188 results about "Traffic intersection" patented technology

The invention discloses a processing method for dynamically adjusting a traffic light signal scheme according to traffic flow, which comprises the following steps of: deploying sensors including a ground induction coil, a camera and a pedestrian button at each traffic intersection, and preprocessing collected traffic flow and pedestrian flow data; and predicting trends of traffic flow, non-motor vehicle flow and pedestrian flow of each intersection in a short period, modeling traffic light states of each intersection into a dynamic system, calculating an optimal traffic light adjustment strategy, and adjusting the traffic light states in real time. By dynamically adjusting the traffic light signals, the system can more effectively respond to the actual on-site conditions including the flow of pedestrians and non-motor vehicles, more reasonable signal duration distribution is achieved, waiting time is shortened, traffic efficiency is improved, meanwhile, the pedestrians and the non-motor vehicles are considered, the safety of the system can be improved through an optimized signal control strategy, and the traffic light control method is suitable for popularization and application. The signal lamp is adjusted to provide more transition time for pedestrians and non-motor vehicles, so that the accident risk is reduced.

The invention provides a traffic signal lamp intelligent control method based on a fiber bragg grating sensing network, and relates to the technical field of traffic signal lamps, and the method comprises the steps: obtaining the detection data of each fiber bragg grating sensing line, and enabling the fiber bragg grating sensing lines to be transversely laid at a selected traffic crossroad along a lane, three fiber bragg grating sensing lines parallel to the stop line are laid on each road; determining the length range of the motor vehicle queuing length of each lane according to the detection data of each fiber Bragg grating sensing line; and controlling the working state of the corresponding traffic signal lamp according to the length range of the motor vehicle queuing length of each lane. The actual traffic flow can be determined through the detection data of the fiber Bragg grating sensing lines, the green light time can be dynamically allocated based on the actual traffic flow, the vehicle queuing time is shortened, and the passing efficiency can be effectively improved.

The invention discloses a sensing intelligent driving multi-mode traffic signal optimization method and system, and relates to the technical field of traffic signal optimization, and the method comprises the steps: collecting traffic flow data; constructing a multi-mode traffic flow model; connecting a plurality of traffic lights in the target area to obtain a street lamp control signal; based on the traffic flow data and the street lamp control signal, using the multi-mode traffic flow model to predict a traffic flow change trend; and configuring a timing optimization scheme of a plurality of traffic lights in the target area according to the traffic flow change trend prediction result. According to the method and the device, the technical problem of low traffic intersection passing efficiency caused by the fact that a traffic signal lamp timing scheme cannot be dynamically adjusted accurately according to real-time traffic conditions and multi-mode traffic behavior interaction relations in the prior art is solved, and the purposes of optimizing the traffic signal lamp timing of the target area according to the prediction result and improving the traffic signal lamp timing efficiency are achieved. And the traffic efficiency of the traffic intersection is effectively improved.

The invention relates to a visual analysis method for traffic intersection collaborative perception, and the method comprises the following steps: S1, employing an intersection rapid calibration algorithm, and obtaining camera images at different positions; s2, for the camera image of each position, inputting a target detection model to obtain a target detection result; s3, inputting the target detection result of each position into a target tracking model, and outputting a target tracking result; s4, converting the target tracking result into a unified global coordinate system based on the affine transformation matrix, and executing global cross-lens ID matching to obtain a cross-camera tracking result; s5, smoothing the cross-camera tracking result to obtain a smooth tracking result; and S6, performing visual perception based on the smooth tracking result to obtain a visual perception result. Compared with the prior art, the method has the advantages of ensuring that the same target can be correctly associated under different camera visual angles, realizing cross-camera consistency identification and the like.

The invention belongs to the field of visual tracking, and relates to a multi-target visual tracking method based on cooperation of a dynamic neural field and a neural network, which takes a cross-modal cooperation architecture as a core and comprises a dynamic neural field module based on multi-target trajectory maintenance and shielding matching and an improved MoESDQ neural network module. Meanwhile, a collaborative decision-making mechanism is designed, when the activation peak value of the dynamic neural field is attenuated to a preset threshold value, neural network feature matching is triggered, and disappearance target reproduction correlation is achieved based on cosine similarity. The objective of the invention is to solve the visual tracking capability under the condition of scene and target motion change in a monitoring range, for example, under an intelligent traffic intersection scene. The problems of high ID switching rate, multi-target misassociation and low tracking precision under a real-time tracking background caused by scene change or frequent shielding of vehicles and pedestrians, similar target appearances, transient disappearance and reproduction of the targets and sudden illumination change are solved.

