Vehicle Control System Coordinated Between Cloud and Mobile Edge Computing

被引:0
作者
Sasaki, Kengo [1 ,2 ]
Suzuki, Naoya [1 ]
Makido, Satoshi [1 ]
Nakao, Akihiro [2 ]
机构
[1] Toyota Cent Res & Dev Labs Inc, Nagakute, Aichi, Japan
[2] Univ Tokyo, Tokyo, Japan
来源
2016 55TH ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS OF JAPAN (SICE) | 2016年
关键词
Autonomous Driving; Network Virtualization; Mobile-Edge Computing;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
One of the challenges in autonomous driving is limited sensing from a single vehicle that causes spurious warnings and dead-lock situations. We posit that cloud-based vehicle control system[1] is promising when a number of vehicles must be controlled, since we can collect information from sensors across multiple vehicles for coordination. However, since cloud based control has inherent challenge in long-haul communication susceptible to prolonged latency and packet loss caused by congestion, mobile edge computing (MEC)[2] recently attracts attention in ITS in the next generation mobile network such as 5G. Although edge servers can perform data processing from the vehicles in ultra low latency in MEC, computational resources at edge servers are limited compared to cloud. Therefore, dynamic resource allocation and coordination between edge and cloud servers are necessary. In this paper, we propose infrastructure-based vehicle control system that shares internal states between edge and cloud servers, dynamically allocates computational resources and switches necessary computation on collected sensors according to network conditions in order to achieve safe driving. We implement a prototype system using micro-cars and evaluate the stability of infrastructure-based vehicle control. We show that proposed system mitigates instability of cloud control caused by latency fluctuation. As a result, when controlled from the cloud with 150ms latency, micro-cars deviate by over 0.095m from the course for the 40% of the entire trajectory possibly causing car accidents. On the other hand, MEC-based control stabilizes the driving trajectory. Also, our proposed system automatically switches control from cloud and from edge server according to the network condition without degrading the stability in driving trajectory. Even when the ratio of time of control by edge server to that by cloud is suppressed to 54%, we can achieve almost the same stability as in full control by edge controller.
引用
收藏
页码:1122 / 1127
页数:6
相关论文
共 50 条
  • [21] Dynamic Task Scheduling in Cloud-Assisted Mobile Edge Computing
    Ma, Xiao
    Zhou, Ao
    Zhang, Shan
    Li, Qing
    Liu, Alex X.
    Wang, Shangguang
    IEEE TRANSACTIONS ON MOBILE COMPUTING, 2023, 22 (04) : 2116 - 2130
  • [22] Dynamic Pricing Strategy for Vehicle Assisted Mobile Edge Computing Systems
    Han, Di
    Chen, Wei
    Fang, Yuguang
    IEEE WIRELESS COMMUNICATIONS LETTERS, 2019, 8 (02) : 420 - 423
  • [23] DQN-based mobile edge computing for smart Internet of vehicle
    Lianhong Zhang
    Wenqi Zhou
    Junjuan Xia
    Chongzhi Gao
    Fusheng Zhu
    Chengyuan Fan
    Jiangtao Ou
    EURASIP Journal on Advances in Signal Processing, 2022
  • [24] DQN-based mobile edge computing for smart Internet of vehicle
    Zhang, Lianhong
    Zhou, Wenqi
    Xia, Junjuan
    Gao, Chongzhi
    Zhu, Fusheng
    Fan, Chengyuan
    Ou, Jiangtao
    EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2022, 2022 (01)
  • [25] Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions
    Elazhary, Hanan
    JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2019, 128 : 105 - 140
  • [26] Joint optimization of computing ratio and access points' density for mixed mobile edge/cloud computing
    Jing, Tianqi
    He, Shiwen
    Yu, Fei
    Huang, Yongming
    Yang, Luxi
    Ren, Ju
    EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2021, 2021 (01)
  • [27] Joint optimization of computing ratio and access points’ density for mixed mobile edge/cloud computing
    Tianqi Jing
    Shiwen He
    Fei Yu
    Yongming Huang
    Luxi Yang
    Ju Ren
    EURASIP Journal on Wireless Communications and Networking, 2021
  • [28] Mobile Edge Cloud System: Architectures, Challenges, and Approaches
    Liu, Hang
    Eldarrat, Fahima
    Alqahtani, Hanen
    Reznik, Alex
    de Foy, Xavier
    Zhang, Yanyong
    IEEE SYSTEMS JOURNAL, 2018, 12 (03): : 2495 - 2508
  • [29] Energy Efficiency Maximization for UAV and Electric Vehicle Assisted Mobile Edge Computing on the Edge Side
    Tang, Qiang
    Li, LinJiang
    Wang, Jin
    Kim, Gwang-Jun
    Tang, Bin
    ARTIFICIAL INTELLIGENCE AND SECURITY, ICAIS 2022, PT III, 2022, 13340 : 469 - 481
  • [30] An Enhanced Green Cloud Based Queue Management (GCQM) System to Optimize Energy Consumption in Mobile Edge Computing
    Gopi, R.
    Suganthi, S. T.
    Rajadevi, R.
    Johnpaul, P.
    Bacanin, Nebojsa
    Kannimuthu, S.
    WIRELESS PERSONAL COMMUNICATIONS, 2021, 117 (04) : 3397 - 3419