Traffic Management System

Abstract

This application relates to the field of vehicle processing technology and provides a traffic management system, including: multiple vehicle-end devices, multiple road-end devices, and a cloud control center; each vehicle-end device acquires first vehicle information of a target vehicle and second vehicle information of each of the other vehicles within its monitoring range, and sends the first vehicle information and the second vehicle information to the cloud control center; each road-end device acquires traffic information within its detection range and sends the traffic information to the cloud control center; the cloud control center predicts the traffic status of each road in the target area based on the first vehicle information, the second vehicle information, and the traffic information; and sends road traffic alert information to each vehicle-end device based on the traffic status of each road. Compared with existing technologies, the cloud control center of this system can accurately predict the future traffic conditions of the area and send relevant alert information to the vehicle-end devices to avoid road congestion.

Description

Technical Field

[0001] This application belongs to the field of automotive technology, and in particular relates to a traffic management system. Background Technology

[0002] In practical applications, with the rapid development of my country's economy, the mileage of roads, the number of motor vehicles and drivers, and the volume of road traffic have continued to increase significantly, putting great pressure on the existing traffic environment. With the rapid development of intelligent transportation, the concept of vehicle-road integration has been applied to real-world traffic scenarios. Intelligent transportation integrates cutting-edge IT technologies such as the Internet of Things, cloud computing, big data, and mobile internet, using these technologies to collect traffic information and provide real-time traffic information services based on traffic data.

[0003] However, the existing traffic management system only broadcasts the current status of the intersection in real time, which is insufficient to meet actual needs. Summary of the Invention

[0004] This application provides a traffic management system that addresses the problem that existing technologies only broadcast the current state of the current intersection in real time, which is insufficient to meet practical needs.

[0005] In a first aspect, embodiments of this application provide a traffic management system, including multiple vehicle-side devices, multiple roadside devices, and a cloud control center; wherein:

[0006] Each of the vehicle-mounted devices is connected to the cloud control center and is used to obtain first vehicle information of the target vehicle corresponding to the vehicle-mounted device, and to obtain second vehicle information of the other vehicles within the monitoring range of the vehicle-mounted device, and send the first vehicle information and each of the second vehicle information to the cloud control center.

[0007] Each of the roadside devices is connected to the cloud control center to acquire traffic information within the detection range of the roadside device and send the traffic information to the cloud control center.

[0008] The cloud control center is used to predict the traffic status of each road in the target area based on the information of each of the first vehicles, the information of each of the second vehicles, and the traffic information; and to send road traffic alert information to each of the vehicle-mounted devices based on the traffic status of each road.

[0009] Optionally, the cloud control center is specifically used for:

[0010] Based on the information of each of the first vehicles and the information of each of the second vehicles, the first traffic flow information of the road corresponding to each of the target vehicles is determined;

[0011] Based on the traffic information, the second traffic flow information of the road where each of the roadside devices is located is determined;

[0012] Based on the first traffic flow information and the second traffic flow information, the traffic status of each road is predicted.

[0013] Optionally, the cloud control center is specifically used for:

[0014] Based on each of the first traffic flow information, each of the second traffic flow information is adjusted to obtain each of the third traffic flow information;

[0015] Based on each of the first traffic flow information and each of the third traffic flow information, the road condition information of each road is determined;

[0016] Based on the road condition information of each road, the traffic status of each road is determined.

[0017] Optionally, each of the vehicle-end devices is also used for:

[0018] Based on the first vehicle information and each of the second vehicle information, the fourth traffic flow information of the road corresponding to the target vehicle is determined, and the fourth traffic flow information is transmitted to the cloud control center;

[0019] Accordingly, the cloud control center is specifically used for:

[0020] Based on each of the first traffic flow information and each of the fourth traffic flow information, the initial state of the road corresponding to each of the target vehicles is determined;

[0021] Based on the initial state of the road corresponding to each target vehicle and the second traffic flow information, the traffic state of each road is predicted.

