A Dynamic Routing for External Communication in Self-driving Vehicles

被引:0
|
作者
Alheeti, Khattab M. Ali [1 ]
Al Dosary, Duaa [1 ]
机构
[1] Univ Anbar, Anbar, Iraq
关键词
Vehicular ad hoc networks; AODV; Routing protocols; Road side units (RSUs); DAODV;
D O I
10.1007/978-3-031-21101-0_15
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Vehicular ad hoc networks (VANETs) are considered as a modification of mobile ad hoc networks (MANETs). They provide wireless ad hoc communication between vehicles and vehicle to roadside equipment. VANET provides an automated, safe, and secure traffic system. Several types of routing protocols have been modified for automated traffic. Many previous protocols such as Ad Hoc On-demand Distance Vector routing (AODV) have shown some weakness in performance. In this paper, a Dynamic AODV (DAODV) routing protocol is proposed that can adapt to various types of mobility and traffic scenarios, such as highway, rural and urban. AODV routing protocol is enhanced by designing a cooperative virtual bridge between vehicles and the nearest Road Side Unit (RSUs) to provide more than one path between the source and destination cars. This work was tested for thirty rounds. In each round, the cooperative routing protocol generated ten various paths between two nodes. In these cases, each RSU finds all available paths between nodes simultaneously. This protocol will have the ability to provide more than one path and reduce the possibility of packet loss between nodes. Simulation results prove that DAODV can improve the communication performance between self-driving and semi self-driving vehicles.
引用
收藏
页码:185 / 198
页数:14
相关论文
共 50 条
  • [21] A Survey of Self-driving Urban Vehicles Development
    Aria, M.
    2ND INTERNATIONAL CONFERENCE ON INFORMATICS, ENGINEERING, SCIENCE, AND TECHNOLOGY (INCITEST 2019), 2019, 662
  • [22] Expanding the Design Horizon for Self-Driving Vehicles
    Blyth, Pascale-L.
    Mladenovic, Milos N.
    Nardi, Bonnie A.
    Ekbia, Hamid R.
    Su, Norman M.
    IEEE TECHNOLOGY AND SOCIETY MAGAZINE, 2016, 35 (03) : 44 - 49
  • [23] Self-Driving Vehicles: The Challenges and Opportunities Ahead
    Rajkumar, Raj
    SenSys'15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, 2015, : 1 - 1
  • [24] Psychological roadblocks to the adoption of self-driving vehicles
    Azim Shariff
    Jean-François Bonnefon
    Iyad Rahwan
    Nature Human Behaviour, 2017, 1 : 694 - 696
  • [25] Efficient Self-driving Control for Electric Vehicles
    Yeom, Kiwon
    SMART GRID AND RENEWABLE ENERGY SYSTEMS, ICRCE 2024, 2024, 1238 : 60 - 69
  • [26] Examining the Sensors That Enable Self-Driving Vehicles
    Gazis A.
    Ioannou E.
    Katsiri E.
    IEEE Potentials, 2020, 39 (01): : 46 - 51
  • [27] Automatic Parameter Tuning of Self-Driving Vehicles
    Wu, Hung-Ju
    Nenchev, Vladislav
    Rathgeber, Christian
    2024 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS, CCTA 2024, 2024, : 555 - 560
  • [28] Online legal driving behavior monitoring for self-driving vehicles
    Wenhao Yu
    Chengxiang Zhao
    Hong Wang
    Jiaxin Liu
    Xiaohan Ma
    Yingkai Yang
    Jun Li
    Weida Wang
    Xiaosong Hu
    Ding Zhao
    Nature Communications, 15
  • [29] Software Failures Prediction in Self-Driving Vehicles
    Abedi, Vajiheh
    Zadeh, Mehrdad H.
    Dargahi, Javad
    Fekri, Pedram
    2020 IEEE 92ND VEHICULAR TECHNOLOGY CONFERENCE (VTC2020-FALL), 2020,
  • [30] Lidar, a key sensor for self-driving vehicles
    Lewis, John
    LASER FOCUS WORLD, 2021, 57 (10): : 5 - 5