The invention belongs to the technical field of intelligent driving decision, and discloses a no-signal crossroad intelligent vehicle decision method, which comprises the following steps: constructing a no-signal crossroad intelligent vehicle decision model including a strategy network and a value network; each of the strategy network and the value network is composed of an LSTM neural network and a full connection layer; the method comprises the following steps of: forming state parameters by self-vehicle information, traffic participation vehicle information, relative position information of a traffic intersection and a self-vehicle and relative position information of a target task point and the self-vehicle from a # imgabs0 # moment to a # imgabs1 # moment as input variables of a strategy network; using an accelerator control signal, a steering wheel angle control signal and a brake control signal corresponding to the # imgabs2 moment state parameter to form an action parameter as an output variable of the strategy network; optimizing the decision model by adopting a PPO algorithm and taking the minimum loss function of the strategy network and the value network as an optimization target to obtain an optimal decision model; and outputting a vehicle decision by using the optimal decision model.

The invention discloses an automatic driving decision-making method and system for a port mixed traffic intersection based on deep reinforcement learning, and relates to the technical field of automatic driving. Comprising the following steps: acquiring real-time state information of automatic driving transportation equipment in a port mixed traffic intersection environment, and fusing the real-time state information into an automatic driving transportation equipment state vector in a heterogeneous manner; constructing a reinforcement learning decision model based on the DDPG; building a port traffic environment simulation platform; the reinforcement learning decision model based on the DDPG is trained; and solidifying and deploying the trained reinforcement learning decision model based on the DDPG in a vehicle-mounted calculation unit of the automatic driving transportation equipment, and controlling the driving of the automatic driving transportation equipment. The traffic efficiency, stability and safety of the automatic driving transportation equipment at the port intersection can be improved, the unmanned transportation process of the port mixed traffic scene is promoted, and the wharf transformation and operation cost is reduced.

The invention relates to the technical field of automatic driving, in particular to an automatic driving system based on pedestrian motivation and a control method, and the system comprises a data acquisition module, a pedestrian detection module, a pedestrian intention reasoning module and a vehicle control module. The data acquisition module is used for acquiring a video sequence and environment information of a traffic intersection when the intelligent driving vehicle drives to the intersection without the signal lamp. The pedestrian detection module detects pedestrians through video processing and a pedestrian recognition strategy and outputs pedestrian inter-frame images. The pedestrian intention reasoning module classifies pedestrian crossing motivations based on the trajectory and attitude information of pedestrians. The vehicle control module dynamically adjusts the vehicle speed according to the pedestrian motivation type and automatically brakes when necessary so as to ensure that pedestrians safely pass through the intersection. According to the system, self-adaptive comity of the intelligent driving vehicle to pedestrians at the intersection without signal lamps is realized, and the traffic safety and the adaptive capacity of intelligent driving are effectively improved.

A method and a system for indicating a presence of a pedestrian using traffic lights at a traffic intersection, the method comprising: setting a human body sensing device to detect whether a pedestrian is present in a pedestrian waiting area. When detecting that there are pedestrians in the pedestrian waiting area, controlling the operation state of a pedestrian warning light among the traffic lights in accordance with the setting position of the human body sensing device and the state information of the driving signal lights of the traffic lights. The method and the system could reduce the likelihood of traffic accidents while protecting the priority right of pedestrians.

The present invention relates to method and system for controlling speed of vehicle by using GPS location, real-time traffic light status and machine learning techniques to recommend cruising speed to vehicle without stopping at the traffic lights. The method includes determining status of traffic lights located on route of traffic junctions and associated time period for status of traffic lights based on data received from sensors. The method includes broadcasting status of traffic lights and associated time period. The method includes calculating in real-time a recommended cruising speed of the vehicle based on GPS location associated with vehicle, status of traffic lights and associated time period, and a time period required by vehicle to arrive at the traffic lights using machine learning techniques. The method includes providing in real-time the recommended cruising speed to vehicle. The method includes adaptively controlling in real-time speed of vehicle to recommended cruising speed.

