汽车偏离车道线的图像分析与视觉计算方法

针对汽车自主驾驶技术的车道检测和跑偏告警问题,提出了一种快速可靠的视觉计算方法。利用方向滤波算子对路面图像进行5×5模板运算,得到边缘图像;采用Otsu自动阈值算法对图像进行二值化处理,并根据车道在图像中的位置特性对边沿图像细化去点,减少后续处理运算量;在此基础上,根据车道几何特性引入约束条件,去除干扰点,并采用Hough 变换检测出车道线;依据针孔摄像机模型建立空间坐标系,用于计算汽车相对于车道线的偏转角和垂直距离,估计驶离车道的时间,为汽车自主驾驶中的安全预警及智能控制提供信息支撑。     

嵌入式车道跑偏告警系统设计

为提高汽车驾驶的安全性,减少高速公路上由于车道跑偏而造成的巨大损失,基于DSP计算和FPGA计算,构造出一套基于视觉的嵌入式车道跑偏告警系统.重点介绍了系统的框架结构以及硬件实现方式,详细研究并解决了视觉系统中大容量数据的传输、存储和处理而造成的带宽问题,采用FPGA内部构造FIFO及DSP上支持的PDT传输方式,加快总线上的数据传输,提高总线的利用率,满足系统的实时性要求.最后,基于构造的系统,设计了典型的视觉处理算法和应用于车道跑偏决策的软件算法,验证了系统的可行性.     

基于STM32的车道保持控制系统设计

针对商用车道保持控制以及实车测试,设计了一种基于STM32的车道保持控制系统.通过CAN总线与车辆行驶状态监测的传感器、基于视觉的车道检测传感器建立通信;并对车身方位角、距左右车道线距离、车速、方向盘转角等数据进行解算;利用基于驾驶体验的三段式控制策略,实现对车道保持所需转向扭矩的计算,进而在实际道路上验证了系统的车道...     

基于视觉识别的机动车辆智能驾驶系统

将机器人视觉原理运用于对机动车辆智能驾驶的研究中,通过识别高速公路车道线的变化,使得车辆能够实现自动驾驶。由CMOS图像传感器摄取道路图像,采用小波处理、线性递推估计、波门跟踪滤波和二次拟合抽取车道线信息,从而得到行驶控制误差;控制命令通过串行232口发送到单片机上,驱动步进电机带动游戏杆转动来仿真实际的自动驾驶过程;获得了很好的实验结果。     

基于图与势场法的多车道编队控制

多车协同驾驶能显著提高交通安全和效率,是未来5G网联自动驾驶技术的重要应用场景之一.传统上,多车协同驾驶的主要形式为单一车道上的无人车队列,其队列稳定性受队列长度、通信距离及延迟的限制.本文提出一种无人车编队方法,将单车道队列扩展为多车道护航编队.针对不同场景下的需求设计多车道编队调整策略,结合基于图的分布式控制,完成任意预定义的编队结构;同时,利用势场法对行车环境建立势场模型,实现无人车的避障轨迹规划,提高编队的避障能力;最后,结合纵横向控制器,实现无人车多车道护航编队控制.仿真实验表明,本文提出的无人车多车道护航编队方法,能适应不同交通场景,如道路变化、障碍车运动等,完成自动变换编队结构,实现安全、高效通行.     

基于机器视觉的轻量级驾驶辅助系统

为了解决计算机视觉应用中数据量大、算法复杂的问题,根据道路结构特征和车辆行为特征,采用单个摄像头作为传感器,实现了一种轻量级的安全辅助驾驶系统。首先采用改进的边缘提取算法和车道线检测算法对摄像机内外参数进行离线标定;接着根据标定结果在二维平面图像上采用标识出实际空间距离的多窗口划分方法,并按不同的车间距将不同窗口划分为不同安全系数的区域,以赋予道路视觉检测的几何先验知识;当区域中出现障碍物时发出相应警示信息进行安全驾驶辅助,能为智能辅助驾驶提供轻量级的视觉检测平台。以便携式计算机和固定在车内的摄像头作为实验装置,在城市道路上进行车载实验。系统在车载实验中能够快速地提取车辆两侧的车道线,并利用离线标定的结果快速生成不同安全系数的警示区域,其中车辆在车道内正常行驶时的误检率和漏检率很小,可以忽略不计。与传统的驾驶辅助系统相比,本系统计算量大大降低,检测流程得到简化,可实现轻量级的车道和车辆检测,为系统在嵌入式系统上的实现奠定基础。     

基于UE4的在线驾驶仿真系统设计与实现

驾驶仿真系统在交通安全研究中发挥着重要作用,但目前仍存在三维道路建模受限、仿真场景还原度不高等问题;研究采用虚幻引擎开发仿真功能模块,根据道路几何线形与交通流原始资料,能高效搭建出驾驶仿真场景;采用像素流送技术将视频帧发送到前端,设置硬件设备的轴属性映射,支持模拟驾驶外部信号输入,并实时将驾驶行为数据存入数据库,设计了一种轻量化、自动化、智能化的在线驾驶仿真信息管理与可视化系统;系统主要实现了道路自动化建模、人-车-路-环境信息管理、车道级真实交通流场景还原、在线模拟驾驶与数据可视化等功能;经测试,系统部署后能在Web端稳定运行,弥补了现有驾驶仿真系统的不足,为实验人员提供轻量高效的驾驶仿真服务。     

