Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World

被引：109

作者：

Fremont, Daniel J. ^{[1
,4
]}

Kim, Edward ^{[1
]}

Pant, Yash Vardhan ^{[1
]}

Seshia, Sanjit A. ^{[1
]}

Acharya, Atul ^{[2
]}

Bruso, Xantha ^{[2
]}

Wells, Paul ^{[2
]}

Lemke, Steve ^{[3
]}

Lu, Qiang ^{[3
]}

Mehta, Shalin ^{[3
]}

机构：

[1] Univ Calif Berkeley, Berkeley, CA 94720 USA

[2] Amer Automobile Assoc AAA Northern Calif Nevada &, Walnut Creek, CA USA

[3] LG Elect Amer R&D Lab, Santa Clara, CA USA

[4] Univ Calif Santa Cruz, Santa Cruz, CA 95064 USA

来源：

2020 IEEE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) | 2020年

基金：

美国国家科学基金会;

关键词：

D O I：

10.1109/itsc45102.2020.9294368

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页数：8

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