Li
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Abstract

In this paper, we propose a new automatic test and evaluation method that can be used for autonomous driving systems in virtual simulation test. Aiming at the problems that the existing automated test method cannot guarantee coverage and validity of the test scenarios, and evaluation lacks the consideration of the complete development life cycle of the systems, we firstly introduce a new scenarios generation method that can generate scenarios with higher overall effectivity on the premise of ensuring coverage. Then, an integrated test platform is constructed, in which the components can be automatically invoked by test scripts. Finally, the automated iterative regression testing is used to ensure the effectiveness of the whole development and test process. The application and verification of a level three (L3) Traffic Jam Pilot (TJP) system is carried out on the above-stated method. The result shows that the proposed method can effectively detect system defects and significantly improve test efficiency.
自动驾驶系统的自动场景生成与迭代回归测试方法
本文提出了一种可用于自动驾驶系统虚拟仿真测试的自动测试与评估方法。针对现有自动化测试方法无法保证测试场景的覆盖和有效性,评估缺乏对系统完整开发生命周期的考虑等问题,首先引入了一种新的场景生成方法,在保证覆盖的前提下,生成整体效率更高的场景。然后,构建了一个集成的测试平台,其中的组件可以被测试脚本自动调用。最后,采用自动化迭代回归测试来保证整个开发和测试过程的有效性。三级(L3)交通阻塞领航员(TJP)系统的应用和验证是根据上述方法进行的。结果表明,该方法能够有效地检测出系统缺陷,显著提高了测试效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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