[0022] Optionally, each of the roadside devices is also used for:

[0023] Based on the traffic information, the fifth traffic flow information of the road where the roadside equipment is located is determined, and the fifth traffic flow information is transmitted to the cloud control center;

[0024] Accordingly, the cloud control center is specifically used for:

[0025] Based on each of the second traffic flow information and each of the fifth traffic flow information, the initial state of the road where each of the roadside devices is located is determined;

[0026] Based on the initial state of the road where each of the roadside devices is located and the first traffic flow information, the traffic state of each road is predicted.

[0027] Optionally, the traffic status includes congestion status; the information of each first vehicle includes the travel path and destination of each target vehicle; the cloud control center is specifically used for:

[0028] Identify the second road that is in a congested state and generate congestion alert information corresponding to the second road;

[0029] Based on each of the aforementioned driving paths, a first vehicle corresponding to the second road is determined; the first vehicle is used to describe a vehicle that is about to enter the second road;

[0030] The congestion alert information is sent to the vehicle-side device corresponding to the first vehicle;

[0031] Accordingly, the vehicle-side equipment corresponding to the first vehicle is also used for:

[0032] Based on the congestion warning information and the destination, route planning is performed on the first vehicle to obtain the target route of the first vehicle.

[0033] Control the first vehicle to travel based on the target path.

[0034] Optionally, the traffic status includes congestion status; the cloud control center is also used for:

[0035] Identify the third road that is in a congested state and generate congestion alert information corresponding to the third road;

[0036] The target road-end equipment is determined based on the location range of the third road;

[0037] The congestion alert information is sent to the target roadside equipment;

[0038] Accordingly, the target roadside equipment is also used for:

[0039] Identify the second vehicle corresponding to the third road;

[0040] The congestion alert is sent to the vehicle-mounted device corresponding to the second vehicle.

[0041] Optionally, the cloud control center is specifically used for:

[0042] The first vehicle information, the second vehicle information, and the traffic information are input into a trained traffic prediction model for processing to obtain the traffic state.

[0043] Optionally, the vehicle-side equipment includes an on-board terminal and / or a domain controller.

[0044] Optionally, the roadside equipment includes roadside units and / or smart base stations.

[0045] The beneficial effects of the embodiments in this application compared with the prior art are:

[0046] This application provides a traffic management system comprising multiple vehicle-mounted devices, multiple roadside devices, and a cloud control center. Each vehicle-mounted device acquires first vehicle information of a target vehicle corresponding to the device, and acquires second vehicle information of each of the remaining vehicles within its monitoring range, and sends the first and second vehicle information to the cloud control center. Each roadside device acquires traffic information within its detection range and sends the traffic information to the cloud control center. The cloud control center predicts the traffic conditions of each road in the target area based on the first, second, and traffic information, and sends road traffic alerts to each vehicle-mounted device based on the traffic conditions of each road. Compared with existing technologies, the cloud control center in this system can accurately predict future traffic conditions in the area by combining information acquired by the vehicle-mounted devices and information acquired by the roadside devices, and sends relevant alerts to the vehicle-mounted devices in real time to avoid road congestion, thereby improving travel efficiency. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a schematic diagram of the structure of a traffic management system provided in an embodiment of this application;

[0049] Figure 2 This is a flowchart illustrating the implementation of a traffic management method provided in an embodiment of this application;

[0050] Figure 3 This is a flowchart illustrating the implementation of a traffic management method according to another embodiment of this application;

[0051] Figure 4 This is a flowchart illustrating the implementation of a traffic management method provided in another embodiment of this application. Detailed Implementation

[0052] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0053] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0054] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0055] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0056] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0057] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0058] In practical applications, with the rapid development of my country's economy, the mileage of roads, the number of motor vehicles and drivers, and the volume of road traffic have continued to increase significantly, putting great pressure on the existing traffic environment. With the rapid development of intelligent transportation, the concept of vehicle-road integration has been applied to real-world traffic scenarios. Intelligent transportation integrates cutting-edge IT technologies such as the Internet of Things, cloud computing, big data, and mobile internet, using these technologies to collect traffic information and provide real-time traffic information services based on traffic data.

[0059] However, the existing traffic management system only broadcasts the current status of the intersection in real time, which is insufficient to meet actual needs.

[0060] Therefore, this application provides a traffic management system that can accurately predict future traffic conditions in a region and send relevant prompts to vehicle-mounted devices in real time to avoid road congestion and thus improve travel efficiency.

