D3: a dynamic deadline-driven approach for building autonomous vehicles
D3: a dynamic deadline-driven approach for building autonomous vehicles
复制标题
DOI：
10.1145/3492321.3519576
复制
发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Ionel Gog;Sukrit Kalra;Peter Schafhalter;Joseph Gonzalez;I. Stoica
中科院分区：
文献类型：
--
作者：
Ionel Gog;Sukrit Kalra;Peter Schafhalter;Joseph Gonzalez;I. Stoica
自动驾驶汽车（AV）必须跨越各种具有挑战性的环境，这些环境不断地施加了持续变化的截止日期，并且在其软件管道上进行了运行时准确的权衡。此类AV管道的截止日期驱动的执行需要一类新的系统，使计算能够在动态变化的截止日期下最大化精度。设计这些系统提出了有趣的挑战，这些挑战是将AV管道的简易开发与截止日期规范和执行机制相结合。我们的工作通过D3（动态截止日期驱动）解决了这些挑战，这是一个新颖的执行模型，它集中了截止日期管理，并允许应用程序通过将错过的截止日期建模为例外来调整其计算。此外，我们设计和实施了ERDOS，这是对AV管道的D3的开源实现，它将良好的执行事件暴露于应用程序中，并提供了在任意事件之间进行投机执行计算和执行截止日期的机制。最后，我们通过最先进的开源AV管道Pylot解决了至关重要的AV基准，该管道在模拟器和真实的AV中无缝地工作。我们通过在跨越50公里的具有挑战性的驾驶场景中驾驶孔驾驶塔，评估了D3和ERDOS的功效，并且与先前的执行模型相比，碰撞降低了68％。
Autonomous vehicles (AVs) must drive across a variety of challenging environments that impose continuously-varying deadlines and runtime-accuracy tradeoffs on their software pipelines. A deadline-driven execution of such AV pipelines requires a new class of systems that enable the computation to maximize accuracy under dynamically-varying deadlines. Designing these systems presents interesting challenges that arise from combining ease-of-development of AV pipelines with deadline specification and enforcement mechanisms. Our work addresses these challenges through D3 (Dynamic Deadline-Driven), a novel execution model that centralizes the deadline management, and allows applications to adjust their computation by modeling missed deadlines as exceptions. Further, we design and implement ERDOS, an open-source realization of D3 for AV pipelines that exposes finegrained execution events to applications, and provides mechanisms to speculatively execute computation and enforce deadlines between an arbitrary set of events. Finally, we address the crucial lack of AV benchmarks through our state-of-the-art open-source AV pipeline, Pylot, that works seamlessly across simulators and real AVs. We evaluate the efficacy of D3 and ERDOS by driving Pylot across challenging driving scenarios spanning 50km, and observe a 68% reduction in collisions as compared to prior execution models.