4results about How to "Avoid heavy traffic" patented technology

The present invention provides a control method of an intelligent traffic control system based on YOLO visual perception, and belongs to the technical field of traffic control. An intelligent traffic control system based on YOLO visual perception includes a video acquisition and processing module and a control module; the present invention connects a camera at an intersection through the video acquisition and processing module, captures the captured video image, analyzes and extracts the image feature information and converts it into control information, and at the same time, the video acquisition and processing module is connected to the control module for communication, and the control module is connected to the traffic light at the intersection, so that the control module can receive the control information and adjust the duration of the traffic light at the intersection in real time according to the control information, that is, according to the traffic volume of different lane types in different directions of the intersection, the duration of the traffic light at the corresponding intersection is controlled in real time, and the duration of the traffic light at the intersection is intelligently and dynamically adjusted, which effectively avoids the problem of traffic congestion in one direction and empty traffic in the other direction in actual road conditions, and improves the traffic efficiency of the road.

The present invention relates to the field of traffic monitoring technology, and in particular to a traffic satellite monitoring method and system based on artificial intelligence. First, an image filling network and a vehicle detection network are trained to obtain a trained image filling network and a trained vehicle detection network; then, the time position sequence distribution data of historical traffic vehicles are obtained based on a historical remote sensing image data set; finally, based on real-time satellite remote sensing images, real-time vehicle distribution data is obtained, and the vehicle distribution data at the next moment is predicted based on the real-time vehicle distribution data and the time position sequence distribution data of historical traffic vehicles. The present invention is based on feature extraction and deep learning, combined with real-time satellite remote sensing images to predict traffic distribution, has no usage restrictions, can be applied to any scene, and provides a good foundation for avoiding traffic congestion.

The application discloses a method, device and equipment for generating a U-turn trajectory based on automatic driving and a medium. The method comprises the following steps: when a vehicle drives to a preset U-turn section, a U-turn space is discretely sampled to obtain a sampling point, wherein the U-turn space is a space between a current lane of the vehicle and an opposite lane in which the vehicle can make a U-turn, and the sampling point is a position on a center line of the U-turn space that is passed by the vehicle when making a U-turn; a vehicle adjustment space is constructed according to a total width of the current lane of the vehicle and the opposite lane and a length of the U-turn space; and a vehicle U-turn trajectory is generated by a preset path planning method according to a current position of the vehicle, the sampling point, the vehicle adjustment space and a road condition of a lane to be driven into. The method can solve the problem that a U-turn trajectory cannot be flexibly generated in the prior art.

The invention discloses a freight navigation optimization method, system and device and a medium. The method is applied to a navigation technology and comprises the following steps: amplifying and displaying an intersection map of a current intersection according to real-time position information of a freight vehicle and position information of the current intersection to obtain an amplified intersection map; drawing the road condition information of the current intersection in the amplified intersection map; and when the freight vehicle is driven out of the current intersection, ending the display of the intersection magnified graph. In the process, through the real-time position information of the freight vehicle and the position information of the current intersection, the intersection map of the current intersection is amplified and displayed, and the intersection information is drawn on the intersection map, so that a user can know the road condition of the intersection in time, and the user can adjust lanes in time to avoid traffic congestion or traffic accidents; and the current road condition can be quickly passed, so that the user experience is improved.