基于视觉和毫米波雷达的车辆检测

根据智能车辆主动驾驶辅助系统中的重要性,提出了一种融合毫米波雷达数据和视觉多特征的车辆检测算法。车辆检测算法通过三个步骤实现,首先,提出一种空间对准算法实现毫米波雷达和视觉的空间对准;其次,根据空间对准结果和搜索策略提取目标车辆的感兴趣区域;最后,融合车底阴影、对称轴、左右边缘等车辆特征实现车辆检测,其中,为了准确得到目标车辆的车底阴影,提出一种改进的车底阴影分割算法。算法的性能在不同的场景下得到证实,实验结果表明该车辆检测算法是有效和可靠的。     

对安全车距的智能检测技术

机器视觉技术的引入使汽车行业进入智能车辆的时代。为了提升汽车的主动安全,提出了对安全车距的智能检测技术。首先依据车道线位置的特殊性进行车道识别。然后利用遗传算法确定最优阈值来进行道路分割。最后利用水平投影与极值提取确定出前方车辆所处区域范围,从而判定出是否符合安全车距。实验结果表明:此种算法能够在保证实时性的同时准确判断出安全结果。     

基于小波分解的弯道识别技术

为了保障车辆安全有效的通过弯道,准确识别弯道曲率尤为重要。因此,文中提出一种基于小波分解的弯道识别技术,该算法通过车载CCD实时采集自车前方的车道线的图像信息,对采集的图像进行小波去噪以及小波边缘检测,最终准确辨识车道线,通过曲线拟合确定道路的曲率半径。经试验验证,该算法能准确辨识出弯道的曲率半径。     

基于激光与视觉的车辆防撞技术的研究

提出了一种基于视觉与激光测距相结合的车辆防撞技术.首先采用改进的Hough变换对车道标志线进行检测与提取,根据车辆底部的阴影特征搜寻车辆可能出现的位置;利用边缘检测排除非车辆区域;然后结合车辆在图像中的宽度,利用激光测距传感器对前方车辆动态扫描,计算出两车的车距和相对速度;最后用卡尔曼滤波算法连续跟踪检测到的车辆;实验表明:该方法能够实时有效地检测前方车辆,在高精度汽车防撞系统中具有一定的意义.     

基于单目视觉的行车环境安全预警系统设计

文中设计了基于机器视觉的行车环境安全预警系统,采用包含视频图像采集、行车环境检测以及行车环境安全预警3个功能模块的系统结构,重点研究并提出了新的行车环境识别方法,包含车道线提取和前方车辆检测方法,并对系统硬件架构及软件流程和算法进行说明,实现高速公路行车环境的识别并进行危险警示。实验结果显示,系统能够准确地对前方车道线和车辆进行检测,实现设计效果。     

基于视频处理的运动车辆检测算法的研究

车辆检测技术是现代智能运输系统的重要组成部分,现有的相关视频检测算法能够检测目标且对环境具有一定的适应性,但其在算法实时性、识别率等方面仍有待提高。提出了一种基于Fisher准则函数法的自适应阈值背景减法和对称差法相结合的运动车辆检测算法,该方法采用surendra算法提取背景,通过背景减法提取出目标前景,再将其与对称差法相结合得到准确的运动目标区域并实时地完成背景更新。实验表明该方法快速、准确,具有一定的实用价值。     

鲁棒的实时多车辆检测与跟踪系统设计

场景中的运动阴影导致多目标粘连,车辆间的相互遮挡使得跟踪识别困难.本文针对这两个影响实时车辆检测与跟踪系统性能的主要因素,采用基于无偏卡尔曼滤波器(UKF)的方法为场景背景建模,提取出运动区域,再通过边缘特征检测出场景中的运动阴影,然后利用角点信息将目标与阴影分离;提出了一种基于运动预测框的目标跟踪算法,将它与基于车辆平行四边形轮廓的遮挡分割方法结合,构建了多车辆目标的实时跟踪系统,并用实验验证了它的实用性与鲁棒性.     

