Automotive systems are integrating artificial intelligence and complex software stacks aiming to interpret the real world, make decisions, and perform actions without human input. The occurrence of soft errors in such systems can lead to wrong decisions, which might ultimately incur in life losses. This brief focuses on the soft error susceptibility assessment of a real automotive application running on top of unmodified Linux kernels, and considering two commercially available processors, and three cross-compilers. Results collected from more than 29 thousand simulation hours show that the occurrence of faults in critical functions may cause $2.16\times $ more failures on the system.

中文翻译：

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一个真正的自动驾驶汽车软件栈的广泛的软错误可靠性分析

汽车系统正在集成人工智能和复杂的软件堆栈，旨在在没有人工输入的情况下解释现实世界、做出决策和执行操作。此类系统中出现软错误可能会导致错误决策，最终可能导致人员伤亡。本简介侧重于在未修改的 Linux 内核之上运行的真实汽车应用程序的软错误敏感性评估，并考虑了两个商用处理器和三个交叉编译器。从超过 29,000 小时的模拟中收集的结果表明，关键功能中出现的故障可能会导致系统故障增加 2.16 倍。