Traffic management method based on virtual traffic signal, medium, equipment and system

Abstract

The invention discloses a traffic management method based on a virtual traffic signal, a medium, equipment and a system. The method comprises the steps that a vehicle and a road cooperatively perceive road condition information of a road where the vehicle and the road are located; inputting the road condition information of the road into a trained first neural network model, wherein the first neural network model identifies whether a traffic event with a safety risk occurs on the road; if the first neural network model recognizes that the traffic event with the safety risk occurs on the road where the first neural network model is located, inputting the traffic event and the road condition information of the current road where the first neural network model is located into a trained second neural network model, so that the second neural network model outputs a management and control strategy of the traffic event; generating a virtual traffic signal sequence based on the management and control strategy; and respectively issuing the virtual traffic signal sequences to related vehicles in the traffic event. According to the scheme, the virtual traffic signals can be published to the related network connection vehicles, so that dynamic and real-time differentiated traffic control and guidance are realized.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation, and in particular to a traffic management method, medium, equipment and system based on virtual traffic signals. Background Art

[0002] Traffic signals can scientifically allocate the right to use road resources and are the main means of controlling and guiding traffic activities. However, traffic activities are complex and changeable, and existing physical traffic signals are usually deployed at fixed locations, with defects such as fixed effective areas, insufficient deployment, and difficulty in dynamic adjustment, making them unable to adapt to the growing real-time and refined traffic management needs in the era of road informatization. Summary of the invention

[0003] In view of the above problems, the present invention provides a traffic management method, medium, device and system based on virtual traffic signals, which solves the problem that existing physical traffic lights cannot meet the real-time and refined traffic management needs.

[0004] To achieve the above objectives, in a first aspect, the present application provides a traffic management method based on a virtual traffic signal, the method comprising the following steps: Vehicle-road collaboration senses road conditions on the road; Inputting the traffic condition information of the road into the trained first neural network model, so that the first neural network model identifies whether a traffic incident with safety risks occurs on the road; If the first neural network model identifies a traffic incident with a safety risk on the road, the traffic incident and the road condition information of the current road are input into the trained second neural network model, so that the second neural network model outputs a control strategy for the traffic incident; generating a virtual traffic signal sequence based on the control strategy; The virtual traffic signal sequence is respectively issued to relevant vehicles in the traffic event.

[0005] Further, the virtual traffic signal includes a virtual traffic light, and the virtual traffic light is a superset of the physical traffic light; The virtual traffic signal also includes a virtual traffic sign or a virtual traffic marking, wherein the virtual traffic sign is a superset of the physical traffic sign, and the virtual traffic marking is a superset of the physical traffic marking; The virtual traffic signal also includes the virtual traffic officer's command, which is a superset of the physical traffic officer's command.

[0006] Furthermore, the virtual traffic signal sequence is a set of virtual traffic signals, which has a one-to-one correspondence with the set of related vehicles in the traffic event.

[0007] Furthermore, the traffic events with safety risks include: any one or more of sudden severe weather, damage to roads and traffic facilities, traffic flow conflicts, traffic violations, and failure of physical traffic signals.

[0008] Furthermore, the road condition information includes any one or more of weather information, road surface status information, physical traffic signal information, location speed and direction information of vehicles and pedestrians, vehicle driving parameters and local path planning information.

[0009] Furthermore, the first neural network model is trained according to the following method: Obtain a sample data set of traffic condition information; Expanding the sample data set of traffic condition information, and randomly arranging and combining the first sample data in the expanded sample data set of traffic condition information to obtain multiple groups of first test cases; Inputting multiple groups of the first test cases into the first neural network model to be trained for iterative training to obtain a trained first neural network model.

[0010] Furthermore, the second neural network model is trained according to the following method: Obtain a sample data set of traffic events; Expanding the sample data set of traffic events, and randomly arranging and combining the second sample data in the expanded sample data set of traffic events and the first sample data in the expanded sample data set of traffic road condition information to obtain multiple groups of second test cases; Input multiple groups of the second test cases into the second neural network model to be trained for iterative training to obtain a trained second neural network model.

[0011] In a second aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the traffic management method based on virtual traffic signals as described in the first aspect of the present application.

[0012] In a third aspect, the present application provides an electronic device having a computer program stored thereon, comprising a processor and a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by the processor, the traffic management method based on virtual traffic signals as described in the first aspect of the present application is implemented.