[0061] The following will provide a detailed description using specific embodiments.

[0062] Please see Figure 1 , Figure 1 This is a schematic diagram of the structure of a traffic management system provided in an embodiment of this application.

[0063] For ease of explanation, only the parts relevant to this embodiment are shown, and are described in detail below:

[0064] like Figure 1 As shown, the traffic management system 1 includes multiple vehicle-side devices 10 (only three are shown in the figure), multiple road-side devices 20 (only three are shown in the figure), and a cloud control center 30. The cloud control center 30 is communicatively connected to the multiple vehicle-side devices 10 and the multiple road-side devices 20.

[0065] In some possible embodiments, each roadside device 20 may also communicate with vehicle-side devices 10 within its detection range.

[0066] In practical applications, each vehicle-side device 10 includes, but is not limited to, vehicle terminals and domain controllers.

[0067] Each roadside device 20 includes, but is not limited to, roadside units and smart base stations.

[0068] The cloud control center 30 includes, but is not limited to, cloud servers.

[0069] Roadside Units (RSUs) play a crucial role in the vehicle-road-cloud integrated system. Their main functions include collecting information on current road and traffic conditions, communicating with roadside sensing devices, traffic lights, electronic signs, and other terminals via communication networks to achieve vehicle-road interconnection and real-time traffic signal interaction, assisting drivers and ensuring the safety of people and vehicles in the transportation sector.

[0070] Smart base stations serve as the infrastructure for building urban vehicle-road cooperative networks. They integrate hardware and software such as cameras, sensors, radar, RSUs, and MECs, enabling them to perceive all traffic elements and perform intelligent calculations. Through smart base stations, information exchange can be achieved between vehicles and between vehicles and roads, assisting autonomous vehicles in making rapid decisions, improving the safety of autonomous driving, effectively supporting the large-scale deployment of autonomous vehicles at all levels, and enabling large-scale and rapid deployment in cities and highways.

[0071] In this embodiment of the application, each vehicle-end device 10 acquires first vehicle information of the target vehicle corresponding to the vehicle-end device 10, and acquires second vehicle information of the other vehicles within the monitoring range of the vehicle-end device 10, and sends the first vehicle information and each second vehicle information to the cloud control center 30.

[0072] Each roadside device 20 is used to acquire traffic information within its detection range and send the traffic information to the cloud control center 30.

[0073] The cloud control center 30 is used to predict the traffic status of each road in the target area based on the information of each first vehicle, each second vehicle, and each traffic information; and to send road traffic alert information to each vehicle-mounted device based on the traffic status of each road.

[0074] It should be noted that, in this embodiment, the traffic management system 1 is used to predict traffic flow in a target area. Therefore, each vehicle-side device 10 and each road-side device 20 in the traffic management system 1 refers to a device located within the target area. The target area can be a city or a district within a city; no limitation is imposed here.

[0075] In this embodiment of the application, in order to accurately predict the future traffic conditions of each road in the target area, each vehicle-mounted device 10 in the traffic management system 1 can obtain the first vehicle information of the target vehicle through various devices installed on its own vehicle, and obtain the second vehicle information of the other vehicles within its monitoring range.

[0076] The aforementioned devices include, but are not limited to: radar, speed sensors, and vehicle navigation systems.

[0077] Based on this, the monitoring range of each vehicle-mounted device 10 can be determined according to the detection range of various devices installed in the corresponding target vehicle.

[0078] It should be noted that the first vehicle information includes, but is not limited to: the target vehicle's speed, location, direction of travel, destination, route, and lane.

[0079] The second vehicle information includes, but is not limited to, the speed, position, direction of travel, and lane of other vehicles.

[0080] In this embodiment of the application, in order to improve the accuracy of predicting the future traffic conditions of various roads in the target area, each roadside device 20 in the traffic management system 1 can acquire traffic information within its own detection range. This traffic information includes, but is not limited to, road traffic flow information within its detection range.

[0081] Subsequently, the cloud control center 30 can predict the traffic status of each road in the target area based on the first vehicle information and the second vehicle information sent by each vehicle-end device 10, as well as the traffic information sent by each road-end device 20. The traffic status includes, but is not limited to, congested and non-congested states.

