Driving abnormity monitoring method, device and system, terminal equipment and medium

Abstract

The invention relates to the technical field of road monitoring, and provides a driving abnormity monitoring method, device and system, a terminal device and a medium, the method is applied to the terminal device in the driving abnormity monitoring system, and the method comprises the following steps: obtaining actual driving information when a target vehicle passes through a first section determined by an ETC door frame and a radar device; wherein the first section is a section corresponding to the ETC door frame; determining predicted driving information when the target vehicle passes through a second section corresponding to the camera according to the actual driving information; the predicted driving information at least comprises predicted passing time; obtaining a vehicle image collected by the camera, identifying a target vehicle in the vehicle image, and determining measurement driving information of the target vehicle when the target vehicle passes through a second section corresponding to the camera; the measured driving information at least comprises measured passing time; and determining whether the target vehicle has abnormal driving according to the predicted driving information and the measured driving information. According to the method, the accuracy of vehicle driving abnormity monitoring can be improved.

Description

Technical Field

[0001] The present application relates to the field of road monitoring technology, and in particular to a driving abnormality monitoring method, device, system, terminal equipment and medium. Background Art

[0002] At present, using perception systems on highways to achieve full perception coverage of highways is one of the key technologies in the construction of smart highways. The corresponding sensors for current perception methods include ETC (Electronic Toll Collection) gantries, cameras, millimeter wave radars, lidars, geomagnetic induction, and grating array vibration sensor cables.

[0003] In order to monitor driving abnormalities of vehicles on highways, the traditional technical solution is to use sensors in the perception system to detect the driving speed of the vehicle at a preset position, and determine whether the vehicle has driving abnormalities based on the driving speed at the preset position and the corresponding driving speed threshold. However, this technical solution may have judgment errors.

[0004] Therefore, how to improve the accuracy of abnormal vehicle driving monitoring is a technical problem that technical personnel in this field currently need to solve. Summary of the invention

[0005] The purpose of this application is to provide a driving abnormality monitoring method, device, system, terminal device and computer-readable storage medium, aiming to improve the accuracy of vehicle driving abnormality monitoring.

[0006] In a first aspect, the present application provides a method for monitoring driving abnormality. The method comprises:

[0007] A terminal device used in a driving abnormality monitoring system, the driving abnormality monitoring system comprising an ETC gantry, a radar device, a camera and a terminal device; the radar device is installed on the ETC gantry; at least one camera is arranged between two adjacent ETC gantries; the method comprises:

[0008] Acquire actual driving information of the target vehicle determined by the ETC gantry and the radar device when passing through a first section; wherein the first section is a section corresponding to the ETC gantry;

[0009] Determine predicted driving information when the target vehicle passes through a second section corresponding to the camera according to the actual driving information; the predicted driving information at least includes the predicted passing time;

[0010] Acquire a vehicle image captured by the camera, identify a target vehicle in the vehicle image, and determine measured driving information of the target vehicle when it passes through a second section corresponding to the camera; the measured driving information at least includes a measured passing time;

[0011] It is determined whether the target vehicle has driving abnormality according to the predicted driving information and the measured driving information.

[0012] In one embodiment, at least two cameras are arranged between two adjacent ETC gantries; the step of determining the predicted driving information of the target vehicle passing through the second section corresponding to the camera according to the actual driving information includes:

[0013] Determine predicted driving information of the target vehicle passing through the second sections respectively corresponding to each of the cameras according to the actual driving information;

[0014] The acquiring of the vehicle image captured by the camera, identifying the target vehicle in the vehicle image, and determining the measured driving information of the target vehicle when passing through the second section corresponding to the camera includes:

[0015] For each of the cameras, a vehicle image captured by the camera is acquired, and a target vehicle in the vehicle image is identified, and measured driving information of the target vehicle when passing through a second section corresponding to the camera is determined;

[0016] The determining whether the target vehicle has driving abnormality according to the predicted driving information and the measured driving information includes:

[0017] For each of the second sections, determining a judgment result of the second section according to the predicted driving information and the measured driving information;

[0018] Determine whether the target vehicle has driving abnormality according to each of the judgment results.

[0019] In one embodiment, determining whether the target vehicle has driving abnormality according to each of the judgment results includes:

[0020] If there are a number of consecutive predetermined judgment results of the second sections that are all abnormal, it is determined that the target vehicle has driving abnormality;

[0021] Otherwise, it is determined that the target vehicle is driving normally.

[0022] In one embodiment, the actual driving information includes actual position information, actual driving speed, actual heading angle, and actual passing time; and the predicted driving information when the target vehicle passes through the second section corresponding to the camera according to the actual driving information includes:

[0023] determining a cross-sectional distance between the first cross-sectional area and a second cross-sectional area corresponding to the camera;

[0024] The predicted driving information of the target vehicle when passing through the second section is determined according to the actual driving information and the section distance; the predicted driving information includes predicted position information, predicted driving speed, predicted heading angle and predicted passing time.

[0025] In one embodiment, acquiring the vehicle image captured by the camera, identifying the target vehicle in the vehicle image, and determining the measured driving information of the target vehicle when passing through the second section corresponding to the camera includes:

[0026] Acquire a vehicle image captured by the camera;

[0027] The target vehicle in the vehicle image is identified using an image recognition model, and measured driving information of the target vehicle when passing through a second section corresponding to the camera is determined.

[0028] In one embodiment, the obtaining of actual driving information of the target vehicle determined by the ETC gantry and the radar device when the target vehicle passes through the first section includes:

[0029] Using the ETC gantry to obtain identification information of vehicles passing through the first section;

[0030] Determining actual driving information of the vehicle when passing through the first section using the radar device;

[0031] A corresponding relationship is set between each piece of identification information and each piece of actual driving information to obtain the actual driving information of the target vehicle passing through the first section.

