Traffic control device, method, equipment and system

Abstract

The invention discloses a traffic control device, method, equipment and system. The method comprises the following steps: acquiring video data of a target road acquired by one or more cameras; determining the traffic state of the target road at the first moment based on the video data, and predicting the traffic state of the target road at the second moment based on the traffic state of the target road at the first moment and the historical traffic state of the target road; generating a marking control strategy and a traffic signal control strategy of the target road based on the traffic state of the target road at the first moment and the traffic state of the target road at the second moment; sending the marking control strategy to a dynamic marking module; the traffic signal control strategy is sent to a traffic signal control module; therefore, flexible layout of lanes and flexible control of traffic signals are realized, and the traffic efficiency of roads is improved.

Description

Technical Field

[0001] The present application relates to the technical field of road traffic management, and in particular to a traffic control device and method, equipment, and system. Background Art

[0002] Traditional lane lines are mostly fixedly painted on the road, and the road layout formed is fixed, for example, the width of the road is fixed, and the driving direction of the road is fixed. With the rapid development of cities, traditional lane lines can no longer meet the complex and changeable road conditions, resulting in low vehicle traffic efficiency. Therefore, it is necessary to propose a new traffic control method to solve the above technical problems. Summary of the invention

[0003] In order to solve the above technical problems, the embodiments of the present application provide a traffic control device and method, equipment, and system.

[0004] The traffic control device provided in the embodiment of the present application is applied to a programmable traffic control system, which at least includes a centralized control middleware, a traffic facility virtualization module, a global data view module and a business application module. The real-time traffic status information is processed by the centralized control middleware, and the processing result is transmitted to the application end, so that the application end generates a control instruction based on information analysis; the centralized control middleware responds to the control instruction to control road vehicles; the centralized control middleware includes a southbound interface and a northbound interface, which is responsible for coordinating the operation of the entire traffic control system, communicating with networked vehicles and roadside perception control equipment through the southbound interface, receiving roadside perception information, and forwarding traffic control instructions and strategies downward, opening a programming interface to upper-level applications through the northbound interface, providing real-time perception data and equipment control capabilities, and realizing the abstraction and centralization of perception control functions; the device includes: An acquisition unit, used for acquiring video data of a target road captured by one or more cameras through the centralized control middleware and the global data view module; a determining unit, configured to determine a traffic state of the target road at a first moment based on the video data; a prediction unit, configured to predict a traffic state of the target road at a second moment based on the traffic state of the target road at the first moment and a historical traffic state of the target road, the second moment being located after the first moment; A strategy generating unit, configured to generate a line marking control strategy and a traffic signal control strategy for the target road based on the traffic state of the target road at a first moment and the traffic state of the target road at a second moment; a communication unit, for sending the line marking control strategy to the dynamic line marking module through the centralized control middleware, the line marking control strategy being used by the dynamic line marking module to control the light emitting states of a plurality of light emitting units, wherein the plurality of light emitting units are arranged on the target road and form line markings corresponding to different lanes under different light emitting states; and for sending the traffic signal control strategy to the traffic signal control module, the traffic signal control strategy being used by the traffic signal control module to control the signal state of a traffic signal unit, wherein the signal state of the traffic signal unit matches the light emitting states of the plurality of light emitting units.

[0005] The traffic facility virtualization module unifies the definitions of different types of traffic element data to achieve consistent mapping of status and performance between physical entities and virtualized digital entities, constructs unified data specifications for geographic information system maps, infrastructure, rules and control instructions, realizes visualization of traffic control tools and supports flexible scheduling and management; the global data view module provides business strategy development and application, and by building a scenario-based data structure, provides the programmable traffic control system with real-time traffic situation perception, prediction and evaluation functions; the business application module is used to at least implement the formulation of marking control strategies, dynamic speed limits, truck management, emergency diversion, and signal control.

[0006] The traffic control method provided in the embodiment of the present application is applied to a programmable traffic control system, which at least includes a centralized control middleware, a traffic facility virtualization module, a global data view module and a business application module. The real-time traffic status information is processed by the centralized control middleware, and the processing result is transmitted to the application end, so that the application end can generate control instructions based on information analysis; the centralized control middleware responds to the control instructions to control road vehicles; the centralized control middleware includes a southbound interface and a northbound interface, which is responsible for coordinating the operation of the entire traffic control system, communicating with networked vehicles and roadside perception control equipment through the southbound interface, receiving roadside perception information, and forwarding traffic control instructions and strategies downward, and opening programming to upper-level applications through the northbound interface. The interface provides real-time perception data and equipment control capabilities to realize the abstraction and centralization of perception control functions; the traffic facility virtualization module realizes the consistent mapping of physical entities and virtualized digital entities in terms of state and performance by unifying the definition of different types of traffic element data, and constructs a unified data specification for geographic information system maps, infrastructure, rules and control instructions to realize the visualization of traffic control tools and support flexible scheduling and management; the global data view module provides business strategy development and application, and provides the programmable traffic control system with real-time traffic situation perception, prediction and evaluation functions by building a scenario-based data structure; the business application module is used to at least realize the formulation of marking control strategies, dynamic speed limits, truck management, emergency diversion, and signal control; the method includes: Acquire video data of a target road collected by one or more cameras through the centralized control middleware and the global data view module; Determine the traffic state of the target road at a first moment based on the video data, and predict the traffic state of the target road at a second moment based on the traffic state of the target road at the first moment and the historical traffic state of the target road, the second moment being after the first moment; Generate a line marking control strategy and a traffic signal control strategy for the target road based on the traffic state of the target road at a first moment and the traffic state of the target road at a second moment; The line marking control strategy is sent to the dynamic line marking module through the centralized control middleware, and the line marking control strategy is used by the dynamic line marking module to control the lighting state of multiple light-emitting units, wherein the multiple light-emitting units are arranged on the target road and form line markings corresponding to different lanes under different lighting states; the traffic signal control strategy is sent to the traffic signal control module, and the traffic signal control strategy is used by the traffic signal control module to control the signal state of the traffic signal unit, wherein the signal state of the traffic signal unit matches the lighting state of the multiple light-emitting units.

[0007] The traffic control device provided in the embodiment of the present application includes: a processor and a memory, the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the steps of the above-mentioned traffic control method.

[0008] The traffic control system provided in the embodiment of the present application includes: a monitoring device, a dynamic road marking module, a traffic signal control module and a traffic control device; wherein, The monitoring device includes one or more cameras, and the one or more cameras are used to collect video data of the target road; The dynamic road marking module includes a plurality of light emitting units, which are arranged on the target road and form road markings corresponding to different lanes in different light emitting states; The traffic signal control module includes a traffic signal unit, and the signal state of the traffic signal unit matches the light-emitting states of the plurality of light-emitting units; The traffic control equipment is used to obtain video data of a target road collected by one or more cameras; determine the traffic state of the target road at a first moment based on the video data, and predict the traffic state of the target road at a second moment based on the traffic state of the target road at the first moment and the historical traffic state of the target road, wherein the second moment is located after the first moment; generate a marking control strategy and a traffic signal control strategy for the target road based on the traffic state of the target road at the first moment and the traffic state of the target road at the second moment; send the marking control strategy to a dynamic marking module, and the marking control strategy is used by the dynamic marking module to control the lighting state of multiple light-emitting units, wherein the multiple light-emitting units are arranged on the target road and form markings corresponding to different lanes under different lighting states; send the traffic signal control strategy to a traffic signal control module, and the traffic signal control strategy is used by the traffic signal control module to control the signal state of a traffic signal unit, wherein the signal state of the traffic signal unit matches the lighting state of the multiple light-emitting units.

