A vehicle-road coordination system and a vehicle passage management method

Abstract

The embodiment of the application relates to the technical field of traffic management, and discloses a vehicle-road coordination system and a vehicle passing management method. The vehicle-road coordination system accurately monitors the number of vehicles turning on a first passing road and reasonably controls traffic lights. When the data of the vehicles to be turned increases and there is an abnormal lane occupation, the green light passing duration of the turning indication traffic light is controlled to be prolonged, so that the waiting time of the vehicles turning on the road is effectively reduced, the passing efficiency is improved, and the traffic jam caused by the abnormal lane occupation vehicles is avoided from being aggravated. In the case that the passing duration of the turning traffic light is prolonged, for a second passing road, the sending interval of prompt information is reasonably arranged according to the traffic flow of the target passing direction, so that the downstream vehicles can make preparation in advance, the smooth fusion of the merging traffic flow and the main road traffic flow is ensured, and the problem of the overall road network passing capacity reduction caused by the poor merging is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of traffic management, in particular to a vehicle-road coordination system and a vehicle passing management method. BACKGROUND

[0002] With the acceleration of urbanization, the road traffic flow increases sharply, and the traffic congestion problem is increasingly serious, which brings great inconvenience to people's travel, and also causes many negative effects such as energy waste and environmental pollution. In the urban road network, it is very common that the first passing road flows into the second passing road through the connecting road, and such intersection or connecting section is often a high-incidence area of traffic congestion.

[0003] The traditional traffic management mode mainly relies on fixed time length of traffic signal lamp setting and simple vehicle flow monitoring equipment, which is difficult to adapt to complex and changeable traffic conditions. For example, in the turning area of the first passing road, only according to the fixed signal lamp timing, it is impossible to adjust in time according to the dynamic change of the number of waiting turning vehicles, especially when the normal waiting vehicles are accumulated too much or abnormal occupation occurs, which is easy to cause long waiting and queuing extension of the turning vehicles, and further affect the passing efficiency of the whole road, and even cause congestion of the first passing road. SUMMARY

[0004] The main purpose of the present application is to provide a vehicle-road coordination system and a vehicle passing management method, which aims to solve the technical problem of low passing efficiency of the whole road caused by congestion at the intersection in the prior art.

[0005] To achieve the above purpose, in a first aspect, the present application provides a vehicle-road coordination system, which is arranged in a target passing road network, the target passing road network comprising a first passing road, a second passing road and a connecting road connecting the first passing road and the second passing road, and the vehicle-road coordination system comprising:

[0006] a camera device arranged at a turning position of the first passing road towards the connecting road, for detecting the number of target vehicles waiting to turn on the first passing road, wherein the number of target vehicles comprises the number of target vehicles normally waiting and the number of target vehicles abnormally occupying;

[0007] a passing vehicle flow monitor arranged on the second passing road, for monitoring the vehicle flow of the target passing direction of the second passing road, the target passing direction being the same as the direction of the connecting road flowing into the second passing road;

[0008] a traffic signal lamp for indicating the passing of vehicles turning from the first passing road to the connecting road.

[0009] a first roadside information interaction device, which is arranged on the first passing road and is spaced apart from the camera by a first preset distance, and is configured to send a first prompt information to a passing vehicle on the first passing road in a case that a number of target vehicles waiting to turn on the first passing road is greater than or equal to a preset number, the first prompt information being used to prompt downstream vehicles on the first passing road to pay attention to a large amount of turning traffic in front;

[0010] a second roadside information interaction device, which is arranged on the second passing road and is spaced apart from the passing traffic flow monitor by a second preset distance, and is configured to send a second prompt information to a passing vehicle in a target passing direction on the second passing road in a case that the traffic signal light prolongs a passing time of a vehicle turning from the first passing road to the connected road, the second prompt information being used to prompt downstream vehicles on the second passing road to pay attention to a large amount of converging traffic in front.

[0011] In a possible implementation, the passing traffic flow monitor includes a loop coil detector arranged on the ground of the second passing road and / or a laser sensor arranged on the roadside of the second passing road.

[0012] The first roadside information interaction device and the second roadside information interaction device include a short-range wireless communication device arranged on the roadside, and the short-range wireless communication device is configured to send the prompt information to a vehicle-mounted unit.

[0013] In a second aspect, the embodiments of the present application further provide a vehicle passing management method, which is applied to the vehicle-road coordination system as described in the first aspect, and the method comprises the following steps:

[0014] obtaining a number of target vehicles waiting to turn on the first passing road according to a photographed image of the camera, wherein the number of target vehicles includes a number of normally waiting target vehicles and a number of abnormally occupying target vehicles;

[0015] in a case that the number of normally waiting target vehicles is greater than or equal to a first preset value and the number of abnormally occupying target vehicles is greater than or equal to a second preset value, adjusting a green light time proportion of the traffic signal light and triggering a vehicle-road coordination instruction;

[0016] obtaining a historical average traffic flow in a target passing direction of the second passing road according to the vehicle-road coordination instruction through the passing traffic flow monitor;

[0017] correcting the historical average traffic flow according to today's traffic data of the target passing direction of the second passing road to obtain a current target traffic flow in the target passing direction of the second passing road;

[0018] When the current target traffic volume in the target direction of the second passage is greater than the preset traffic volume, the second roadside information interaction device repeatedly sends a second prompt message to the vehicles passing through the second passage every first preset time interval. When the current target traffic volume in the target direction of the second passage is less than or equal to the preset traffic volume, the second roadside information interaction device repeatedly sends a second prompt message to the vehicles passing through the second passage every second preset time interval. The first preset time interval is less than the second preset time interval. The second prompt message is used to remind downstream vehicles on the second passage to pay attention to the large merging traffic ahead.