[0082] In some possible embodiments, traffic conditions may also include congestion levels. Congestion levels include, but are not limited to, light congestion, moderate congestion, and heavy congestion.

[0083] It should be noted that mild, moderate, and severe congestion can all be determined based on actual needs, and no restrictions are imposed here.

[0084] In one embodiment of this application, the cloud control center can input the information of each first vehicle, each second vehicle, and each traffic information into a trained traffic prediction model for processing, thereby predicting the traffic status of each road in the target area.

[0085] In this embodiment, the traffic prediction model can be obtained by training a pre-built neural network model based on a preset sample set. Each sample data point in the preset sample set includes sample information (including sample vehicle information corresponding to each vehicle-end device 10, sample vehicle information of other vehicles, and sample traffic information of each road-end device 20) and the sample traffic state of each road corresponding to that sample information. When training the pre-built neural network model, the sample information in each sample data point is used as the input to the neural network model, and the sample traffic state corresponding to the sample information in each sample data point is used as the output of the neural network model. Through training, the neural network model can learn the correspondence between all possible sample information and sample traffic states, and the trained neural network model becomes the traffic prediction model.

[0086] In another embodiment of this application, the cloud control center can also specifically be through, as... Figure 2 The steps S101 to S103 of the traffic management method shown are for predicting the traffic conditions of each road, and are detailed below:

[0087] In S101, based on the information of each of the first vehicles and the information of each of the second vehicles, the first traffic flow information of the road corresponding to each of the target vehicles is determined.

[0088] It should be noted that the first traffic flow information is used to describe the traffic conditions of the roads corresponding to each target vehicle. The roads corresponding to each target vehicle include, but are not limited to, the roads where the target vehicle is currently located and the roads it will enter in the future.

[0089] The roads that the vehicles will soon enter can be determined based on their travel routes and destinations.

[0090] In this embodiment, the cloud control center can input the information of each first vehicle and each second vehicle into the trained first traffic flow prediction model for processing, thereby predicting the first traffic flow information of the road corresponding to each target vehicle.

[0091] It should be noted that the first traffic flow prediction model can be obtained by training a pre-built first deep learning model based on a preset sample set. Each sample data point in the preset sample set includes sample information (including sample vehicle information corresponding to each vehicle-mounted device and sample vehicle information for other vehicles) and sample traffic flow information for the road where each target vehicle is located. When training the pre-built first deep learning model, the sample information from each sample data point is used as the input to the first deep learning model, and the corresponding sample traffic flow information is used as the output. Through training, the first deep learning model can learn the correspondence between all possible sample information and sample traffic flow information, and the trained first deep learning model becomes the first traffic flow prediction model.

[0092] In one embodiment of this application, since the first vehicle information includes the location and driving path of the target vehicle, and the second vehicle information includes the location of the remaining vehicles, the cloud control center can determine the number of first vehicles on each road at this time and the number of second vehicles on each road in the future based on the location and driving path included in each of the first vehicle information and the location included in the second vehicle information.

[0093] In this embodiment, the cloud control center can determine the first traffic flow information of each target vehicle's corresponding road based on the current number of first vehicles on each road and the future number of second vehicles on each road.

[0094] In S102, based on the traffic information, the second traffic flow information of the road where each of the roadside devices is located is determined.

[0095] In this embodiment, the cloud control center can input various traffic information into a trained second traffic flow prediction model for processing, thereby predicting the second traffic flow information of the roads where each roadside device is located. The second traffic flow information describes the future traffic conditions of the roads where each roadside device is located.

[0096] It should be noted that the second traffic flow prediction model can be obtained by training a pre-built second deep learning model based on a preset sample set. Each sample data point in the preset sample set includes sample information (including sample traffic information for each roadside device) and sample traffic flow information for the road where the corresponding roadside device is located. When training the pre-built second deep learning model, the sample information from each sample data point is used as the input to the second deep learning model, and the corresponding sample traffic flow information is used as the output. Through training, the second deep learning model can learn the correspondence between all possible sample information and sample traffic flow information. The trained second deep learning model is then used as the second traffic flow prediction model.