[0013] In a fourth aspect, the present application provides a traffic management system based on virtual traffic signals, the system comprising: An electronic device, which is the electronic device as described in the third aspect of the present application; The vehicle-mounted device is connected to the electronic device for receiving the published virtual traffic signal.

[0014] Different from the prior art, the above technical solution provides a traffic management method, medium, equipment and system based on virtual traffic signals, the method comprising: vehicle-road collaborative perception of road condition information of the road; inputting the road condition information of the road into a first neural network model that has been trained, and the first neural network model identifies whether a traffic event with safety risks occurs on the road; if the first neural network model identifies that a traffic event with safety risks occurs on the road, the traffic event and the road condition information of the current road are input into a second neural network model that has been trained, so that the second neural network model outputs a control strategy for the traffic event; generating a virtual traffic signal sequence based on the control strategy; and publishing the virtual traffic signal sequence to relevant vehicles in the traffic event respectively.

[0015] The above solution can realize dynamic and real-time differentiated traffic control and guidance by issuing virtual traffic signals to relevant connected vehicles, and realize real-time and refined management of traffic activities.

[0016] The above-mentioned records related to the content of the invention are only an overview of the technical solution of the present invention. In order to enable ordinary technicians in the field to more clearly understand the technical solution of the present invention, and then implement it according to the text of the specification and the contents recorded in the drawings, and in order to make the above-mentioned purpose and other purposes, features and advantages of the present invention easier to understand, the following is a description in combination with the specific implementation mode of the present invention and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings are only used to illustrate the principles, implementations, applications, features and effects of the specific embodiments of the present invention and other related contents, and shall not be considered as limiting the present invention.

[0018] In the drawings of the specification: Figure 1 A first flow chart of a traffic management method based on a virtual traffic signal according to a specific implementation method; Figure 2 A second flow chart of a traffic management method based on a virtual traffic signal according to a specific implementation method; Figure 3 A third flow chart of a traffic management method based on a virtual traffic signal according to a specific implementation method; Figure 4 It is a module schematic diagram of the electronic device described in the specific implementation mode; Figure 5 A module architecture diagram of a traffic management system based on virtual traffic signals according to a specific implementation method; The reference numerals in the above drawings are described as follows: 10. Electronic equipment; 101. Processor; 102. Storage medium. DETAILED DESCRIPTION

[0019] In order to explain in detail the possible application scenarios, technical principles, specific schemes that can be implemented, and the purposes and effects of the present invention, the following is a detailed description in combination with the specific embodiments listed and the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of the present invention, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present invention.

[0020] Reference to "embodiment" herein means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The word "embodiment" appearing in various places in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or association with other embodiments. In principle, in the present invention, as long as there is no technical contradiction or conflict, the various technical features mentioned in each embodiment can be combined in any way to form a corresponding implementable technical solution.

[0021] Unless otherwise defined, the technical terms used herein have the same meanings as those generally understood by those skilled in the art to which the present invention belongs; the use of relevant terms herein is only for describing specific embodiments and is not intended to limit the present invention.

[0022] In the description of the present invention, the term "and / or" is an expression used to describe the logical relationship between objects, indicating that three relationships may exist, for example, A and / or B, which means: A exists, B exists, and A and B exist at the same time. In addition, the character " / " in this article generally indicates that the objects before and after are in a logical relationship of "or".

[0023] In the present invention, terms such as "first" and "second" are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship of quantity, priority or sequence between these entities or operations.

[0024] Without further restrictions, in the present invention, the words "include", "comprises", "has" or other similar expressions used in sentences are intended to cover non-exclusive inclusion. These expressions do not exclude the presence of additional elements in the process, method or product including the elements, so that the process, method or product including a series of elements may include not only those limited elements, but also other elements not explicitly listed, or also include elements inherent to such process, method or product.

[0025] In the present invention, expressions such as "greater than", "less than", "exceed" and the like are understood to exclude the number itself; expressions such as "above", "below", "within" and the like are understood to include the number itself. In addition, in the description of the embodiments of the present invention, "multiple" means more than two (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups", "multiple times", etc., unless otherwise clearly and specifically limited.

