Deception detection method and device, equipment and storage medium

Abstract

The invention belongs to the field of computers, and discloses a cheating detection method and device, equipment and a storage medium. The method comprises the following steps: acquiring vehicle state information of a vehicle, wherein the vehicle state information is information generated when the vehicle travels according to a received GNSS (Global Navigation Satellite System) signal; determining a vehicle jumping distance, a vehicle moving distance and a vehicle direction angle according to the vehicle state information; and judging whether GNSS spoofing exists or not based on the vehicle jumping distance, the vehicle moving distance and the vehicle direction angle. The vehicle jumping distance, the vehicle moving distance and the vehicle direction angle can be determined according to the vehicle state information, whether GN SS cheating exists or not is judged on the basis of the vehicle jumping distance, the vehicle moving distance and the vehicle direction angle, the service quality requirement of a user can be guaranteed, and the vehicle-road cooperation safety is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a deception detection method, device, equipment and storage medium. Background Art

[0002] Vehicle to Everything (V2X) is a new generation of information and communication technology that connects vehicles to everything, where V stands for vehicle and X stands for any object that interacts with the vehicle. Currently, X mainly includes vehicles, people, roadside infrastructure and networks. The information modes of V2X interaction include: vehicle to vehicle (V2V), vehicle to infrastructure (V2I), vehicle to pedestrian (V2P), and vehicle to network (V2N). C-V2X operates in the 5.905-5.925GHz frequency band and relies on the Global Navigation Satellite System (GNSS), as well as more advanced autonomous driving applications in the future, including vehicle platooning and sensor sharing. The roadside unit RSU broadcasts MAP map messages, RSI road warning prompt messages, RSM roadside safety messages, and SPAT signal light messages to the OBU on the PC interface, all of which rely on GNSS signal high-precision positioning to provide longitude and latitude information. Once GNSS is spoofed, it will broadcast wrong V2X messages, lose the vehicle-road collaboration function, and even seriously threaten driving safety. Therefore, how to accurately identify whether there is GNSS spoofing has become a technical problem that needs to be solved urgently.

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present invention is to provide a deception detection method, device, equipment and storage medium, aiming to solve the technical problem that the prior art cannot accurately identify GNSS deception.

[0005] To achieve the above object, the present invention provides a deception detection method, which comprises the following steps:

[0006] Acquiring vehicle status information of the vehicle, wherein the vehicle status information is information generated when the vehicle is traveling according to the received GNSS signal;

[0007] Determine the vehicle jump distance, vehicle movement distance and vehicle direction angle according to the vehicle state information;

[0008] Whether GNSS spoofing exists is determined based on the vehicle jump distance, the vehicle movement distance, and the vehicle direction angle.

[0009] Optionally, the step of judging whether GNSS spoofing exists based on the vehicle jump distance, the vehicle moving distance and the vehicle direction angle includes:

[0010] Determining whether the vehicle jump distance is greater than the vehicle moving distance;

[0011] If the vehicle jump distance is less than or equal to the vehicle moving distance, determining the average direction angle of the vehicle according to the vehicle state information;

[0012] Determining whether the vehicle average direction angle and the vehicle direction angle meet a preset deception condition;

[0013] If the vehicle average direction angle and the vehicle direction angle meet a preset deception condition, it is determined that GNSS deception occurs in the vehicle.

[0014] Optionally, after the step of determining whether the vehicle jump distance is greater than the vehicle moving distance, the method further includes:

[0015] If the vehicle jump distance is greater than the vehicle moving distance, it is determined that GNSS spoofing occurs in the vehicle.

[0016] Optionally, the step of determining the average direction angle of the vehicle according to the vehicle state information comprises:

[0017] Collecting the direction angle information in the vehicle status information according to a preset sampling period;

[0018] Determine the sampling quantity and the direction angle corresponding to each sampling data according to the direction angle information;

[0019] An average vehicle heading angle is determined based on the heading angle and the number of samples.

[0020] Optionally, after the step of determining whether the vehicle average direction angle and the vehicle direction angle meet a preset deception condition, the method further includes:

[0021] When the vehicle average direction angle and the vehicle direction angle do not satisfy a preset deception condition, collecting vehicle driving data within a next sampling period from the vehicle state information according to a preset sampling period;

[0022] Determine a target jump distance and a target moving distance according to the vehicle driving data;

[0023] Whether GNSS spoofing exists is determined according to the target jump distance and the target movement distance.