[0019] In one possible implementation, when the number of normally waiting target vehicles is greater than or equal to a first preset value and the number of abnormally occupied target vehicles is greater than or equal to a second preset value, adjusting the green light duration of the traffic signal and triggering a vehicle-road coordination command includes:

[0020] The percentage increase in the green light duration of the traffic signal is determined based on the number of target vehicles waiting normally and the number of target vehicles abnormally occupying the lane.

[0021] The original green light duration percentage is adjusted based on the increase in the green light duration percentage of the traffic signal to extend the travel time for vehicles turning from the first traffic road to the connecting road.

[0022] In one possible implementation, determining the increase in the green light duration percentage based on the number of normally waiting target vehicles and the number of abnormally occupied target vehicles includes:

[0023] The queue length of the normally waiting vehicles is obtained based on the target number of vehicles waiting normally and the pre-set average vehicle length.

[0024] The queue length of vehicles abnormally occupying lanes is obtained based on the number of target vehicles and the average vehicle length.

[0025] The queue lengths of normally waiting vehicles and abnormally occupying lanes are input into a pre-trained green light percentage increment prediction model to obtain the increase in the green light duration percentage.

[0026] The green light percentage increment prediction model satisfies the following expression: ΔP=(W1*L1+W2*L2)*P0, where W2>W1, ΔP is the increase in green light duration percentage, L1 is the queue length of normally waiting vehicles, L2 is the queue length of abnormally occupying lanes, W1 is the influence weight of the queue length of normally waiting vehicles on the green light percentage increment, W2 is the influence weight of the queue length of abnormally occupying lanes on the green light percentage increment, and P0 is the baseline value of the green light percentage increment.

[0027] In a possible implementation, after the green light duration proportion of the traffic signal lamp is regulated and the vehicle-road coordination instruction is triggered, the method further includes:

[0028] The first roadside information interaction device sends first prompt information to the vehicles on the first passing road, and the first prompt information is used to prompt the downstream vehicles on the first passing road to pay attention to the large turning flow in front.

[0029] In a possible implementation, the first roadside information interaction device sends the first prompt information to the vehicles on the first passing road, and the method further includes:

[0030] The expected congestion duration is determined according to the target vehicle quantity in normal waiting and the target vehicle quantity in abnormal lane occupation.

[0031] When the expected congestion duration is greater than or equal to the preset congestion duration, the first roadside information interaction device sends first prompt information for detouring to the vehicles on the first passing road.

[0032] In a possible implementation, the historical average traffic flow is an average value of historical traffic flows in a preset time period corresponding to a traffic signal lamp starting regulation time, and the current target traffic flow of the target passing direction of the second passing road is obtained by correcting the historical average traffic flow according to the current traffic flow data of the target passing direction of the second passing road, and the method further includes:

[0033] The current average traffic flow data of the target passing direction of the second passing road is obtained.

[0034] When the current average traffic flow of the target passing direction of the second passing road is greater than or equal to the historical average traffic flow, the historical average traffic flow is positively corrected to obtain the current target traffic flow of the target passing direction of the second passing road.

[0035] When the current average traffic flow of the target passing direction of the second passing road is less than the historical average traffic flow, the historical average traffic flow is negatively corrected to obtain the current target traffic flow of the target passing direction of the second passing road.

[0036] In a possible implementation, the historical average traffic flow is positively corrected to obtain the current target traffic flow of the target passing direction of the second passing road, and the method further includes:

[0037] The difference between the current average traffic flow data and the historical average traffic flow data is calculated to obtain a positive correction increment.

[0038] The historical average traffic flow is added to the product of the positive correction increment and a first preset correction coefficient to obtain the current target traffic flow.

[0039] The negative correction on the historical average traffic flow obtains a current target traffic flow of a target passing direction of the second passing road, and the current target traffic flow comprises:

[0040] The difference between the historical average traffic flow data and the average traffic flow data of the day is calculated to obtain a negative correction reduction amount;

[0041] The historical average traffic flow is reduced by the product of the negative correction reduction amount and a second preset correction coefficient to obtain the current target traffic flow, wherein the first preset correction coefficient and the second preset correction coefficient are not equal.

[0042] In a possible implementation, the camera device comprises a first camera device and a second camera device, the shooting angles of the first camera device and the second camera device are different, and the first passing road target vehicle quantity waiting to turn is obtained according to the shooting image of the camera device, and the method comprises the following steps:

[0043] The shooting images of the first camera device and the second camera device are respectively acquired;

[0044] The shooting images of different angles are fused by using an image fusion technology, the normal target vehicle quantity waiting and the abnormal target vehicle quantity occupying the road are obtained by analyzing the fused image through a pre-trained target detection model.

[0045] Compared with the prior art, the vehicle-road coordination system and the vehicle passing management method provided by the embodiments of the present application can accurately monitor the first passing road target vehicle quantity and reasonably control the traffic signal lamp, that is, when the data of the target vehicle to be turned increases and there is an abnormal road occupation, the green light passing time of the turning indication traffic signal lamp is extended to effectively reduce the waiting time of the road turning vehicle and improve the passing efficiency, and meanwhile, the traffic jam caused by the abnormal road occupation vehicle is avoided from being aggravated. Moreover, in the case that the passing time of the turning traffic signal lamp is extended, for the second passing road, the prompt information sending interval is reasonably arranged according to the traffic flow of the target passing direction, so that the downstream vehicle can make preparation in advance, the smooth fusion of the inflow and the main road traffic flow is ensured, the problem of the overall road network passing capacity reduction caused by the poor inflow is reduced, and the optimal allocation of the target passing road network traffic flow is realized. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of the drawings shown.