[0026] In the description of the embodiments of the present invention, the space-related expressions used, such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "vertical", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or position relationship based on the orientation or position relationship shown in the specific embodiments or drawings, and are only for the convenience of describing the specific embodiments of the present invention or facilitating the reader's understanding, and do not indicate or imply that the referred device or component must have a specific position, a specific orientation, or be constructed or operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present invention.

[0027] Unless otherwise expressly specified or limited, in the description of the embodiments of the present invention, the terms such as "install", "connect", "connect", "fix", "set" and the like used should be understood in a broad sense. For example, the "connection" can be a fixed connection, a detachable connection, or an integrated setting; it can be a mechanical connection, an electrical connection, or a communication connection; it can be a direct connection, or an indirect connection through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. For those skilled in the art of the technical field to which the present invention belongs, the specific meanings of the above terms in the embodiments of the present invention can be understood according to the specific circumstances.

[0028] In the first aspect, Figure 1 As shown, the present application provides a traffic management method based on virtual traffic signals, the method comprising the following steps: Step S101: vehicle-road collaboration senses road condition information of the road on which it is located; Step S102: inputting the road condition information of the road into the trained first neural network model, and the first neural network model identifies whether a traffic incident with safety risks occurs on the road; Step S103: If the first neural network model identifies a traffic event with a safety risk on the road, the traffic event and the road condition information of the current road are input into the trained second neural network model, so that the second neural network model outputs a control strategy for the traffic event; Step S104: generating a virtual traffic signal sequence based on the control strategy; Step S105: issuing the virtual traffic signal sequence to the relevant vehicles in the traffic event respectively.

[0029] In step S101, the road condition information includes weather information, road surface status information, physical traffic signal information, position speed and direction information of vehicles and pedestrians, driving parameters of vehicles and local path planning information. Compared with global path planning, local path planning refers to the algorithm used to generate the local motion path of the vehicle in the field of vehicle navigation or mobile vehicles. These algorithms are designed to enable the vehicle to reach the target point as efficiently as possible while avoiding obstacles and maintaining a safe distance.

[0030] In step S102, the traffic events with safety risks include: any one or more of sudden severe weather, damage to roads and traffic facilities, traffic flow conflicts, traffic violations, and failure of physical traffic signals.

[0031] In steps S102 and S103, the first neural network model and / or the second neural network model may be a convolutional neural network (CNN) or a recurrent neural network (RNN) and variants thereof (such as LSTM, GRU).

[0032] In step S104, the virtual traffic signal sequence is a collection of virtual traffic signals. It is a collection of control strategy implementation measures for all vehicles related to the traffic event. The virtual traffic signals in the collection are matched one by one with the related vehicles, that is, the virtual traffic signal received by each vehicle is calculated based on the road condition information around the current vehicle (including surrounding vehicle information and road information), so as to realize the customization of control strategy.

[0033] In step S105, the relevant vehicles in the traffic event refer to the vehicles traveling on the road.

[0034] The above scheme can output control strategies based on the road condition information and traffic events on the road, and publish the virtual traffic signals generated based on the control strategies to relevant connected vehicles, so as to realize dynamic and real-time differentiated traffic control and guidance, and realize real-time and refined management of traffic activities.

[0035] In some embodiments, the virtual traffic signal includes a virtual traffic light, which is a superset of a physical traffic light. The virtual traffic light includes a virtual left lane change light, a virtual right lane change light, a virtual deceleration light, a virtual acceleration light, etc.

[0036] The virtual traffic signal also includes virtual traffic signs and road markings, and the virtual traffic signs and road markings are supersets of physical traffic signs and road markings.

[0037] The virtual traffic signal also includes the command of the virtual traffic personnel, which is a superset of the command of the physical traffic personnel. The command of the virtual traffic personnel not only has richer command gestures, but can also be presented through the image of a virtual character, and the virtual character can send traffic command signals to specific connected vehicles through language dialogue or text form.

[0038] In general, virtual traffic signals are consistent with physical traffic signals, and their priority is no higher than that of physical traffic signals. However, in special circumstances (such as when the physical traffic signal is inoperative or there is no physical traffic signal on the road section), virtual traffic signals will take over control of the physical traffic signal to achieve emergency response to traffic accidents.

[0039] In some embodiments, the virtual traffic signal is a virtualized electronic traffic signal dynamically generated by a cloud server based on road traffic conditions, and is issued to relevant connected vehicles via a communication network to achieve dynamic, real-time and differentiated control and guidance of traffic activities, enabling refined management of traffic activities.