[0032] In the second aspect, the present application also provides a driving abnormality monitoring device. A terminal device used in a driving abnormality monitoring system, the driving abnormality monitoring system includes an ETC gantry, a radar device, a camera and a terminal device; the radar device is installed on the ETC gantry; at least one camera is arranged between two adjacent ETC gantries; the device includes:

[0033] A first acquisition module is used to acquire actual driving information of a target vehicle determined by the ETC gantry and the radar device when the target vehicle passes through a first section; wherein the first section is a section corresponding to the ETC gantry;

[0034] A prediction module, used to determine predicted driving information when the target vehicle passes through a second section corresponding to the camera according to the actual driving information;

[0035] A second acquisition module is used to acquire the vehicle image captured by the camera, identify the target vehicle in the vehicle image, and determine the measured driving information of the target vehicle when passing through the second section corresponding to the camera;

[0036] A judgment module is used to determine whether the target vehicle has driving abnormality according to the predicted driving information and the measured driving information.

[0037] In a third aspect, the present application also provides a driving abnormality monitoring system. The system includes an ETC gantry, a radar device, a camera and a terminal device; the radar device is installed on the ETC gantry; at least one camera is arranged between two adjacent ETC gantries; the ETC gantry, the radar device and the camera are respectively connected to the terminal device for communication;

[0038] The ETC gantry is used to obtain identification information of a vehicle passing through a first section corresponding to the ETC gantry;

[0039] The radar device is used to determine the actual driving information of the vehicle when passing through the first section;

[0040] The camera is used to collect a vehicle image of the vehicle when the vehicle passes through a second section corresponding to the camera;

[0041] The terminal device is used to set a correspondence between each piece of identification information and each piece of actual driving information to obtain actual driving information of the target vehicle passing through a first section; determine predicted driving information of the target vehicle when passing through a second section corresponding to the camera based on the actual driving information; obtain the vehicle image captured by the camera, identify the target vehicle in the vehicle image, and determine measured driving information of the target vehicle when passing through the second section corresponding to the camera; determine whether the target vehicle has driving abnormalities based on the predicted driving information and the measured driving information.

[0042] In a fourth aspect, the present application also provides another driving abnormality monitoring system. The system includes an ETC gantry, a radar device, a camera, an edge device and a terminal device; the radar device is installed on the ETC gantry, and at least one camera is arranged between two adjacent ETC gantries; the ETC gantry and the radar device are respectively connected to the terminal device in communication, the edge device is arranged close to the camera and connected to the camera in communication; the edge device is connected to the terminal device in communication;

[0043] The ETC gantry is used to obtain identification information of a vehicle passing through a first section corresponding to the ETC gantry;

[0044] The radar device is used to determine the actual driving information of the vehicle when passing through the first section;

[0045] The camera is used to collect a vehicle image of the vehicle when the vehicle passes through a second section corresponding to the camera, and send the vehicle image to the edge device;

[0046] The terminal device is used to set a corresponding relationship between each piece of identification information and each piece of actual driving information to obtain actual driving information of the target vehicle passing through the first section; determine predicted driving information when the target vehicle passes through the second section corresponding to the camera according to the actual driving information, and send the predicted driving information to the edge device;

[0047] The edge device is used to obtain the vehicle image captured by the camera, identify the target vehicle in the vehicle image, and determine the measured driving information of the target vehicle when passing through the second section corresponding to the camera; and determine whether the target vehicle has driving abnormalities based on the predicted driving information and the measured driving information.

[0048] In one embodiment, the ETC gantry is also used for:

[0049] Get license plate information, vehicle dimensions, and vehicle type;

[0050] Filter out vehicles with a preset license plate type according to the license plate information;

[0051] And / or, vehicles of a preset vehicle type are screened out according to the vehicle size and / or the vehicle type.

[0052] In a fifth aspect, the present application further provides a terminal device, wherein the terminal device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0053] In a sixth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0054] The present application provides a driving abnormality monitoring method, which is applied to a terminal device in a driving abnormality monitoring system, wherein the driving abnormality monitoring system includes an ETC gantry, a radar device, a camera and a terminal device; the radar device is installed on the ETC gantry; at least one camera is arranged between two adjacent ETC gantries; the method uses the ETC gantry and the radar device to determine the actual driving information of the target vehicle when it passes through a first section, and predicts the predicted driving information of the target vehicle when it passes through a second section corresponding to the camera; then the vehicle image captured by the camera is used to determine the measured driving information of the target vehicle when it passes through the second section corresponding to the camera; that is, if the target vehicle has driving abnormality, it will affect the passing time of the target vehicle when passing through the second section, that is, the measured passing time; the predicted passing time and the measured passing time of the target vehicle passing through the second section are used to determine whether the target vehicle has driving abnormality, thereby avoiding using a single driving speed threshold to determine whether the target vehicle has driving abnormality, so the method can improve the accuracy of vehicle driving abnormality monitoring.

[0055] It can be understood that the driving abnormality monitoring device, system, terminal device and computer-readable storage medium provided in the embodiments of the present application have the same beneficial effects as the above-mentioned driving abnormality monitoring method, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0057] Figure 1 A flowchart of a driving abnormality monitoring method provided in an embodiment of the present application;

[0058] Figure 2 A schematic cross-sectional view of a highway provided in an embodiment of the present application;

[0059] Figure 3 A schematic diagram of the structure of a driving abnormality monitoring device provided in an embodiment of the present application;

[0060] Figure 4 A schematic diagram of the structure of a terminal device provided in an embodiment of the present application;

[0061] Figure 5 A schematic diagram of the structure of a driving abnormality monitoring system provided in an embodiment of the present application;

[0062] Figure 6A schematic diagram of the structure of another driving abnormality monitoring system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0063] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0064] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0065] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0066] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0067] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0068] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways. "Multiple" means "two or more".

