CN118072525A - Fake-licensed vehicle detection method, computer equipment and readable medium - Google Patents

Abstract

The present disclosure provides a fake-licensed vehicle detection method, the method comprising: basic safety information of the first vehicle and perception data of the second vehicle corresponding to the preset time are obtained, and a fake-licensed result is determined according to a comparison result of the basic safety information and the perception data. According to the fake-licensed vehicle identification method and device, the fake-licensed vehicles are detected by using the perception data of the vehicles, so that the existence of the fake-licensed vehicles can be found, the fake-licensed vehicles can be identified, and the fake-licensed vehicles with the same appearance can be identified; the vehicle-mounted unit identification method and device can identify whether the vehicle provided with the vehicle-mounted unit is licensed or not, and whether other vehicles on the road are licensed or not. The present disclosure also provides a computer device and a readable medium.

Description

Fake-licensed vehicle detection method, computer equipment and readable medium

Technical Field

The disclosure relates to the technical field of vehicle-road coordination, in particular to a fake-licensed vehicle detection method, computer equipment and a readable medium.

Background

The V2X (Veh ic le to EVERYTH ING) technology is also called a vehicle wireless communication technology, is essentially an Internet of things technology, V represents a vehicle, and X represents all devices which can be connected, such as roads, people, vehicles, equipment and the like. The essence of V2X is to realize the intellectualization of the whole road transportation through the coordination among roads, pedestrians and vehicles. If there is a front vehicle to be combined, the front vehicle can send a command to the base station, and the base station informs the rear vehicle. For example, when a person passes through a road, the person can send an instruction in advance through a mobile phone, and the vehicles of the same person are required to pay attention to avoiding. Such collaboration requires the coordination of the vehicle manufacturer, the communication equipment manufacturer and the operation service provider, is a huge industry chain coordination division, and requires the promotion of relevant national standards.

At present, the problem of low efficiency exists in vehicle supervision, and particularly, the problem that vehicles with the same appearance and license plate number cannot be reasonably and effectively detected.

Disclosure of Invention

The present disclosure provides a fake-licensed vehicle detection method, a computer device, and a readable medium.

In a first aspect, an embodiment of the present disclosure provides a fake-licensed vehicle detection method, the method including: basic safety information of a first vehicle and perception data of a second vehicle corresponding to preset time are obtained; and determining a fake-licensed result according to the comparison result of the basic security information and the perception data.

In yet another aspect, the disclosed embodiments also provide a computer device, comprising: one or more processors; a storage device having one or more programs stored thereon; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the fake-licensed vehicle detection method as previously described.

In yet another aspect, the disclosed embodiments also provide a computer readable medium having a computer program stored thereon, wherein the program when executed implements a fake-licensed vehicle detection method as previously described.

The fake-licensed vehicle detection method provided by the embodiment of the disclosure comprises the following steps: basic safety information of the first vehicle and perception data of the second vehicle corresponding to the preset time are obtained, and a fake-licensed result is determined according to a comparison result of the basic safety information and the perception data. According to the fake-licensed vehicle identification method and device, the fake-licensed vehicles are detected by using the perception data of the vehicles, so that the existence of the fake-licensed vehicles can be found, the fake-licensed vehicles can be identified, and the fake-licensed vehicles with the same appearance can be identified; the vehicle-mounted unit identification method and device can identify whether the vehicle provided with the vehicle-mounted unit is licensed or not, and whether other vehicles on the road are licensed or not.

Drawings

FIG. 1 is a schematic diagram of a V2X system architecture according to an embodiment of the present disclosure;

Fig. 2 is a schematic diagram of a fake-licensed vehicle detection flow chart according to an embodiment of the present disclosure;

FIG. 3 is a schematic flow chart of determining a fake-licensed vehicle according to a comparison result of basic security information and perception data according to an embodiment of the present disclosure;

FIG. 4 is a flow chart of a fake-licensed vehicle determination according to a comparison result of basic security information and perceived data provided by another embodiment of the present disclosure;

fig. 5 is a schematic flow chart of detecting whether a vehicle with an OBU installed is a fake-licensed other vehicle according to still another embodiment of the present disclosure;

fig. 6 is a schematic flow chart of detecting whether other vehicles are licensed to install an OBU vehicle according to still another embodiment of the present disclosure.