[0047] Figure 1 This is a schematic diagram of the equipment layout of the vehicle-road coordination system in some embodiments of this application;

[0048] Figure 2 This is a flowchart illustrating the vehicle traffic management method in some embodiments of this application;

[0049] Figure 3 This is a flowchart illustrating the vehicle traffic management method in other embodiments of this application;

[0050] Figure 4 This is a schematic diagram of the module structure of the vehicle-road coordination system in some embodiments of this application.

[0051] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0053] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0054] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the term "and / or" throughout the text includes three solutions; taking A and / or B as an example, it includes technical solution A, technical solution B, and a technical solution that simultaneously satisfies A and B. Furthermore, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0055] With the acceleration of urbanization, road traffic flow has increased dramatically, and traffic congestion has become increasingly serious, causing great inconvenience to people's travel and also resulting in many negative effects such as energy waste and environmental pollution. In urban road networks, it is very common for a primary road to merge into a secondary road via a connecting road, and such intersections or connecting sections are often high-incidence areas of traffic congestion.

[0056] Traditional traffic management methods rely primarily on fixed-duration traffic lights and simple traffic flow monitoring equipment, which are ill-suited to complex and ever-changing traffic conditions. For example, in the turning area of ​​the primary traffic lane, relying solely on fixed traffic light timings cannot be adjusted in a timely manner according to the dynamic changes in the number of vehicles waiting to turn. Especially when there is a large backlog of normally waiting vehicles or abnormal lane obstruction, it can easily lead to long waiting times and extended queues for turning vehicles, thereby affecting the overall traffic efficiency of the road and even causing congestion on the primary traffic lane.

[0057] like Figure 1 As shown in this embodiment, the vehicle-road coordination system is deployed in the target traffic road network, which includes a first traffic road D100, a second traffic road D200, and a connecting road D300 connecting the first traffic road D100 and the second traffic road D200. The vehicle-road coordination system includes:

[0058] A camera device 100 is installed at the turning position of the first traffic road D100 toward the connecting road D300, and is used to detect the number of target vehicles waiting to turn on the first traffic road D100, wherein the number of target vehicles includes the number of target vehicles waiting normally and the number of target vehicles abnormally occupying the road.

[0059] Traffic flow monitoring device 200 is installed on the second traffic road D200 to monitor the traffic flow in the target traffic direction of the second traffic road D200. The target traffic direction is the same as the direction in which the connecting road D300 merges into the second traffic road D200. The traffic flow monitoring device 200 can be a loop detector installed on the ground of the second traffic road or a laser sensor installed on the side of the second traffic road.

[0060] Traffic signal light 300, the traffic signal light 300 being used to indicate the passage of vehicles turning from the first traffic road D100 to the connecting road D300;

[0061] The first roadside information interaction device 400 is located on the first traffic road D100 and at a first preset distance (e.g., 800M away) from the camera device 100. It is used to send a first prompt message to the vehicles traveling on the first traffic road D100 when the number of target vehicles waiting to turn on the first traffic road D100 is greater than or equal to a preset number. The first prompt message is used to remind downstream vehicles on the first traffic road to pay attention to the large number of turning vehicles ahead.

[0062] The second roadside information interaction device 500 is located on the second traffic road D200 and at a second preset distance (e.g., 1 km away) from the traffic flow monitoring device 200. When the traffic signal light 300 extends the travel time for vehicles turning from the first traffic road D100 to the connecting road D300, the second roadside information interaction device 500 sends a second prompt message to vehicles traveling in the target direction of the second traffic road D200. The second prompt message is used to remind downstream vehicles on the second traffic road D200 to pay attention to the large merging traffic ahead.

[0063] It should be noted that the first roadside information interaction device and the second roadside information interaction device include short-range wireless communication devices (such as local area network communication or Bluetooth communication) installed on the roadside. These short-range wireless communication devices are used to send prompt information to the vehicle-mounted unit.

[0064] like Figures 1-3 As shown, the following explanation uses a vehicle-road coordination system to illustrate this vehicle traffic management method. It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order. Please refer to the appendix. Figure 2 The method includes the following steps S100-S500:

[0065] Step S100: Obtain the number of target vehicles waiting to turn on the first traffic road based on the image captured by the camera device, wherein the number of target vehicles includes the number of target vehicles waiting normally and the number of target vehicles abnormally occupying the road.

[0066] In one embodiment, the camera device first captures images of the turning positions of the first traffic road toward the connecting road at a fixed frame rate (e.g., 25 frames per second), acquiring a high-resolution (e.g., 1080p) image sequence. The acquired images are then grayscaled to reduce data volume and highlight vehicle contours. Gaussian filtering is then applied to remove noise interference, making vehicle edges clearer and facilitating subsequent vehicle detection and recognition. A deep learning-based target detection algorithm (e.g., the YOLOv5 model) is then used to analyze the preprocessed images. This algorithm, pre-trained on a large number of traffic scene images containing different types of vehicles, can accurately identify vehicles in the images and locate and classify them (distinguishing between vehicles waiting normally and vehicles illegally occupying lanes). For each detected vehicle, a corresponding counter is incremented by 1, thereby counting the number of vehicles waiting normally and vehicles illegally occupying lanes.