[0069] The embodiment of the present application provides a driving abnormality monitoring method, which is applied to a terminal device in a driving abnormality monitoring system, and the processor of the terminal device executes the driving abnormality monitoring method when running a corresponding computer program. The driving abnormality monitoring system includes an ETC gantry, a radar device, a camera, and a terminal device; the radar device is installed on the ETC gantry; and at least one camera is arranged between two adjacent ETC gantries. Figure 1 This is a flowchart of a driving abnormality monitoring method provided in an embodiment of the present application. For the sake of convenience, only the part related to the present embodiment is shown. The method provided in the present embodiment includes the following steps:

[0070] S100: Acquire actual driving information of a target vehicle determined by the ETC gantry and radar equipment when the target vehicle passes through a first section; wherein the first section is a section corresponding to the ETC gantry.

[0071] Among them, the ETC gantry is a facility similar to a traffic probe installed above the highway. It is mainly composed of high-definition license plate image recognition system, road side equipment (Road Side Unit, RSU), lane controller and other equipment and facilities. It reads the information of the on-board unit (On board Unit, OBU) through a radio frequency device to achieve accurate recording of the vehicle's driving path, and at the same time can obtain the identification information corresponding to each vehicle.

[0072] The radar device may include a laser radar or a millimeter wave radar, which is not limited in this embodiment.

[0073] In this step, the high-definition license plate image recognition system in the ETC gantry is used to identify each vehicle and determine the vehicle's identification information; or the information of the on-board unit is read by the radio frequency device in the ETC gantry to obtain the vehicle's identification information; identification information refers to information used to uniquely identify the vehicle, which can be a license plate number.

[0074] The first section refers to the section corresponding to the ETC gantry; for each vehicle, based on the vehicle identification information and the actual driving information collected by the radar equipment, the actual driving information corresponding to each vehicle when passing through the first section is determined. The target vehicle refers to a vehicle for driving abnormality monitoring, which can be one vehicle or multiple vehicles for driving abnormality monitoring, and this embodiment does not limit this. The actual driving information includes the actual position information, actual driving speed, actual heading angle, and actual passing time of the vehicle, and this embodiment does not limit the specific content of the actual driving information.

[0075] S200: Determine predicted driving information when the target vehicle passes through a second section corresponding to the camera according to actual driving information; the predicted driving information at least includes a predicted passing time.

[0076] It should be noted that the ETC gantry and camera are generally set at different section positions, that is, the first section and the second section are not at the same position. The gantry system and camera composed of the ETC gantry and radar equipment can both accurately detect the driving status of the target vehicle, but a detection blind spot without accurate detection is formed between the first section corresponding to the gantry system and the second section corresponding to the camera. Therefore, after determining the actual driving information corresponding to the first section, the predicted driving information of the target vehicle when passing through the second section is calculated based on the actual driving information; wherein, the predicted driving information at least includes the predicted passing time.

[0077] Specifically, the predicted passing time can be the specific time point when the target vehicle passes through the second section, such as 9:30 in the morning; it can also be the time difference between the target vehicle in the first section and the second section, such as the target vehicle arrives at the second section 10 minutes after passing the first section; this embodiment does not limit the specific value method of the predicted passing time, and the measured passing time and the predicted passing time can be set accordingly.

[0078] S300: Acquire a vehicle image captured by a camera, identify a target vehicle in the vehicle image, and determine measured driving information of the target vehicle when passing through a second section corresponding to the camera; the measured driving information at least includes measured passing time.

[0079] Specifically, a camera is pre-set at the second section, and the camera is used to collect vehicle images; then the collected vehicle images are recognized, and the measured driving information of the target vehicle when passing through the second section is calculated. The measured driving information refers to the driving information of the target vehicle determined by using the vehicle image; the measured driving information at least includes the measured passing time, and may also include the measured position information, the measured driving speed, and the measured heading angle.

[0080] S400: Determine whether the target vehicle has driving abnormality according to the predicted driving information and the measured driving information.

[0081] Specifically, after determining the predicted driving information and measured driving information of the target vehicle passing through the same second section, the theoretical value and actual value of the target vehicle passing through the second section are determined; by determining the difference between the predicted driving information and the measured driving information, and judging whether the difference between the two is greater than a difference threshold; if the difference is greater than the difference threshold, it means that the target vehicle did not arrive at the second section as expected, so it is determined that the target vehicle has driving abnormalities; if the difference is less than or equal to the difference threshold, it means that the target vehicle arrived at the second section as expected, so it is determined that the target vehicle does not have driving abnormalities.

[0082] An embodiment of the present application provides a driving abnormality monitoring method, which is applied to a terminal device in a driving abnormality monitoring system, wherein the driving abnormality monitoring system includes an ETC gantry, a radar device, a camera and a terminal device; the radar device is installed on the ETC gantry; at least one camera is arranged between two adjacent ETC gantries; the method uses the ETC gantry and the radar device to determine the actual driving information of the target vehicle when passing through a first section, and predicts the predicted driving information of the target vehicle when passing through a second section corresponding to the camera; then the vehicle image captured by the camera is used to determine the measured driving information of the target vehicle when passing through the second section corresponding to the camera; that is, if the target vehicle has driving abnormalities, it will affect the passing time of the target vehicle when passing through the second section, that is, the measured passing time; the predicted passing time and the measured passing time of the target vehicle passing through the second section are used to determine whether the target vehicle has driving abnormalities, thereby avoiding using a single driving speed threshold to determine whether the target vehicle has driving abnormalities, so the method can improve the accuracy of vehicle driving abnormality monitoring.

[0083] Based on the above embodiment, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, at least two cameras are arranged between two adjacent ETC gantries; the predicted driving information of the target vehicle passing through the second section corresponding to the camera is determined according to the actual driving information, including:

[0084] Determine predicted driving information of the target vehicle passing through the second sections corresponding to each camera according to the actual driving information;

[0085] Acquire a vehicle image captured by a camera, identify a target vehicle in the vehicle image, and determine measured driving information of the target vehicle when passing through a second section corresponding to the camera, including:

[0086] For each camera, a vehicle image captured by the camera is acquired, and a target vehicle in the vehicle image is identified to determine measured driving information of the target vehicle when it passes through a second section corresponding to the camera;

[0087] Determine whether the target vehicle has driving abnormalities based on the predicted driving information and the measured driving information, including:

[0088] For each second section, determining a judgment result of the second section according to the predicted driving information and the measured driving information;

[0089] Determine whether the target vehicle has driving abnormality based on each judgment result.