Detailed Description

Example embodiments will be described more fully hereinafter with reference to the accompanying drawings, but may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

Embodiments described herein may be described with reference to plan and/or cross-sectional views with the aid of idealized schematic diagrams of the present disclosure. Accordingly, the example illustrations may be modified in accordance with manufacturing techniques and/or tolerances. Thus, the embodiments are not limited to the embodiments shown in the drawings, but include modifications of the configuration formed based on the manufacturing process. Thus, the regions illustrated in the figures have schematic properties and the shapes of the regions illustrated in the figures illustrate the particular shapes of the regions of the elements, but are not intended to be limiting.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

The embodiment of the disclosure provides a fake-licensed vehicle detection method which can be applied to a V2X system in vehicle-road cooperation. V2X technology is a set of systems, and V2X technology is mainly used to provide information for a vehicle in which an OBU (On Board u it, on-Board unit) is installed. Fig. 1 is a schematic view of a V2X system architecture, as shown in fig. 1, where the system may include, but is not limited to, a cloud device 10, a Road Side Unit (RSU) 20, a Road Side sensing unit 30, an OBU-mounted vehicle 40, and an OBU-not-mounted vehicle 50, where the Road Side sensing unit 30 includes a camera 301 and an edge computing device (Mu lt i-ACCESS EDGE computing, MEC) 302.

In the running process of the vehicle 40 with the OBU, the road side sensing unit 30 reports the BSM (Bas IC SAFETY MESSAGE, basic safety information) information of the vehicle to the cloud device 10 in real time, wherein the basic safety information includes information such as the position, speed, heading and license plate number of the vehicle. The road side sensing unit 30 collects vehicle images on the road by using the camera 301, the edge computing device 302 can acquire sensing data such as the position, the speed, the heading, the license plate number and the like of the vehicle by using an image recognition technology, the sensing data are synchronously broadcast to the vehicle 40 provided with the OBU and the cloud device 10 by the road side sensing unit 30, the cloud device 10 can display the sensing data in a three-dimensional map, the vehicle 40 provided with the OBU can apply the sensing data to related scenes such as pedestrian early warning between vehicles and people (Veh ic to PEDESTR IAN, V2P), collision early warning between vehicles (Veh ic le to Veh ic le, V2V) and the like. The vehicle 40 on which the OBU is installed needs to be registered on a V2X cloud platform, and the registered information includes information such as a vehicle identifier, a license plate number, a color, a brand, a vehicle type, and the like, and the V2X cloud platform establishes a correspondence relationship between the vehicle identifier and the license plate number for each registered vehicle. The cloud device 10 may be a V2X cloud platform or a local area server, and is responsible for data storage, analysis and calculation, and may identify a fake-licensed vehicle according to BSM messages and perception data.

The embodiment of the disclosure provides a fake-licensed vehicle detection method, which can be applied to cloud equipment or independently run on equipment capable of acquiring relevant information and performing corresponding processing, such as MEC, vehicles and the like.

As shown in fig. 2, the fake-licensed vehicle detection method may include, but is not limited to, the following operations.

And 11, acquiring basic safety information of the first vehicle and perception data of the second vehicle corresponding to the preset time.

In the running process of the vehicle provided with the OBU, the BSM information can be broadcast in real time through the V2X network, the BSM information carries basic safety information, and after the RSU nearby the vehicle receives the BSM information, the BSM information is forwarded to the cloud device, so that the cloud device can analyze the BSM information to obtain the basic safety information. The cloud device can acquire basic safety information of the first vehicle corresponding to the preset time. In an exemplary embodiment, the OBU may continuously report to the cloud device at a period of 0.1S.

The method comprises the steps that a camera collects vehicle images on a road, the MEC obtains sensing data of a vehicle by using an image recognition technology, and the sensing data are synchronously broadcast to the vehicle provided with the OBU and cloud equipment through the RSU. The cloud device acquires basic safety information and perception data corresponding to preset time from the acquired mass data. It should be noted that, the sensing data is not limited to MEC reporting, but may also come from devices such as a vehicle end, RSCU (Road Side Comput ing Un it ), and the like.

In an exemplary embodiment, the cloud device may acquire basic safety information of the first vehicle and perceived data of the second vehicle according to a preset period, where the preset time is a preset period arrival time.

In an exemplary embodiment, the BSM message sent by the RSU may carry a timestamp, the sensing data synchronized by the MEC to the RSU may also carry a timestamp, and the cloud device may screen the acquired mass data according to the timestamp based on the time in the user instruction, so as to obtain the basic security information of the first vehicle and the sensing data of the second vehicle corresponding to the corresponding time.