[0067] To improve the accuracy of counting, in other embodiments, multi-frame image data can be fused and analyzed to avoid missed or false detections caused by factors such as vehicle obstruction.

[0068] In another embodiment, the camera device includes a first camera device and a second camera device, wherein the first camera device and the second camera device have different shooting angles. Step S100: obtaining the number of target vehicles waiting to turn on the first traffic road based on the images captured by the camera devices includes:

[0069] The images captured by the first camera device and the second camera device are acquired respectively;

[0070] Image fusion technology is used to fuse images taken from different perspectives. A pre-trained target detection model is then used to analyze the fused images to obtain the number of vehicles waiting normally and the number of vehicles abnormally occupying the road.

[0071] Specifically, the first and second camera devices are installed near the turning point of the first traffic road towards the connecting road, but their shooting angles differ. For example, the first camera device is installed at a higher position, shooting from a top-down perspective, which can cover a larger waiting area and obtain the overall distribution of vehicles; the second camera device is installed at a slightly lower position, shooting from an oblique perspective, focusing on capturing detailed features of vehicles, such as license plate information and the specific posture of the vehicles, which helps to more accurately identify abnormal lane occupancy. The two camera devices simultaneously acquire high-definition images at a predetermined frame rate (e.g., 25 frames per second) in a synchronous manner (e.g., by connecting to the same clock signal source to ensure that the acquisition time of each frame is basically consistent), and transmit the acquired image data to the back-end image processing server in real time via wired (e.g., Ethernet) or wireless (e.g., Wi-Fi 6 standard wireless network). On the image processing server, the images from the first and second camera devices are first pre-processed separately. This process includes converting the color image to grayscale to reduce data volume and highlight the vehicle's outline features for easier subsequent processing. Next, Gaussian filtering is applied to remove noise interference, making the vehicle edges clearer and smoother, avoiding false detections caused by noise. Then, histogram equalization is performed to enhance image contrast, making the difference between the vehicle and the background more obvious and further improving vehicle detection accuracy. Since the first and second cameras have different shooting angles, the acquired images differ in spatial location and scale, necessitating image registration. Feature point matching methods, such as SIFT (Scale Invariant Feature Transform) or SURF (Accelerated Robust Feature Transform), are used to extract representative feature points from the two images and match them using feature descriptors. Then, based on the matched feature points, affine or perspective transformation models are used to calculate the transformation relationship between the images, mapping the image from the second camera to the coordinate system of the first camera, thus achieving image registration. Finally, image fusion is performed on the registered images. A pixel-weighted averaging fusion method can be used. Based on factors such as image sharpness and contrast, different weights are assigned to pixels at different locations. The pixel values ​​of the two images are then weighted and summed to obtain the fused image. This fused image combines the advantages of both camera devices, providing more comprehensive and accurate vehicle information. A pre-trained object detection model is then used to analyze the fused image. This object detection model can be built on a deep learning framework (such as TensorFlow or PyTorch), using a convolutional neural network (CNN) structure, and trained on a large dataset of images containing vehicles in different traffic scenarios. It can accurately identify vehicles in the images and distinguish between vehicles waiting normally and vehicles illegally occupying lanes.During the detection process, the model outputs a bounding box for each vehicle target in the image, identifying the vehicle's location and providing the vehicle's category probability (normal vehicle or abnormal vehicle blocking the lane). By statistically analyzing these bounding boxes, the number of target vehicles waiting normally and the number of target vehicles abnormally blocking the lane are calculated.

[0072] Step S200: When the number of target vehicles waiting normally is greater than or equal to a first preset value and the number of target vehicles abnormally occupying the lane is greater than or equal to a second preset value, adjust the green light duration of the traffic signal and trigger a vehicle-road coordination command.

[0073] In one embodiment of the present invention, when the number of normally waiting target vehicles waiting to turn on the first traffic lane is detected to be greater than or equal to a first preset value, and the number of target vehicles abnormally occupying the lane is greater than or equal to a second preset value, the green light duration of the traffic signal needs to be adjusted, and a vehicle-road coordination command is triggered. The specific steps are as follows:

[0074] S210. Determine the percentage increase in the green light duration of the traffic signal based on the number of target vehicles waiting normally and the number of target vehicles abnormally occupying the lane.

[0075] This application first calculates the queue length of normally waiting vehicles by multiplying the number of vehicles normally waiting based on the number of vehicles normally waiting, obtained by the camera device, and a pre-set average vehicle length. This average vehicle length can be determined based on long-term statistical analysis of vehicles passing through this road segment. Similarly, the queue length of abnormally obstructing vehicles is calculated based on the number of vehicles abnormally obstructing traffic and the average vehicle length. Then, the queue lengths of normally waiting vehicles and abnormally obstructing vehicles are input into a pre-trained green light percentage increment prediction model to obtain the increase in the green light duration percentage.

[0076] This application employs a pre-trained green light percentage increment prediction model. This model is optimized and trained based on actual traffic flow and historical data to achieve accurate prediction of the increase in the green light duration percentage. The model satisfies the expression: ΔP = (W1*L1 + W2*L2)*P0, where W2 > W1 (e.g., W2 is 0.6, W1 is 0.4, indicating that the queue length of abnormally occupied vehicles has a more significant impact on the increase in the green light duration percentage), ΔP is the increase in the green light duration percentage, L1 is the queue length of normally waiting vehicles, L2 is the queue length of abnormally occupied vehicles, W1 is the weight of the influence of the queue length of normally waiting vehicles on the increase in the green light percentage, W2 is the weight of the influence of the queue length of abnormally occupied vehicles on the increase in the green light percentage, and P0 is the benchmark value for the increase in the green light percentage. This benchmark value is determined based on a large amount of actual traffic data and simulation experiments and is used to calibrate the calculation of the increase in the green light duration percentage.