[0090] In this embodiment, at least two cameras are arranged between two adjacent ETC gantries, that is, there are at least two second sections between two adjacent first sections. Therefore, for each second section, the predicted driving information corresponding to the target vehicle when passing through each second section is calculated based on the actual driving information of the target vehicle in the first section and the section distances corresponding to the first section and each second section.

[0091] For example, combined with Figure 2 A schematic cross-sectional diagram of a highway provided in an embodiment of the present application is shown; it is assumed that three cameras are arranged between two adjacent ETC gantries, that is, there are three second sections between two adjacent first sections; the second sections are second section 1, second section 2, and second section 3 according to the driving direction; after determining the actual driving information corresponding to the first section, it is predicted that the target vehicle will travel to the predicted driving information corresponding to the second section 1, second section 2, and second section 3 according to the driving direction; for example, it is determined that the predicted passing time of the target vehicle through the second section 1 is T1, the predicted passing time through the second section 2 is T2, and the predicted passing time through the second section 3 is T3.

[0092] For each second section, the camera corresponding to each second section is used to collect a vehicle image, identify the target vehicle in the vehicle image, and determine the measured driving information of the target vehicle when it passes through the second section corresponding to the camera. Figure 2 , assuming that the measured passing time of the target vehicle through the second section 1 is t1, the measured passing time through the second section 2 is t2, and the measured passing time through the second section 3 is t3.

[0093] Specifically, for each second section, the corresponding predicted driving information and measured driving information are determined; therefore, for each second section, the judgment result of the second section is determined according to the predicted driving information and the measured driving information; and according to each judgment result, it is determined whether the target vehicle has driving abnormality.

[0094] According to the method of this embodiment, predicted driving information corresponding to multiple second sections can be calculated based on actual driving information, and the predicted driving information of multiple second sections can be used to monitor driving abnormality, which can improve the accuracy of driving abnormality monitoring.

[0095] In a specific embodiment, determining whether the target vehicle has driving abnormality according to each judgment result includes:

[0096] If the judgment results of a preset number of second sections are all abnormal, it is determined that the target vehicle has driving abnormality;

[0097] Otherwise, it is determined that the target vehicle is driving normally.

[0098] Specifically, since there are at least two second sections between two adjacent first sections, based on the actual driving information of the first sections, the predicted driving information corresponding to at least two second sections can be determined; for each second section, the difference between the predicted driving information and the measured driving information is calculated, and it is determined whether the difference between the two is greater than the difference threshold; if the difference is greater than the difference threshold, it means that the judgment result of the second section corresponding to the predicted driving information is abnormal; after determining the judgment results corresponding to each second section, it is determined whether there are a continuous preset number of abnormal judgment results; if there are a continuous preset number of second sections whose judgment results are all abnormal, it is determined that the target vehicle has driving abnormalities; otherwise, it is determined that the target vehicle is driving normally.

[0099] According to the method of this embodiment, it is possible to avoid the judgment error of determining whether the target vehicle has driving abnormality based on a single second section, and to improve the accuracy of driving abnormality monitoring.

[0100] Based on the above embodiment, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the actual driving information includes actual position information, actual driving speed, actual heading angle and actual passing time; the predicted driving information when the target vehicle passes through the second section corresponding to the camera is determined according to the actual driving information, including:

[0101] Determining a cross-sectional distance between a first cross-sectional plane and a second cross-sectional plane corresponding to the camera;

[0102] The predicted driving information when the target vehicle passes through the second section is determined based on the actual driving information and the section distance; the predicted driving information includes predicted position information, predicted driving speed, predicted heading angle and predicted passing time.

[0103] Specifically, for each second section, the section distance between the first section and the second section is determined; wherein the section distance is the distance required for the target vehicle to travel from the first section to the second section. The actual driving information corresponding to the target vehicle in the first section is obtained, and the actual driving information includes the actual position information, the actual driving speed, the actual heading angle, and the actual passing time; the predicted driving information of the target vehicle when passing through the second section is calculated based on the actual position information, the actual driving speed, the actual heading angle, and the section distance of the target vehicle; the predicted driving information includes the predicted position information, the predicted driving speed, the predicted heading angle, and the predicted passing time.

[0104] According to the method of this embodiment, the predicted driving information of the target vehicle can be determined efficiently and conveniently, and the efficiency of abnormal driving monitoring can be improved.

[0105] Based on the above embodiment, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, a vehicle image captured by a camera is obtained, and a target vehicle in the vehicle image is identified, and measured driving information of the target vehicle when passing through a second section corresponding to the camera is determined, including:

[0106] Obtain vehicle images captured by the camera;

[0107] The image recognition model is used to recognize the target vehicle in the vehicle image, and the measured driving information of the target vehicle when passing through the second section corresponding to the camera is determined.

[0108] Specifically, the camera is calibrated based on the high-precision map in advance, and the camera is used to collect vehicle images. In actual applications, the initial shooting angle of the camera can be further set, and the camera can be controlled to track and shoot the vehicle; when the camera zoom or tracking shooting rotation operation ends, the camera is controlled to return to the initial shooting angle.

[0109] Specifically, after the camera captures the vehicle image, it sends the vehicle image to the terminal device. The terminal device uses the image recognition model to identify the target vehicle in the vehicle image and determines the measured driving information of the target vehicle when it passes through the second section corresponding to the camera.

[0110] In actual applications, the terminal device uses a large number of sample vehicle images to train the initial neural network model in advance to obtain an image recognition model; subsequently, the vehicle image can be input into the image recognition model, and the image recognition model can be used to identify the vehicle in the vehicle image and determine the target vehicle. Furthermore, after determining the target vehicle, the time when the target vehicle passes through the second section is further obtained, that is, the measured passing time is determined.

[0111] According to the method of this embodiment, the measured driving information of the target vehicle when passing through the second section can be determined efficiently and conveniently, which can further improve the efficiency of abnormal driving monitoring.