[0009] In the technical solution of the embodiment of the present application, a plurality of light-emitting units are arranged on the target road, and the plurality of light-emitting units can form markings corresponding to different lanes under different light-emitting states, providing infrastructure for flexible adjustment of lane lines. Based on this, the traffic control device determines the traffic state of the target road at the first moment based on the number of videos of the target road; further, the traffic state of the target road at the second moment is predicted based on the traffic state of the target road at the first moment and the historical traffic state of the target road. Since the traffic state of the target road is predicted with the assistance of the historical traffic state, the predicted traffic state at the future moment (i.e., the second moment) is more accurate; further, a marking control strategy and a traffic signal control strategy of the target road are generated based on the traffic state of the target road at the first moment and the traffic state of the target road at the second moment; the marking control strategy is sent to the dynamic marking module, so that the dynamic marking module controls the light-emitting states of the plurality of light-emitting units to realize flexible adjustment of the lane lines; in addition, the traffic signal control strategy is sent to the traffic signal control module, so that the traffic signal control module controls the signal state of the traffic signal unit and matches the light-emitting states of the plurality of light-emitting units, thereby improving the traffic efficiency of the road. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic diagram of the structure of a programmable traffic control system according to an embodiment of the present application; Figure 2 It is a flow chart of the traffic control method provided in the embodiment of the present application; Figure 3 is a schematic diagram of predicting the state transition probability provided by an embodiment of the present application; Figure 4 is a schematic diagram of a crossroads provided in an embodiment of the present application; Figure 5 is a schematic diagram before the lane layout is changed provided in an embodiment of the present application; Figure 6 is a schematic diagram of a lane layout after a change provided in an embodiment of the present application; Figure 7 It is a schematic diagram of the structure of the traffic control device provided in the embodiment of the present application; Figure 8 It is a schematic diagram of the structural composition of the traffic control system provided in the embodiment of the present application; Fig. 9 It is a schematic structural diagram of a traffic control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The following will describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0012] It should be noted that in the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the embodiments of the present application, the character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0013] In the description of the embodiments of the present application, the term "corresponding" may indicate that there is a direct or indirect correspondence relationship between two things, or may indicate that there is an associative relationship between the two things.

[0014] Figure 1 Schematic diagram of the structure of the programmable traffic control system of the present application embodiment. Figure 1 As shown, the programmable traffic control system of the embodiment of the present application is based on the software-defined traffic control concept. Through technical means such as traffic signal digital twins, standardization of facility and vehicle communication protocols, traffic operation data visualization, and perception control hardware virtualization, it realizes functions such as hardware plug-and-play, layered decoupling of software and hardware, flexible programming of control strategies, and data governance scenario adaptation. It has centralized control middleware and a unified application development framework.

[0015] The programmable traffic control system of the embodiment of the present application, as a new generation of traffic control architecture, has the characteristics of virtualization of traffic elements, decoupling of operation and control, parallelization of control process, open programmability, etc., which makes it possible to realize forward-looking traffic control strategies such as individual dynamic speed limit, global variable lane, and saturated utilization of road network. Through virtualization and standardized data definition, the digital twin of the traffic system is constructed, and vehicles, roads, traffic lights, signs, etc. are converted into virtual digital models to dynamically map the operating status of the physical system. This real-time virtual-real fusion can not only accurately perceive the holographic changes of the traffic system, but also support the causal tracing of traffic phenomena and the strategy optimization of traffic control. Virtualization technology is based on unified standardized data specifications, covering traffic infrastructure, vehicles and control instructions, and realizing a unified digital definition of traffic elements. Through standardized data architecture, high compatibility and interoperability can be achieved between different systems and devices, effectively breaking the information islands in the traditional traffic control system, and providing technical guarantee for the coordinated operation of multiple systems. Virtualization technology also realizes the consistency alignment and resource integration of traffic elements, forming a closed-loop management mode of virtual-real interaction. Traffic managers can simulate complex scenarios in a virtual environment, predict changes in traffic flow, optimize signal timing strategies, and conduct traffic accident warnings, thereby improving the operational efficiency and safety of the traffic system. The concept of decoupling control and transfer separates the functions and responsibilities of the control layer (traffic control center) and the execution layer (field equipment) of the traffic system, thereby realizing a control paradigm that is simple locally (field equipment) and complex remotely (traffic control center). The control layer usually refers to the traffic control center or command center, which is responsible for formulating strategies, monitoring traffic conditions, analyzing data, and making decisions. It realizes macro-control of the entire traffic network through intelligent traffic control systems, such as adjusting signal cycles, issuing traffic information, and guiding emergency responses. The execution layer includes on-site traffic lights, cameras, sensors and other equipment, which directly interact with traffic participants and execute specific instructions issued by the control layer. For example, according to the instructions of the control center, the on-site signal lights can change their light states to control traffic flow, or the cameras can capture traffic violations. The advantage of this separation model is that it allows the traffic system to be more flexible and responsive. The control layer can centrally process complex traffic situations and data, while the execution layer focuses on performing specific operational tasks. In addition, this separation also helps improve the reliability and safety of the system, because the control layer can be maintained and upgraded without affecting the operation of the execution layer. Through the decoupling of control, the traffic control method will be transformed from point to surface, and the single traffic control of independent intersections will be transitioned to global mixed traffic control at the road network level. The traffic control center will analyze the road network data centrally, use large-scale computing and intelligent algorithm optimization, perform global situation deduction and generate mixed traffic control strategies, and the generated strategies will be sent to the corresponding intersections as needed to ensure the timeliness and effectiveness of the control strategies.The global dispatch system can also coordinate with other traffic elements such as public transportation, emergency rescue transportation and autonomous driving vehicles, and ultimately achieve flexible, efficient and reliable traffic control goals for "one network management" and "dispatching integration". Through the full digitization of traffic elements, the full standardization of traffic instructions, and the full spatial and temporal integration of road networks, control networks, and vehicle networks, an artificial traffic system for Cyber-Physical-Social Systems (CPSS) is constructed, and parallel execution of computational experiments based on artificial systems and virtual-real interactions is used to achieve credible, reliable and efficient parallel traffic control. In parallel traffic control, the artificial traffic system will serve as the "experimental field" and "control center" of traffic control, and assume the core functions of traffic control. Based on computational experiments of artificial traffic systems, through knowledge-driven scenario engineering, the skill training of real-world traffic managers and the optimization and evaluation of traffic control strategies can be achieved. In the parallel control stage, traffic managers call upper-layer applications and services in a standardized manner, monitor the global road network traffic status on a unified control platform, respond to traffic accidents and congestion in real time through virtual control, optimize signal light control, predict and adjust traffic flow, realize centralized coordination of command and dispatch, duty arrangement and flexible allocation of traffic management resources, and combine blockchain and federal intelligent technology to ensure that the traffic control process is credible, reliable and efficient. The programmable traffic control system of the embodiment of the present application is oriented to the needs of open and scalable application development, and supports the secondary development of flexible access and control functions of standardized traffic facilities. Standardized traffic facilities such as traffic lights, variable traffic signs, variable traffic markings, mobile isolation facilities, roadside sensors, etc. are plug-and-play in the programmable traffic control system. The unified interface and virtualization technology provided by the programmable traffic control system of the embodiment of the present application ensures the horizontal compatibility and interactive operation of different hardware. The programmable traffic control system of the embodiment of the present application supports the custom expansion of traffic control business instructions through software programming, allowing researchers and developers to experiment with new algorithms and control strategies. The system control function can be customized according to different cities, road types and traffic conditions to flexibly meet specific management goals and user needs. Programmable traffic control systems break the technical barriers of traditional closed traffic control hardware facilities, promote the standardization and efficient use of traffic resources, provide platform support for the continuous iteration and innovation of traffic control technology, and meet the diverse needs of future urban traffic control and travel services.