[0077] S220. Adjust the original green light duration percentage according to the increase value of the green light duration percentage of the traffic signal to extend the travel time of vehicles turning from the first traffic road to the connecting road.

[0078] This embodiment first obtains the original green light duration percentage. The traffic signal control system pre-stores the original green light duration percentage data for each direction of travel. This data is set comprehensively based on factors such as traffic flow characteristics and road capacity at different times. Then, the increase in the green light duration percentage calculated by the green light duration percentage prediction model in step S210 is combined with the original green light duration percentage for the direction from the first road to the connecting road, thereby extending the travel time for vehicles in that direction. Specifically, by adding the increase in the green light duration percentage to the original green light duration percentage, the adjusted green light duration percentage is obtained. The timing of the traffic signals is then adjusted accordingly to extend the travel time for vehicles turning from the first road to the connecting road, thus prioritizing the smooth passage of vehicles turning from the first road to the connecting road. Meanwhile, after the traffic signal control system completes the adjustment of the green light duration ratio, due to the increase in vehicles merging into the second traffic lane (especially when the connecting road is short), it needs to trigger a vehicle-road coordination command to transmit the signal light status change information to other relevant equipment in the vehicle-road coordination system, such as traffic flow monitoring devices and roadside information interaction devices, so that the entire system can work together to cope with changes in traffic flow and improve road traffic efficiency and safety.

[0079] Step S300: According to the vehicle-road coordination instruction, obtain the historical average traffic flow of the target traffic direction of the second traffic road through the traffic flow monitoring instrument;

[0080] After the system triggers the vehicle-road coordination command, it indicates that other roads need to be coordinated as a whole to jointly respond to changes in traffic flow and improve road traffic efficiency and safety.

[0081] In this embodiment, after the system triggers the vehicle-road coordination command, the traffic flow monitoring device acquires the historical average traffic flow in the target direction of the second traffic road, providing accurate data support for subsequent vehicle-road coordination. Specifically, the historical average traffic flow can be the average traffic flow at the same time each day (e.g., 10 minutes before and after the control time) within the month preceding the traffic light control time. For example, if the traffic light control is activated at 10:00, the historical average traffic flow is the average traffic flow from 9:50 to 10:10 each day in the previous month.

[0082] Step S400: Correct the historical average traffic flow based on the daily traffic flow data of the target traffic direction of the second traffic road to obtain the current target traffic flow of the target traffic direction of the second traffic road;

[0083] In one embodiment, step S400, which involves correcting the historical average traffic flow based on the daily traffic flow data of the target direction of the second road to obtain the current target traffic flow of the target direction, includes: acquiring the daily average traffic flow data of the target direction of the second road; determining that the daily average traffic flow of the target direction of the second road is greater than or equal to the historical average traffic flow, and performing a positive correction on the historical average traffic flow to obtain the current target traffic flow of the target direction of the second road; and determining that the daily average traffic flow of the target direction of the second road is less than the historical average traffic flow, and performing a negative correction on the historical average traffic flow to obtain the current target traffic flow of the target direction of the second road.

[0084] Specifically, the average daily traffic flow data for the target direction of the second route is first obtained using a traffic flow monitoring device. This data is a comprehensive statistical value of the traffic flow on that road segment that day, reflecting the actual traffic flow situation at that time. Then, the obtained average daily traffic flow data is compared with historical average traffic flow data: if the average daily traffic flow for the target direction of the second route is greater than or equal to the historical average traffic flow, it indicates that the traffic flow on that road segment that day has increased or remained the same compared to historical levels. In this case, a positive correction needs to be applied to the historical average traffic flow to more accurately reflect the current traffic flow situation, thus obtaining the current target traffic flow for the target direction of the second route. If the average daily traffic flow for the target direction of the second route is less than the historical average traffic flow, it indicates that the traffic flow on that road segment that day has decreased compared to historical levels. In this case, a negative correction needs to be applied to the historical average traffic flow to obtain the current target traffic flow that reflects the actual situation.

[0085] In other embodiments, to improve the accuracy of historical average traffic flow correction, the step of positively correcting the historical average traffic flow to obtain the current target traffic flow in the target direction of the second road includes: calculating the difference between the current day's average traffic flow data and the historical average traffic flow data to obtain a positive correction increment; adding the historical average traffic flow to the product of the positive correction increment and a first preset correction coefficient to obtain the current target traffic flow; the step of negatively correcting the historical average traffic flow to obtain the current target traffic flow in the target direction of the second road includes: calculating the difference between the historical average traffic flow data and the current day's average traffic flow data to obtain a negative correction decrement; subtracting the product of the negative correction decrement and a second preset correction coefficient from the historical average traffic flow to obtain the current target traffic flow, wherein the first preset correction coefficient and the second preset correction coefficient are not equal.