[0040] Virtual traffic signals are a complement, enhancement and emergency adjustment to physical traffic signals. When physical traffic signals are missing, damaged or obscured, this application generates a corresponding traffic control strategy by comprehensively analyzing traffic conditions information and combining the operating parameter information of relevant vehicles, and generates virtual traffic signals based on the traffic control strategy. By laying out virtual traffic signs and markings, issuing virtual traffic lights to networked vehicles, and assigning virtual traffic personnel to networked vehicles for command, the physical traffic signals are enhanced to meet the traffic management operation requirements under complex road conditions.

[0041] In some embodiments, Figure 2 As shown, the first neural network model is trained according to the following method: Step S201: Acquire a sample data set of traffic condition information; Step S202: expanding the sample data set of traffic condition information, and randomly arranging and combining the first sample data in the expanded sample data set of traffic condition information to obtain multiple groups of first test cases; Step S203: inputting the plurality of groups of the first test cases into the first neural network model to be trained for iterative training to obtain a trained first neural network model.

[0042] In some embodiments, Figure 3 As shown, the first neural network model is trained according to the following method: Step S301: Obtain a sample data set of traffic events; Step S302: expanding the sample data set of traffic events, and randomly arranging and combining the second sample data in the expanded sample data set of traffic events and the first sample data in the expanded sample data set of traffic road condition information to obtain multiple groups of second test cases; Step S303: inputting multiple groups of the second test cases into the second neural network model to be trained for iterative training to obtain a trained second neural network model.

[0043] In some embodiments, the vehicle-road collaboration senses the road condition information of the road, and vehicle A traveling in the slow lane of the road attempts to change lanes left to the fast lane, while vehicle B in the fast lane is traveling behind vehicle A. The trained first neural network model recognizes the traffic event of the left lane change, and the traffic event has a safety risk.

[0044] The first neural network model inputs the traffic event information and the road condition information around vehicle A into the trained second neural network model. The second neural network model generates a traffic control strategy that authorizes vehicle A to complete the left lane change within a limited time and vehicle B to slow down and give way, and generates a virtual traffic signal sequence based on the traffic control strategy. This virtual traffic signal sequence includes two groups of virtual traffic signals: one group is issued to vehicle A, the signal content is the left lane change green light signal, and the time point of signal failure is set; the other group is issued to vehicle B, the signal content is a sign indicating vehicle B to slow down and give way, and the signal failure condition is set to keep a safe distance between vehicles A and B after vehicle A completes the lane change. Then, the virtual traffic signal sequence is issued to vehicles A and B respectively. After obtaining the virtual lane change signal, vehicle A completes the left lane change within the specified time limit. After vehicles A and B maintain a safe distance on the fast lane, the control of this traffic event is completed.

[0045] In a second aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the traffic management method based on virtual traffic signals as described in the first aspect of the present invention is implemented.

[0046] The computer-readable storage medium may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories.

[0047] 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), a ferromagnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD ROM); the magnetic surface memory can be a disk memory or a tape memory.

[0048] The volatile memory may 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), synchronous static random access memory (SSRAM), 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), direct memory bus random access memory (DRRAM). The computer-readable storage medium described in the embodiments of the present invention is intended to include these and any other suitable types of memory.

[0049] like Figure 4 As shown, in a third aspect, the present invention provides an electronic device 10, comprising a processor 101 and a storage medium 102, wherein a computer program is stored on the storage medium, and when the computer program is executed by the processor, the traffic management method based on virtual traffic signals as described in the first aspect of the present invention is implemented.

[0050] In some embodiments, the processor can be implemented by software, hardware, firmware or a combination thereof, and can use circuits, single or multiple application specific integrated circuits (Application Specific Integrated Circuit, ASIC), digital signal processors (Digital Signal Processor, DSP), digital signal processing devices (Digital Signal Processing Device, DSPD), programmable logic devices (Programmable Logic Device, PLD), field programmable gate arrays (Field Programmable Gate Array, FPGA), central processing units (Central Processing Unit, CPU), controllers, microcontrollers, at least one of microprocessors, so that the processor can execute some or all of the steps or any combination of the steps in the traffic management method based on virtual traffic signals described in the various embodiments of the present application.