[0016] like Figure 1As shown, the programmable traffic control system of the embodiment of the present application includes the following main modules: centralized control middleware, traffic facility virtualization module, global data view module, business application module, integrated development framework, traffic control facilities and networked vehicles. The centralized control middleware receives the perception data from traffic control facilities and networked vehicles through the communication network, performs unified data aggregation and governance, and maps the road data to the traffic facility virtualization module and the global data view module respectively, so as to realize the digitization, visualization presentation and structured expression of the traffic system and scene data. The virtual system and structured data are integrated in the application development framework (Software Development Kit, SDK) and are open to the business application module, allowing traffic managers, engineers and researchers to flexibly write upper-level applications and algorithms for traffic control. Ultimately, these applications will realize the coordinated control of road traffic control facilities and networked vehicles through the centralized controller module. The centralized control middleware is the central software platform of the programmable traffic control system, including the southbound interface and the northbound interface, which is responsible for coordinating the operation of the entire traffic control system. The centralized control middleware communicates with connected vehicles and roadside perception control devices through the southbound interface, receives roadside perception information, and forwards traffic control instructions and policies downward. It opens programming interfaces to upper-level applications through the northbound interface, provides real-time perception data and device control capabilities, and realizes the abstraction and centralization of perception control functions.

[0017] The traffic facility virtualization module achieves consistent mapping of physical entities and virtualized digital entities in terms of status and performance by unifying the definition of different types of traffic element data. This module digitizes and structures three types of traffic elements, including Geographic Information System (GIS) maps, infrastructure, rules, and control instructions, to build a unified data specification, realize the visualization of traffic control tools, and support flexible scheduling and management. Among the three types of traffic elements in the virtualization module, map elements include digital representations of elements such as roads, intersections, and buildings. As a digital base map, map elements provide basic structural information of the road network, including road information, intersection information, building information, and road network topology. These digital data not only support accurate traffic flow simulation, but also provide important basic data for traffic planning and management. Facility elements cover the digital definition of various traffic management facilities such as signs and markings, variable information signs, isolation facilities, signal machines, sensing facilities, and roadside units. The digitization of facility elements realizes the unified description of their physical properties, dynamic status, and performance indicators, which is crucial for the global management and real-time monitoring of the traffic system. The command elements include the digital processing of traffic rules and control commands, covering traffic signal control, speed limit, lane use rules and other contents. Through digital twin technology, these commands can be synchronously controlled in virtual and real environments, providing accurate traffic management and real-time response capabilities. First, through the collection and production of high-precision maps, accurate digitization and standardized modeling of traffic elements such as roads, intersections, and buildings are carried out to build the foundation of the virtual world. Secondly, the digital expression of dynamic traffic rules, real-time traffic information and facility status is connected to build a digital twin model that runs in parallel with the actual traffic system. On this basis, the system can set dynamic traffic rules and controls, issue these rules to traffic facilities and vehicles in the virtual world, and complete the update of the infrastructure twin model. Finally, in the physical world, vehicles perform corresponding operations according to the received dynamic rules, and the status of physical traffic facilities will be synchronously updated according to the adjustments in the virtual world. This virtual-real combination process not only ensures the real-time consistency between the virtual world and the physical world, but also greatly improves the flexibility and responsiveness of traffic management.

[0018] The traffic facility virtualization module constructs a virtual world corresponding to the physical world. On this basis, a two-stage progressive approach is adopted to achieve parallel traffic control that combines the virtual and the real. In the first stage, the virtual controls the real. Through the dispatch of the traffic command center, the traffic control facilities in the virtual world are adjusted in real time, thereby simultaneously affecting the traffic operation in the real world. The core is that the traffic facilities in the real world can be controlled at the traffic command center to achieve real-time linkage between the virtual and the real. With the comprehensive digitization and networking of traffic facilities and vehicles in the real world, vehicles will operate according to virtualized traffic signs and markings, and no longer rely on physical traffic facilities. This means that traffic management is completely based on virtual instructions, and networked vehicles no longer need to identify traffic signs and markings in reality, thereby achieving more accurate, flexible and full-space and time-covered traffic control.

[0019] The global data view module is a core component of the programmable traffic control system, providing a key data foundation for business strategy development and application. By building a scenario-based data structure, the system is provided with the functions of perceiving, predicting and evaluating real-time traffic situations. The global data view module can be seamlessly integrated with the traffic facility virtualization module to realize full-factor visualization and map matching of the operation status of the traffic system, covering various traffic information from macro road network traffic flow to micro single vehicle. This all-round data support not only improves the system's ability to monitor traffic flow and traffic events, but also provides a scientific basis for traffic control decisions, which helps to optimize resource allocation and improve the ability to respond to traffic events. The global data view module contains diversified data from roadside sensors, vehicle-side sensors and environmental sensors, covering various dynamic information in the traffic system. Roadside sensors include roadside cameras, radars and other equipment, which are used to monitor traffic conditions such as traffic flow, speed and distance in real time; vehicle-side sensors collect information on the operation status and surrounding environment of networked vehicles, and their transmission and reception processes are encrypted; environmental data includes external factors such as weather conditions and lighting conditions. These data are uniformly verified and managed through scenario-based data structures to ensure data accuracy and consistency, and are easy to be called and analyzed by different application modules. The scenario-based data of the global data view module covers multiple dimensions such as time, space and content. The time dimension provides full coverage of multiple time scales such as year, month, day, and real-time data, providing a basis for long-term traffic mode analysis and immediate response. The spatial dimension data covers from regions, road sections to specific intersections and individual vehicles, and can show traffic information at all scales of the road network. The content dimension integrates multiple scenario data such as traffic flow parameters, traffic events, and vehicle movement status. This multi-dimensional data view provides comprehensive and in-depth data support for flexible and efficient traffic control, and helps to conduct comprehensive analysis and optimization decisions in complex scenarios. The global data view module has powerful data abstraction and modeling capabilities, and can digitally abstract the traffic flow and individual status of the entire network to build a detailed road network operation status view. This view not only shows the topological structure and traffic flow distribution of the entire network, but also includes real-time monitoring and early warning functions of traffic events. The information in the global view is provided to the control layer through a standardized interface, providing support for various traffic control decisions such as early warning, guidance, control, and signal cycle adjustment. For example, when the system detects a traffic accident or other traffic incident, it can quickly identify the affected areas and roads through the global view, and quickly formulate emergency response measures based on real-time data to alleviate traffic congestion and improve road safety. This makes the global data view module not only a tool for data display and monitoring, but also provides flexible and powerful support for traffic management and decision-making.

[0020] The business application module is responsible for implementing various traffic control applications, such as dynamic speed limit, truck management, emergency diversion, signal control, etc. This module not only provides traffic managers with a wealth of control methods, but also provides engineers and researchers with a platform for developing and testing new control strategies. Through an open architecture, the business application module allows third-party developers to use the unified SDK interface provided by the centralized controller for development, making full use of the flexibility and scalability of the system. In specific applications, the business application module can achieve unified and coordinated control of various facilities and connected vehicles in the traffic system based on the virtualization scene provided by the traffic facility virtualization module and the scenario data structure provided by the global data view module. Developers can call the digital twin facility model in the virtualization scene through the SDK interface to obtain real-time traffic flow, road conditions and other data, and write and deploy traffic control applications according to actual needs. For example, the dynamic speed limit function can automatically adjust the speed limit sign according to the real-time traffic flow and road conditions to optimize the vehicle's driving speed; the truck management function can restrict or guide trucks on a specific road section to avoid excessive pressure on urban traffic; the emergency diversion function can quickly formulate a diversion plan when a traffic incident occurs to ensure the priority passage of emergency vehicles and the safe evacuation of ordinary vehicles. In addition, the business application module also supports intelligent management of signal control. By collaborating with the centralized controller and the global data view module, the system can monitor the operating status of traffic signals in real time and dynamically adjust signal timing according to changes in traffic flow to improve traffic efficiency and capacity. Developers and researchers can also use this module to test and verify new control algorithms, such as adaptive signal control, intelligent lane management and other new technologies, to provide efficient and flexible solutions for future traffic system upgrades.

