程慧玲,
丁仪,
张晖,
朱洪波
1.教育部泛在网络健康服务系统工程研究中心 南京 210003
2.江苏省无线通信重点实验室 南京 210003
3.南京邮电大学通信与信息工程学院 南京 210003
基金项目:国家自然科学基金(61771252)，江苏省自然科学基金(BK20171444)，江苏省高校重点自然科学研究重大项目(18KJA510005)，江苏省“六大人才高峰”B类资助项目(DZXX-041)，江苏省科协青年科技人才托举工程资助培养项目，江苏省研究生科研创新计划项目(KYCX19_0949)
详细信息
作者简介:赵海涛：男，1983年生，博士，副教授，研究方向为物联网与移动边缘计算
程慧玲：女，1995年生，硕士生，研究方向为移动边缘计算与人工智能
丁仪：女，1995年生，硕士生，研究方向为物联网路由优化和边缘计算
张晖：男，1982年生，博士，副教授，研究方向为未来无线网络
朱洪波：男，1956年生，博士，教授，研究方向为移动通信与宽带无线技术、无线通信与电磁兼容
通讯作者:赵海涛 zhaoht@njupt.edu.cn
中图分类号:TP399计量
文章访问数:1948
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被引次数:0
出版历程
收稿日期:2019-08-06
修回日期:2019-11-05
网络出版日期:2019-11-13
刊出日期:2020-01-21
Research on Traffic Accident Risk Prediction Algorithm of Edge Internet of Vehicles Based on Deep Learning
Haitao ZHAO,,Huiling CHENG,
Yi DING,
Hui ZHANG,
Hongbo ZHU
1. Ministry of Education Ubiquitous Network Health Service System Engineering Research Center, Nanjing 210003, China
2. Jiangsu Key Wireless Communication Laboratory, Nanjing 210003, China
3. College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Funds:The National Natural Science Foundation of China (61771252), The Natural Science Foundation Project of Jiangsu Province (BK20171444), The Jiangsu Province University Natural Science Research Major Project (18KJA510005), “The Six talents High Peaks” Class B Funding Project of Jiangsu Province (DZXX-041), The Jiangsu Provincial Association for Science and Technology Talents Entrustment Project, Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX19_0949)
摘要
摘要:针对传统交通事故风险预测算法无法自动判别数据特征，且模型表达能力差等问题。该文提出一种基于深度学习的车联边缘网络交通事故风险预测算法，该算法首先针对车载自组织网络中采集的大量交通数据，采用边缘服务器中建立的卷积神经网络自主提取多维特征，经归一化、去均值等预处理后，再将得到的新变量输入卷积层、采样层进行训练，最后根据全连接层输出的判别值，得到模拟预测交通事故发生的风险性。仿真结果表明，该算法被验证能够预测交通事故发生的风险性，较传统的机器学习算法BP神经网络、逻辑回归具有更低的损失与更高的预测准确度。
关键词:车联边缘网络/
机器学习/
卷积神经网络/
边缘服务器
Abstract:For the problem that the traditional traffic accident risk prediction algorithm can not automatically discriminate data features, and the model expression ability is poor, a traffic accident risk prediction algorithm based on deep learning is proposed. The algorithm firstly extracts multi-dimensional features by using the convolutional neural network established in the edge server for a large amount of traffic data collected in the edge network of vehicles. After normalization, de-equalization and other pre-processing, the new variables are input into the convolutional layer and the pooling layer for training. Finally, based on the output discrimination value of the fully connected layer, the risk of traffic accidents can be predicted by simulation. The simulation results show that the algorithm is validated to predict the risk of traffic accidents, and has lower loss and higher prediction accuracy than the traditional machine learning BP neural network algorithm and Logical Regression algorithm.
Key words:Edge network of vehicles/
Machine learning/
Convolutional neural network/
Edge server
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