Poster: Sim-ATAV: Simulation-Based Adversarial Testing Framework for Autonomous Vehicles

被引：19

作者：

Tuncali, Cumhur Erkan ^{[1
]}

Fainekos, Georgios ^{[2
]}

Ito, Hisahiro ^{[1
]}

Kapinski, James ^{[1
]}

机构：

[1] Toyota Tech Ctr, Ann Arbor, MI 48105 USA

[2] Arizona State Univ, CIDSE, Tempe, AZ USA

来源：

HSCC 2018: PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE ON HYBRID SYSTEMS: COMPUTATION AND CONTROL (PART OF CPS WEEK) | 2018年

关键词：

D O I：

10.1145/3178126.3187004

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

One of the main challenges in testing autonomous driving systems is the presence of machine learning components, such as neural networks, for which formal properties are difficult to establish. We present a simulation-based testing framework that supports methods used to evaluate cyber-physical systems, such as test case generation and automatic falsification. We demonstrate how the framework can be used to evaluate closed-loop properties of autonomous driving system models that include machine learning components.

引用

页码：283 / 284

页数：2

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