[0021] Figure 2 is a flow chart of the traffic control method provided in the embodiment of the present application, such as Figure 2As shown, the traffic control method is applied to a programmable traffic control system. The programmable traffic control system of the embodiment of the present application at least includes a centralized control middleware, a traffic facility virtualization module, a global data view module and a business application module. The real-time traffic status information is processed by the centralized control middleware, and the processing result is transmitted to the application end, so that the application end can generate control instructions based on information analysis; the centralized control middleware responds to the control instructions to control road vehicles; the centralized control middleware includes a southbound interface and a northbound interface, which is responsible for coordinating the operation of the entire traffic control system, communicating with networked vehicles and roadside perception control equipment through the southbound interface, receiving roadside perception information, and forwarding traffic control instructions and strategies downward, and opening to upper-level applications through the northbound interface. The programming interface provides real-time perception data and equipment control capabilities, and realizes the abstraction and centralization of perception control functions; the traffic facility virtualization module can achieve consistent mapping of physical entities and virtualized digital entities in terms of state and performance by unifying the definition of different types of traffic element data, and build a unified data specification for geographic information system maps, infrastructure, rules and control instructions to realize the visualization of traffic control tools and support flexible scheduling and management; the global data view module provides business strategy development and application, and provides the perception, prediction and evaluation functions of real-time traffic situation for the programmable traffic control system by building a scenario-based data structure; the business application module is used to at least realize the formulation of marking control strategies, dynamic speed limits, truck management, emergency diversion, and signal control. It can process real-time traffic status information through the centralized control middleware, and transmit the processing results to the application end, so that the application end can generate control instructions based on information analysis. The centralized control middleware responds to the control instruction to control road vehicles; the embodiment of the present application obtains real-time traffic status information through intelligent traffic perception devices such as video surveillance equipment and radar and intelligent networked vehicles; then the real-time traffic status information is standardized through the centralized control middleware, and the processing results are transmitted to the application end, so that the application end generates control instructions based on information analysis; and the centralized control middleware controls intelligent traffic control devices such as signal machines and variable traffic signs based on control instructions to control or induce vehicle driving, or directly sends virtual traffic signals to intelligent networked vehicles to achieve precise control. In this way, relevant personnel can understand the real-time road traffic status at the application end, and issue instructions, accurately and efficiently coordinate various intelligent traffic perception and control devices and intelligent networked vehicles, and realize unified organization and control of the entire road network traffic. The real-time traffic status information is standardized through the preset centralized control middleware, and the processing results are transmitted to the application end. Among them, the standardized processing may include processing the data format, obtaining data such as data packets that can be recognized or used by the application end, and then transmitting the packets to the application end, so that the application end generates appropriate control instructions based on the processing results.The control instructions can be generated by relevant personnel such as traffic police based on the analysis results. The central control middleware controls the state of the intelligent traffic control equipment based on the control instructions to manage and control road vehicles; or controls the communication equipment to send virtual traffic signals to the intelligent networked vehicles. For example, intelligent traffic control equipment including variable traffic signs can be controlled to change, control the flow of vehicles, and send virtual traffic signals directly to intelligent networked vehicles through communication equipment for use by the vehicle control system to achieve traffic control of individual machines. The specific structure is shown above. Figure 1 And its description will not be repeated here.

[0022] like Figure 2 As shown, the traffic control method of the embodiment of the present application includes the following steps: Step 101: Obtain video data of a target road captured by one or more cameras.

[0023] In the embodiment of the present application, the relevant video data is obtained through the centralized control middleware and the global data view module. The target road can be a road in an urban area, but is not limited thereto. The target road can also be a road in other areas, such as a road in a township area. The present application does not limit the area where the target road is located.

[0024] In the embodiment of the present application, one or more cameras are arranged on the target road. In one example, multiple cameras are arranged at both ends of the target road and in the middle of the target road, and the arrangement positions of the cameras can be flexibly adjusted according to actual needs, for example, a larger number of cameras can be arranged at both ends of the target road, and a smaller number of cameras can be arranged in the middle of the target road.

[0025] One or more cameras deployed on the target road collect video data of the target road in real time and transmit the video data to the traffic control device, so that the traffic control device obtains the video data of the target road collected by one or more cameras. The transmission method of video data can include but is not limited to the following methods: 1) wireless transmission method; 2) wired transmission method.

[0026] Step 102: Determine the traffic status of the target road at a first moment based on the video data, and predict the traffic status of the target road at a second moment based on the traffic status of the target road at the first moment and the historical traffic status of the target road, where the second moment is after the first moment.

[0027] In the embodiment of the present application, the traffic status of the target road includes information such as traffic flow and vehicle speed.

[0028] In some implementations, the video data is input into a traffic status analysis model, and the traffic status analysis model is used to perform the following processing: S11, extracting key frames from the video data, wherein the key frames refer to video frames containing target objects; the target objects here include vehicles, and optionally, pedestrians; S12, analyzing each key frame to obtain the number of target objects contained in each key frame and the speed of the target objects; S13: Based on the number of target objects and the speed of the target objects corresponding to each key frame, the traffic status of the target road at the first moment is counted.

[0029] In the embodiment of the present application, the first moment refers to the current moment, or refers to the moment of video data collection. The second moment refers to a moment after the first moment, and the second moment can be understood as a future moment (i.e., a moment that has not yet occurred).

[0030] In some implementations, the traffic state of the target road at the first moment and the historical traffic state of the target road are input into a traffic state prediction model; the traffic state prediction model is used to perform the following processing: S21, extracting a first feature representation of a traffic state at a first moment, and extracting a first feature representation set of historical traffic states.

[0031] The historical traffic state includes multiple historical traffic states, each of which corresponds to a historical time. Exemplarily, the historical traffic state can be represented as {S1, S2, ..., Sk}, representing k traffic states, where Si represents the traffic state corresponding to the historical time ti, 1≤i≤k.

[0032] In some embodiments, the traffic state prediction model includes a feature extraction network, and the feature extraction network is used to extract a first feature representation of the traffic state at a first moment, and a first feature representation set of historical traffic states is extracted.

[0033] Exemplarily, the first feature representation can be expressed as F11 , represents the traffic status of the target road at the first moment; the first feature representation set can be expressed as { F21, F22, …, F2k},in, F2i Represents the feature representation corresponding to the traffic state Si in the historical traffic state, 1≤i≤k.

[0034] S22. Based on the first feature representation and the first feature representation set, learn the state transition probabilities between the traffic state at the first moment and each traffic state in the historical traffic state to obtain a state transition probability matrix.

[0035] In some embodiments, the first feature representation set includes a plurality of second feature representations, each second feature representation corresponding to a traffic state in the historical traffic states; The learning of the state transition probability between the traffic state at the first moment and each traffic state in the historical traffic state based on the first feature representation and the first feature representation set includes: Determine condition control information corresponding to the target road at a first moment, the condition control information including road position information of the target road, surrounding environment information of the target road, and the number of users of the target road; A state transition probability between the first feature representation and each second feature representation is calculated based on the conditional control information.

[0036] Specifically, Figure 3 As shown, the road position information of the target road is embeddedly represented to obtain the corresponding road representation; the surrounding environment information of the target road is embeddedly represented to obtain the corresponding road representation; the user volume of the target road is embeddedly represented to obtain the corresponding user volume representation. Further, the road representation, the road representation and the user volume representation are concatenated to obtain the representation corresponding to the conditional control information. Further, the representation corresponding to the conditional control information, the first feature representation and the plurality of second feature representations are input into the Markov model (which is part of the traffic state prediction model), and the first feature representation is predicted by the Markov model. F11 With each second feature representation F2i The state transition probability between the traffic state at the first moment and each traffic state in the historical traffic state is the state transition probability matrix. The predicted state transition probability meets the constraints of the conditional control information and is therefore more accurate. The matrix formed by the state transition probability between the traffic state at the first moment and each traffic state in the historical traffic state is the state transition probability matrix.