The first vehicles are vehicles corresponding to the reported basic safety information, the second vehicles are road side vehicles identified by the MEC, the number of the first vehicles can be one or more, and the number of the second vehicles can be one or more.

And step 12, determining a fake-licensed result according to the comparison result of the basic security information and the perception data.

The cloud device compares the basic safety information and the perception data which correspond to each other at the same time, and can determine whether a fake-licensed vehicle exists or not and identify which vehicle is the fake-licensed vehicle according to the comparison result.

The fake-licensed vehicle detection method provided by the embodiment of the disclosure comprises the following steps: basic safety information of the first vehicle and perception data of the second vehicle corresponding to the preset time are obtained, and a fake-licensed result is determined according to a comparison result of the basic safety information and the perception data. According to the fake-licensed vehicle identification method and device, the fake-licensed vehicles are detected by using the perception data of the vehicles, so that the existence of the fake-licensed vehicles can be found, the fake-licensed vehicles can be identified, and the fake-licensed vehicles with the same appearance can be identified; the vehicle-mounted unit identification method and device can identify whether the vehicle provided with the vehicle-mounted unit is licensed or not, and whether other vehicles on the road are licensed or not.

The sensing data is obtained by performing image processing on the image of the vehicle acquired by the camera, the transmission delay is large, and the basic safety information is reported by the OBU in real time, so that the basic safety information and the sensing data are not synchronously received. In order to realize fake-licensed vehicle detection, the cloud device can buffer basic safety information first, and compare the basic safety information with the perception data after the perception data are received.

Thus, in an exemplary embodiment, after acquiring the basic safety information of the first vehicle corresponding to the preset time, the fake-licensed vehicle detection method further includes the steps of: and under the condition that the delay of the perceived data is larger than a preset threshold value, caching the acquired basic security information. Accordingly, the determining the fake-licensed result (i.e. step 12) according to the comparison result of the basic security information and the perception data includes the following steps: after the perception data is acquired, a fake-licensed result is determined according to the comparison result of the basic security information and the perception data.

In an exemplary embodiment, the basic security information and the sensory data may include, but are not limited to: the basic safety information can further comprise a vehicle identifier, and the perception data can further comprise a second license plate number. That is, the basic security information may include, but is not limited to: the characteristic parameters, the driving parameters, the location parameters, and the vehicle identification, the sensory data may include, but is not limited to: the vehicle license plate comprises a characteristic parameter, a driving parameter, a position parameter and a second license plate number. The location parameter may be latitude and longitude information, for example.

In an exemplary embodiment, the travel parameters may include, but are not limited to, speed and heading.

In an exemplary embodiment, the characteristic parameters may include color, or the characteristic parameters may include, but are not limited to, color and vehicle model. The characteristic parameters may also include, but are not limited to, vehicle attribute parameters such as gears, brakes, lights, steering wheels, etc. of the vehicle.

The OBU can acquire the current position parameter, speed and heading of the vehicle through a GPS (G loba l Pos it ion ING SYSTEM ) satellite, and basic safety information such as the position parameter, speed, heading, vehicle identification, color and the like is broadcast out through a V2X network by using the RSU in a BSM message mode, so that the cloud device can acquire the basic safety information.

After the vehicle runs on a road section provided with the camera, the camera acquires a vehicle image, the MEC utilizes an AI (ART IFICIA LI NTE L L IGENCE ) pattern recognition technology to recognize characteristic parameters such as the color and the vehicle type of the vehicle and the license plate number (namely a second license plate number) of the vehicle, then utilizes a vehicle tracking algorithm to calculate running parameters such as the speed and the course of the vehicle, acquires position parameters of the vehicle through coordinate mapping, and reports the characteristic parameters, the running parameters, the position parameters and the second license plate number to cloud equipment so that the cloud equipment can acquire the sensing data.

In an exemplary embodiment, as shown in fig. 3, the determining the fake-licensed result (i.e., step 12) according to the comparison result of the basic security information and the perceived data may include, but is not limited to, the following steps.

Step 31, calculating a first distance between each first vehicle and each second vehicle according to the position parameters in the basic safety information and the position parameters in the perception data.

