虚拟自动驾驶测试中基于覆盖的场景模糊技术
Coverage-based Scene Fuzzing for Virtual Autonomous Driving Testing
摘要：
基于仿真的虚拟测试已成为确保自动驾驶系统安全性的重要步骤。测试人员需要手工制作虚拟驾驶场景，并配置各种环境设置，如周围的交通，天气条件等。由于配置的可能性巨大，人类的努力受到检测工业级自动驾驶系统缺陷的效率低下的影响。本文提出了一种覆盖率驱动的模糊技术，自动生成不同的配置参数，形成新的驾驶场景。实验结果表明，我们的模糊化方法可以显着降低成本，在派生新的风险场景从初始设置设计的测试人员。我们预计自动模糊将成为自动驾驶系统虚拟测试的常见做法。
英文摘要：
Simulation-based virtual testing has become an essential step to ensure the safety of autonomous driving systems. Testers need to handcraft the virtual driving scenes and configure various environmental settings like surrounding traffic, weather conditions, etc. Due to the huge amount of configuration possibilities, the human efforts are subject to the inefficiency in detecting flaws in industry-class autonomous driving system. This paper proposes a coverage-driven fuzzing technique to automatically generate diverse configuration parameters to form new driving scenes. Experimental results show that our fuzzing method can significantly reduce the cost in deriving new risky scenes from the initial setup designed by testers. We expect automated fuzzing will become a common practice in virtual testing for autonomous driving systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