[0024] Optionally, the step of judging whether GNSS spoofing exists according to the target jump distance and the target moving distance includes:

[0025] Determining whether the target jump distance is greater than the target moving distance;

[0026] When the target jump distance is greater than the target moving distance, determining that GNSS spoofing exists;

[0027] When the target jump distance is less than or equal to the target moving distance, the method returns to the step of determining the vehicle jump distance, the vehicle moving distance and the vehicle direction angle according to the vehicle state information.

[0028] Optionally, the step of determining the vehicle jump distance, the vehicle moving distance and the vehicle direction angle according to the vehicle state information includes:

[0029] Collecting the vehicle position information and vehicle speed information in the vehicle status information based on a preset sampling period;

[0030] Determine the vehicle jump distance and the vehicle direction angle according to the vehicle position information;

[0031] The vehicle moving distance is determined according to the preset sampling period and the vehicle speed information.

[0032] Optionally, the step of determining the vehicle jump distance and the vehicle direction angle according to the vehicle position information includes:

[0033] Determining an initial latitude, an initial longitude, a target latitude, and a target longitude within a preset sampling period according to the vehicle position information;

[0034] Determining a vehicle jump distance according to the initial latitude, the initial longitude, the target latitude, the target longitude and the radius of the earth;

[0035] The vehicle direction angle is determined based on the vehicle position information using a preset direction angle algorithm.

[0036] Optionally, the step of determining the vehicle jump distance according to the initial latitude, initial longitude, target latitude, target longitude and earth radius includes:

[0037] The vehicle jump distance is calculated according to the initial latitude, initial longitude, target latitude, target longitude and earth radius using the following formula:

[0038]

[0039] Among them, S is used to represent the vehicle jump distance, θ1 is used to represent the initial latitude, φ1 is used to represent the initial longitude, θ n Used to represent the target latitude, φ nIt is used to represent the target longitude, R is used to represent the radius of the earth, and n is used to represent the number of data collected within the preset sampling period.

[0040] Optionally, the step of determining the vehicle moving distance according to the preset sampling period and the vehicle speed information includes:

[0041] Determine the vehicle speed corresponding to each sampling data within a preset sampling period according to the vehicle speed information;

[0042] The vehicle moving distance is calculated according to the vehicle speed and the preset sampling period by the following formula:

[0043]

[0044] Among them, L is used to represent the vehicle's moving distance, speed i It is used to represent the vehicle speed corresponding to the i-th sampling data, T is used to represent the preset sampling period, and n is used to represent the number of data collected within the preset sampling period.

[0045] Optionally, before the step of determining whether GNSS spoofing exists based on the vehicle jump distance, the vehicle moving distance and the vehicle direction angle, the step further includes:

[0046] When the vehicle direction angle is less than a preset direction angle threshold, obtaining a preset direction angle gain value;

[0047] The vehicle direction angle is corrected according to the preset direction angle gain value, and the vehicle direction angle is a corrected vehicle direction angle.

[0048] In addition, to achieve the above object, the present invention also provides a deception detection device, the device comprising:

[0049] An acquisition module, used to acquire vehicle status information of the vehicle, wherein the vehicle status information is information generated when the vehicle is traveling according to the received GNSS signal;

[0050] A determination module, used to determine a vehicle jump distance, a vehicle moving distance and a vehicle direction angle according to the vehicle state information;

[0051] The judgment module is used to judge whether GNSS spoofing exists based on the vehicle jump distance, the vehicle movement distance and the vehicle direction angle.

[0052] In addition, to achieve the above-mentioned purpose, the present invention also proposes a deception detection device, which includes: a memory, a processor, and a deception detection program stored on the memory and executable on the processor, wherein the deception detection program is configured to implement the steps of the deception detection method described above.

[0053] In addition, to achieve the above-mentioned purpose, the present invention further proposes a storage medium, on which a deception detection program is stored, and when the deception detection program is executed by a processor, the steps of the deception detection method described above are implemented.