The basic safety information of the first vehicle and the perception data of the second vehicle corresponding to the preset time comprise basic safety information and perception data of a plurality of vehicles, in the step, the cloud device randomly selects one position parameter of the first vehicle from the basic safety information, randomly selects one position parameter of the second vehicle from the perception data, and calculates a first distance between the first vehicle and the second vehicle according to the two position parameters. The acquired basic safety information and perception data are traversed in the above manner, and thus a first distance between each first vehicle and each second vehicle is calculated.

Step 32, determining a first target vehicle and a corresponding second target vehicle, wherein the first distance is smaller than a preset first threshold, the first target vehicle is at least one of the first vehicles, and the second target vehicle is at least one of the second vehicles.

And (3) screening the plurality of first distances calculated in the step (31) by using a preset first threshold value, and selecting first distances smaller than the preset first threshold value, wherein the first distances smaller than the preset first threshold value correspond to a group of vehicles, and the group of vehicles comprise first vehicles and corresponding second vehicles, wherein the first vehicles are first target vehicles, and the second vehicles corresponding to the first vehicles are second target vehicles. That is, each first distance smaller than the preset first threshold corresponds to one first target vehicle and one second target vehicle.

It should be noted that, for the first vehicle and the second vehicle corresponding to the first distance greater than or equal to the preset first threshold, the two vehicles are far away from each other, and may be considered as vehicles at different places, that is, two different vehicles at different places at the same time, there is no comparability, and data of these vehicles is not processed.

Step 33 compares the driving parameters of the first target vehicle with the driving parameters of the corresponding second target vehicle, and compares the characteristic parameters of the first target vehicle with the characteristic parameters of the corresponding second target vehicle.

The driving parameters and the characteristic parameters of the first target vehicle and the second target vehicle are compared, respectively, and in an exemplary embodiment, the driving parameters include speed and heading, and the characteristic parameters include at least color.

Step 34, determining the first license plate number according to the vehicle identification of the first target vehicle under the condition that the running parameters of the first target vehicle and the corresponding running parameters of the second target vehicle are the same and the characteristic parameters of the first target vehicle and the corresponding characteristic parameters of the second target vehicle are the same.

If the running parameters and the characteristic parameters of a group of fake-licensed vehicles to be detected are the same, determining a first license plate number of the first target vehicle according to the vehicle identification of the first target vehicle so as to compare with a second license plate number of a corresponding second target vehicle.

If at least one of the running parameters and the characteristic parameters of a group of fake-licensed vehicles to be detected is different, namely at least one of the speed, the course, the color and the vehicle type of the vehicles is different, the first vehicle and the second vehicle are two different vehicles which are close to each other and appear at the same place at the same time, and the data of the vehicles are not processed.

And 35, determining that the corresponding second target vehicle is a fake-licensed vehicle under the condition that the first license plate number is inconsistent with the second license plate number of the corresponding second target vehicle.

In an exemplary embodiment, the second license plate number of the corresponding second target vehicle is identified by the MEC and reported to the cloud device. The cloud device compares the first license plate number of the first target vehicle determined in the step 34 with the second license plate number of the corresponding second target vehicle reported by the MEC, if the first license plate number and the second license plate number are different, the same vehicle appearing at the same time and the same place is indicated, the running parameters and the characteristic parameters registered on the V2X cloud platform are consistent with the running parameters and the characteristic parameters actually perceived from the road, but the license plate number registered on the V2X cloud platform is inconsistent with the license plate number actually perceived from the road, so that the second target vehicle can be determined to be a fake-licensed vehicle, namely, the fake-licensed vehicle of the vehicle with the OBU is provided with the license plate of other vehicles, and the other vehicles with the fake-licensed license plate can be vehicles with the OBU or vehicles without the OBU. After the fake-licensed vehicle is determined, relevant personnel can be informed to call relevant videos to obtain evidence.

If the first license plate number is consistent with the second license plate number of the corresponding second target vehicle, the license plate number, the running parameters and the characteristic parameters registered on the V2X cloud platform are consistent with the license plate number, the running parameters and the characteristic parameters actually perceived from the road, and the vehicle actually perceived from the road is the vehicle registered on the V2X cloud platform, so that it can be determined that the second target vehicle does not cover other vehicles.

Through the steps 31-35, whether the vehicle provided with the OBU is licensed with other vehicles or not can be detected.

In an exemplary embodiment, the determining the first license plate number according to the vehicle identification of the first target vehicle includes the steps of: and determining a first license plate number corresponding to the vehicle identification of the first target vehicle according to the preset corresponding relation between the vehicle identification and the license plate number.