[0037] S23. Predicting the traffic state of the target road at the second moment based on the state transition probability matrix.

[0038] In some implementations, the traffic state corresponding to the maximum state transition probability in the state transition probability matrix may be used as the traffic state of the target road at the second moment.

[0039] Step 103: Generate a line marking control strategy and a traffic signal control strategy for the target road based on the traffic status of the target road at the first moment and the traffic status of the target road at the second moment.

[0040] Specifically, a congestion trend of the target road in the first driving direction and a congestion trend of the target road in the second driving direction are determined based on the traffic status of the target road at the first moment and the traffic status of the target road at the second moment.

[0041] The first case: if the congestion trend of the target road in the first driving direction is higher than the congestion trend in the second driving direction, the first line marking control strategy and the first traffic signal control strategy of the target road are generated; wherein the first line marking control strategy is to widen the lane width in the first driving direction or increase the number of lanes in the first driving direction or adjust the direction of some or all lanes in the second driving direction to the first driving direction; the first traffic signal control strategy is to lengthen the release signal in the first driving direction.

[0042] The second situation: if the congestion trend of the target road in the first driving direction is lower than the congestion trend in the second driving direction, a second line marking control strategy and a second traffic signal control strategy for the target road are generated; wherein the second line marking control strategy is to narrow the lane width in the first driving direction or to reduce the number of lanes in the first driving direction or to adjust the direction of some or all lanes in the first driving direction to the second driving direction; and the second traffic signal control strategy is to shorten the release signal in the first driving direction or to maintain it unchanged.

[0043] Through the above-mentioned marking control strategy and traffic signal control strategy, the traffic flow on the target road in both driving directions can be flexibly adjusted, so as to maximize the road traffic efficiency under limited road conditions.

[0044] Step 104: Send the marking control strategy to the dynamic marking module, where the marking control strategy is used by the dynamic marking module to control the lighting state of multiple light-emitting units, wherein the multiple light-emitting units are arranged on the target road and form markings corresponding to different lanes under different lighting states.

[0045] Taking the first case above as an example, refer to Figure 4 , Figure 4 The diagram is a schematic diagram of an intersection. It can be seen that each road has two driving directions. Taking the road below the intersection as an example, the first driving direction is upward (i.e. north), and the second driving direction is downward (i.e. south). If the congestion trend of the road in the first driving direction is higher than the congestion trend in the second driving direction, a first marking control strategy is generated. The first marking control strategy is to increase the number of lanes in the first driving direction or adjust the direction of some or all lanes in the second driving direction to the first driving direction. Figure 5 and Figure 6 , Figure 5 This is the lane layout before the first marking control strategy is executed. It can be seen that there are a total of 3 lanes in the first driving direction, including a left turn lane, a straight lane and a right turn lane. When the congestion trend in the first driving direction is higher than the congestion trend in the second driving direction, the layout of the lanes is adjusted by controlling the light states of the light-emitting units arranged on the road. Figure 6This is the lane layout after the execution of the first marking control strategy. It can be seen that there are a total of 4 lanes in the first driving direction, including a left-turn lane, two straight lanes and a right-turn lane. The number of lanes has increased. In addition, the number of vehicles in the second driving direction has decreased accordingly, so that the effect of a tidal lane can be achieved.

[0046] Step 105: Send the traffic signal control strategy to the traffic signal control module, where the traffic signal control strategy is used by the traffic signal control module to control the signal state of the traffic signal unit, wherein the signal state of the traffic signal unit matches the light-emitting states of the plurality of light-emitting units.

[0047] It should be noted that step 104 and step 105 are not limited in execution order and can be executed in parallel.

[0048] Taking the first case mentioned above as an example, when the congestion trend in the first driving direction is higher than the congestion trend in the second driving direction, the duration of the release signal in the first driving direction is adjusted by controlling the signal state of the traffic signal unit. Specifically, the release signal in the first driving direction is lengthened. In this way, the congestion trend in the first driving direction can be alleviated to a greater extent.

[0049] In addition, since the lane layout is adjusted in step 104 , the signal status of the corresponding traffic signal unit also needs to match it.

[0050] In some embodiments, the method further comprises: Obtain the driving status of the target vehicle through the centralized control middleware; A road guidance signal is generated based on the driving state of the target vehicle and the marking control strategy of the target road, and the road guidance signal is sent to the target vehicle, wherein the road guidance signal is used to present a virtual road guidance line on the augmented reality device of the target vehicle, and the virtual road guidance line matches the markings of the lane formed by multiple light-emitting units.

[0051] Here, the target vehicle refers to any vehicle located on the target road. In some embodiments, the driving state of the target vehicle is obtained through the vehicle network, and here, the driving state includes the driving position and the driving speed.

[0052] Here, the target vehicle can present the road guidance signal through the augmented reality device. For example, the windshield of the target vehicle is built with a translucent display device, which can display the road guidance line, and the road guidance line will be superimposed on the real environment to form an augmented reality effect. The road guidance line can assist the driver of the target vehicle to accurately grasp the layout of the current lane line and drive in the right direction.

[0053] Here, the road guide line matches the lane markings formed by multiple light-emitting units. For example, if the light-emitting units form a left-turn lane, the road guide line is the left-turn guide line. For example, if the light-emitting units form a straight lane, the road guide line is the straight guide line. For example, if the light-emitting units form a right-turn lane, the road guide line is the right-turn guide line.

[0054] In some embodiments, the method further comprises: A driving speed control signal is generated based on the driving state of the target vehicle and the traffic signal control strategy, and the driving speed control signal is sent to the target vehicle through the centralized control middleware, wherein the driving speed control signal is used to present virtual driving speed information on the augmented reality device of the target vehicle.

[0055] Here, the target vehicle can present the driving speed control signal through the augmented reality device. For example, the windshield of the target vehicle is equipped with a translucent display device, which can display the driving speed information, and the driving speed information will be superimposed on the real environment to form an augmented reality effect. The driving speed information can assist the driver of the target vehicle to accurately grasp the driving speed recommended by the current road conditions, thereby avoiding traffic accidents caused by excessive speed.

[0056] In some embodiments, if the traffic signal control strategy is to lengthen the release signal in the first driving direction, then the virtual driving speed information presented based on the driving speed control signal will increase. Conversely, if the traffic signal control strategy is to shorten the release signal in the first driving direction, then the virtual driving speed information presented based on the driving speed control signal will decrease.

[0057] In some embodiments, if the driving state of the target vehicle is that the speed in the first driving direction exceeds a first speed threshold, then the virtual driving speed information presented based on the driving speed control signal is reduced. Conversely, if the driving state of the target vehicle is that the speed in the first driving direction is lower than a second speed threshold, then the virtual driving speed information presented based on the driving speed control signal is increased.

[0058] In some embodiments, the method further comprises: Generate multiple twin objects corresponding to the multiple light-emitting units, wherein each light-emitting unit corresponds to one twin object; The display states of the multiple twin objects are adjusted based on the marking control strategy, wherein the display states of the multiple twin objects correspond to the markings of the lanes formed by the multiple light-emitting units.

[0059] Here, in order to flexibly control multiple light-emitting units, multiple twin objects corresponding to the multiple light-emitting units can be generated, and the display status of the multiple twin objects can be adjusted based on the marking control strategy, so that the display status of the multiple twin objects corresponds to the lane markings formed by the multiple light-emitting units, which is convenient for the background traffic control monitoring personnel to understand the current lane layout status in real time and realize the visualization of the lane layout.