[0112] Based on the above embodiment, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the actual driving information of the target vehicle when passing through the first section determined by the ETC gantry and the radar device is obtained, including:

[0113] Using the ETC gantry to obtain identification information of vehicles passing through the first section;

[0114] Using radar equipment to determine actual driving information of the vehicle when it passes through the first section;

[0115] A corresponding relationship is set between each piece of identification information and each piece of actual driving information to obtain the actual driving information of the target vehicle passing through the first section.

[0116] Specifically, the high-definition license plate image recognition system in the ETC gantry is used to obtain the identification information of each vehicle; or the radio frequency device in the ETC gantry reads the information of the on-board unit to obtain the vehicle's identification information; the identification information includes the license plate number.

[0117] Specifically, the actual driving information of the vehicle when passing through the first section is obtained by using the radar equipment; wherein, the process of obtaining the actual driving information of the vehicle by using the radar equipment includes: the radar equipment transmits a laser to the vehicle, and then measures parameters such as the time for the reflected or scattered signal to reach the transmitter, the signal strength and frequency change, so as to determine the distance between the vehicle and the radar equipment, the driving speed and the direction.

[0118] It should be noted that the vehicle will be in the corresponding lane during the driving process; therefore, the license plate numbers and actual driving information of the vehicles in the same lane can be bound, that is, the actual driving information corresponding to each vehicle can be determined; then the target vehicle can be determined from each vehicle, and the actual driving information of the target vehicle passing through the first section can be obtained.

[0119] It can be seen that according to the method of this embodiment, the actual driving information of the target vehicle passing through the first section can be determined efficiently and conveniently, thereby improving the efficiency of abnormal driving monitoring.

[0120] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0121] Figure 3 FIG. 1 is a schematic diagram of the structure of a driving abnormality monitoring device provided in an embodiment of the present application. Figure 3 As shown, the driving abnormality monitoring device of this embodiment includes a first acquisition module 310, a prediction module 320, a second acquisition module 330 and a judgment module 340; wherein,

[0122] The first acquisition module 310 is used to acquire actual driving information of the target vehicle when it passes through the first section determined by the ETC gantry and the radar device; wherein the first section is the section corresponding to the ETC gantry;

[0123] A prediction module 320, for determining predicted driving information when the target vehicle passes through a second section corresponding to the camera according to actual driving information;

[0124] The second acquisition module 330 is used to acquire the vehicle image captured by the camera, identify the target vehicle in the vehicle image, and determine the measured driving information of the target vehicle when passing through the second section corresponding to the camera;

[0125] The judgment module 340 is used to determine whether the target vehicle has driving abnormality according to the predicted driving information and the measured driving information.

[0126] An abnormal driving monitoring device provided in an embodiment of the present application has the same beneficial effects as the abnormal driving monitoring method described above.

[0127] In one embodiment, at least two cameras are arranged between two adjacent ETC gantries; the prediction module includes:

[0128] A prediction submodule, used to determine predicted driving information of the target vehicle passing through the second sections corresponding to each camera according to the actual driving information;

[0129] The second acquisition module includes:

[0130] The second acquisition submodule is used to acquire the vehicle image captured by the camera for each camera, identify the target vehicle in the vehicle image, and determine the measured driving information of the target vehicle when passing through the second section corresponding to the camera;

[0131] The judgment module includes:

[0132] A first judgment submodule, used for determining a judgment result of the second section according to the predicted driving information and the measured driving information for each second section;

[0133] The second judgment submodule is used to determine whether the target vehicle has driving abnormality according to each judgment result.

[0134] In one embodiment, the second determination submodule includes:

[0135] The judgment unit is used to determine that the target vehicle has driving abnormality if the judgment results of a preset number of second sections are all abnormal; otherwise, determine that the target vehicle is driving normally.

[0136] In one embodiment, the actual driving information includes actual position information, actual driving speed, actual heading angle and actual passing time; and the prediction module includes:

[0137] A distance determination submodule, used to determine a section distance between a first section and a second section corresponding to the camera;

[0138] The time determination submodule is used to determine the predicted driving information when the target vehicle passes through the second section based on the actual driving information and the section distance; the predicted driving information includes predicted position information, predicted driving speed, predicted heading angle and predicted passing time.

[0139] In one embodiment, the second acquisition module includes:

[0140] An image acquisition submodule, used to acquire vehicle images captured by a camera;

[0141] The vehicle driving information determination submodule is used to identify the target vehicle in the vehicle image using the image recognition model, and determine the measured driving information of the target vehicle when it passes through the second section corresponding to the camera.

[0142] In one embodiment, the first acquisition module includes:

[0143] An identification information acquisition submodule, used to acquire identification information of vehicles passing through the first section by using the ETC gantry;

[0144] A driving information acquisition submodule, used to determine actual driving information of the vehicle when passing through the first section by using radar equipment;

[0145] The actual driving information determination submodule is used to set a corresponding relationship between each piece of identification information and each piece of actual driving information to obtain the actual driving information of the target vehicle passing through the first section.

[0146] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0147] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0148] Figure 4 A schematic diagram of the structure of a terminal device provided in an embodiment of the present application. Figure 4As shown, the terminal device 400 of this embodiment includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and executable on the processor 402; when the processor 402 executes the computer program 403, the steps in the above-mentioned driving abnormality monitoring method embodiments are implemented; or when the processor 402 executes the computer program 403, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0149] Exemplarily, the computer program 403 may be divided into one or more modules / units, one or more modules / units are stored in the memory 401, and are executed by the processor 402 to implement the method of the embodiment of the present application. One or more modules / units may be a series of computer program instruction segments that can perform specific functions, and the instruction segments are used to describe the execution process of the computer program 403 in the terminal device 400. For example, the computer program 403 may be divided into a first acquisition module, a prediction module, a second acquisition module, and a judgment module, and the specific functions of each module are as follows:

[0150] A first acquisition module is used to acquire actual driving information of a target vehicle determined by the ETC gantry and the radar device when the target vehicle passes through a first section; wherein the first section is a section corresponding to the ETC gantry;

[0151] A prediction module, used to determine predicted driving information when the target vehicle passes through a second section corresponding to the camera according to actual driving information;

[0152] A second acquisition module is used to acquire a vehicle image captured by the camera, identify a target vehicle in the vehicle image, and determine measured driving information of the target vehicle when passing through a second section corresponding to the camera;

[0153] The judgment module is used to determine whether the target vehicle has driving abnormality based on the predicted driving information and the measured driving information.

