共查询到20条相似文献,搜索用时 498 毫秒
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Front Vehicle Identification Under Different Lighting Conditions 总被引:1,自引:0,他引:1
选取车辆底部水平方向特征和车辆左右两侧垂直方向特征,提取出受全局灰度影响较小的局部灰度特征、局部梯度特征和局部波动特征.应用加权证据理论将车辆水平和垂直方向的特征分别进行信息融合,并根据光强的不同调整各特征的权重.为提高识别的实时性,将人工鱼群算法应用于识别中,并增添了车辆识别模块以增强人工鱼的引导能力.根据人工鱼群的搜索结果,再通过对称性特征进行进一步判定,实现前方车辆的准确定位.实验结果表明本方法可在不同光照条件下对前方车辆进行识别并准确定位,具有良好的适应性、准确性和实时性. 相似文献
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针对智能车辆安全辅助驾驶系统中利用单目视觉进行车道识别的问题,提出了1种基于平行直线对模型的车道检测方法。该方法根据高速公路图像特征构建平行直线对模型,在此基础上先利用 Hough变换提取直线,再由改进的级联 Hough变换检测出平行直线对的消失点,最后通过消失点和先验信息来提取当前车道线。使用M atlab对高速公路上不同路段、不同光照情况、不同车辆干扰下共150幅道路图像进行实验,检测精度达88.6%,平均检测时间为0.24 s。实验结果表明,这一方法在高速公路行驶环境下能较准确地检测出当前车道线,具有很好的光照适应性、抗车辆干扰性和一定的实时性。 相似文献
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弯曲路径识别中感兴趣区设定方法的研究 总被引:1,自引:0,他引:1
在路径识别中设置搜索感兴趣区以提高弯曲道路边界的识别实时性。用抛物线模型拟合弯曲道路边界,根据道路边界位置不会发生突变的特性,在识别出的道路抛物线模型上选取有代表性的3个点,针对每个点建立预测模型,应用Kalman滤波理论准确预测道路边界的位置,并据此设置搜索感兴趣区。依照合理的目标函数,在感兴趣区范围内搜索并确定抛物线参数,从而将搜索范围较为准确地限定在较小区域,能够有效提高识别的实时性。试验验证该方法在大大提高识别实时性的同时,增强了识别的精确性和鲁棒性。 相似文献
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基于当前智能驾驶背景下道路特征模型的车道线识别现状,对应用于智能汽车的图像预处理中的灰度化处理算法、滤波处理算法和感兴趣区域提取技术分别进行对比分析,研究不同的图像预处理方法在车道线识别算法的应用适用性。对车道线实时提取算法中的边缘检测技术原理、道路特征条件转化算法进行综合运用分析,搭建基于道路特征的车道线识别算法模型,经过在Visual Studio平台验证,算法模型满足智能驾驶汽车车道线识别要求。 相似文献
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《公路交通科技》2018,(12)
本文通过一种改进的基于RGB空间的颜色增强算法变换RGB空间颜色值并分割图像,锁定道路两边的图像信息并利用其特有的几何形状检测并定位交通标志,提取其内部图形,建立基于边缘梯度特征的交通标志匹配和污损识别算法模型。该算法利用少量样本快速创建不同尺度和角度下的模板,解决了基于机器学习方法下需要大量样本并训练时间过长的问题,同时基于梯度特征进行匹配,解决了基于灰度的模板匹配对光照变化过于敏感的问题。最后,通过采集了大量图像数据,并研发基于服务端和移动端的原型系统进行模型算法的验证,实例实验表明,本文提出的算法具有较高的识别准确率和匹配效率,检测准确率可达到83%,且能满足基于移动端的应用需要。 相似文献
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A. López J. Serrat C. Cañero F. Lumbreras T. Graf 《International Journal of Automotive Technology》2010,11(3):395-407
Detection of lane markings based on a camera sensor can be a low-cost solution to lane departure and curve-over-speed warnings.