Technical problems and their solution are disclosed regarding the location of mobile devices requesting services near a site from a server. Embodiments adapt and / or configure the transmitting device near the site, the mobile device communicating with the transmitting device using a short haul wireless communications protocol to deliver a token based upon a key shared with the server but invisible to the mobile device. The server can determine the proximity of the mobile device to the site to control actuation of the requested service or disable the service request, and possibly flushing the service request from the server. Solutions are disclosed for traffic intersections involving one or more traffic lights, elevators in buildings, fire alarms in buildings and valet parking facilities.

Technical problems and their solution are disclosed regarding the location of mobile devices requesting services near a site from a server. Embodiments adapt and / or configure the transmitting device near the site, the mobile device communicating with the transmitting device using a short haul wireless communications protocol to deliver a token based upon a key shared with the server but invisible to the mobile device. The server can determine the proximity of the mobile device to the site to control actuation of the requested service or disable the service request, and possibly flushing the service request from the server. Solutions are disclosed for traffic intersections involving one or more traffic lights, elevators in buildings, fire alarms in buildings and valet parking facilities.

The invention discloses a non-motor vehicle passenger-vehicle identification method and system in a crowded scene, and belongs to the technical field of intelligent traffic management. In order to solve the problem of accurate recognition of non-motor vehicles, persons and vehicles in a crowded scene, image data of a traffic intersection is collected through a traffic camera, target detection processing is performed on the image data by using a target detection model, and a person target and a non-motor vehicle target are detected to obtain a person target frame and a non-motor vehicle target frame; judging whether the scene category to which the image data of the traffic intersection belongs is a crowded scene or not to obtain crowded scene image data; personnel key points and non-motor vehicle key points in the crowded scene image data are extracted; associating the personnel key points with the non-motor vehicle key points to determine personnel key points associated with the non-motor vehicle key points to obtain a personnel-vehicle association target; and identifying whether the person-vehicle associated target has traffic violation behaviors or not through an image tracking technology, and if so, identifying the identity information of the person target for traffic violation early warning and processing.

The invention discloses an intelligent network connection traffic signal lamp and vehicle cloud side cooperative control method, and the method comprises the steps: predicting the overall traffic flow direction through the traffic flow information of a graph structure formed by the topological relation between a plurality of traffic intersections, and planning the phase duration of each traffic signal lamp according to the predicted traffic flow direction; the method improves the coupling among a plurality of intelligent network connection traffic intersections, employs a federal learning method, builds a cloud-side cooperative training mode of a traffic signal lamp time length planning network, enables each side end device to form a cloud-side cooperative intelligent traffic whole under the guidance of a cloud end device, effectively reduces the training turns of the side end devices, and improves the training efficiency. The training efficiency is improved, and the side end load is reduced.

The invention discloses an image quality enhancement method and system based on an MESH ad-hoc network, and relates to the technical field of ad-hoc network image processing. The image quality enhancement method based on the MESH ad-hoc network comprises the following steps: node data acquisition delay error evaluation; evaluating a signal collision delay error; and evaluating a link fracture delay error. According to the method, whether detection node optimization is carried out or not is judged through a data acquisition delay error result, signal delay error evaluation is carried out through signal collision data so as to judge whether routing signal transmitting power fraction optimization is carried out or not, and link delay error evaluation is carried out through link fracture data so as to judge whether link gain optimization is carried out or not; the effect of improving the accuracy of the real-time passenger flow image analysis of the smart city traffic intersection is achieved, and the problem that the accuracy of the real-time passenger flow image analysis of the smart city traffic intersection is not high due to the fact that node image data corresponds to MESH ad hoc network route switching delay in the multi-hop transmission process in the prior art is solved.

The present invention discloses a pneumatic lifting speed bump, which comprises a lifting plate, a power assembly and a load-bearing bracket. The lifting plate is composed of longitudinal guide rails and side plates. The side plates are connected to both sides of the longitudinal guide rails. The power assembly is composed of a lifting cylinder, a damping cylinder, a high-pressure gas storage tank and a low-pressure gas storage tank. The high-pressure gas storage tank and the low-pressure gas storage tank are welded by a cross beam frame to form a groove rail with an integral structure. A number of lifting cylinders and damping cylinders are arranged alternately and evenly installed on the cross beam frame inside the groove rail. The width of the longitudinal guide rail corresponds to the groove width of the groove rail. The piston rod end of the lifting cylinder is connected to the longitudinal guide rail, so that the lifting plate and the power assembly form a mechanical body of the pneumatic lifting speed bump. An edge bracket is provided on the outer edge of the groove rail to be located on the load-bearing bracket, and the load-bearing bracket is embedded and installed in a preset ground groove pit at a traffic intersection. The present invention adopts a pneumatic lifting method with multiple cylinders and double gas storage tanks for voltage stabilization, achieving the purpose of a lifting speed bump that can pass smoothly when decelerating, give a warning when speeding, and impose a penalty through jolting.

