S. Kato, Shota Tokunaga, Yuya Maruyama, Seiya Maeda, Manato Hirabayashi, Yuki Kitsukawa, Abraham Monrroy, Tomohito Ando, Yusuke Fujii, Takuya Azumi
{"title":"Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems","authors":"S. Kato, Shota Tokunaga, Yuya Maruyama, Seiya Maeda, Manato Hirabayashi, Yuki Kitsukawa, Abraham Monrroy, Tomohito Ando, Yusuke Fujii, Takuya Azumi","doi":"10.1109/ICCPS.2018.00035","DOIUrl":null,"url":null,"abstract":"This paper presents Autoware on Board, a new profile of Autoware, especially designed to enable autonomous vehicles with embedded systems. Autoware is a popular open-source software project that provides a complete set of self-driving modules, including localization, detection, prediction, planning, and control. We customize and extend the software stack of Autoware to accommodate embedded computing capabilities. In particular, we use DRIVE PX2 as a reference computing platform, which is manufactured by NVIDIA Corporation for development of autonomous vehicles, and evaluate the performance of Autoware on ARM-based embedded processing cores and Tegra-based embedded graphics processing units (GPUs). Given that low-power CPUs are often preferred over high-performance GPUs, from the functional safety point of view, this paper focuses on the application of Autoware on ARM cores rather than Tegra ones. However, some Autoware modules still need to be executed on the Tegra cores to achieve load balancing and real-time processing. The experimental results show that the execution latency imposed on the DRIVE PX2 platform is capped at about three times as much as that on a high-end laptop computer. We believe that this observed computing performance is even acceptable for real-world production of autonomous vehicles in certain scenarios.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"344","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCPS.2018.00035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 344
Abstract
This paper presents Autoware on Board, a new profile of Autoware, especially designed to enable autonomous vehicles with embedded systems. Autoware is a popular open-source software project that provides a complete set of self-driving modules, including localization, detection, prediction, planning, and control. We customize and extend the software stack of Autoware to accommodate embedded computing capabilities. In particular, we use DRIVE PX2 as a reference computing platform, which is manufactured by NVIDIA Corporation for development of autonomous vehicles, and evaluate the performance of Autoware on ARM-based embedded processing cores and Tegra-based embedded graphics processing units (GPUs). Given that low-power CPUs are often preferred over high-performance GPUs, from the functional safety point of view, this paper focuses on the application of Autoware on ARM cores rather than Tegra ones. However, some Autoware modules still need to be executed on the Tegra cores to achieve load balancing and real-time processing. The experimental results show that the execution latency imposed on the DRIVE PX2 platform is capped at about three times as much as that on a high-end laptop computer. We believe that this observed computing performance is even acceptable for real-world production of autonomous vehicles in certain scenarios.
本文介绍了车载汽车软件(Autoware on Board),这是一种新的汽车软件,专门为实现嵌入式系统的自动驾驶汽车而设计。Autoware是一个流行的开源软件项目,它提供了一套完整的自动驾驶模块,包括定位、检测、预测、规划和控制。我们定制和扩展Autoware的软件堆栈,以适应嵌入式计算能力。我们特别以NVIDIA公司为开发自动驾驶汽车而制造的DRIVE PX2作为参考计算平台,在基于arm的嵌入式处理内核和基于tegra的嵌入式图形处理单元(gpu)上评估Autoware的性能。考虑到低功耗的cpu往往比高性能的gpu更受青睐,从功能安全的角度考虑,本文主要研究Autoware在ARM内核上的应用,而不是在Tegra内核上的应用。然而,一些Autoware模块仍然需要在Tegra内核上执行,以实现负载平衡和实时处理。实验结果表明,DRIVE PX2平台上的执行延迟上限约为高端笔记本电脑的三倍。我们相信,在某些情况下,这种观察到的计算性能甚至可以用于自动驾驶汽车的实际生产。