[0086] Specifically, the positive correction steps are as follows: First, calculate the difference between the current day's average traffic flow data and the historical average traffic flow data; this difference is the positive correction increment. Let the current day's average traffic flow data be Davg, and the historical average traffic flow data be Havg, then the positive correction increment Δ1 = Davg - Havg. Then, add the historical average traffic flow to the product of the positive correction increment and the first preset correction coefficient K1 to obtain the current target traffic flow C+. That is, C+ = Hag + Δ1 x K1. The first preset correction coefficient K1 is a value pre-set based on actual traffic conditions and experience, used to adjust the correction magnitude to ensure that the corrected traffic flow more closely matches the actual traffic situation.

[0087] Similarly, the specific steps for negative correction are as follows: First, calculate the difference between the historical average traffic flow data and the current day's average traffic flow data. This difference is the negative correction reduction. That is, the negative correction reduction Δ2 = Havg - Davg.

[0088] Then, the product of the negative correction reduction and the second preset correction coefficient K2 is subtracted from the historical average traffic flow to obtain the current target traffic flow C-. That is, C- = Havg - △2x K2. Here, the second preset correction coefficient K2 is also a value preset based on actual traffic conditions and experience. Generally speaking, the first preset correction coefficient K1 and the second preset correction coefficient K2 are affected by different factors, therefore, positive and negative corrections require different adjustment ranges to more accurately reflect changes in traffic flow under different conditions. Therefore, K1 ≠ K2 (for example, K1 = 0.8, K2 = 0.7). In this way, even when the current traffic flow is less than the historical average traffic flow, a relatively accurate current target traffic flow can still be obtained.

[0089] The above correction scheme can reasonably correct the historical average traffic flow based on the actual traffic flow data of the target traffic direction on the second access road, thereby obtaining a more realistic current target traffic flow and providing accurate data support for subsequent traffic decisions and information prompts.

[0090] Step S500: When the current target traffic flow in the target direction of the second passage is greater than the preset traffic flow, the second roadside information interaction device repeatedly sends a second prompt message to the vehicles passing through the second passage every first preset time interval. When the current target traffic flow in the target direction of the second passage is less than or equal to the preset traffic flow, the second roadside information interaction device repeatedly sends a second prompt message to the vehicles passing through the second passage every second preset time interval. The first preset time interval is less than the second preset time interval. The second prompt message is used to remind downstream vehicles on the second passage to pay attention to the large merging traffic ahead.

[0091] In this embodiment of the application, after obtaining the current target traffic flow of the corrected second traffic road in the target direction, it is necessary to compare it with the preset traffic flow, and based on the comparison result, to precisely control the time interval for the second roadside information interaction device to send the second prompt information to the vehicles traveling on the second traffic road.

[0092] If the current target traffic volume exceeds the preset traffic volume, it indicates that the target direction of traffic on the second access road will soon face significant merging traffic pressure. To ensure that downstream drivers are promptly and fully aware of the road conditions ahead, and to guarantee driving safety and smooth traffic flow, the second roadside information interaction equipment will repeatedly send a second alert message to vehicles traveling on the second access road at short, first preset time intervals. This alert message aims to clearly remind downstream vehicles of the large merging traffic ahead, prompting drivers to prepare in advance, such as adjusting their speed and maintaining a safe following distance.

[0093] Conversely, if the current target traffic flow is less than or equal to the preset traffic flow, it indicates that the merging traffic pressure on that road segment is relatively low. In this case, to avoid excessive prompts that may interfere with drivers, while ensuring effective information delivery, the second roadside information interaction device will repeatedly send a second prompt message to passing vehicles at relatively long second preset time intervals. By dynamically adjusting the prompt frequency according to traffic flow, the accuracy and rationality of information prompts are achieved, ensuring road traffic safety and improving the driver's travel experience. It should be noted that the first preset time interval is shorter than the second preset time interval to meet the differentiated needs for information prompt frequency under different traffic flow conditions.

[0094] In other embodiments, after the system completes the adjustment of the green light duration of the traffic signal and triggers the vehicle-road coordination command, it will further use the first roadside information interaction device to send a targeted first prompt message to the vehicles passing through the first passage road, thereby reminding the downstream vehicles of the road to pay attention to the large flow of turning traffic ahead.

[0095] Specifically, the system first determines the estimated congestion duration based on the number of vehicles waiting normally and the number of vehicles abnormally occupying lanes, using a specific algorithm. This algorithm comprehensively considers multiple factors such as average vehicle speed, road capacity, and traffic light cycles, enabling it to accurately predict the potential congestion duration caused by turning traffic. Then, the estimated congestion duration is compared with a preset congestion duration. If the estimated congestion duration is greater than or equal to the preset duration, it indicates severe congestion. In this case, the first roadside information interaction device will issue an initial detour prompt to vehicles on the first access road, guiding them to choose alternative routes to alleviate traffic pressure. Conversely, if the estimated congestion duration is less than the preset duration, it indicates relatively mild congestion, and vehicles can still pass within an acceptable time. In this situation, the first roadside information interaction device will issue a first deceleration prompt to vehicles on the first access road, reminding drivers to drive cautiously, maintain a safe distance, and pass through the section in an orderly manner to avoid traffic accidents caused by excessive speed, ensuring road safety and smooth traffic flow.

[0096] Based on this, the vehicle-road coordination system and vehicle traffic management method provided in this application accurately monitor the number of turning vehicles on the first traffic road and rationally control the traffic lights. Specifically, when the number of vehicles waiting to turn increases and there are abnormal lane occupancy issues, the green light duration of the turning signal traffic light is extended to effectively reduce the waiting time of turning vehicles on that road, improve traffic efficiency, and avoid exacerbating traffic congestion caused by abnormal lane occupancy. Furthermore, when the turning signal traffic light duration is extended, for the second traffic road, the interval for sending prompt information is rationally arranged according to the traffic flow in its target direction, allowing downstream vehicles to prepare in advance. This ensures the smooth integration of merging traffic flow with the main road traffic flow, reduces the problem of reduced overall road network capacity due to poor merging, and achieves optimized allocation of traffic flow on the target traffic road network.