[0154] In application, the terminal device 400 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device 400 may include but is not limited to a memory 401 and a processor 402. Those skilled in the art will appreciate that Figure 4 It is only an example of a terminal device and does not constitute a limitation of the terminal device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.; among which, the input and output devices may include cameras, audio acquisition / playback devices, display screens, etc.; the network access device may include a communication module for wireless communication with external devices.

[0155] In applications, the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0156] In applications, the memory can be an internal storage unit of a terminal device, such as a hard disk or memory of the terminal device; it can also be an external storage device of the terminal device, such as a plug-in hard disk equipped on the terminal device, a smart media card (SMC), a secure digital (SD) card, a flash card, etc.; it can also include both the internal storage unit and the external storage device of the terminal device. The memory is used to store operating systems, applications, boot loaders, data, and other programs, such as program codes of computer programs. The memory can also be used to temporarily store data that has been output or is about to be output.

[0157] Figure 5 FIG. 1 is a schematic diagram of the structure of a driving abnormality monitoring system provided by an embodiment of the present application. Figure 5 As shown, a driving abnormality monitoring system includes an ETC gantry 510, a radar device 520, a camera 530 and a terminal device 540; the radar device 520 is installed on the ETC gantry 510; at least one camera 530 is arranged between two adjacent ETC gantries 510; the ETC gantry 510, the radar device 520 and the camera 530 are respectively connected to the terminal device 540 for communication;

[0158] The ETC gantry 510 is used to obtain identification information of a vehicle passing through a first section corresponding to the ETC gantry 510;

[0159] The radar device 520 is used to determine the actual driving information of the vehicle when passing through the first section;

[0160] The camera 530 is used to collect a vehicle image when the vehicle passes through a second section corresponding to the camera 530;

[0161] The terminal device 540 is used to set a correspondence between each identification information and each actual driving information, and obtain the actual driving information of the target vehicle passing through the first section; determine the predicted driving information when the target vehicle passes through the second section corresponding to the camera 530 based on the actual driving information; obtain the vehicle image captured by the camera 530, and identify the target vehicle in the vehicle image, and determine the measured driving information when the target vehicle passes through the second section corresponding to the camera 530; determine whether the target vehicle has driving abnormalities based on the predicted driving information and the measured driving information.

[0162] In this embodiment, the terminal device is used to determine the actual driving information of the target vehicle when passing through the first section; the predicted driving information of the target vehicle when passing through the second section corresponding to the camera is determined based on the actual driving information; and after obtaining the vehicle image captured by the camera, the measured driving information of the target vehicle when passing through the second section is determined based on the vehicle image information; and then, whether the target vehicle has driving abnormalities is determined based on the predicted driving information and the measured driving information.

[0163] The present application provides a driving abnormality monitoring system. If a target vehicle has driving abnormality, it will affect the passing time of the target vehicle when passing through a second section, that is, the measured passing time; the predicted passing time and the measured passing time of the target vehicle passing through the second section are used to determine whether the target vehicle has driving abnormality, avoiding the use of a single driving speed threshold to determine whether the target vehicle has driving abnormality. Therefore, the present method can improve the accuracy of vehicle driving abnormality monitoring.

[0164] Figure 6 FIG. 2 is a schematic diagram of another driving abnormality monitoring system provided by an embodiment of the present application. Figure 6 As shown, a driving abnormality monitoring system includes an ETC gantry 610, a radar device 620, a camera 630, an edge device 640 and a terminal device 650; the radar device 620 is installed on the ETC gantry 610, and at least one camera 630 is arranged between two adjacent ETC gantries 310; the ETC gantry 610 and the radar device 620 are respectively connected to the terminal device 650 for communication, and the edge device 640 is arranged close to the camera 630 and is connected to the camera 630 for communication; the edge device 640 is connected to the terminal device 650 for communication;

[0165] The ETC gantry 610 is used to obtain identification information of a vehicle passing through a first section corresponding to the ETC gantry 610;

[0166] The radar device 620 is used to determine the actual driving information of the vehicle when passing through the first section;

[0167] The camera 630 is used to collect a vehicle image when the vehicle passes through a second section corresponding to the camera, and send the vehicle image to the edge device 640;

[0168] The terminal device 650 is used to set a corresponding relationship between each identification information and each actual driving information, and obtain the actual driving information of the target vehicle passing through the first section; determine the predicted driving information when the target vehicle passes through the second section corresponding to the camera according to the actual driving information, and send the predicted driving information to the edge device 640;

[0169] The edge device 640 is used to obtain the vehicle image captured by the camera, identify the target vehicle in the vehicle image, and determine the measured driving information of the target vehicle when it passes through the second section corresponding to the camera; determine whether the target vehicle has driving abnormalities based on the predicted driving information and the measured driving information.

