车辆检测-MobileNetSSD 车辆计数检测模型，可以读取实时移动物体并定义其类别。该模型使用DNN来检测和分类可能在任何给定帧中的不同对象。我们使用的模型是Caffe版本的模型，称为MobileNet-SSD，它使用了Google的称为MobileNet的框架与另一个称为Single Shot Detector（SSD）MultiBox的框架的混合体。 输出已保存为名称“ Output_mobilenet-ssd.mp4”。

optaweb-vehicle-routing-master.zip

微信小程序-虚拟车牌输入键盘组件vehicleKeyboard-master.zip

Global-Guide-to-Autonomous-Vehicles-2021（自动驾驶汽车2021年全球指南）.pdf

Autonomous Vehicle Implementation Predictions（自动驾驶汽车的实施预测对交通规划的影响）.pdf

Space Vehicle Navigation Guidance And Control.pdf s report contains a general summary plus a detailed bibliography on Space Vehicle Guidance, Navigation, and Attitude Control. The report is intended as a general reference source for further research in various areas of Space Vehicle Guidance and Control. Only a general summarization is provided since a detailed analysis of all areas in this vast field would be unreasonable in terms of time and costs. Launch vehicle guidance and control is presented in I_SIC-494, entitled "Methods of Launch Vehicle Control."

This edited volume includes thoroughly collected on sensing and control for autonomous vehicles. Guidance, navigation and motion control systems for autonomous vehicles are increasingly important in land-based, marine and aerial operations. Autonomous underwater vehicles may be used for pipeline inspection, light intervention work, underwater survey and collection of oceanographic/biological data. Autonomous unmanned aerial systems can be used in a large number of applications such as inspection, monitoring, data collection, surveillance, etc. At present, vehicles operate with limited autonomy and a minimum of intelligence. There is a growing interest for cooperative and coordinated multi-vehicle systems, real-time re-planning, robust autonomous navigation systems and robust autonomous control of vehicles. Unmanned vehicles with high levels of autonomy may be used for safe and efficient collection of environmental data, for assimilation of climate and environmental models and to complement global satellite systems. The target audience primarily comprises research experts in the field of control theory, but the book may also be beneficial for graduate students.

Haar级联的车辆检测 最后页面更新时间： 2016年10月19日 最新版本： 1.0.0 （有关更多信息，请参见发行说明） 大家好，执行车辆检测的一种简单方法是使用Haar Cascades。 当前，我没有关于它的详细教程，但是您可以在OpenCV主页上获得一些其他信息，请参阅页面。 另请参阅以训练您自己的级联分类器。 haar-cascade cars.xml是使用来自后方的526张汽车图像（360 x 240像素，无比例缩放）进行训练的。 这些图像是从布拉德·菲利普（Brad Philip）和保罗·厄普代克（Paul Updike）提出的Car数据集中提取的，该数据集取自南加州的高速公路。 有关更多信息，请参见： 训练自己的OpenCV Haar分类器 相关论文： 奥利维拉，M。 Santos，V.使用类似Haar的功能自动检测实际道路上的汽车（ ） 一些其他资源：

控制领域很大牛人写的书，搞控制的必看！Khac Duc Do • Jie Pan这两个人一定得知道的。

几百种种车辆品牌的型号的平均价格、车辆图片、车标等