Towards C-V2X Enabled Collaborative Autonomous Driving

被引:13
|
作者
He, Yuankai [1 ]
Wu, Baofu [1 ,2 ]
Dong, Zheng [1 ]
Wan, Jian [2 ,3 ]
Shi, Weisong [4 ]
机构
[1] Wayne State Univ, Dept Comp Sci, Detroit, MI 48202 USA
[2] Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Peoples R China
[3] Zhejiang Univ Sci & Technol, Sch Informat & Elect Engn, Hangzhou 310023, Peoples R China
[4] Univ Delaware, Dept Comp Sci, Newark, DE 19716 USA
关键词
ADAS; autonomous driving; C-V2X; collaborative driving; cooperative driving; edge computing;
D O I
10.1109/TVT.2023.3299844
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Intelligent vehicles, including autonomous vehicles and vehicles equipped with ADAS systems, are single-agent systems that navigate solely on the information collected by themselves. However, despite rapid advancements in hardware and algorithms, many accidents still occur due to the limited sensing coverage from a single-agent perception angle. These tragedies raise a critical question of whether single-agent autonomous driving is safe. Preliminary investigations on this safety issue led us to create a C-V2X-enabled collaborative autonomous driving framework (CCAD) to observe the driving circumstance from multiple perception angles. Our framework uses C-V2X technology to connect infrastructure with vehicles and vehicles with vehicles to transmit safety-critical information and to add safety redundancies. By enabling these communication channels, we connect previously independent single-agent vehicles and existing infrastructure. This paper presents a prototype of our CCAD framework with RSU and OBU as communication devices and an edge-computing device for data processing. We also present a case study of successfully implementing an infrastructure-based collaborative lane-keeping with the CCAD framework. Our case study evaluations demonstrate that the CCAD framework can transmit, in real-time, personalized lane-keeping guidance information when the vehicle cannot find the lanes. The evaluations also indicate that the CCAD framework can drastically improve the safety of single-agent intelligent vehicles and open the doors to many more collaborative autonomous driving applications.
引用
收藏
页码:15450 / 15462
页数:13
相关论文
共 50 条
  • [21] Performance Analysis of C-V2X Mode 4 Communication Introducing an Open-Source C-V2X Simulator
    Eckermann, Fabian
    Kahlert, Moritz
    Wietfeld, Christian
    2019 IEEE 90TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2019-FALL), 2019,
  • [22] Federated Reinforcement Learning for Collaborative Intelligence in UAV-Assisted C-V2X Communications
    Gupta, Abhishek
    Fernando, Xavier
    DRONES, 2024, 8 (07)
  • [23] C-V2X Assisted mmWave V2V Scheduling
    Molina-Galan, Alejandro
    Coll-Perales, Baldomero
    Gozalvez, Javier
    2019 IEEE 2ND CONNECTED AND AUTOMATED VEHICLES SYMPOSIUM (CAVS), 2019,
  • [24] Deep Reinforcement Learning Enabled Power Allocation for Multi-Connectivity C-V2X Downlink
    Xue, Jianzhe
    Yu, Kai
    Zhang, Tianqi
    Zhou, Haibo
    Shen, Xuemin
    2023 IEEE 34TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS, PIMRC, 2023,
  • [25] Edge Computing-enabled Intrusion Detection for C-V2X Networks using Federated Learning
    Selamnia, Aymene
    Brik, Bouziane
    Senouci, Sidi Mohammed
    Boualouache, Abdelwahab
    Hossain, Shajjad
    2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022), 2022, : 2080 - 2085
  • [26] C-V2X network slicing framework for 5G-enabled vehicle platooning applications
    Lekidis, Alexios
    Bouali, Faouzi
    2021 IEEE 93RD VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-SPRING), 2021,
  • [27] 蜂窝车联网(C-V2X)综述
    陈山枝
    时岩
    胡金玲
    中国科学基金, 2020, 34 (02) : 179 - 185
  • [28] 携手推进C-V2X产业发展
    吕晓峰
    智能网联汽车, 2019, (01) : 49 - 49
  • [29] C-V2X无线技术演进研究
    牟晋宏
    山东通信技术, 2021, 41 (03) : 18 - 22
  • [30] C-V2X技术演变与研究
    宋爱慧
    赵慧麟
    孙向前
    廖臻
    通信世界, 2021, (21) : 35 - 36