The preset correspondence between the vehicle identifier and the license plate number may be established by the V2X cloud platform when the vehicle on which the OBU is installed registers on the V2X cloud platform, or may be obtained from a third party system, which includes, but is not limited to, a vehicle management system. The corresponding relation between the vehicle identification and the license plate number is inquired, so that the first license plate number corresponding to the vehicle identification of the first target vehicle can be determined. If a new OBU-mounted vehicle is registered on the V2X cloud platform, the V2X cloud platform updates the corresponding relation between the vehicle identification and the license plate number; the V2X cloud platform may also periodically obtain the latest correspondence between the vehicle identifier and the license plate number from the third party system.

In an exemplary embodiment, the basic security information and the sensory data may include, but are not limited to, location parameters, the basic security information may further include a vehicle identification, and the sensory data may further include a second license plate number. That is, the basic security information may include, but is not limited to, a location parameter and a vehicle identification, and the sensory data may include, but is not limited to, a location parameter and a second license plate number. The location parameter may be latitude and longitude information, for example.

As shown in FIG. 4, the determination of the fake-licensed result (i.e., step 12) based on the comparison of the basic security information and the perceived data may include, but is not limited to, the following steps.

Step 41, determining the first license plate numbers according to the vehicle identifications, and comparing each first license plate number with each second license plate number.

In the step, the cloud device determines first license plate numbers corresponding to all vehicle identifications according to the basic safety information, and compares all the first license plate numbers with all second license plate numbers in the perception data. The cloud device randomly selects a first license plate number from the basic security information and compares the first license plate number with each second license plate number in the perception data. Traversing the basic security information in this way, and comparing all the first license plate numbers with all the second license plate numbers one by one respectively.

In step 42, a third target vehicle and a fourth target vehicle with the same first license plate number and the second license plate number are determined, wherein the third target vehicle is at least one of the first vehicles, and the fourth target vehicle is at least one of the second vehicles.

In the step, the cloud device screens out a group of vehicles with the same first license plate number and second license plate number, wherein the group of vehicles comprises a third target vehicle corresponding to basic safety information and a fourth target vehicle corresponding to perception data.

The data of the vehicles with different first license plate numbers and second license plate numbers are not processed.

Step 43, calculating a second distance between the third target vehicle and the fourth target vehicle according to the position parameter of the third target vehicle and the position parameter of the fourth target vehicle.

And (3) calculating a second distance between the third target vehicle and the fourth target vehicle according to the position parameters for each group of third target vehicle and fourth target vehicle with the same license plate numbers screened in the step (42).

And step 44, determining that the fourth target vehicle is a fake-licensed vehicle when the second distance is greater than a preset second threshold value.

If the second distance is greater than the preset second threshold value, the third target vehicle and the fourth target vehicle with the same license plate number are far, and the third target vehicle is a vehicle registered on the V2X cloud platform and the information is legal, so that the fourth target vehicle can be determined to be a fake license plate vehicle. After the fake-licensed vehicle is determined, relevant personnel can be informed to call relevant videos to obtain evidence.

If the second distance is smaller than or equal to the preset second threshold value, the third target vehicle and the fourth target vehicle with the same license plate number are the same vehicle because the distance between the third target vehicle and the fourth target vehicle is relatively close, and therefore data of the vehicles are not processed.

It can be detected whether other vehicles will nest the mounted OBU vehicle by the above steps 41-44.

In an exemplary embodiment, the determining the first license plate number according to the vehicle identification (i.e., step 41) includes the steps of: and determining a first license plate number corresponding to the vehicle identifier according to a preset corresponding relation between the vehicle identifier and the license plate number.

The preset correspondence between the vehicle identifier and the license plate number may be established by the V2X cloud platform when the vehicle on which the OBU is installed registers on the V2X cloud platform, or may be obtained from a third party system, which includes but is not limited to a vehicle management system. The first license plate number corresponding to the vehicle identification in the basic safety information can be determined by inquiring the corresponding relation between the vehicle identification and the license plate number.

In order to clearly illustrate the solution of the embodiment of the present disclosure, a flow of detecting whether a vehicle on which an OBU is installed is licensed to other vehicles will be described in detail with reference to fig. 5 by way of a specific example. As shown in fig. 5, the process may include, but is not limited to, the following steps.