The invention discloses a traffic signal real-time monitoring control system, which comprises monitoring equipment arranged at each traffic intersection in an area, signal lamp controllers in wireless communication connection with the monitoring equipment, and a central control system connected with all signal lamp controllers in the area, and is characterized in that the monitoring equipment is used for shooting the state of each intersection; the central control system is used for shooting traffic lights, preprocessing and feature extraction are carried out on shot images, state data of all intersections are extracted, the information is transmitted to the traffic light controller, and the traffic light controller adopts a reinforcement learning algorithm to adjust the signal conditions of the traffic lights and sends an adjustment scheme to the central control system. The central control system collects the adjustment scheme of each intersection, readjusts the scheme by adopting Nash equilibrium, and sends the adjusted scheme to the signal lamp controller again, and the signal lamp controller performs signal adjustment according to the adjustment scheme sent by the central control system. According to the invention, the traffic flow at the traffic port is improved, and traffic congestion is reduced.

The present invention relates to a vehicle-road collaborative detection method based on V2X communication and multi-sensor fusion, which belongs to the field of intelligent vehicle target detection. The method comprises the following steps: obtaining a vehicle-perspective road image, point cloud data, and a BEV road image at a traffic intersection, and obtaining a target detection result in the vehicle-perspective image; obtaining vehicle and pedestrian target detection results in a laser radar point cloud, and projecting the result onto a vehicle acquisition image plane; obtaining vehicle and pedestrian target detection results in a BEV image at a traffic intersection by using V2X communication technology; performing a perspective conversion on the BEV image at the traffic intersection by using a weight conversion method based on perspective transformation, and obtaining converted target information; correcting vehicle and pedestrian targets at the vehicle-perspective according to the converted target; and cross-modally fusing the corrected vehicle-perspective target with the projected point cloud target by constructing a convolutional neural network, and obtaining an accurate road target detection result.

The invention relates to the technical field of automatic driving and radar signal processing, in particular to an unmanned vehicle identification method based on radar perception. According to the technical scheme of the unmanned vehicle identification method based on radar perception, radar signals emitted by passing vehicles are collected in real time through a receiver installed at an urban traffic intersection or a key position. Secondly, preprocessing and time-frequency analysis are carried out on the collected signals through wavelet transform, and time-domain features and frequency-domain information of the signals are extracted; next, the processed signal image is identified and classified through an improved YOLOv10 target detection algorithm, whether the image is an LFMCW signal of a vehicle-mounted radar is determined, signal information is analyzed, accurate identification and tracking of an unmanned vehicle in urban traffic are realized, and through combination of radar signal processing and a deep learning technology, accurate identification and tracking of the unmanned vehicle in urban traffic are realized. And the limitation of a traditional visual identification method in a complex environment is effectively overcome.

The invention discloses a congestion traffic road dredging method and system, and the method comprises the steps: synchronously collecting traffic road intersection congestion data through an image collection device and a GPS positioning device which are disposed at a traffic road intersection, and uploading the data to a cloud server; the cloud server issues the congestion data to a corresponding monitoring terminal, and the monitoring terminal obtains related data of a congested road section according to the congestion data; the congestion data and the related data of the congested road section are input into a traffic road dredging virtual machine, the current traffic road congestion condition is simulated, and a plurality of dredging methods are adopted to simulate the dredging process in a virtual environment; acquiring a fastest dredging method for simulating the dredging process in the virtual environment; and the simulated dredging process of the fastest dredging method is sent to traffic road intersection signal lamp control equipment, and related commands of the dredging process are implemented through the traffic road intersection signal lamp control equipment, so that the congestion condition of a plurality of road intersections can be quickly dredged.

