With the recent progress of techniques in computer vision and processor design, vehicles are able to perform a greater number of functions, and are reaching higher levels of autonomy. As the list of autonomous tasks that the car is supposed to perform grows, two design questions arise: how to group these tasks into modules and which processors and data buses should instantiate these modules and their links in the physical architecture. Both questions are linked, as the processing capacity of the processors influences how centralized the architecture can be, and the modularization influences the overall system latency as well. Furthermore, our interest lies in designing architectures that perform the tasks rapidly, while minimizing cost. This multiobjective problem is intractable without architecture exploration and an analysis tool. This paper presents a linear optimization formulation to capture these tradeoffs, and to systematically find relevant architectures with optimal latency and cost. The results show that enforcing all safety constraints on the architecture leads to a worst case increase of 17% in latency and 18% component cost per vehicle. The increase in latency is significant at the scale of human driver reaction times.

中文翻译：

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自动驾驶系统硬件和软件架构探索：在安全约束下优化延迟和成本

随着计算机视觉和处理器设计技术的最新进展，车辆能够执行更多的功能，并达到更高的自治性。随着汽车应执行的自主任务列表的增加，出现了两个设计问题：如何将这些任务分组为模块，以及哪些处理器和数据总线应实例化这些模块及其在物理体系结构中的链接。这两个问题都联系在一起，因为处理器的处理能力会影响架构的集中程度，而模块化也会影响整个系统的延迟。此外，我们的兴趣在于设计可快速执行任务并最小化成本的体系结构。如果没有架构探索和分析工具，这个多目标问题将是棘手的。本文提出了一种线性优化公式，以捕获这些折衷，并系统地找到具有最佳延迟和成本的相关架构。结果表明，在架构上强制执行所有安全约束会导致最坏的情况，即每辆车的等待时间增加17％，部件成本增加18％。在人类驾驶员反应时间的尺度上，等待时间的增加是显着的。