Step 101, acquiring basic safety information of a first vehicle and perception data of a second vehicle corresponding to preset time.

Step 102, a first distance between each first vehicle and each second vehicle is calculated.

Step 103, judging whether the first distance is smaller than a first threshold value, if so, executing step 104; if the first distance is greater than or equal to the first threshold, ending the flow.

Step 104, determining a first target vehicle and a corresponding second target vehicle having a first distance less than a first threshold.

Step 105, comparing the speed and the heading of the first target vehicle with the speed and the heading of the corresponding second target vehicle, and comparing the color of the first target vehicle with the color of the corresponding second target vehicle, if the speed, the heading and the color of the first target vehicle and the corresponding second target vehicle B are the same, executing step 106; if at least one of the speed, heading and color of the first target vehicle and the corresponding second target vehicle B are different, the flow is ended.

Step 106, determining the first license plate number according to the vehicle identification of the first target vehicle.

Step 107, comparing the first license plate number with a corresponding second license plate number of a second target vehicle, and if the first license plate number is the same as the second license plate number, executing step 108; if the first license plate number is different from the second license plate number, ending the flow.

Step 108, determining that the corresponding second target vehicle is a fake-licensed vehicle, i.e. the license plate of the second target vehicle is not the license plate registered by the second target vehicle on the V2X cloud platform.

The steps 101-108 can show that the vehicles at the same time and at the same place have the same speed, course and color, but the perceived license plate number is inconsistent with the license plate number registered by the V2X cloud platform, and the vehicles are considered as fake license plate vehicles. Suppose vehicle a has an OBU installed and has access to a V2X network, i.e., the first target vehicle is referred to as vehicle a and the second target vehicle is referred to as vehicle B. Through the flow, the BSM reported by the vehicle and the perceived data perceived and identified by the road side are compared, so that whether the vehicle A is sleeved with license plates of other vehicles can be identified. If the appearance of the other vehicles of the fake-licensed is the same as that of the a vehicle, it can be recognized whether the vehicle (a vehicle) of the same appearance is a fake-licensed vehicle.

In order to clearly illustrate the solution of the embodiment of the present disclosure, a flow of detecting whether other vehicles are licensed to have an OBU vehicle installed will be described in detail with reference to fig. 6 by way of a specific example. As shown in fig. 6, the flow includes the following steps.

Step 201, basic safety information of a first vehicle and perception data of a second vehicle corresponding to a preset time are obtained.

The basic safety information comprises a position parameter and a vehicle identifier, and the perception data comprises the position parameter and a second license plate number.

Step 202, determining a first license plate number in the basic security information according to the vehicle identification.

And determining a first license plate number corresponding to the vehicle identifier according to a preset corresponding relation between the vehicle identifier and the license plate number.

Step 203, comparing each first license plate number with each second license plate number, judging whether the first license plate number is the same as the second license plate number, if so, executing step 204; if the two types are different, ending the flow.

Step 204, determining a third target vehicle and a fourth target vehicle with the same first license plate number and the same second license plate number.

Step 205 calculates a second distance between the third target vehicle and the fourth target vehicle.

Step 206, determining whether the second distance is greater than a preset second threshold, and if the second distance is greater than the preset second threshold, executing step 207; if the second distance is smaller than or equal to the preset second threshold value, ending the flow.

Step 207, determining that the fourth target vehicle is a fake-licensed vehicle.

It can be seen from steps 201-207 that if a vehicle is present at two different locations at the same time, the perceptively identified vehicle is considered a fake-licensed vehicle. Assuming that the vehicle a is equipped with an OBU and accesses the V2X network, the vehicle B is not equipped with an OBU but is licensed with the license plate of the vehicle a, i.e., the third target vehicle is the vehicle a and the fourth target vehicle is the vehicle B. Through the flow, the BSM reported by the vehicle A and the perceived data of the vehicle B identified by the road side are compared, so that whether the vehicle B is sleeved with the license plate of the vehicle A can be identified, and whether the vehicles with the same appearance are sleeved vehicles can be identified.

The embodiment of the disclosure provides a fake-licensed vehicle which is identified by comparing basic safety information and road side perception data through V2X information and road side perception information and comparing the characteristics of the vehicle and running information, and solves the problem that fake-licensed license plates of vehicles with the same appearance cannot be identified.