[0060] In the technical solution of the embodiment of the present application, a plurality of light-emitting units are arranged on the target road, and the plurality of light-emitting units can form markings corresponding to different lanes under different light-emitting states, providing infrastructure for flexible adjustment of lane lines. Based on this, the traffic control device determines the traffic state of the target road at the first moment based on the number of videos of the target road; further, the traffic state of the target road at the second moment is predicted based on the traffic state of the target road at the first moment and the historical traffic state of the target road. Since the traffic state of the target road is predicted with the assistance of the historical traffic state, the predicted traffic state at the future moment (i.e., the second moment) is more accurate; further, a marking control strategy and a traffic signal control strategy of the target road are generated based on the traffic state of the target road at the first moment and the traffic state of the target road at the second moment; the marking control strategy is sent to the dynamic marking module, so that the dynamic marking module controls the light-emitting states of the plurality of light-emitting units to realize flexible adjustment of the lane lines; in addition, the traffic signal control strategy is sent to the traffic signal control module, so that the traffic signal control module controls the signal state of the traffic signal unit and matches the light-emitting states of the plurality of light-emitting units, thereby improving the traffic efficiency of the road.

[0061] Figure 7 Schematic diagram of the structure of the traffic control device provided in the embodiment of the present application. Figure 7 As shown, the traffic control device of the embodiment of the present application is applied to a programmable traffic control system. The programmable traffic control system of the embodiment of the present application at least includes a centralized control middleware, a traffic facility virtualization module, a global data view module and a business application module. The real-time traffic status information is processed by the centralized control middleware, and the processing result is transmitted to the application end, so that the application end can generate control instructions based on information analysis; the specific structure of the programmable traffic control system of the embodiment of the present application and the specific functions of each module, see the aforementioned Figure 1 The traffic control device of the embodiment of the present application includes: An acquisition unit 601 is used to acquire video data of a target road captured by one or more cameras through the centralized control middleware and the global data view module; A determination unit 602, configured to determine a traffic state of the target road at a first moment based on the video data; A prediction unit 603, configured to predict a traffic state of the target road at a second moment based on the traffic state of the target road at the first moment and a historical traffic state of the target road, the second moment being after the first moment; A strategy generating unit 604, configured to generate a line marking control strategy and a traffic signal control strategy for the target road based on the traffic state of the target road at the first moment and the traffic state of the target road at the second moment; The communication unit 605 is used to send the line marking control strategy to the dynamic line marking module, and the line marking control strategy is used by the dynamic line marking module to control the light-emitting states of multiple light-emitting units, wherein the multiple light-emitting units are arranged on the target road and form line markings corresponding to different lanes under different light-emitting states; send the traffic signal control strategy to the traffic signal control module, and the traffic signal control strategy is used by the traffic signal control module to control the signal state of the traffic signal unit, wherein the signal state of the traffic signal unit matches the light-emitting states of the multiple light-emitting units.

[0062] In some implementations, the prediction unit 603 is specifically configured to input the traffic state of the target road at the first moment and the historical traffic state of the target road into a traffic state prediction model; and use the traffic state prediction model to perform the following processing: Extracting a first feature representation of the traffic state at the first moment, and extracting a first feature representation set of the historical traffic state; Based on the first feature representation and the first feature representation set, learning the state transition probability between the traffic state at the first moment and each traffic state in the historical traffic state to obtain a state transition probability matrix; The traffic state of the target road at a second moment is predicted based on the state transition probability matrix.

[0063] In some embodiments, the first feature representation set includes multiple second feature representations, each second feature representation corresponds to a traffic state in the historical traffic states; the prediction unit 603 is specifically used to determine the condition control information corresponding to the target road at the first moment, the condition control information including the road position information of the target road, the surrounding environment information of the target road and the user volume of the target road; based on the condition control information, the state transition probability between the first feature representation and each second feature representation is calculated.

[0064] In some embodiments, the strategy generating unit 604 is specifically used to determine the congestion trend of the target road in the first driving direction and the congestion trend in the second driving direction based on the traffic status of the target road at the first moment and the traffic status of the target road at the second moment; if the congestion trend of the target road in the first driving direction is higher than the congestion trend in the second driving direction, then generate a first line marking control strategy and a first traffic signal control strategy for the target road; wherein the first line marking control strategy is to widen the lane width in the first driving direction, increase the number of lanes in the first driving direction, or adjust the direction of some or all lanes in the second driving direction to the first driving direction; the first traffic signal control strategy is to lengthen the release signal in the first driving direction; if the congestion trend of the target road in the first driving direction is lower than the congestion trend in the second driving direction, then generate a second line marking control strategy and a second traffic signal control strategy for the target road; wherein the second line marking control strategy is to narrow the lane width in the first driving direction, reduce the number of lanes in the first driving direction, or adjust the direction of some or all lanes in the first driving direction to the second driving direction; the second traffic signal control strategy is to shorten or maintain the release signal in the first driving direction.

[0065] In some implementations, the communication unit 605 is further used to obtain the driving status of the target vehicle through the centralized control middleware; The device further includes: a guidance signal generating unit 606, configured to generate a road guidance signal based on the driving state of the target vehicle and the marking control strategy of the target road; The communication unit 605 is also used to send the road guidance signal to the target vehicle, wherein the road guidance signal is used to present a virtual road guidance line on the augmented reality device of the target vehicle, and the virtual road guidance line matches the lane markings formed by the multiple light-emitting units.

[0066] In some embodiments, the apparatus further comprises: a speed control signal generating unit 607, configured to generate a driving speed control signal based on the driving state of the target vehicle and the traffic signal control strategy; The communication unit 605 is further used to send the driving speed control signal to the target vehicle through the centralized control middleware, wherein the driving speed control signal is used to present virtual driving speed information on the augmented reality device of the target vehicle.

[0067] In some embodiments, the device also includes: a twin simulation unit 608, which is used to generate multiple twin objects corresponding to the multiple light-emitting units, wherein each light-emitting unit corresponds to a twin object; and adjust the display status of the multiple twin objects based on the marking control strategy, wherein the display status of the multiple twin objects corresponds to the markings of the lanes formed by the multiple light-emitting units.

[0068] Those skilled in the art should understand that Figure 7 The functions implemented by each unit in the traffic control device shown can be understood by referring to the relevant description of the aforementioned method. Figure 7 The functions of each unit in the traffic control device shown can be implemented by a program running on a processor, or by a specific logic circuit.

[0069] Figure 8 Schematic diagram of the structure of the traffic control system provided in the embodiment of the present application. Figure 8 As shown, the traffic control system includes monitoring equipment 701, dynamic road marking module 702, traffic signal control module 703 and traffic control equipment 704; wherein, The monitoring device 701 includes one or more cameras, and the one or more cameras are used to collect video data of the target road; The dynamic road marking module 702 includes a plurality of light emitting units, which are arranged on the target road and form road markings corresponding to different lanes in different light emitting states; The traffic signal control module 703 includes a traffic signal unit, and the signal state of the traffic signal unit matches the light-emitting states of the plurality of light-emitting units; The traffic control device 704 is used to obtain video data of a target road collected by one or more cameras; determine the traffic state of the target road at a first moment based on the video data, and predict the traffic state of the target road at a second moment based on the traffic state of the target road at the first moment and the historical traffic state of the target road, wherein the second moment is located after the first moment; generate a marking control strategy and a traffic signal control strategy for the target road based on the traffic state of the target road at the first moment and the traffic state of the target road at the second moment; send the marking control strategy to the dynamic marking module 702, and the marking control strategy is used by the dynamic marking module 702 to control the lighting state of multiple light-emitting units, wherein the multiple light-emitting units are arranged on the target road and form markings corresponding to different lanes under different lighting states; send the traffic signal control strategy to the traffic signal control module 703, and the traffic signal control strategy is used by the traffic signal control module 703 to control the signal state of the traffic signal unit, wherein the signal state of the traffic signal unit matches the lighting state of the multiple light-emitting units.

