Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems

被引:359
作者
Kato, Shinpei [1 ,3 ,4 ]
Tokunaga, Shota [2 ]
Maruyama, Yuya [2 ]
Maeda, Seiya [2 ]
Hirabayashi, Manato [3 ]
Kitsukawa, Yuki [3 ]
Monrroy, Abraham [3 ]
Ando, Tomohito [3 ]
Fujii, Yusuke [4 ]
Azumi, Takuya [2 ,4 ,5 ]
机构
[1] Univ Tokyo, Grad Sch Informat Sci & Technol, Tokyo, Japan
[2] Osaka Univ, Grad Sch Engn, Suita, Osaka, Japan
[3] Nagoya Univ, Grad Sch Informat, Nagoya, Aichi, Japan
[4] Tier IV Inc, Nagoya, Aichi, Japan
[5] JST, PRESTO, Tokyo, Japan
来源
2018 9TH ACM/IEEE INTERNATIONAL CONFERENCE ON CYBER-PHYSICAL SYSTEMS (ICCPS 2018) | 2018年
关键词
D O I
10.1109/ICCPS.2018.00035
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:287 / 296
页数:10
相关论文
共 25 条
  • [1] [Anonymous], 2005, PROC CVPR IEEE
  • [2] [Anonymous], 2009, THESIS
  • [3] [Anonymous], 2016, ARXIV PREPRINT
  • [4] [Anonymous], 2009, ICRA WORKSH OP SOURC
  • [5] [Anonymous], 2009, SEMANTIC 3D OBJECT M
  • [6] [Anonymous], 1992, TECH REP
  • [7] A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
    Arulampalam, MS
    Maskell, S
    Gordon, N
    Clapp, T
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2002, 50 (02) : 174 - 188
  • [8] A METHOD FOR REGISTRATION OF 3-D SHAPES
    BESL, PJ
    MCKAY, ND
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1992, 14 (02) : 239 - 256
  • [9] The normal distributions transform: A new approach to laser scan matching
    Biber, P
    [J]. IROS 2003: PROCEEDINGS OF THE 2003 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-4, 2003, : 2743 - 2748
  • [10] Buehler M, 2009, SPRINGER TRAC ADV RO, V56, P1, DOI 10.1007/978-3-642-03991-1