The disclosed embodiments also provide a computer device comprising: one or more processors and a storage device; the storage device stores one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the fake-licensed vehicle detection method provided in the foregoing embodiments.

The disclosed embodiments also provide a computer readable medium having a computer program stored thereon, wherein the computer program when executed implements the fake-licensed vehicle detection method as provided by the foregoing embodiments.

Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, functional modules/units in the apparatus disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.

Example embodiments have been disclosed herein, and although specific terms are employed, they are used and should be interpreted in a generic and descriptive sense only and not for purpose of limitation. In some instances, it will be apparent to one skilled in the art that features, characteristics, and/or elements described in connection with a particular embodiment may be used alone or in combination with other embodiments unless explicitly stated otherwise. It will therefore be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the scope of the present invention as set forth in the following claims.

Claims (9)

1. A fake-licensed vehicle detection method, the method comprising:

basic safety information of a first vehicle and perception data of a second vehicle corresponding to preset time are obtained;

And determining a fake-licensed result according to the comparison result of the basic security information and the perception data.

2. The method of claim 1, wherein after acquiring the basic safety information of the first vehicle corresponding to the preset time, the method further comprises: caching the acquired basic security information under the condition that the delay of the perceived data is larger than a preset threshold value;

The determining the fake-licensed result according to the comparison result of the basic security information and the perception data comprises the following steps:

and after the perception data is acquired, determining a fake-licensed result according to the comparison result of the basic security information and the perception data.

3. The method of claim 1 or 2, wherein the basic safety information and the perceived data each include a characteristic parameter, a driving parameter, and a position parameter, the basic safety information further includes a vehicle identification, the perceived data further includes a second license plate number, and the determining the fake-licensed result according to the comparison result of the basic safety information and the perceived data includes:

Calculating a first distance between each first vehicle and each second vehicle according to the position parameters in the basic safety information and the position parameters in the perception data;

Determining a first target vehicle and a corresponding second target vehicle, wherein the first distance is smaller than a preset first threshold value, the first target vehicle is at least one of the first vehicles, and the second target vehicle is at least one of the second vehicles;

comparing the driving parameters of the first target vehicle with the driving parameters of the corresponding second target vehicle, and comparing the characteristic parameters of the first target vehicle with the characteristic parameters of the corresponding second target vehicle;

determining a first license plate number according to the vehicle identification of the first target vehicle under the condition that the running parameters of the first target vehicle and the corresponding running parameters of the second target vehicle are the same and the characteristic parameters of the first target vehicle and the corresponding characteristic parameters of the second target vehicle are the same;

and under the condition that the first license plate number is inconsistent with the second license plate number of the corresponding second target vehicle, determining that the corresponding second target vehicle is a fake license plate vehicle.

4. The method of claim 3, wherein said determining a first license plate number from said vehicle identification of said first target vehicle comprises:

and determining a first license plate number corresponding to the vehicle identification of the first target vehicle according to a preset corresponding relation between the vehicle identification and the license plate number.

5. The method of claim 3, wherein the travel parameters include speed and heading; and, the characteristic parameter includes a color, or the characteristic parameter includes a color and a vehicle type.

6. The method of claim 1 or 2, wherein the basic security information and the perceived data each include a location parameter, the basic security information further includes a vehicle identification, the perceived data further includes a second license plate number, and the determining a fake-licensed result based on the comparison of the basic security information and the perceived data includes:

determining a first license plate number according to the vehicle identification, and comparing each first license plate number with each second license plate number;

Determining a third target vehicle and a fourth target vehicle with the same first license plate number and the same second license plate number, wherein the third target vehicle is at least one of the first vehicles, and the fourth target vehicle is at least one of the second vehicles;

calculating a second distance between the third target vehicle and the fourth target vehicle according to the position parameter of the third target vehicle and the position parameter of the fourth target vehicle;

and under the condition that the second distance is larger than a preset second threshold value, determining that the fourth target vehicle is a fake-licensed vehicle.

7. The method of claim 6, wherein said determining a first license plate number from said vehicle identification comprises:

and determining a first license plate number corresponding to the vehicle identifier according to a preset corresponding relation between the vehicle identifier and the license plate number.

8. A computer device, comprising:

One or more processors;

a storage device having one or more programs stored thereon;

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the fake-licensed vehicle detection method of any one of claims 1-7.

9. A computer readable medium having stored thereon a computer program, wherein the program when executed implements the fake-licensed vehicle detection method of any one of claims 1-7.