基于机器视觉的车道线精确检测算法

车道线检测是车辆智能驾驶系统的重要组成部分.针对传统的车道线检测方法精度低、实时性能差的问题,提出一种基于机器视觉的车道线精确检测算法.该算法采用车道内侧边缘线代表车道线,具体包括预处理和车道线提取两个步骤:预处理部分包括灰度化、Sobel边缘检测、ROI设定、二值化,最终得到车道线部分的二值图像;车道线提取部分包括图像切片、改进的Hough直线检测、DBSCAN直线聚类以及直线拟合,最终得到精确的车道边缘线信息.最后将算法应用于各种场景下的路况测试,实验结果表明:该算法的平均准确率为94.9％,平均处理时长为25.6 ms/f,具有很好的实时性和鲁棒性.     

面向嵌入式平台的车道线检测方法

车道线检测在自动驾驶和高级辅助驾驶中起着举足轻重的作用,然而,传统的车道线检测技术鲁棒性较差,而大多数基于深度学习的方法复杂度又较高,难以在嵌入式平台实时应用。提出一种面向嵌入式平台的轻量级车道线检测网络,将车道线检测转化为语义分割问题,该网络借鉴U-Net与Segnet网络结构,使用了小尺度卷积等轻量化组件设计计算高效的语义分割网络。在检测车道线的基础上,计算车辆距离两侧车道线的距离,以及车道线的曲率,同时当车辆偏离车道线或检测出现异常时进行预警,最后将整个系统移植到海思平台。实验结果表明:该系统具有较高的检测精度以及检测速度,准确率达到97.5%,速度达到50 FPS,满足实时性要求,因此该系统能够用于面向嵌入式平台的实时车道线的检测、测距、曲率计算以及预警。     

夜间车道线检测与跟踪算法研究

夜间车道线的检测与跟踪是汽车智能辅助安全驾驶系统在夜间工作的前提.根据夜间车道图像照度低,光照不均匀的特点,提出先进行基于光密度差的对数Prewitt边缘检测,再进行Hough变换检测单帧车道线,最后融合固定区域法和Kalman滤波法预测感兴趣区域跟踪连续车道线的算法.实验结果表明,该算法具有准确、实时的夜间车道线检测与跟踪能力.     

基于RGB彩色通道的结构化道路车道线检测

车道线检测是车辆智能辅助系统的重要组成部分,为提高检测准确性,文中采用一种基于RGB颜色特征的车道线检测方法。根据车道线颜色特征设计转移函数标记图像中的车道线区域,并应用基于形态学的边缘检测算法提取车道线边缘,最终检测出车道线。文中算法原理简单,在车道线边缘识别上,具有较高的准确度,对自动车辆车道线检测有一定的意义。     

Vehicle counting based on double virtual lines

Traditionally, magnetic loop detectors are often used to count vehicles passing over them in intelligent transportation system. Real-time image sequences are captured by video surveillance system. Virtual loop, which emulates the functionality of inductive loop detectors, is placed on images. It is more convenient, but it occurs in false detection and discrimination when vehicles are lane departure due to overtaking or crossing. This paper presents an effective approach for vehicle counting based on double virtual lines (DVL). Double virtual lines are assigned on images, which are across bidirectional multi-lane. The region between DVL is the detection zone, rather than virtual loop zone in each lane, so as to reduce the proportion of false detection and misjudgment from lane departure for vehicles. Then, in the detection zone, the dual-template convolution is designed to detect and locate moving vehicles to eliminate the mapping of one to many, many to one. The effective rules are given in terms of the constraint of the horizontal and vertical distances to improve the accuracy of vehicle counting. Experimental comparisons with the other method demonstrate the performance of the proposed method.     