The invention relates to the technical field of traffic monitoring. The traffic flow dynamic monitoring method and device, the equipment and the medium are provided, and the method comprises the steps that independent game nodes are arranged at key traffic intersections of a target road network in a distributed node deployment mode, and a remote game control framework is constructed; collecting real-time traffic flow data and associated environment parameters, and generating an initial signal control strategy; performing cross-regional information exchange on the initial signal control strategy to generate a traffic resource allocation game model; performing iterative optimization on the traffic resource allocation game model by adopting a distributed Nash equilibrium algorithm to generate a global coordination signal timing scheme; performing scenarized strategy customization on the global coordination signal timing scheme according to the road function partition features to obtain a scenarized signal control instruction; and issuing the scenarized signal control instruction to an execution terminal of a corresponding intersection so as to improve the multi-intersection collaborative decision-making capability, enhance the scenarized strategy adaptability and optimize the dynamic optimization real-time performance.

The application discloses a traffic signal lamp trigger type countdown control output method, comprising the following steps: S100, configuring signal lamp fixed time countdown time length and configuring current cycle phase information; S200, reading the cycle phase information of the traffic signal lamp at the current time node and the signal lamp fixed time countdown time length, and generating a running phase chain table; S300, judging whether the time before the non-traffic light color of the current lost traffic right phase meets the configured fixed time countdown time length; S400, if the configured fixed time countdown time length is met, triggering the corresponding countdown timer; if the configured fixed time countdown time length is not met, performing countdown correction and calculating the countdown extension time; and S500, repeating steps S200-S400. The application can generate a running phase chain table under the condition that traffic intersection parameters are pre-configured, pre-add, real-time update and calculate signal control data to achieve accurate control of the time when the trigger signal triggers the countdown timer, and realize the trigger type pulse control countdown algorithm.

The invention relates to the technical field of image recognition, and provides a computer-aided infrared imaging target simulation recognition method and device, and the method comprises the steps: carrying out the clustering analysis of all vehicle heat source regions based on a driving correlation index, and obtaining an independent vehicle heat source region and a suspected adhesion vehicle heat source region; according to the position distribution of the vehicle heat source area in the independent vehicle heat source area, obtaining a corrected structural element size of the independent vehicle heat source area; according to the adhesion suspicion degree, the corrected structural element size of the suspected adhesion vehicle heat source area is obtained; enhancement is carried out based on the corrected structure element size, and an enhanced traffic intersection monitoring infrared image is obtained; and vehicle target identification is carried out based on the enhanced traffic intersection monitoring infrared image. According to the invention, the accuracy of vehicle segmentation in a complex traffic scene is improved, and the accuracy of vehicle target identification and the integrity of a vehicle target structure are ensured.

The utility model relates to a non-motor vehicle driving indicating device, and belongs to the technical field of traffic indicating devices. The non-motor vehicle driving indicating device comprises an indicating board and an LED indicating lamp arranged on the indicating board. The LED indicating lamp is powered by a solar storage battery; the LED indicating lamps can be turned on and off to form an indicating pattern. A solar panel for supplying power to the solar storage battery is arranged at the upper end of the sign board; a plurality of underground indicating lamps are further arranged on the ground to form a channel for the non-motor vehicles to turn left; and the underground indicating lamp and the LED indicating lamp on the indicating board are in linkage control. The utility model discloses a non-motor vehicle driving indicating device at a traffic intersection, which can indicate the left turn of a non-motor vehicle, so that the non-motor vehicle can turn left at one time, and the passing efficiency is improved. According to the device, through indication of the underground indicator lamp, a left-turn driving channel of a non-motor vehicle can be indicated at night, and the safety is improved.

The invention relates to the field of path planning, in particular to a vehicle path planning method based on road network division and graph reinforcement learning, comprising the following steps: forming a directed graph according to a traffic road network, nodes in the graph representing traffic intersections, and edges representing connections between the intersections; dividing the real-time traffic flow data into a plurality of control regions according to the directed graph, assigning adjacent nodes among the regions as control nodes, and endowing each edge with a weight according to the real-time traffic flow data; inputting the directed graph into the graph convolutional network, extracting a road condition flow characteristic matrix, and combining the flow characteristic matrix, the current position matrix and the destination position matrix to obtain the current state of the intelligent agent; and the intelligent agent selects the advancing direction according to the decision network in the current state, updates the state according to the selected action, and optimizes the driving route. According to the method, efficient path planning can be realized in a complex and dynamically changing traffic environment, and the method is suitable for optimizing a vehicle driving route in real time.