基于视觉的缩微智能车自主换道

基于简单视觉的缩微智能车自主驾驶行为,提出一种完全基于视觉的超车换道策略及超车开关模糊控制算法.根据检测车道线拟合出的行驶轨迹,获得车辆中心轴与行驶轨迹之间的偏移距离E及其变化率Ec,针对直道行驶和超车换道行驶采用不同的模糊转向控制算法.此外,本文通过实验研究像素点距与实际距离关系、安全超车距离与速度的关系;并通过实验验证视觉换道策略的有效性及车辆切换模糊控制算法的高效性和稳定性.     

基于机器视觉的车道线检测研究进展综述

针对车道线检测技术在车道偏离预警、自动泊车和车道变换等各种辅助驾驶系统中的重要作用,国内外专家学者对车道线检测技术做了较多的研究,但是近年来少见有关于车道线检测的综述,因此本文主要阐述了近几年国内外机器视觉的车道线检测研究进展。首先简单介绍了机器视觉的车道线检测的基本流程;其次重点阐述了基于特征、基于模型和基于深度学习三种典型方法的基本检测原理和研究现状,并对比三种典型研究方法;最后,提出了机器视觉的车道线检测方法主要存在的问题,并针对问题提出未来的发展方向。     

一种基于机器视觉的跑偏角估计算法

在移动机器人、汽车等自主行进过程中,由于受到道路情况等因素的影响,极易出现方向跑偏,导致其无法按照预定路径前进。为了防止出现跑偏等危险情况,研究了利用机器视觉估计预定运动方向和实际运动方向夹角即跑偏角的方法。介绍了利用机器视觉测量跑偏角的基本原理;研究了基于特征点跟踪的跑偏角估计算法。实验研究表明:该方法可以有效地在行进过程中计算跑偏角。     

基于单目视觉的汽车防偏防追尾预警系统研究

该文设计了一个基于单目视觉的汽车防偏防追尾预警系统,应用于主要障碍物为车辆的结构化道路环境.利用视觉传感器提供的道路图像序列亮度、纹理、形状等丰富特征,对汽车前方道路和车辆进行检测和跟踪,根据其危险程度,向驾驶员提供可靠的防偏指令和防追尾预警信号,并辅以声光报警.介绍了系统软硬件结构,给出了主程序流程图.对定位前方道路及车辆的算法和预警方案等关键技术进行了研究,给出了系统可行性和有效性验证的仿真和初步试验结果.     

Ego-Lane Analysis System (ELAS): Dataset and algorithms

Decreasing costs of vision sensors and advances in embedded hardware boosted lane related research – detection, estimation, tracking, etc. – in the past two decades. The interest in this topic has increased even more with the demand for advanced driver assistance systems (ADAS) and self-driving cars. Although extensively studied independently, there is still need for studies that propose a combined solution for the multiple problems related to the ego-lane, such as lane departure warning (LDW), lane change detection, lane marking type (LMT) classification, road markings detection and classification, and detection of adjacent lanes (i.e., immediate left and right lanes) presence. In this paper, we propose a real-time Ego-Lane Analysis System (ELAS) capable of estimating ego-lane position, classifying LMTs and road markings, performing LDW and detecting lane change events. The proposed vision-based system works on a temporal sequence of images. Lane marking features are extracted in perspective and Inverse Perspective Mapping (IPM) images that are combined to increase robustness. The final estimated lane is modeled as a spline using a combination of methods (Hough lines with Kalman filter and spline with particle filter). Based on the estimated lane, all other events are detected. To validate ELAS and cover the lack of lane datasets in the literature, a new dataset with more than 20 different scenes (in more than 15,000 frames) and considering a variety of scenarios (urban road, highways, traffic, shadows, etc.) was created. The dataset was manually annotated and made publicly available to enable evaluation of several events that are of interest for the research community (i.e., lane estimation, change, and centering; road markings; intersections; LMTs; crosswalks and adjacent lanes). Moreover, the system was also validated quantitatively and qualitatively on other public datasets. ELAS achieved high detection rates in all real-world events and proved to be ready for real-time applications.     