[0097] The vehicle-road coordination system provided in this application embodiment further includes a memory 110 and a processor 120, wherein the memory 110 is used to store computer-readable instructions, and the processor 120 is used to call the computer-readable instructions to execute the vehicle traffic management method as described above.

[0098] The processor 120 provides computing and control capabilities to control the vehicle-road coordination system to perform corresponding tasks, such as controlling the vehicle-road coordination system to perform the vehicle traffic management method in any of the above method embodiments. The method includes: acquiring the number of target vehicles waiting to turn on a first traffic road based on images captured by a camera device, wherein the number of target vehicles includes the number of normally waiting target vehicles and the number of abnormally occupying lanes; adjusting the green light duration ratio of the traffic lights and triggering a vehicle-road coordination command when the number of normally waiting target vehicles is greater than or equal to a first preset value and the number of abnormally occupying lanes is greater than or equal to a second preset value; acquiring the historical average traffic flow of the target traffic direction on a second traffic road through the traffic flow monitoring device according to the vehicle-road coordination command; and acquiring the historical average traffic flow of the target traffic direction on the second traffic road according to the target traffic direction... The current target traffic flow of the second roadway is obtained by correcting the historical average traffic flow based on the daily traffic flow data of the current direction. When the current target traffic flow of the second roadway is greater than the preset traffic flow, the second roadside information interaction device repeatedly sends a second prompt message to the vehicles traveling on the second roadway every first preset time interval. When the current target traffic flow of the second roadway is less than or equal to the preset traffic flow, the second roadside information interaction device repeatedly sends a second prompt message to the vehicles traveling on the second roadway every second preset time interval. The first preset time interval is less than the second preset time interval. The second prompt message is used to remind downstream vehicles on the second roadway to pay attention to the large merging traffic ahead.

[0099] The processor 120 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The aforementioned PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0100] The memory 110, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the vehicle access management method in the embodiments of this application. The processor 120 can implement the vehicle access management method in any of the above method embodiments by running the non-transitory software programs, instructions, and modules stored in the memory 110.

[0101] Specifically, memory 110 may include volatile memory (VM), such as random access memory (RAM); memory 110 may also include non-volatile memory (NVM), such as read-only memory (ROM), flash memory, hard disk drive (HDD), solid-state drive (SSD), or other non-transitory solid-state storage devices; memory 110 may also include combinations of the above types of memory.

[0102] In summary, the vehicle-road coordination system of this application adopts the technical solution of any of the above-mentioned vehicle traffic management method embodiments. Therefore, it has at least the beneficial effects brought about by the technical solutions of the above embodiments, which will not be elaborated here.

[0103] This application also provides a computer-readable storage medium, such as a memory including program code, which can be executed by a processor to complete the vehicle access management method described above. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CDROM), magnetic tape, floppy disk, or optical data storage device, etc.

[0104] This application also provides a computer program product comprising one or more lines of program code stored in a computer-readable storage medium. The processor of the vehicle-road coordination system reads the program code from the computer-readable storage medium and executes the program code to complete the vehicle traffic management method steps provided in the above embodiments.

[0105] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware, or by a program or program code related to hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0106] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0107] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software and a general-purpose hardware platform, or of course, using hardware. Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0108] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A vehicle traffic management method, characterized in that, The system is applied to a vehicle-road coordination system, which is located in a target road network. The target road network includes a first road, a second road, and connecting roads connecting the first road and the second road. The vehicle-road coordination system includes: A camera device is installed at the turning point of the first traffic road toward the connecting road, and is used to detect the number of target vehicles waiting to turn on the first traffic road, wherein the number of target vehicles includes the number of target vehicles waiting normally and the number of target vehicles abnormally occupying the road. A traffic flow monitoring device is installed on the second traffic road to monitor the traffic flow in the target direction of the second traffic road, wherein the target traffic direction is the same as the direction in which the connecting road merges into the second traffic road; Traffic lights, the traffic lights being used to indicate the passage of vehicles turning from the first road of traffic onto the connecting road; The first roadside information interaction device is located on the first road and is spaced a first preset distance from the camera device. It is used to issue a first prompt message to the vehicles traveling on the first road when the number of target vehicles waiting to turn on the first road is greater than or equal to a preset number. The first prompt message is used to remind downstream vehicles on the first road to pay attention to the large number of turning vehicles ahead. The second roadside information interaction device is located on the second traffic road and is spaced at a second preset distance from the traffic flow monitoring instrument. It is used to issue a second prompt message to vehicles traveling in the target direction of the second traffic road when the traffic signal light extends the travel time of vehicles turning from the first traffic road to the connecting road. The second prompt message is used to remind downstream vehicles on the second traffic road to pay attention to the large merging traffic ahead. The method includes: The number of target vehicles waiting to turn on the first traffic road is obtained from the images captured by the camera device, wherein the number of target vehicles includes the number of target vehicles waiting normally and the number of target vehicles abnormally occupying the road. When the number of target vehicles waiting normally is greater than or equal to a first preset value and the number of target vehicles abnormally occupying the lane is greater than or equal to a second preset value, the green light duration of the traffic signal light is adjusted and a vehicle-road coordination command is triggered. According to the vehicle-road coordination instruction, the historical average traffic flow of the target traffic direction of the second traffic road is obtained through the traffic flow monitoring device; The historical average traffic flow is corrected based on the daily traffic flow data of the target direction of the second access road to obtain the current target traffic flow of the target direction of the second access road; When the current target traffic volume in the target direction of the second passage is greater than the preset traffic volume, the second roadside information interaction device repeatedly sends a second prompt message to the vehicles passing through the second passage every first preset time interval. When the current target traffic volume in the target direction of the second passage is less than or equal to the preset traffic volume, the second roadside information interaction device repeatedly sends a second prompt message to the vehicles passing through the second passage every second preset time interval. The first preset time interval is less than the second preset time interval. The second prompt message is used to remind downstream vehicles on the second passage to pay attention to the large merging traffic ahead.