[0097] In S103, the traffic status of each road is predicted based on each of the first traffic flow information and each of the second traffic flow information.

[0098] In this embodiment, the cloud control center can input the first traffic flow information and the second traffic flow information into the trained traffic state prediction model for processing, thereby predicting the traffic state of each road.

[0099] It should be noted that the traffic state prediction model can be obtained by training a pre-built third-party deep learning model based on a preset sample set. Each sample data point in the preset sample set includes sample information (including sample traffic flow information for each vehicle and sample traffic flow information for each roadside device) and the sample traffic state for each road corresponding to that sample information. When training the pre-built third-party deep learning model, the sample information from each sample data point is used as the input to the third-party deep learning model, and the sample traffic state for each road corresponding to that sample information is used as the output of the third-party deep learning model. Through training, the third-party deep learning model can learn the correspondence between all possible sample information and the sample traffic states of each road. The trained third-party deep learning model then becomes the traffic state prediction model.

[0100] In one embodiment of this application, since each vehicle-end device and each road-end device are located in the target area, that is, each first traffic flow information and each second traffic flow information may contain traffic flow information describing the same road, in order to improve the prediction accuracy of the traffic status of each road, the cloud control center can specifically implement step S203 according to the following steps, detailed as follows:

[0101] Based on each of the first traffic flow information, each of the second traffic flow information is adjusted to obtain each of the third traffic flow information;

[0102] Based on each of the first traffic flow information and each of the third traffic flow information, the road condition information of each road is determined;

[0103] Based on the road condition information of each road, the traffic status of each road is determined.

[0104] In this embodiment, since each roadside device monitors the overall traffic conditions within its detection range, there may be issues with insufficient monitoring. Meanwhile, each vehicle-side device can accurately monitor other vehicles within its own monitoring range. Therefore, in order to improve the accuracy of traffic condition prediction for each road, the cloud control center can adjust the second traffic flow information based on the first traffic flow information. That is, the traffic conditions of the same road corresponding to the first traffic flow information in the second traffic flow information are adjusted to the first traffic flow information corresponding to that same road. Thus, the adjusted first traffic flow information becomes the third traffic flow information.

[0105] Subsequently, the cloud control center can obtain more accurate intersection information for each road based on the primary and tertiary traffic flow information. Among these, traffic condition information is used to describe the traffic flow situation on the road.

[0106] In this embodiment, the cloud control center can determine whether each road is congested based on the road condition information of each road, that is, the traffic flow of each road, thereby obtaining the congestion status of each road.

[0107] In this embodiment, after obtaining the traffic status of each road, the cloud control center can send road traffic alert information to each vehicle-end device based on the traffic status of each road, so as to alert each vehicle-end device that a congested road is about to be sent, so that each vehicle-end device can replan its driving route in time to avoid congestion and thus improve travel efficiency.

[0108] In some possible implementations, the cloud control center can directly send road traffic alerts to the various vehicle-mounted devices with which it communicates.

[0109] In some other possible embodiments, since the number of vehicle-side devices in the target area is usually much greater than the number of road-side devices, in order to reduce the workload of the cloud control center, the cloud control center can send road traffic alert information to each road-side device, and then send the road traffic alert information to each vehicle-side device through each road-side device.

[0110] As can be seen from the above, the traffic management system provided in this application includes multiple vehicle-mounted devices, multiple roadside devices, and a cloud control center. Each vehicle-mounted device is used to acquire first vehicle information of a target vehicle corresponding to the device, and second vehicle information of each of the other vehicles within the monitoring range of the vehicle-mounted device, and sends the first vehicle information and each second vehicle information to the cloud control center. Each roadside device is used to acquire traffic information within the detection range of the roadside device and send the traffic information to the cloud control center. The cloud control center is used to predict the traffic status of each road in the target area based on the first vehicle information, the second vehicle information, and the traffic information; and sends road traffic alert information to each vehicle-mounted device based on the traffic status of each road. Compared with the prior art, the cloud control center in this system can accurately predict the future traffic conditions of the area by combining the information acquired by the vehicle-mounted devices and the information acquired by the roadside devices, and send relevant alert information to the vehicle-mounted devices in real time to avoid road congestion, thereby improving travel efficiency.