A number of methods and implementations have been reported in the literature. However, reliable detection is still an issue
because of cast shadows, worn and occluded markings, variable ambient lighting conditions, for example. We focus on increasing
detection reliability in two ways. First, we employed an image feature other than the commonly used edges: ridges, which we
claim addresses this problem better. Second, we adapted RANSAC, a generic robust estimation method, to fit a parametric model
of a pair of lane lines to the image features, based on both ridgeness and ridge orientation. In addition, the model was fitted
for the left and right lane lines simultaneously to enforce a consistent result. Four measures of interest for driver assistance
applications were directly computed from the fitted parametric model at each frame: lane width, lane curvature, and vehicle
yaw angle and lateral offset with regard the lane medial axis. We qualitatively assessed our method in video sequences captured
on several road types and under very different lighting conditions. We also quantitatively assessed it on synthetic but realistic
video sequences for which road geometry and vehicle trajectory ground truth are known. 相似文献
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针对L2/L3级自动驾驶汽车的仿真测试和封闭场地测试认证需求,结合现有L2/L3级自动驾驶汽车量产车型的主要功能特点,提出自动驾驶汽车基础测试场景群的构建方法。首先针对指定的道路交通环境,分析主车和周围交通参与者可能的相对位置和运动方向的组合,确定复杂场景群。其次分别以主车功能所确定的各个可能运动方向,依此与各干扰车辆的可能运动方向(包括任一干扰车辆不存在的情形)进行组合,组合时采用PICT组合测试工具,并添加必要的运动约束条件,选择参数组合覆盖标准自动生成全部的组合场景群。最后结合场景筛选规则,筛选出具有测试价值的覆盖各个层级及功能的基础测试场景群。采用场景构建方法,对于主车处于三车道中间车道的路段场景和无红绿灯的十字路口场景,分别构建62种和33种基础测试场景。根据驾驶人行为特性、交通规则、汽车在城市、郊区和高速公路工况下的典型车速、加减速度、横向加速度、交通事故和自然驾驶数据库的有关场景数据等,设计主车换道工况的测试用例。采用模型预测控制框架建立主车局部路径规划和控制仿真模型,并对主车危险换道场景进行仿真。研究结果表明:主车在邻车道前车大减速的情况下实现了减速换道并避免了与本车道前车和邻车道前后车的碰撞,同时跟踪到期望跟车间距,验证了该换道测试用例的有效性。 相似文献
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为了探讨道路线形变化对侧碰、刮擦等侧向安全事故的影响,以三维空间线形的曲率和挠率作为公路线形几何特征描述参数,以车道偏移量作为侧向行车安全的表征指标,剖析了线形在空间层面发生的几何突变对车道偏离的影响。在山区高速公路开展实车试验,采用侧向行车视频记录连续的车道偏移,进行图像距离与实际距离的标定,并通过图像识别技术自动读取连续的车道偏移曲线,从中获取最大车道偏移作为分析变量。采用单因素方差分析方法,对新手驾驶人和熟练驾驶人在线形空间几何特征不同的曲线路段所表现的最大车道偏移结果展开统计分析和检验。分析结果表明:当空间曲率突变超过一定的临界值时,空间曲率突变与最大车道偏移显著正相关;挠率突变对车道偏移产生的影响主要取决于线形扭转的方向,当线形扭转方向与路拱横坡反向时,会明显加剧最大车道偏移;而线形扭转方向与路拱横坡同向时,会降低最大车道偏移但降低效果不明显;熟练驾驶人的最大车道偏移小于新手驾驶人,这种现象在空间曲率突变较大和挠率突变不利的路段尤为明显。研究结论可为公路线形安全性评价、线形设计优化和路侧安全改善提供参考。 相似文献
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《国际交通安全学会研究报告》2020,44(2):155-161
This study aims to investigate the contributing factors affecting the occurrence of crashes while lane-changing maneuvers of drivers. Two different data sets were used from the same drivers' population. The first data set was collected from the traffic police crash reports and the second data set was collected through a questionnaire survey that was conducted among 429 drivers. Two different logistic regression models were developed by employing the two sets of the collected data. The results of the crash occurrence model showed that the drivers' factors (gender, nationality and years of experience in driving), location and surrounding condition factors (non-junction locations, light and road surface conditions) and roads feature (road type, number of lanes and speed limit value) are the significant variables that affected the occurrence of lane-change crashes. About 57.2% of the survey responders committed that different sources of distractions were the main reason for their sudden or unsafe lane change including 21.2% was due to mobile usage. The drivers' behavior model results showed that drivers who did sudden lane change are more likely to be involved in traffic crashes with 2.53 times than others. The drivers who look towards the side mirrors and who look out the windows before lane-change intention have less probability to be involved in crashes by 4.61 and 3.85 times than others, respectively. Another interesting finding is that drivers who reported that they received enough training about safe lane change maneuvering during issuing the driving licenses are less likely to be involved in crashes by 2.06 times than other drivers. 相似文献