高速公路场景的车路视觉协同行车安全预警算法  

Vehicle-road visual cooperative driving safety early warning algorithm for expressway scenes

作　　者：汪长春 高尚兵[1,2] 蔡创新 陈浩霖 Wang Changchun;Gao Shangbing;Cai Chuangxin;Chen Haolin(College of Computer and Software Engineering,Huaiyin Institute of Technology,Huai′an 223001,China;Laboratory for Internet of Things and Mobile Internet Technology of Jiangsu Province,Huai′an 223001,China)

机构地区：[1]淮阴工学院计算机与软件学院,淮安223001 [2]江苏省物联网移动互联技术工程实验室,淮安223001

出　　处：《中国图象图形学报》2022年第10期3058-3067,共10页Journal of Image and Graphics

基　　金：国家重点研发计划资助(2018YFB1004904);江苏省六大人才高峰资助项目(XYDXXJS-011);江苏省333工程资助项目(BRA2016454);江苏省教育厅重大研究项目(18KJA520001)。

摘　　要：目的基于视觉的车辆行驶安全性预警分析技术是目前车辆辅助驾驶的一个重要研究方向,对前方多车道快速行驶的车辆进行精准的跟踪定位并建立稳定可靠的安全距离预警模型是当前研究难点。为此,提出面向高速公路场景的车路视觉协同行车安全预警算法。方法首先提出一种深度卷积神经网络SF_YOLOv4(single feature you look only once v4)对前方车辆进行精准的检测跟踪;然后提出一种安全距离模型对车辆刹车距离进行计算,并根据单目视觉原理计算车辆间距离;最后提出多车道预警模型对自车行驶过程的安全性进行分析,并对司机给予相应安全提示。结果实验结果表明,提出的SF_YOLOv4算法对车辆检测的准确率为93.55%,检测速度(25帧/s)领先对比算法,有效降低了算法的时间和空间复杂度;提出的安全距离模型计算的不同类型车辆的刹车距离误差小于0.1 m,与交通法建议的距离相比,本文方法计算的安全距离精确度明显提升;提出的多车道安全预警模型与马自达6(ATENZA)自带的前方碰撞系统相比,能对相邻车道车辆进行预警,并提前0.7 s对前方变道车辆发出预警。结论提出的多车道预警模型充分考虑高速公路上相邻车道中的车辆位置变化发生的碰撞事故;本文方法与传统方法相比,具有较高实用性,其预警效果更加客观,预警范围更广,可以有效提高高速公路上的行车安全。Objective Vehicles motion are prone to traffic accidents on the expressway due to their high speed,which mainly include rear-end collisions,punctures,scratches,side collisions,etc.Among them,high-speed rear-end collisions,overtaking and lane changing accounted for t the most losses of them.Therefore,it is essential to analyze the driving safety and reduce the occurrence of accidents.Thanks to the development of deep learning,vision-based vehicle driving safety early warning analysis technology is currently an important research direction for vehicle aided driving.We propose an early warning algorithm for vehicle-road visual collaborative driving safety in expressway scenes.Method The vehicles motion safety early warning algorithm in synchronized vehicles-road visual expressway scenarios is facilitated.First,we illustrated a vehicle motion recorder to monitor and combine vehicle target recognition and positioning,a safe distance model,and analyzes driving safety based on a multi-lane early warning algorithm.It is composed of three parts like vehicle target recognition and positioning technology,safety distance model and multi-lanes warning algorithm.The image processing technology is as the input to detect the distance between the vehicle ahead and the vehicle body.A safe distance model early warning fusion algorithm is,performed to safety analysis on the motion of the vehicle.Our deep convolutional neural network of single feature you look only once v4(SF_YOLOv4)detects and tracks the vehicle ahead accurately.Then,the range of the vehicles is calculated in terms of the perspective transformation principle combined with the vehicle position information.Finally,a safe distance model and fusion algorithm are proposed to analyze the vehicle safety.In the target detection part,our method is improved on the basis of YOLOv4.The backbone network is replaced by CSPDarknet53 with a smaller layer of cross stage paritial Darknet17(CSPDarknet17)network,which reduces the number of model parameters and calculations,improves the

关 键 词：安全性分析 防碰撞预警 车辆目标检测 安全距离模型 YOLOv4 车距计算 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]