[0170] In order to enable people in the technical field to better understand the technical solutions in this application, the technical solutions in the embodiments of this application are described in detail below in combination with actual application scenarios. In the embodiments of this application, the existing ETC gantry, radar equipment and cameras on the highway are used to form a driving abnormality monitoring system; the specific implementation steps are as follows:

[0171] Use the high-definition license plate image recognition system in the ETC gantry to obtain the license plate number of each vehicle;

[0172] Use the radar equipment on the ETC gantry to obtain the vehicle's actual location information, actual driving speed, actual heading angle, vehicle size, vehicle type and other information; classify vehicles according to vehicle type and filter out vehicles of preset vehicle types; the detection rate and accuracy rate must be higher than 99%;

[0173] According to the lane where the vehicle is located, the license plate number and the actual driving information are bound, that is, the license plate number and the actual driving information corresponding to each vehicle are obtained, which may also include vehicle characteristics, such as vehicle type, vehicle size, and vehicle color;

[0174] Filter out vehicles of preset vehicle types such as hazardous chemical transport vehicles and new energy vehicles based on vehicle characteristics;

[0175] For the target vehicle, based on the traffic flow simulation prediction technology, the predicted driving information of the target vehicle when passing through the second section corresponding to the camera is calculated according to the actual driving information of the target vehicle in the first section; the predicted driving information at least includes the predicted passing time; the predicted passing time is sent to the edge device corresponding to the second section;

[0176] The camera is calibrated based on the high-precision map in advance, and the edge device is used to communicate with the camera. After the camera captures the vehicle image, it sends the vehicle image to the edge device.

[0177] The edge device selects the vehicle images within the effective pixel range for detection, and performs target recognition based on the vehicle images to determine the target vehicle, and detects the target vehicle's measured driving speed, measured heading angle, vehicle type, vehicle color and other information. The effective detection area is 50 to 100 meters, and the detection accuracy is less than 1 meter.

[0178] Set the initial shooting angle of the camera and control the camera to track and shoot the vehicle; when the camera zoom or tracking shooting rotation operation ends, control the camera to return to the initial shooting angle;

[0179] Compare the measured driving information and the predicted driving information of the target vehicle passing through the second section, and determine whether the measured driving information when the target vehicle passes through the second section is within the error range of the predicted driving information, that is, whether the difference between the measured driving information and the predicted driving information is less than or equal to the difference threshold; if the difference is less than or equal to the difference threshold, determine that the judgment result is normal; otherwise, determine that the judgment result is abnormal; if there are a continuous preset number of second sections where the judgment results are all abnormal, determine that the target vehicle has driving abnormalities; otherwise, determine that the target vehicle is driving normally.

[0180] The accident location of the target vehicle is located according to the position of the second section, so that the monitoring personnel can quickly locate the on-site location of the target vehicle to rescue the target vehicle; the traffic flow simulation prediction result output is less than 90 seconds.

[0181] An embodiment of the present application provides a driving abnormality monitoring method, which is applied to a terminal device in a driving abnormality monitoring system, wherein the driving abnormality monitoring system includes an ETC gantry, a radar device, a camera and a terminal device; the radar device is installed on the ETC gantry; at least one camera is arranged between two adjacent ETC gantries; the method uses the ETC gantry and the radar device to determine the actual driving information of the target vehicle when passing through a first section, and predicts the predicted driving information of the target vehicle when passing through a second section corresponding to the camera; then the vehicle image captured by the camera is used to determine the measured driving information of the target vehicle when passing through the second section corresponding to the camera; that is, if the target vehicle has driving abnormalities, it will affect the passing time of the target vehicle when passing through the second section, that is, the measured passing time; the predicted passing time and the measured passing time of the target vehicle passing through the second section are used to determine whether the target vehicle has driving abnormalities, thereby avoiding using a single driving speed threshold to determine whether the target vehicle has driving abnormalities, so the method can improve the accuracy of vehicle driving abnormality monitoring.

[0182] In this embodiment, an edge device corresponding to each camera is set, and each edge device determines the measured driving information of the target vehicle according to the vehicle image collected by the corresponding camera; and determines whether the target vehicle has driving abnormality according to the corresponding predicted driving information sent by the terminal device. In other words, this embodiment uses the terminal device and the edge device to work together to determine whether the target vehicle has driving abnormality; therefore, the accuracy of vehicle driving abnormality monitoring can be improved.

[0183] In one embodiment, the ETC gantry is also used to:

[0184] Get license plate information, vehicle dimensions, and vehicle type;

[0185] Filter out vehicles with preset license plate types based on license plate information;

[0186] And / or, filtering out vehicles of a preset vehicle type according to vehicle size and / or vehicle type.

[0187] Specifically, the high-definition license plate image recognition system in the ETC gantry is used to identify each vehicle and determine the vehicle's license plate information. In actual applications, vehicles of preset license plate types can be directly screened out based on the license plate information, such as vehicles corresponding to white license plates based on the license plate color; or vehicles of preset vehicle types such as hazardous chemical transport vehicles and new energy vehicles can be screened out based on vehicle size and / or vehicle type.

[0188] In this embodiment, vehicles of a preset license plate type can be further screened, or vehicles of a preset vehicle type can be selected, so as to facilitate abnormal driving monitoring of different vehicle types.

[0189] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0190] A computer-readable storage medium provided in an embodiment of the present application has the same beneficial effects as the above-mentioned driving abnormality monitoring method.

[0191] The present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying the computer program code to a terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0192] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0193] Those of ordinary skill in the art will appreciate that the devices 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.

[0194] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices, which can be electrical, mechanical or other forms.

[0195] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A driving abnormality monitoring method, characterized in that: A terminal device used in a driving abnormality monitoring system, wherein the driving abnormality monitoring system includes an ETC gantry, a radar device, a camera and a terminal device; The radar device is installed on the ETC gantry; At least one camera is arranged between two adjacent ETC gantries; the method comprises: Acquire actual driving information of the target vehicle determined by the ETC gantry and the radar device when passing through a first section; wherein the first section is a section corresponding to the ETC gantry; Determine predicted driving information when the target vehicle passes through a second section corresponding to the camera according to the actual driving information; the predicted driving information at least includes the predicted passing time; Acquire a vehicle image captured by the camera, identify a target vehicle in the vehicle image, and determine measured driving information of the target vehicle when it passes through a second section corresponding to the camera; the measured driving information at least includes a measured passing time; It is determined whether the target vehicle has driving abnormality according to the predicted driving information and the measured driving information.

