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Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World
arXiv - CS - Machine Learning Pub Date : 2020-03-17 , DOI: arxiv-2003.07739
Daniel J. Fremont, Edward Kim, Yash Vardhan Pant, Sanjit A. Seshia, Atul Acharya, Xantha Bruso, Paul Wells, Steve Lemke, Qiang Lu, Shalin Mehta

We present a new approach to automated scenario-based testing of the safety of autonomous vehicles, especially those using advanced artificial intelligence-based components, spanning both simulation-based evaluation as well as testing in the real world. Our approach is based on formal methods, combining formal specification of scenarios and safety properties, algorithmic test case generation using formal simulation, test case selection for track testing, executing test cases on the track, and analyzing the resulting data. Experiments with a real autonomous vehicle at an industrial testing facility support our hypotheses that (i) formal simulation can be effective at identifying test cases to run on the track, and (ii) the gap between simulated and real worlds can be systematically evaluated and bridged.

中文翻译:

自动驾驶汽车基于场景的正式测试:从模拟到现实世界

我们提出了一种基于场景的自动驾驶汽车安全性自动化测试的新方法,尤其是那些使用先进的基于人工智能的组件,涵盖基于模拟的评估以及现实世界中的测试。我们的方法基于形式方法,结合场景和安全属性的形式规范、使用形式模拟的算法测试用例生成、轨道测试的测试用例选择、在轨道上执行测试用例并分析结果数据。在工业测试设施中对真正的自动驾驶汽车进行的实验支持我们的假设:(i) 形式模拟可以有效地识别在赛道上运行的测试用例,以及 (ii) 可以系统地评估和弥合模拟世界和现实世界之间的差距.
更新日期:2020-07-14
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