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PTL : Liya Yu (13 April 2021)


Blueprint species:

...

Use Case Attributes

Description

Informational

Type

New

 

Blueprint Family

Integrated Edge Cloud (IEC)

The AI Edge

 

Use Case

Safety and security, driving assistant

Autonomousdrivingtaxi

 

Blueprint proposed Name

IEC Type 3: Autonomous vehicles as the edge

RoboTaxi

 

Initial POD Cost (capex)

N/A

Leverage Unicycle POD - less than $150k

 

Scale & Type

One per vehicle

Up to 4 servers, x86/Arm server or deep edge class

With nVIDIA Tesla P4/T4 GPUs

 

Applications

Video Processing for AutoDriving (especially training deep learning models)

  • lane guidance
  • safety and security applications
  • remote control

    Autonomousdrivingtaxi

     

    Power and memory restrictions

    Autonoumous vehicles are power and memory constrained.

    Less than 10Kw

     

    Infrastructure orchestration

    Need Geolocation correlation with deep learning model (the trained ones in binary format)

    Docker 1.13.1 or above

    K8s 1.12.5 or above- Container Orchestration

    OS – CentOS 7.0 or above

     

    SDN

    Calico

    and K8s, and Containers serving as client/server (a database to collect raw videos/video streams) and server/client (a database for trained models in binary format)

    container networking,or OVS-DPDK

     

    Workload Type

    Containers

    (Tensoflow, Keras containers)
  • VMs
  •  

    Additional Details

    Different bus connection: Ethernet, CANbus, etc.

    There are mainly 4 pieces:

    1. a database to collect raw videos/video streams
    2. tons of containers with various models on training
    3. a database for trained models in binary format
    4. a database for queries records and transactions of database mentioned in 1 and 3.

     

    Proposed PTL: 

    Ken Yi

    Contributors: 

    @soshun Tina Tsou (Deactivated) @soshun.arai@arm.com

    Committers: 

     

    PaaSOTE Stack + Baetyl + OpenNESS

    Additional Details

    N/A

     



    View file
    nameRoboTaxi_V1.3.pdf
    height250

    As per the Akraino Community process and directed by TSC, any person wishing to be PTL shall self nominate themselves by putting a Y in the column.   A blueprint which has only one nominee for Project Technical Lead (PTL) will be the elected lead.   If there are two or more, an election will take place.

    Self Nominations begin on 25 March 2021 and will conclude on 1 April 2021


    Committer

    Committer

    Company

     Committer Contact Info

    Committer Bio

    Committer Picture

    Self Nominate for PTL (Y/N)

    Hechun Zhang

    Baidu

    zhanghechun@baidu.com

    zhanghechun@gmail.com

    Seasoned MEC and 5G Solution Architect of Baidu, with impressive 9 years  experience in the ICT industry.

    Project Manager of ODCC Edge Computing Group.

    张贺纯.jpegImage Added



    Zhenhua XuBaiduxuzhenhua02@baidu.com


    Liya YuBaiduyuliya@baidu.com

    Y
    Zhuming Zhang(Simmy Zhang)Yishi17555827@qq.com


    Zhongwang Hao(haozw)Yishizw.hao@msn.cn


    Yipan DengIntelyipan.deng@intel.com


    Haitao WangIntelhai.tao.wang@intel.com


    Xiaopeng TongIntelxiaopeng.tong@intel.com


    Tina TsouArmtina.tsou@arm.com


    Dechao KongBaidukongdechao@baidu.com


    Wenhui ZhangPSU




    N



    Proposed PTL: 

    Hechun Zhang

    Contributors: 

    Tina Tsou (Deactivated) 

    Wenhui Zhang