2. The method according to claim 1, characterized in that At least two cameras are arranged between two adjacent ETC gantries; the method of determining the predicted driving information of the target vehicle passing through the second section corresponding to the camera according to the actual driving information includes: Determine predicted driving information of the target vehicle passing through the second sections respectively corresponding to each of the cameras according to the actual driving information; The acquiring of the vehicle image captured by the camera, identifying the target vehicle in the vehicle image, and determining the measured driving information of the target vehicle when passing through the second section corresponding to the camera includes: For each of the cameras, a vehicle image captured by the camera is acquired, and a target vehicle in the vehicle image is identified, and measured driving information of the target vehicle when passing through a second section corresponding to the camera is determined; The determining whether the target vehicle has driving abnormality according to the predicted driving information and the measured driving information includes: For each of the second sections, determining a judgment result of the second section according to the predicted driving information and the measured driving information; Determine whether the target vehicle has driving abnormality according to each of the judgment results.

3. The method according to claim 2, characterized in that Determining whether the target vehicle has driving abnormality according to each of the judgment results includes: If there are a number of consecutive predetermined judgment results of the second sections that are all abnormal, it is determined that the target vehicle has driving abnormality; Otherwise, it is determined that the target vehicle is driving normally.

4. The method according to claim 1, characterized in that: The actual driving information includes actual position information, actual driving speed, actual heading angle and actual passing time; the predicted driving information when the target vehicle passes through the second section corresponding to the camera is determined according to the actual driving information, including: determining a cross-sectional distance between the first cross-sectional area and a second cross-sectional area corresponding to the camera; The predicted driving information of the target vehicle when passing through the second section is determined according to the actual driving information and the section distance; the predicted driving information includes predicted position information, predicted driving speed, predicted heading angle and predicted passing time.

5. The method according to claim 1, characterized in that The acquiring of the vehicle image captured by the camera, identifying the target vehicle in the vehicle image, and determining the measured driving information of the target vehicle when passing through the second section corresponding to the camera includes: Acquire a vehicle image captured by the camera; The target vehicle in the vehicle image is identified using an image recognition model, and measured driving information of the target vehicle when passing through a second section corresponding to the camera is determined.

6. The method according to claim 1, characterized in that The obtaining of actual driving information of the target vehicle determined by the ETC gantry and the radar device when the target vehicle passes through the first section includes: Using the ETC gantry to obtain identification information of vehicles passing through the first section; Determining actual driving information of the vehicle when passing through the first section using the radar device; A corresponding relationship is set between each piece of identification information and each piece of actual driving information to obtain the actual driving information of the target vehicle passing through the first section.

7. A driving abnormality monitoring device, characterized in that: A terminal device used in a driving abnormality monitoring system, wherein the driving abnormality monitoring system includes an ETC gantry, a radar device, a camera and a terminal device; The radar device is installed on the ETC gantry; At least one camera is arranged between two adjacent ETC gantries; the device comprises: A first acquisition module is used to acquire actual driving information of a target vehicle determined by the ETC gantry and the radar device when the target vehicle passes through a first section; wherein the first section is a section corresponding to the ETC gantry; A prediction module, used to determine predicted driving information when the target vehicle passes through a second section corresponding to the camera according to the actual driving information; A second acquisition module is used to acquire the vehicle image captured by the camera, identify the target vehicle in the vehicle image, and determine the measured driving information of the target vehicle when passing through the second section corresponding to the camera; A judgment module is used to determine whether the target vehicle has driving abnormality according to the predicted driving information and the measured driving information.

8. A driving abnormality monitoring system, characterized in that: The system comprises an ETC gantry, a radar device, a camera and a terminal device; the radar device is installed on the ETC gantry; at least one camera is arranged between two adjacent ETC gantries; the ETC gantry, the radar device and the camera are respectively connected to the terminal device for communication; The ETC gantry is used to obtain identification information of a vehicle passing through a first section corresponding to the ETC gantry; The radar device is used to determine the actual driving information of the vehicle when passing through the first section; The camera is used to collect a vehicle image of the vehicle when the vehicle passes through a second section corresponding to the camera; The terminal device is used to set a corresponding relationship between each piece of identification information and each piece of actual driving information to obtain the actual driving information of the target vehicle passing through the first section; Determine predicted driving information when the target vehicle passes through a second section corresponding to the camera based on the actual driving information; obtain the vehicle image captured by the camera, identify the target vehicle in the vehicle image, and determine measured driving information when the target vehicle passes through the second section corresponding to the camera; determine whether the target vehicle has driving abnormalities based on the predicted driving information and the measured driving information.

9. A driving abnormality monitoring system, characterized in that: The system comprises an ETC gantry, a radar device, a camera, an edge device and a terminal device; the radar device is installed on the ETC gantry, and at least one camera is arranged between two adjacent ETC gantries; the ETC gantry and the radar device are respectively connected to the terminal device for communication, the edge device is arranged close to the camera and is connected to the camera for communication; the edge device is connected to the terminal device for communication; The ETC gantry is used to obtain identification information of a vehicle passing through a first section corresponding to the ETC gantry; The radar device is used to determine the actual driving information of the vehicle when passing through the first section; The camera is used to collect a vehicle image of the vehicle when the vehicle passes through a second section corresponding to the camera, and send the vehicle image to the edge device; The terminal device is used to set a corresponding relationship between each piece of identification information and each piece of actual driving information to obtain the actual driving information of the target vehicle passing through the first section; Determine predicted driving information when the target vehicle passes through a second section corresponding to the camera according to the actual driving information, and send the predicted driving information to the edge device; The edge device is used to obtain the vehicle image captured by the camera, identify the target vehicle in the vehicle image, and determine the measured driving information of the target vehicle when passing through the second section corresponding to the camera; and determine whether the target vehicle has driving abnormalities based on the predicted driving information and the measured driving information.

10. The system according to claim 9, characterized in that The ETC gantry is also used for: Get license plate information, vehicle dimensions, and vehicle type; Filter out vehicles with a preset license plate type according to the license plate information; And / or, vehicles of a preset vehicle type are screened out according to the vehicle size and / or the vehicle type.

11. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

12. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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