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A Retargetable Fault Injection Framework for Safety Validation of Autonomous Vehicles

机译:用于自动驾驶汽车安全性验证的可重定位故障注入框架

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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.
机译:自动驾驶汽车使用运行复杂软件的电子控制单元来改善乘客的舒适度和安全性。为了测试车载电子设备的安全性,有关功能安全性的ISO 26262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)会在运行时引发难以触发的硬件和软件故障,从而能够分析故障传播效果。车辆中各种交互组件的数量和复杂性不断增长,因此需要在车辆级别使用多功能FIF。在本文中,我们提出了一种基于可在许多目标系统上使用的调试器接口的新颖的可重定位FIF。我们在基于恩智浦BlueBox原型开发平台的三种用于自动驾驶的硬件在环设置中验证了FIF。为了触发故障注入过程,我们开发了基于机器人操作系统的交互式用户界面,该界面还可视化了车辆系统的健康状况。我们基于调试器的可重定向目标的故障注入机制确认了各种汽车系统的安全特性并确定了安全缺陷。

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