[0070] In the embodiment of the present application, the traffic control equipment includes a traffic control device. Those skilled in the art should understand that the traffic control device can refer to the aforementioned Figure 7 Understand the relevant description.

[0071] Fig. 9 It is a schematic structural diagram of a traffic control device provided in an embodiment of the present application. Fig. 9 The traffic control device shown includes a processor 810, which can call and run a computer program from a memory to implement the method in the embodiment of the present application.

[0072] Alternatively, if Fig. 9 As shown, the traffic control device may further include a memory 820. The processor 810 may call and run a computer program from the memory 820 to implement the method in the embodiment of the present application.

[0073] The memory 820 may be a separate device independent of the processor 810 , or may be integrated into the processor 810 .

[0074] Alternatively, if Fig. 9 As shown, the traffic control device may also include a transceiver 830, and the processor 810 may control the transceiver 830 to communicate with other devices. Specifically, it may send information or data to other devices, or receive information or data sent by other devices.

[0075] The transceiver 830 may include a transmitter and a receiver. The transceiver 830 may further include an antenna, and the number of the antennas may be one or more.

[0076] The traffic control equipment can implement the corresponding processes of the various methods implemented in the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0077] It should be understood that the processor of the embodiment of the present application may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method embodiment can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor to be executed. The software module may be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0078] It can be understood that the memory in the embodiments of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DRRAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0079] It should be understood that the above-mentioned memory is exemplary but not restrictive. For example, the memory in the embodiments of the present application may also be static random access memory (static RAM, SRAM), dynamic random access memory (dynamic RAM, DRAM), synchronous dynamic random access memory (synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (double data rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (synch link DRAM, SLDRAM) and direct memory bus random access memory (Direct Rambus RAM, DRRAM), etc. That is to say, the memory in the embodiments of the present application is intended to include but not limited to these and any other suitable types of memory.

[0080] The embodiment of the present application further provides a computer-readable storage medium for storing a computer program. The computer program enables a computer to execute the corresponding processes implemented by the traffic control device in each method of the embodiment of the present application, which will not be described in detail for the sake of brevity.

[0081] The embodiment of the present application further provides a computer program product, including computer program instructions. The computer program instructions enable a computer to execute the corresponding processes implemented by the traffic control device in each method of the embodiment of the present application, which will not be described in detail for the sake of brevity.

[0082] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0083] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0084] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0085] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0086] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0087] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0088] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A traffic control device, applied to a programmable traffic control system, the programmable traffic control system at least comprising a centralized control middleware, a traffic facility virtualization module, a global data view module and a business application module, the centralized control middleware processes real-time traffic status information and transmits the processing result to an application end, so that the application end generates a control instruction based on information analysis; the centralized control middleware responds to the control instruction to control road vehicles; The centralized control middleware includes a southbound interface and a northbound interface, and is responsible for coordinating the operation of the entire traffic control system. It communicates with networked vehicles and roadside perception control equipment through the southbound interface, receives roadside perception information, and forwards traffic control instructions and strategies downward. It opens a programming interface to upper-layer applications through the northbound interface, provides real-time perception data and equipment control capabilities, and realizes the abstraction and centralization of perception control functions. It is characterized by: The device comprises: An acquisition unit, used for acquiring video data of a target road captured by one or more cameras through the centralized control middleware and the global data view module; a determining unit, configured to determine a traffic state of the target road at a first moment based on the video data; a prediction unit, configured to predict a traffic state of the target road at a second moment based on the traffic state of the target road at the first moment and a historical traffic state of the target road, the second moment being located after the first moment; A strategy generating unit, configured to generate a line marking control strategy and a traffic signal control strategy for the target road based on the traffic state of the target road at a first moment and the traffic state of the target road at a second moment; a communication unit, for sending the line marking control strategy to the dynamic line marking module through the centralized control middleware, the line marking control strategy being used by the dynamic line marking module to control the light emitting states of a plurality of light emitting units, wherein the plurality of light emitting units are arranged on the target road and form line markings corresponding to different lanes under different light emitting states; and for sending the traffic signal control strategy to the traffic signal control module, the traffic signal control strategy being used by the traffic signal control module to control the signal state of a traffic signal unit, wherein the signal state of the traffic signal unit matches the light emitting states of the plurality of light emitting units.

2. The device according to claim 1, characterized in that The traffic facility virtualization module unifies the definitions of different types of traffic element data to achieve consistent mapping of status and performance between physical entities and virtualized digital entities, constructs unified data specifications for geographic information system maps, infrastructure, rules and control instructions, realizes visualization of traffic control tools and supports flexible scheduling and management; the global data view module provides business strategy development and application, and by building a scenario-based data structure, provides the programmable traffic control system with real-time traffic situation perception, prediction and evaluation functions; the business application module is used to at least implement the formulation of marking control strategies, dynamic speed limits, truck management, emergency diversion, and signal control.

3. The device according to claim 1, characterized in that The prediction unit is further used to input the traffic state of the target road at the first moment and the historical traffic state of the target road into a traffic state prediction model; and use the traffic state prediction model to perform the following processing: Extracting a first feature representation of the traffic state at the first moment, and extracting a first feature representation set of the historical traffic state; Based on the first feature representation and the first feature representation set, learning the state transition probability between the traffic state at the first moment and each traffic state in the historical traffic state to obtain a state transition probability matrix; The traffic state of the target road at a second moment is predicted based on the state transition probability matrix.

4. The device according to claim 3, characterized in that The first feature representation set includes multiple second feature representations, each second feature representation corresponds to a traffic state in the historical traffic states; the prediction unit is also used to determine the condition control information corresponding to the target road at the first moment, the condition control information includes the road position information of the target road, the surrounding environment information of the target road and the user volume of the target road; based on the condition control information, the state transition probability between the first feature representation and each second feature representation is calculated.

5. The device according to claim 1, characterized in that The strategy generating unit is further used to determine the congestion trend of the target road in the first driving direction and the congestion trend of the target road in the second driving direction based on the traffic state of the target road at the first moment and the traffic state of the target road at the second moment; If the congestion trend of the target road in the first driving direction is higher than that in the second driving direction, a first line marking control strategy and a first traffic signal control strategy for the target road are generated; wherein the first line marking control strategy is to widen the lane width in the first driving direction, increase the number of lanes in the first driving direction, or adjust the directions of some or all lanes in the second driving direction to the first driving direction; the first traffic signal control strategy is to lengthen the release signal in the first driving direction; if the congestion trend of the target road in the first driving direction is lower than that in the second driving direction, a second line marking control strategy and a second traffic signal control strategy for the target road are generated; wherein the second line marking control strategy is to narrow the lane width in the first driving direction, reduce the number of lanes in the first driving direction, or adjust the directions of some or all lanes in the first driving direction to the second driving direction; the second traffic signal control strategy is to shorten the release signal in the first driving direction or keep it unchanged.

6. The device according to claim 1, characterized in that The communication unit is further used to obtain the driving status of the target vehicle through the centralized control middleware; The device further comprises: a guidance signal generating unit, configured to generate a road guidance signal based on the driving state of the target vehicle and the marking control strategy of the target road; The communication unit is also used to send the road guidance signal to the target vehicle through the centralized control middleware, wherein the road guidance signal is used to present a virtual road guidance line on the augmented reality device of the target vehicle, and the virtual road guidance line matches the lane markings formed by the multiple light-emitting units.

7. The device according to claim 6, characterized in that The device further comprises: a speed control signal generating unit, configured to generate a travel speed control signal based on the travel state of the target vehicle and the traffic signal control strategy; The communication unit is further used to send the driving speed control signal to the target vehicle through the centralized control middleware, wherein the driving speed control signal is used to present virtual driving speed information on an augmented reality device of the target vehicle.