2. The vehicle traffic management method as described in claim 1, characterized in that, When the number of target vehicles waiting normally is greater than or equal to a first preset value and the number of target vehicles abnormally occupying the lane is greater than or equal to a second preset value, adjusting the green light duration of the traffic signal and triggering a vehicle-road coordination command includes: The percentage increase in the green light duration of the traffic signal is determined based on the number of target vehicles waiting normally and the number of target vehicles abnormally occupying the lane. The original green light duration percentage is adjusted based on the increase in the green light duration percentage of the traffic signal to extend the travel time for vehicles turning from the first traffic road to the connecting road.

3. The vehicle traffic management method as described in claim 2, characterized in that, The step of determining the increase in the green light duration percentage of the traffic signal based on the number of target vehicles waiting normally and the number of target vehicles abnormally occupying the lane includes: The queue length of the normally waiting vehicles is obtained based on the target number of vehicles waiting normally and the pre-set average vehicle length. The queue length of vehicles abnormally occupying lanes is obtained based on the number of target vehicles and the average vehicle length. The queue lengths of normally waiting vehicles and abnormally occupying lanes are input into a pre-trained green light percentage increment prediction model to obtain the increase in the green light duration percentage. The green light percentage increment prediction model satisfies the following expression: ΔP=(W1*L1+W2*L2)*P0, where W2>W1, ΔP is the increase in green light duration percentage, L1 is the queue length of normally waiting vehicles, L2 is the queue length of abnormally occupying lanes, W1 is the influence weight of the queue length of normally waiting vehicles on the green light percentage increment, W2 is the influence weight of the queue length of abnormally occupying lanes on the green light percentage increment, and P0 is the baseline value of the green light percentage increment.

4. The vehicle traffic management method as described in claim 1, characterized in that, After adjusting the green light duration of the traffic lights and triggering the vehicle-road coordination command, the process also includes: The first roadside information interaction device sends a first warning message to vehicles traveling on the first road, the first warning message being used to remind downstream vehicles on the first road to pay attention to the large volume of turning traffic ahead.

5. The vehicle traffic management method as described in claim 4, characterized in that, The step of issuing a first notification message to vehicles traveling on the first road via the first roadside information interaction device includes: The estimated congestion duration is determined based on the number of target vehicles waiting normally and the number of target vehicles abnormally occupying the lane. If the estimated congestion duration is greater than or equal to the preset congestion duration, a first detour prompt message is sent to vehicles traveling on the first road via the first roadside information interaction device.

6. The vehicle traffic management method as described in claim 1, characterized in that, The historical average traffic flow is the average of historical traffic flow within a preset time period corresponding to the traffic light activation control time. The step of correcting the historical average traffic flow based on the current day's traffic flow data for the target direction of the second traffic road to obtain the current target traffic flow for the target direction of the second traffic road includes: Obtain the average daily traffic flow data for the target traffic direction on the second access road; If the average daily traffic flow in the target direction of the second access road is greater than or equal to the historical average traffic flow, the current target traffic flow in the target direction of the second access road is obtained by positively correcting the historical average traffic flow. If the average daily traffic flow in the target direction of the second access road is determined to be less than the historical average traffic flow, the current target traffic flow in the target direction of the second access road is obtained by negatively correcting the historical average traffic flow.

7. The vehicle traffic management method as described in claim 6, characterized in that, The step of positively correcting the historical average traffic flow to obtain the current target traffic flow in the target direction of the second access road includes: Calculate the difference between the average traffic flow data for the day and the historical average traffic flow data to obtain the positive correction increment; The current target traffic flow is obtained by adding the product of the positive correction increment and the first preset correction coefficient to the historical average traffic flow. The process of negatively correcting the historical average traffic flow to obtain the current target traffic flow in the target direction of the second access road includes: The difference between historical average traffic flow data and daily average traffic flow data is calculated to obtain the negative correction reduction. The current target traffic flow is obtained by subtracting the product of the negative correction reduction and the second preset correction coefficient from the historical average traffic flow, wherein the first preset correction coefficient and the second preset correction coefficient are not equal.

8. The vehicle traffic management method as described in claim 1, characterized in that, The camera device includes a first camera device and a second camera device, wherein the first camera device and the second camera device have different shooting angles. The step of obtaining the number of target vehicles waiting to turn on the first traffic road based on the images captured by the camera devices includes: The images captured by the first camera device and the second camera device are acquired respectively; Image fusion technology is used to fuse images taken from different perspectives. A pre-trained target detection model is then used to analyze the fused images to obtain the number of vehicles waiting normally and the number of vehicles abnormally occupying the road.

Images