基于车辆底盘阴影的车辆精确分割算法研究

在基于视频的智能交通系统中，利用车辆底盘阴影特征，并结合车辆边缘及稳定背景和可信前景等特征，抑制局部遮挡导致的车辆粘连问题，将遮挡的车辆准确分离。实验结果表明，车辆底盘特征能较好地用于粘连车辆的精确分割。     

视频图像中的车辆检测及阴影去除方法研究

视频监控系统成为近年来的研究热点。文中针对交通智能视频检测系统的车辆检测问题,采用基于高斯混合模型方法来检测视频中的运动车辆,结合车辆阴影纹理不变的特性分割阴影,实现了运动车辆的检查识别和阴影去除。仿真实验达到了预期效果。     

Vision-based vehicles in Japan: machine vision systems and drivingcontrol systems

This paper surveys three intelligent vehicles developed in Japan, and in particular the configurations, the machine vision systems, and the driving control systems. The first one is the Intelligent Vehicle, developed since the mid 1970's, which has a machine vision system for obstacle detection and a dead reckoning system for autonomous navigation on a compact car. The machine vision system with stereo TV cameras is featured by real time processing using hard-wired logic. The dead reckoning function and a new lateral control algorithm enable the vehicle to drive from a starting point to a goal. It drove autonomously at about 10 km/h while avoiding an obstacle. The second one is the Personal Vehicle System (PVS), developed in the late 1980's, which is a comprehensive test system for a vision-based vehicle. The machine vision system captures lane markings at both road edges along which the vehicle is guided. The PVS has another machine vision system for obstacle detection with stereo cameras. The PVS drove at 10-30 km/h along lanes with turnings and crossings. The third one is the Automated Highway Vehicle System (AHVS) with a single TV camera for lane-keeping by PD control. The machine vision system uses an edge extraction algorithm to detect lane markings. The AHVS drove at 50 km/h along a lane with a large curvature     

GOLD: a parallel real-time stereo vision system for genericobstacle and lane detection

This paper describes the generic obstacle and lane detection system (GOLD), a stereo vision-based hardware and software architecture to be used on moving vehicles to increment road safety. Based on a full-custom massively parallel hardware, it allows to detect both generic obstacles (without constraints on symmetry or shape) and the lane position in a structured environment (with painted lane markings) at a rate of 10 Hz. Thanks to a geometrical transform supported by a specific hardware module, the perspective effect is removed from both left and right stereo images; the left is used to detect lane markings with a series of morphological filters, while both remapped stereo images are used for the detection of free-space in front of the vehicle. The output of the processing is displayed on both an on-board monitor and a control-panel to give visual feedbacks to the driver. The system was tested on the mobile laboratory (MOB-LAB) experimental land vehicle, which was driven for more than 3000 km along extra-urban roads and freeways at speeds up to 80 km/h, and demonstrated its robustness with respect to shadows and changing illumination conditions, different road textures, and vehicle movement.     

结合Hough变换与运动估计的车道线提取方法

车道线识别是安全辅助驾驶和智能驾驶系统的核心研究内容,对控制危险驾驶和疲劳驾驶均有显著的作用,通常利用Hough变换对直线检测的容错性和鲁棒性,可以对车载摄像头拍摄到的车道线进行有效的检出.巧妙地将分块Hough变换和图像块的运动估计相结合,极大地降低了车道线检测和跟踪的算法复杂度,实现了车道线的实时识别与跟踪.实验表明,采用该方法既可以得到稳定的检测结果,又能提高检测的速度,保留了Hough变换的容错性和鲁棒性.     

Perception for collision avoidance and autonomous driving

The Navlab group at Carnegie Mellon University has a long history of development of automated vehicles and intelligent systems for driver assistance. The earlier work of the group concentrated on road following, cross-country driving, and obstacle detection. The new focus is on short-range sensing, to look all around the vehicle for safe driving. The current system uses video sensing, laser rangefinders, a novel light-stripe rangefinder, software to process each sensor individually, a map-based fusion system, and a probability based predictive model. The complete system has been demonstrated on the Navlab 11 vehicle for monitoring the environment of a vehicle driving through a cluttered urban environment, detecting and tracking fixed objects, moving objects, pedestrians, curbs, and roads.     

复杂行车环境下的前方车辆检测算法研究

针对复杂行车环境下的智能车辆行车安全问题,提出了一种基于特征的多目标前方车辆检测算法。算法首先利用车辆底部阴影特征、车辆轮廓特征的先验知识,探测前方感兴趣区域,然后利用车辆边界特征、对称性特征对感兴趣区域进行判别。该算法针对复杂路况下的车辆分布特征进行了适应性设计。能够快速、准确的检测到具有潜在安全威胁的前方行驶车辆。