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Adaptable Demonstrator Platform for the Simulation of Distributed Agent-Based Automotive Systems

机译:适用于分布式代理的汽车系统仿真的适应性示范性平台

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Future autonomous vehicles will no longer have a driver as a fallback solution in case of critical failure scenarios. However, it is costly to add hardware redundancy to achieve a fail-operational behaviour. Here, graceful degradation can be used by repurposing the allocated resources of non-critical applications for safety-critical applications. The degradation problem can be solved as a part of an application mapping problem. As future automotive software will be highly customizable to meet customers' demands, the mapping problem has to be solved for each individual configuration and the architecture has to be adaptable to frequent software changes. Thus, the mapping problem has to be solved at run-time as part of the software platform. In this paper we present an adaptable demonstrator platform consisting of a distributed simulation environment to evaluate such approaches. The platform can be easily configured to evaluate different hardware architectures. We discuss the advantages and limitations of this platform and present an exemplary demonstrator configuration running an agent-based graceful degradation approach.
机译:在关键失败情景的情况下,未来的自动车辆将不再具有驾驶员作为后备解决方案。但是,添加硬件冗余以实现故障操作行为是昂贵的。在这里,通过重新修复安全关键应用程序的非关键应用程序的分配资源,可以使用优常的劣化。劣化问题可以作为应用映射问题的一部分来解决。由于未来的汽车软件将高度可自定义以满足客户的需求,必须为每个配置配置来解决映射问题,并且该架构必须适应频繁的软件更改。因此,必须在作为软件平台的一部分的运行时解决映射问题。在本文中,我们提供了一个适应性的演示平台,包括分布式仿真环境来评估这些方法。该平台可以很容易地配置为评估不同的硬件架构。我们讨论了该平台的优点和局限性,并呈现了运行基于代理的优雅劣化方法的示范器配置。

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