Identification of driver state for lane-keeping tasks

Identification of driver state is a desirable element of many proposed vehicle active safety systems (e.g., collision detection and avoidance, automated highway, and road departure warning systems). In the paper, driver state assessment is considered in the context of a road departure warning and intervention system. A system identification approach, using vehicle lateral position as the input and steering wheel position as the output, is used to develop a model and to update its parameters during driving. Preliminary driving simulator results indicate that changes in the bandwidth and/or parameters of such a model may be useful indicators of driver fatigue. The approach is then applied to data from 12 2-h highway driving runs conducted in a full-vehicle driving simulator. The identified model parameters (ζ ω_n , and DC gain) do not exhibit the trends expected as lane keeping performance deteriorates, despite having acceptably white residuals. As an alternative, model residuals are compared in a process monitoring approach using a model fit to an early portion of the 2-h driver run. Model residuals show the expected trends and have potential in serving as the basis for a driver state monitor     

基于计算机视觉的车道标线与障碍物自动检测

车道标线与障碍物检测是智能车辆辅助驾驶系统的关键技术问题。论文提出了一种基于计算机视觉的车道和障碍物检测新方法。它根据摄影几何投影变换从图像内容重建出道路平面图,解决了图像中远方车道过于细小、难以检测的缺点,算法对虚线车道特别有效。文中对重建参数选择进行了分析比较,实验表明重建结果对参数变化具有很好的鲁棒性。     

Lane detection and tracking using a new lane model and distance transform

Lane detection is a significant component of driver assistance systems. Highway-based lane departure warning solutions are in the market since the mid-1990s. However, improving and generalizing vision-based lane detection remains to be a challenging task until recently. Among various lane detection methods developed, strong lane models, based on the global assumption of lane shape, have shown robustness in detection results, but are lack of flexibility to various shapes of lane. On the contrary, weak lane models will be adaptable to different shapes, as well as to maintain robustness. Using a typical weak lane model, particle filtering of lane boundary points has been proved to be a robust way to localize lanes. Positions of boundary points are directly used as the tracked states in the current research. This paper introduces a new weak lane model with this particle filter-based approach. This new model parameterizes the relationship between points of left and right lane boundaries, and can be used to detect all types of lanes. Furthermore, a modified version of an Euclidean distance transform is applied on an edge map to provide information for boundary point detection. In comparison to an edge map, properties of this distance transform support improved lane detection, including a novel initialization and tracking method. This paper fully explains how the application of this distance transform greatly facilitates lane detection and tracking. Two lane tracking methods are also discussed while focusing on efficiency and robustness, respectively. Finally, the paper reports about experiments on lane detection and tracking, and comparisons with other methods.     

A Computer Vision System for Lateral Localization

For urban driving, knowledge of ego‐vehicle's position is a critical piece of information that enables advanced driver‐assistance systems or self‐driving cars to execute safety‐related, autonomous driving maneuvers. This is because, without knowing the current location, it is very hard to autonomously execute any driving maneuvers for the future. The existing solutions for localization rely on a combination of a Global Navigation Satellite System, an inertial measurement unit, and a digital map. However, in urban driving environments, due to poor satellite geometry and disruption of radio signal reception, their longitudinal and lateral errors are too significant to be used for an autonomous system. To enhance the existing system's localization capability, this work presents an effort to develop a vision‐based lateral localization algorithm. The algorithm aims at reliably counting, with or without observations of lane‐markings, the number of road‐lanes and identifying the index of the road‐lane on the roadway upon which our vehicle happens to be driving. Tests of the proposed algorithms against intercity and interstate highway videos showed promising results in terms of counting the number of road‐lanes and the indices of the current road‐lanes.     

基于模型驱动的车线偏离警告系统

车线偏离警告系统(LDWS)是车载主动安全系统的一项主要功能,研究了LDWS中的关键技术,以车道模型为核心,采用基于模型驱动的方法探测车道,为解决弯道探测的问题,将车线探测区域划分为多个子区域,独立探测每个子区域中的车线段,为提高探测效率并减少误识别,每个子区域的尺寸根据每次探测的结果和车道模型的拟合值动态变化,为增加探测的稳定性,对近处子区域采用Sobel滤波处理,对远处子区域采用根据车道几何特征动态设置的Gabor滤波处理,实验表明,系统能准确探测弯道并精确测算车道偏离范围,在噪声环境中有良好的鲁棒性.     

Probabilistic lane estimation for autonomous driving using basis curves

Lane estimation for autonomous driving can be formulated as a curve estimation problem, where local sensor data provides partial and noisy observations of spatial curves forming lane boundaries. The number of lanes to estimate are initially unknown and many observations may be outliers or false detections (due e.g. to shadows or non-boundary road paint). The challenges lie in detecting lanes when and where they exist, and updating lane estimates as new observations are made. This paper describes an efficient probabilistic lane estimation algorithm based on a novel curve representation. The key advance is a principled mechanism to describe many similar curves as variations of a single basis curve. Locally observed road paint and curb features are then fused to detect and estimate all nearby travel lanes. The system handles roads with complex multi-lane geometries and makes no assumptions about the position and orientation of the vehicle with respect to the roadway. We evaluate our algorithm using a ground truth dataset containing manually-labeled, fine-grained lane geometries for vehicle travel in two large and diverse datasets that include more than 300,000 images and 44 km of roadway. The results illustrate the capabilities of our algorithm for robust lane estimation in the face of challenging conditions and unknown roadways.