8. The device according to any one of claims 1 to 7, characterized in that The device also includes: a twin simulation unit, used to generate multiple twin objects corresponding to the multiple light-emitting units, wherein each light-emitting unit corresponds to a twin object; and adjust the display states of the multiple twin objects based on the marking control strategy, wherein the display states of the multiple twin objects correspond to the markings of the lanes formed by the multiple light-emitting units.

9. A traffic control method, applied to a programmable traffic control system, the programmable traffic control system at least comprising a centralized control middleware, a traffic facility virtualization module, a global data view module and a business application module, the centralized control middleware processes real-time traffic status information and transmits the processing result to an application end, so that the application end generates a control instruction based on information analysis; the centralized control middleware responds to the control instruction to control road vehicles; The centralized control middleware includes a southbound interface and a northbound interface, which is responsible for coordinating the operation of the entire traffic control system. It communicates with networked vehicles and roadside perception control equipment through the southbound interface, receives roadside perception information, and forwards traffic control instructions and strategies downward. It opens a programming interface to upper-level applications through the northbound interface, provides real-time perception data and equipment control capabilities, and realizes the abstraction and centralization of perception control functions; the traffic facility virtualization module unifies the definition of different types of traffic element data to achieve consistent mapping of physical entities and virtualized digital entities in state and performance, and constructs a unified data specification for geographic information system maps, infrastructure, rules and control instructions to realize the visualization of traffic control tools and support flexible scheduling and management; the global data view module provides business strategy development and application, and provides the programmable traffic control system with real-time traffic situation perception, prediction and evaluation functions by building a scenario-based data structure; the business application module is used to at least realize the formulation of marking control strategies, dynamic speed limits, truck management, emergency diversion, and signal control; it is characterized in that The method comprises: Acquire video data of a target road collected by one or more cameras through the centralized control middleware and the global data view module; Determine the traffic state of the target road at a first moment based on the video data, and predict the traffic state of the target road at a second moment based on the traffic state of the target road at the first moment and the historical traffic state of the target road, the second moment being after the first moment; Generate a line marking control strategy and a traffic signal control strategy for the target road based on the traffic state of the target road at a first moment and the traffic state of the target road at a second moment; The line marking control strategy is sent to the dynamic line marking module through the centralized control middleware, and the line marking control strategy is used by the dynamic line marking module to control the lighting state of multiple light-emitting units, wherein the multiple light-emitting units are arranged on the target road and form line markings corresponding to different lanes under different lighting states; the traffic signal control strategy is sent to the traffic signal control module, and the traffic signal control strategy is used by the traffic signal control module to control the signal state of the traffic signal unit, wherein the signal state of the traffic signal unit matches the lighting state of the multiple light-emitting units.

10. The method according to claim 9, characterized in that The predicting the traffic state of the target road at the second moment based on the traffic state of the target road at the first moment and the historical traffic state of the target road includes: Inputting the traffic state of the target road at the first moment and the historical traffic state of the target road into a traffic state prediction model; The traffic state prediction model is used to perform the following processing: Extracting a first feature representation of the traffic state at the first moment, and extracting a first feature representation set of the historical traffic state; Based on the first feature representation and the first feature representation set, learning the state transition probability between the traffic state at the first moment and each traffic state in the historical traffic state to obtain a state transition probability matrix; The traffic state of the target road at a second moment is predicted based on the state transition probability matrix.

11. The method according to claim 10, characterized in that The first feature representation set includes a plurality of second feature representations, each second feature representation corresponding to one of the historical traffic states; The learning, based on the first feature representation and the first feature representation set, of the state transition probabilities between the traffic state at the first moment and each traffic state in the historical traffic state includes: Determine condition control information corresponding to the target road at the first moment, the condition control information including road position information of the target road, surrounding environment information of the target road, and the number of users on the target road; A state transition probability between the first feature representation and each of the second feature representations is calculated based on the conditional control information.

12. The method according to claim 9, characterized in that The generating of the marking control strategy and the traffic signal control strategy of the target road based on the traffic state of the target road at the first moment and the traffic state of the target road at the second moment includes: Determine a congestion trend of the target road in a first driving direction and a congestion trend of the target road in a second driving direction based on a traffic state of the target road at a first moment and a traffic state of the target road at a second moment; If the congestion trend of the target road in the first driving direction is higher than that in the second driving direction, a first line marking control strategy and a first traffic signal control strategy for the target road are generated; wherein the first line marking control strategy is to widen the lane width in the first driving direction or to increase the number of lanes in the first driving direction or to adjust the direction of some or all lanes in the second driving direction to the first driving direction; and the first traffic signal control strategy is to lengthen the release signal in the first driving direction; If the congestion trend of the target road in the first driving direction is lower than the congestion trend in the second driving direction, a second line marking control strategy and a second traffic signal control strategy for the target road are generated; wherein the second line marking control strategy is to narrow the lane width in the first driving direction or to reduce the number of lanes in the first driving direction or to adjust the direction of some or all lanes in the first driving direction to the second driving direction; and the second traffic signal control strategy is to shorten the release signal in the first driving direction or to maintain it unchanged.

13. The method according to claim 9, characterized in that The method further comprises: Acquiring the driving status of the target vehicle through the centralized control middleware; A road guidance signal is generated based on the driving state of the target vehicle and the marking control strategy of the target road, and the road guidance signal is sent to the target vehicle through the centralized control middleware, wherein the road guidance signal is used to present a virtual road guidance line on an augmented reality device of the target vehicle, and the virtual road guidance line matches the markings of the lane formed by the multiple light-emitting units.

14. The method according to claim 13, characterized in that The method further comprises: A driving speed control signal is generated based on the driving state of the target vehicle and the traffic signal control strategy, and the driving speed control signal is sent to the target vehicle through the centralized control middleware, wherein the driving speed control signal is used to present virtual driving speed information on an augmented reality device of the target vehicle.

15. The method according to any one of claims 9 to 14, characterized in that The method further comprises: Generate a plurality of twin objects corresponding to the plurality of light-emitting units, wherein each light-emitting unit corresponds to a twin object; The display states of the multiple twin objects are adjusted based on the marking control strategy, wherein the display states of the multiple twin objects correspond to the markings of the lanes formed by the multiple light-emitting units.

16. A traffic control device, characterized in that: include: A processor and a memory, the memory being used to store a computer program, the processor being used to call and run the computer program stored in the memory to execute the method as claimed in any one of claims 9 to 15.

17. A traffic control system, characterized in that: include: Monitoring equipment, dynamic road marking modules, traffic signal control modules and traffic control equipment; among which, The monitoring device includes one or more cameras, and the one or more cameras are used to collect video data of the target road; The dynamic road marking module includes a plurality of light emitting units, which are arranged on the target road and form road markings corresponding to different lanes in different light emitting states; The traffic signal control module includes a traffic signal unit, and the signal state of the traffic signal unit matches the light-emitting states of the plurality of light-emitting units; The traffic control equipment is used to obtain video data of a target road collected by one or more cameras; determine the traffic state of the target road at a first moment based on the video data, and predict the traffic state of the target road at a second moment based on the traffic state of the target road at the first moment and the historical traffic state of the target road, wherein the second moment is located after the first moment; generate a marking control strategy and a traffic signal control strategy for the target road based on the traffic state of the target road at the first moment and the traffic state of the target road at the second moment; send the marking control strategy to a dynamic marking module, and the marking control strategy is used by the dynamic marking module to control the lighting state of multiple light-emitting units, wherein the multiple light-emitting units are arranged on the target road and form markings corresponding to different lanes under different lighting states; send the traffic signal control strategy to a traffic signal control module, and the traffic signal control strategy is used by the traffic signal control module to control the signal state of a traffic signal unit, wherein the signal state of the traffic signal unit matches the lighting state of the multiple light-emitting units.

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