A Design of a Lightweight In-Vehicle Edge Gateway for the Self-Diagnosis of an Autonomous Vehicle

被引:6
|
作者
Jeong, YiNa [1 ]
Son, SuRak [1 ]
Jeong, EunHee [2 ]
Lee, ByungKwan [1 ]
机构
[1] Catholic Kwandong Univ, Dept Comp Engn, Kangnung 25601, South Korea
[2] Kangwon Natl Univ, Dept Reg Econ, Samcheok 25913, South Korea
来源
APPLIED SCIENCES-BASEL | 2018年 / 8卷 / 09期
基金
新加坡国家研究基金会;
关键词
Address Mapping Table; In-Vehicle Edge Gateway; self-diagnosis;
D O I
10.3390/app8091594
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
This paper proposes a Lightweight In-Vehicle Edge Gateway (LI-VEG) for the self-diagnosis of an autonomous vehicle, which supports a rapid and accurate communication between in-vehicle sensors and a self-diagnosis module and between in-vehicle protocols. A paper on the self-diagnosis module has been published previously, thus this paper only covers the LI-VEG, not the self-diagnosis. The LI-VEG consists of an In-Vehicle Sending and Receiving Layer (InV-SRL), an InV-Management Layer (InV-ML) and an InV-Data Translator Layer (InV-DTL). First, the InV-SRL receives the messages from FlexRay, Control Area Network (CAN), Media Oriented Systems Transport (MOST), and Ethernet and transfers the received messages to the InV-ML. Second, the InV-ML manages the message transmission and reception of FlexRay, CAN, MOST, and Ethernet and an Address Mapping Table. Third, the InV-DTL decomposes the message of FlexRay, CAN, MOST, and Ethernet and recomposes the decomposed messages to the frame suitable for a destination protocol. The performance analysis of the LI-VEG shows that the transmission delay time about message translation and transmission is reduced by an average of 10.83% and the transmission delay time caused by traffic overhead is improved by an average of 0.95%. Therefore, the LI-VEG has higher compatibility and is more cost effective because it applies a software gateway to the OBD, compared to a hardware gateway. In addition, it can reduce the transmission error and overhead caused by message decomposition because of a lightweight message header.
引用
收藏
页数:19
相关论文
共 5 条
  • [1] A Deep Learning Part-diagnosis Platform(DLPP) based on an In-vehicle On-board gateway for an Autonomous Vehicle
    Kim, KyungDeuk
    Son, SuRak
    Jeong, YiNa
    Lee, ByungKwan
    KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2019, 13 (08): : 4123 - 4141
  • [2] Autonomous Self-Diagnosis System
    Stefanescu, Vlad Adrian
    Radoi, Ion Emilian
    2020 19TH ROEDUNET CONFERENCE: NETWORKING IN EDUCATION AND RESEARCH (ROEDUNET), 2020,
  • [3] Study of a self-diagnosis system for an autonomous mobile robot
    Okina, S
    Kawabata, K
    Fujii, T
    Kunii, Y
    Asama, H
    Endo, I
    ADVANCED ROBOTICS, 2000, 14 (05) : 339 - 341
  • [4] Classification of Exogenous Anomalies and Self-Diagnosis in Autonomous Robots
    Schleyer, Gustavo
    Russell, Andrew
    2019 IEEE CHILEAN CONFERENCE ON ELECTRICAL, ELECTRONICS ENGINEERING, INFORMATION AND COMMUNICATION TECHNOLOGIES (CHILECON), 2019,
  • [5] An App Visualization design based on IoT Self-diagnosis Micro Control Unit for car accident prevention
    Jeong, YiNa
    Jeong, EunHee
    Lee, ByungKwan
    KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2017, 11 (02): : 1005 - 1018