[0051] In a fourth aspect, the present application provides a traffic management system based on virtual traffic signals, the system comprising: An electronic device, which is the electronic device as described in the third aspect of the present application; The vehicle-mounted device is communicatively connected with the electronic device and is used for receiving the virtual traffic signal.

[0052] like Figure 5 As shown, the electronic device involved in the present application may be built with a cloud computing unit, which has a virtual traffic signal system built on a physical traffic signal system. The function of the virtual traffic signal system is to generate corresponding virtual traffic signals according to the surrounding traffic situation of each vehicle road condition and publish them to the corresponding vehicles for traffic control. The virtual traffic signal is a virtualized electronic traffic signal dynamically generated by the computing unit according to the road traffic conditions and the driving intention of the networked vehicle, and is published to the relevant networked vehicles through the communication network.

[0053] Finally, it should be noted that although the above embodiments have been described in the specification and drawings of this application, this does not limit the scope of patent protection of this application. All technical solutions generated by replacing or modifying equivalent structures or equivalent processes based on the essential concept of this application using the contents recorded in the specification and drawings of this application, as well as directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, are included in the scope of patent protection of this application.

Claims

1. A traffic management method based on virtual traffic signals, characterized in that: The method comprises the following steps: Vehicle-road collaboration senses road conditions on the road; Inputting the traffic condition information of the road into the trained first neural network model, so that the first neural network model identifies whether a traffic incident with safety risks occurs on the road; If the first neural network model identifies a traffic incident with a safety risk on the road, the traffic incident and the road condition information of the current road are input into the trained second neural network model, so that the second neural network model outputs a control strategy for the traffic incident; generating a virtual traffic signal sequence based on the control strategy; issuing the virtual traffic signal sequence to the relevant vehicles in the traffic event respectively; The virtual traffic signal sequence is a collection of virtual traffic signals; The virtual traffic signal includes a virtual traffic light, and the virtual traffic light is a superset of a physical traffic light; The virtual traffic signal also includes a virtual traffic sign or a virtual traffic marking, wherein the virtual traffic sign is a superset of the physical traffic sign, and the virtual traffic marking is a superset of the physical traffic marking; The virtual traffic signal also includes the virtual traffic officer's command, which is a superset of the physical traffic officer's command.

2. The traffic management method based on virtual traffic signals according to claim 1, characterized in that: There is a one-to-one correspondence between the virtual traffic signal sequence and the set of related vehicles in the traffic event.

3. The traffic management method based on virtual traffic signals according to claim 1, characterized in that: The traffic events with safety risks include: any one or more of sudden severe weather, damage to roads and traffic facilities, traffic flow conflicts, traffic violations, and failure of physical traffic signals.

4. The traffic management method based on virtual traffic signals according to claim 1, characterized in that: The road condition information includes any one or more of weather information, road surface status information, physical traffic signal information, location speed and direction information of vehicles and pedestrians, vehicle driving parameters and local path planning information.

5. The traffic management method based on virtual traffic signals according to claim 1, characterized in that: The first neural network model is trained according to the following method: Obtain a sample data set of traffic condition information; Expanding the sample data set of traffic condition information, and randomly arranging and combining the first sample data in the expanded sample data set of traffic condition information to obtain multiple groups of first test cases; Inputting multiple groups of the first test cases into the first neural network model to be trained for iterative training to obtain a trained first neural network model.

6. The traffic management method based on virtual traffic signals according to claim 5, characterized in that: The second neural network model is trained according to the following method: Obtain a sample data set of traffic events; Expanding the sample data set of traffic events, and randomly arranging and combining the second sample data in the expanded sample data set of traffic events and the first sample data in the expanded sample data set of traffic road condition information to obtain multiple groups of second test cases; Input multiple groups of the second test cases into the second neural network model to be trained for iterative training to obtain a trained second neural network model.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the traffic management method based on virtual traffic signals as described in any one of claims 1 to 6 is implemented.

8. An electronic device having a computer program stored thereon, characterized in that: It comprises a processor and a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by the processor, the traffic management method based on virtual traffic signals as described in any one of claims 1 to 6 is implemented.

9. A traffic management system based on virtual traffic signals, characterized in that: The system comprises: The electronic device is the electronic device as claimed in claim 8; The vehicle-mounted device is communicatively connected with the electronic device and is used for receiving the virtual traffic signal.

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