[0111] Please see Figure 3 , Figure 3 This is a flowchart illustrating the implementation of a traffic management method according to another embodiment of this application. Compared to... Figure 1 In a corresponding embodiment, after determining the traffic status of each road, the cloud control center can also execute steps S201 to S203. Correspondingly, the vehicle-side equipment corresponding to the first vehicle can also execute steps S204 to S205, as detailed below:

[0112] In S201, the cloud control center determines the second road that is in a congested state and generates congestion alert information corresponding to the second road.

[0113] In this embodiment, since vehicles on roads that are not congested, or vehicles about to enter roads that are not congested, do not require congestion alerts, in order to reduce the workload of the cloud control center and improve work efficiency, the cloud control center can determine the second road that is congested based on the traffic status of each road, and generate congestion alert information corresponding to the second road. The congestion alert information is used to indicate that the second road is about to become congested.

[0114] It should be noted that there can be one or more second roads.

[0115] In S202, the cloud control center determines the first vehicle corresponding to the second road based on each of the driving paths; the first vehicle is used to describe the vehicle that is about to enter the second road.

[0116] In S203, the cloud control center sends the congestion alert information to the vehicle-side device corresponding to the first vehicle.

[0117] In this embodiment, since the information of each first vehicle includes the driving path of the target vehicle, the cloud control center can determine the vehicle that is about to enter the second road, i.e., the first vehicle, based on the driving path of each target vehicle.

[0118] It should be noted that the first vehicle can be one or more vehicles.

[0119] Afterwards, the cloud control center can send congestion alerts to the vehicle-side devices corresponding to the first vehicle mentioned above.

[0120] In S204, the vehicle-side device corresponding to the first vehicle performs route planning for the first vehicle based on the congestion warning information and the destination, and obtains the target route of the first vehicle.

[0121] In S205, the vehicle-mounted equipment corresponding to the first vehicle controls the first vehicle to travel based on the target path.

[0122] In this embodiment, after receiving the congestion alert information sent by the cloud control center, the vehicle-side device corresponding to the first vehicle can re-plan the route of the first vehicle based on the congestion alert information and the destination of the first vehicle to obtain the target route of the first vehicle.

[0123] After that, the vehicle-mounted equipment corresponding to the first vehicle can control the first vehicle to travel along the target path.

[0124] As can be seen from the above, the traffic management method provided in this embodiment can prevent vehicles from entering roads that are about to become congested, thereby avoiding road congestion. It can also automatically adjust the vehicle's travel path in real time based on the road congestion situation, thereby improving travel efficiency.

[0125] Please see Figure 4 , Figure 4 This is a flowchart illustrating the implementation of a traffic management method according to another embodiment of this application. Compared to... Figure 1 In the corresponding embodiment, since the number of vehicle-side devices in the target area is usually much greater than the number of road-side devices, in order to reduce the workload of the cloud control center, after determining the traffic status of each road, the cloud control center can also execute steps S301 to S303. Correspondingly, the target road-side devices can also execute steps S304 to S305, and the vehicle-side devices corresponding to the second vehicle can also execute steps S306 to S307, as detailed below:

[0126] In S301, the cloud control center identifies the third road that is in a congested state and generates congestion alert information corresponding to the third road.

[0127] In this embodiment, since vehicles on roads that are not congested, or vehicles about to enter roads that are not congested, do not require congestion alerts, in order to further reduce the workload of the cloud control center and improve work efficiency, the cloud control center can determine the third road that is congested based on the traffic status of each road, and generate congestion alert information corresponding to the third road. The congestion alert information is used to indicate that the third road is about to become congested.

[0128] It should be noted that there can be one or more third roads.

[0129] In S302, the cloud control center determines the target road-end equipment based on the location range of the third road.

[0130] In S303, the cloud control center sends the congestion alert information to the target roadside equipment.

[0131] In this embodiment, after determining the third road, the cloud control center can determine the target roadside equipment for detecting the traffic conditions of the third road based on the location range of the third road.

[0132] Afterwards, the cloud control center can send congestion alerts to the target roadside equipment.