The application provides a traffic intersection vehicle trajectory fusion method and device, electronic equipment and medium, and relates to the technical field of intelligent transportation, which comprises the following steps: standardizing and preprocessing the perception data of a millimeter wave radar and a camera to obtain effective data; matching single-camera and multi-camera fusion prediction targets to determine a visual measurement state vector; combining Mahalanobis distance and target binding ID to complete target association; performing adaptive Kalman filtering fusion tracking to obtain fusion target state data; performing new birth and disappearance judgment on unassociated targets after clustering to obtain an effective fusion tracking target set; extracting a preliminary trajectory, matching the preliminary trajectory with a new birth trajectory after interrupting trajectory prediction in a ROI (Region of Interest) of the intersection, and performing interpolation completion to obtain an optimized trajectory; and improving multi-target tracking accuracy, trajectory speed and position consistency, adapting to complex traffic scenarios at the intersection, and providing reliable trajectory data for traffic management and vehicle-road cooperation.

The invention provides a traffic road ghost probe warning lamp, and relates to the technical field of traffic signals. The warning lamp comprises a detection device, a warning device and a control device. The detection device comprises at least two groups of laser correlation sensors, an ultrasonic distance measuring sensor and a networking monitoring camera, and is used for detecting approaching conditions of pedestrians, non-motor vehicles and vehicles at an intersection. The warning device comprises an LED lamp strip, a full-color LED dot matrix display screen and a motor vehicle display screen and is used for carrying out real-time early warning on passing vehicles and pedestrians. The control device comprises a control circuit board and a power management circuit. According to the invention, through a plurality of sensor fusion technologies, accurate detection of pedestrians and vehicles at the intersection is realized, and real-time early warning is carried out on passing vehicles and pedestrians by using the LED lamp strip and the display screen, so that traffic accidents caused by a ghost probe phenomenon are effectively prevented, the safety and traffic efficiency of the traffic intersection are improved, and accident risks are reduced.

The invention provides an intelligent scheduling and prediction method and system for a star large model by combining low-altitude economy with an intelligent calculation center, and the method comprises the steps: collecting radio wave data transmitted between a VLC transmitting end installed on each vehicle and a VLC receiving end installed at a traffic intersection, and forming a radio wave constellation; performing data classification on the radio wave constellation, and separating radio wave data received by each VLC receiving end according to a time sequence; acquiring traffic flow data distributed according to a time sequence by adopting a star large model according to the classified radio wave data; according to the traffic flow data of each traffic intersection, an optimal path algorithm is adopted to plan an optimal path for the target vehicle to arrive at the destination, and meanwhile, the congestion trend of arriving at the next traffic intersection is predicted; and after the planning result and the prediction result are integrated, report information is generated through the intelligent calculation center and transmitted to the target vehicle. According to the method, the star large model and the intelligent calculation center are used for intelligent scheduling and prediction of the vehicle network, and the effects of path planning and congestion prediction are improved.

This invention discloses a perception-driven intelligent multi-modal traffic signal optimization method and system, belonging to the field of traffic signal optimization technology. The method includes: collecting traffic flow data; constructing a multi-modal traffic flow model; connecting multiple traffic lights within a target area to obtain street light control signals; predicting traffic flow trends based on the traffic flow data and the street light control signals, using the multi-modal traffic flow model; and configuring a timing optimization scheme for multiple traffic lights within the target area based on the traffic flow trend prediction results. This invention solves the technical problem in the prior art where traffic light timing schemes cannot be accurately adjusted dynamically based on real-time traffic conditions and multi-modal traffic behavior interactions, leading to low traffic intersection efficiency. It achieves the technical effect of optimizing traffic light timing in a target area based on prediction results, effectively improving the traffic intersection efficiency.

The invention relates to the technical field of traffic control, and discloses a traffic intersection adaptive control traffic signal machine and a traffic control method, and the method comprises the steps: obtaining local point cloud data of a to-be-turned vehicle through a multi-frame image collection and three-dimensional reconstruction technology; according to the geometrical characteristics of the vehicle local point cloud, dynamically distributing weight coefficients of shape completion and semantic completion, and fusing the two completion results to generate a high-precision vehicle global point cloud; and then the vehicle length value is calculated through bounding box fitting, and when the number of vehicles meeting the length condition reaches a threshold value, the system automatically prolongs the green light duration of the U-turn signal lamp. According to the scheme, the problem of size calculation errors caused by local missing of vehicle images in a traditional method is effectively solved, accurate recognition and passing demand matching of large turning-around vehicles (such as long-wheelbase trucks) are achieved, the intersection space-time resource utilization rate in a complex traffic scene is remarkably improved, and the overall passing efficiency and safety are improved.