A Retargetable Fault Injection Framework for Safety Validation of Autonomous Vehicles

Yuting Fu, A. Terechko, T. Bijlsma, P. Cuijpers, J. Redegeld, A. Ors
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引用次数: 10

Abstract

Autonomous vehicles use Electronic Control Units running complex software to improve passenger comfort and safety. To test safety of in-vehicle electronics, the ISO 26262 standard on functional safety recommends using fault injection during component and system-level design. A Fault Injection Framework (FIF) induces hard-to-trigger hardware and software faults at runtime, enabling analysis of fault propagation effects. The growing number and complexity of diverse interacting components in vehicles demands a versatile FIF at the vehicle level. In this paper, we present a novel retargetable FIF based on debugger interfaces available on many target systems. We validated our FIF in three Hardware-In-the-Loop setups for autonomous driving based on the NXP BlueBox prototyping platform. To trigger a fault injection process, we developed an interactive user interface based on Robot Operating System, which also visualized vehicle system health. Our retargetable debugger-based fault injection mechanism confirmed safety properties and identified safety shortcomings of various automotive systems.
自动驾驶汽车安全验证的可重定向故障注入框架
自动驾驶汽车使用运行复杂软件的电子控制单元来提高乘客的舒适度和安全性。为了测试车载电子设备的安全性,ISO 26262功能安全标准建议在组件和系统级设计期间使用故障注入。故障注入框架(FIF)在运行时诱导难以触发的硬件和软件故障,从而能够分析故障传播效应。车辆中各种相互作用部件的数量和复杂性不断增加,要求在车辆层面上实现多功能的FIF。本文提出了一种基于多种目标系统上可用的调试器接口的可重定向FIF。我们在三个基于NXP BlueBox原型平台的自动驾驶硬件在环设置中验证了我们的FIF。为了触发故障注入过程,我们开发了一个基于机器人操作系统的交互式用户界面,该界面还可以可视化车辆系统的健康状况。我们基于可重定向调试器的故障注入机制确认了各种汽车系统的安全特性并识别了安全缺陷。
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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