[0133] In S304, the target roadside equipment identifies the second vehicle corresponding to the third road.

[0134] In S305, the target roadside equipment sends the congestion warning information to the vehicle-side equipment corresponding to the second vehicle.

[0135] It should be noted that the target roadside equipment can communicate with the vehicle-side equipment of a second vehicle within its detection range.

[0136] In this embodiment, after receiving congestion alert information from the cloud control center, the target roadside device can identify the second vehicle corresponding to the third road. Specifically, the second vehicle corresponding to the third road refers to vehicles already on the third road and vehicles about to enter the third road.

[0137] Afterwards, the target roadside equipment can send congestion alert information to the vehicle-side equipment 20 corresponding to the second vehicle.

[0138] In S306, the vehicle-side equipment corresponding to the second vehicle performs route planning for the second vehicle based on the congestion warning information and the destination, and obtains the target route of the second vehicle.

[0139] In S307, the vehicle-mounted equipment corresponding to the second vehicle controls the second vehicle to travel based on the target path.

[0140] In this embodiment, after receiving the congestion alert information sent by the target roadside equipment, the vehicle-side equipment corresponding to the second vehicle can re-plan the route of the first vehicle based on the congestion alert information and the destination of the second vehicle, so as to obtain the target route of the second vehicle.

[0141] After that, the vehicle-mounted equipment corresponding to the second vehicle can control the second vehicle to travel along the target path.

[0142] As can be seen from the above, the traffic management method provided in this embodiment can prevent vehicles from entering roads that are about to become congested, thereby avoiding road congestion. It can also automatically adjust the vehicle's travel path in real time based on the road congestion situation, thereby improving travel efficiency.

[0143] In one embodiment of this application, combined with Figure 2 To further improve the accuracy of traffic condition prediction for each road, each vehicle-mounted device can determine the fourth traffic flow information of the road corresponding to the target vehicle based on the first vehicle information and the second vehicle information it has acquired, and send the fourth traffic flow information to the cloud control center.

[0144] In this embodiment, each vehicle-end device can combine the first vehicle information and various second vehicle information it has acquired to make an initial prediction of the road corresponding to the target vehicle, thereby determining the fourth traffic flow information of the road corresponding to the target vehicle.

[0145] Subsequently, the cloud control center can re-predict the traffic conditions of the roads corresponding to each target vehicle based on the first traffic flow information it has determined and the fourth traffic flow information sent by each vehicle-end device, thereby obtaining the initial state of the roads corresponding to each target vehicle.

[0146] Then, the cloud control center can predict the final traffic state of each road based on the initial state of the road corresponding to each target vehicle and the information of each secondary traffic flow.

[0147] As can be seen from the above, the traffic management method provided in this embodiment further improves the accuracy of predicting the traffic conditions of various roads.

[0148] In another embodiment of this application, combined with Figure 2 In order to further improve the accuracy of traffic condition prediction for each road, each roadside device can also make an initial prediction of the traffic condition of its own road based on the traffic information it has acquired, obtain the fifth traffic flow information of the road where each roadside device is located, and send the fifth traffic flow information to the cloud control center.

[0149] Afterwards, the cloud control center can predict the traffic conditions of the roads where each road terminal device is located based on the second traffic flow information it has determined and the fifth traffic flow information sent by each road terminal device, thereby obtaining the initial state of the roads where each road terminal device is located.

[0150] Afterwards, the cloud control center can predict the final traffic status of each road based on the initial state of the road where each roadside device is located and the first traffic flow information.

[0151] As can be seen from the above, the traffic management method provided in this embodiment further improves the accuracy of predicting the traffic conditions of various roads.

[0152] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0153] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A traffic management system, characterized in that, This includes multiple vehicle-mounted devices, multiple roadside devices, and a cloud control center; among which: Each of the vehicle-mounted devices is connected to the cloud control center and is used to obtain first vehicle information of the target vehicle corresponding to the vehicle-mounted device, and to obtain second vehicle information of the other vehicles within the monitoring range of the vehicle-mounted device, and send the first vehicle information and each of the second vehicle information to the cloud control center. Each of the roadside devices is connected to the cloud control center to acquire traffic information within the detection range of the roadside device and send the traffic information to the cloud control center. The cloud control center is used to predict the traffic status of each road in the target area based on the information of each of the first vehicles, the information of each of the second vehicles, and the traffic information; and to send road traffic alert information to each of the vehicle-mounted devices based on the traffic status of each road.

