摘要
智能车联网发展对车载计算能力提出挑战,传统云计算模式传输延迟高,难以满足实时性需求,边缘计算成为关键技术。聚焦边缘计算环境下智能车联网的任务卸载与资源分配协同优化问题,分析了协同优化模型在强化自动驾驶安全、提升交通调控效率、推动绿色交通等方面的应用价值,阐述了任务卸载、资源分配及协同优化的研究现状,构建了融合动态场景特性的协同优化模型,明确了系统模型、优化目标和约束条件。研究为提升车联网系统性能提供了理论与技术参考,同时指出未来需在模型实时性、多目标平衡等方面进一步探索。
The rapid development of intelligent connected vehicles poses a severe challenge to the computing capabilities of vehicles,and traditional cloud computing models are unable to meet real-time requirements due to transmission delays.Edge computing,bydeploying distributed nodes at the edge of the network,provides near processing capability for vehicle tasks,and becomes the key technology to solve this problem.Focusing on the collaborative optimization of task unloading and resource allocation of the internet of vehicles in the edge computing environment,this paper systematically analyzes the relevant theoretical basis and research status,builds acollaborative optimization model integrating dynamic scene characteristics,and discusses its application value in intelligent transportation,providing theoretical and technical reference for improving the performance of the Internet of vehicles system.
作者
朱艳艳
秦晓磊
付朝
史津鸿
Zhu Yanyan;Qin Xiaolei;Fu Zhao;Shi Jinhong(Science and Technology College of Hubei University of Arts and Science,Xiangyang 441025,China)
出处
《专用汽车》
2025年第8期36-38,共3页
Special Purpose Vehicle
基金
湖北省教育厅科学研究计划项目“基于边缘计算的智能车联网资源优化技术研究”(B2023481)。
关键词
边缘计算
智能车联网
任务卸载
资源分配
协同优化
Edge computing
Intelligent vehicle networking
Task uninstallation
Resource allocation
Collaborative optimization