2. The traffic management system as described in claim 1, characterized in that, The cloud control center is specifically used for: Based on the information of each of the first vehicles and the information of each of the second vehicles, the first traffic flow information of the road corresponding to each of the target vehicles is determined; Based on the traffic information, the second traffic flow information of the road where each of the roadside devices is located is determined; Based on the first traffic flow information and the second traffic flow information, the traffic status of each road is predicted.

3. The traffic management system as described in claim 2, characterized in that, The step of predicting the traffic state of each road based on each of the first traffic flow information and each of the second traffic flow information includes: Based on each of the first traffic flow information, each of the second traffic flow information is adjusted to obtain each of the third traffic flow information; Based on each of the first traffic flow information and each of the third traffic flow information, the road condition information of each road is determined; Based on the road condition information of each road, the traffic status of each road is determined.

4. The traffic management system as described in claim 2, characterized in that, Each of the aforementioned vehicle-end devices is also used for: Based on the first vehicle information and each of the second vehicle information, the fourth traffic flow information of the road corresponding to the target vehicle is determined, and the fourth traffic flow information is transmitted to the cloud control center; Accordingly, predicting the traffic state of each road based on each of the first traffic flow information and each of the second traffic flow information includes: Based on each of the first traffic flow information and each of the fourth traffic flow information, the initial state of the road corresponding to each of the target vehicles is determined; Based on the initial state of the road corresponding to each target vehicle and the second traffic flow information, the traffic state of each road is predicted.

5. The traffic management system as described in claim 2, characterized in that, Each of the aforementioned roadside devices is also used for: Based on the traffic information, the fifth traffic flow information of the road where the roadside equipment is located is determined, and the fifth traffic flow information is transmitted to the cloud control center; Accordingly, predicting the traffic state of each road based on each of the first traffic flow information and each of the second traffic flow information includes: Based on each of the second traffic flow information and each of the fifth traffic flow information, the initial state of the road where each of the roadside devices is located is determined; Based on the initial state of the road where each of the roadside devices is located and the first traffic flow information, the traffic state of each road is predicted.

6. The traffic management system as described in claim 1, characterized in that, The traffic status includes congestion status; the information of each of the first vehicles includes the travel path and destination of each target vehicle; sending road traffic alert information to each vehicle-mounted device based on the traffic status of each road includes: Identify the second road that is in a congested state and generate congestion alert information corresponding to the second road; Based on each of the aforementioned driving paths, a first vehicle corresponding to the second road is determined; the first vehicle is used to describe a vehicle that is about to enter the second road; The congestion alert information is sent to the vehicle-side device corresponding to the first vehicle; Accordingly, the vehicle-side equipment corresponding to the first vehicle is also used for: Based on the congestion warning information and the destination, route planning is performed on the first vehicle to obtain the target route of the first vehicle. Control the first vehicle to travel based on the target path.

7. The traffic management system as described in claim 1, characterized in that, The traffic status includes congestion status; the cloud control center is also used for: Identify the third road that is in a congested state and generate congestion alert information corresponding to the third road; The target road-end equipment is determined based on the location range of the third road; The congestion alert information is sent to the target roadside equipment; Accordingly, the target roadside equipment is also used for: Identify the second vehicle corresponding to the third road; The congestion alert is sent to the vehicle-mounted device corresponding to the second vehicle.

8. The traffic management system as described in claim 1, characterized in that, The step of predicting the traffic status of each road in the target area based on the first vehicle information, the second vehicle information, and the traffic information includes: The first vehicle information, the second vehicle information, and the traffic information are input into a trained traffic prediction model for processing to obtain the traffic state.

9. The traffic management system according to any one of claims 1-8, characterized in that, The vehicle-mounted equipment includes an on-board terminal and / or a domain controller.

10. The traffic management system according to any one of claims 1-8, characterized in that, The roadside equipment includes roadside units and / or smart base stations.

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