[0054] The present invention obtains vehicle status information of a vehicle, wherein the vehicle status information is information generated when the vehicle is traveling according to the received GNSS signal; determines the vehicle jump distance, vehicle moving distance and vehicle direction angle according to the vehicle status information; and judges whether GNSS spoofing exists based on the vehicle jump distance, vehicle moving distance and vehicle direction angle. The present invention can determine the vehicle jump distance, vehicle moving distance and vehicle direction angle according to the vehicle status information, and then judge whether GNSS spoofing exists based on the vehicle jump distance, vehicle moving distance and vehicle direction angle, thereby ensuring the user's service quality requirements and improving the safety of vehicle-road collaboration. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a structural diagram of a deception detection device in a hardware operating environment involved in an embodiment of the present invention;

[0056] Figure 2 A schematic diagram of a flow chart of a first embodiment of a fraud detection method of the present invention;

[0057] Figure 3 A schematic diagram of a flow chart of a second embodiment of a fraud detection method of the present invention;

[0058] Figure 4 A schematic diagram of a flow chart of a third embodiment of a fraud detection method of the present invention;

[0059] Figure 5 This is a structural block diagram of the first embodiment of the deception detection device of the present invention.

[0060] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0061] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0062] Reference Figure 1 , Figure 1 A schematic diagram of the structure of a deception detection device in a hardware operating environment according to an embodiment of the present invention.

[0063] like Figure 1As shown, the deception detection device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyb oard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fid elity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM), or a stable non-volatile memory (Non-Volatile Me mory, NVM), such as a disk storage. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0064] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the fraud detection device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0065] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a fraud detection program.

[0066] exist Figure 1 In the deception detection device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the deception detection device of the present invention can be set in the deception detection device, and the deception detection device calls the deception detection program stored in the memory 1005 through the processor 1001, and executes the deception detection method provided by the embodiment of the present invention.

[0067] Based on the above-mentioned deception detection device, an embodiment of the present invention provides a deception detection method, referring to Figure 2 , Figure 2 FIG. 4 is a flow chart of the first embodiment of the fraud detection method of the present invention.

[0068] In this embodiment, the deception detection method includes the following steps:

[0069] Step S10: Acquire vehicle status information of the vehicle, where the vehicle status information is information generated when the vehicle is traveling according to the received GNSS signal.

[0070] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a mobile phone, a tablet computer, a personal computer, etc., or an electronic device or a vehicle V2X device that can realize the above functions. The following takes the vehicle V2X device as an example to illustrate this embodiment and the following embodiments.

[0071] It should be noted that the vehicle status information may be a basic safety message BSM message broadcast by the V2X device (on-board unit OBU and roadside unit RSU) on the PC5 interface at a specific frequency (e.g. 10 Hz). BSM is the basic safety information broadcast by each vehicle during the V2X communication process, which may include vehicle speed speed, heading angle heading, position pos, acceleration acce lSet, predicted path and historical path. Its information structure is as follows:

[0072]

[0073]

[0074] Step S20: Determine the vehicle jump distance, vehicle moving distance and vehicle direction angle according to the vehicle state information.

[0075] It should be noted that the determination of the vehicle jump distance according to the vehicle status information may be to collect the distance of the change in the position of the vehicle in longitude and latitude within the sampling period from the vehicle status information according to a preset sampling period, and the sampling period may be a preset sampling time period, which is used to extract the vehicle driving data from the vehicle status information according to the sampling period, and then determine whether there is GNSS spoofing based on the vehicle driving data. The determination of the vehicle moving distance according to the vehicle status information may be to determine the vehicle starting position and the end position of the vehicle within the sampling period according to the vehicle status information, and calculate the moving distance of the vehicle within the sampling period according to the vehicle starting position and the end position. The vehicle direction angle may be the direction angle of the vehicle offset within the sampling period. The difference between the direction angle in the first vehicle status data within the sampling period and the direction angle in the last vehicle status data within the sampling period may be calculated as the direction angle of the vehicle offset within the sampling period.

[0076] It should be understood that after the vehicle's V2X device receives the GNSS signal and works normally, it can store the BSM message sent by the V2X device and extract the pos, speed, heading and other information in the BSM message in the order of message timestamps, and use the sampling period T (T>=0.2s, which can be set to 1s. If the frequency of sending BSM messages is 10hz, there are 10 BSM messages in 1s) sliding window as a unit to obtain, and select N consecutive periods for statistical analysis. In order to improve the number and rationality of sampling, the pos position changes within the period T and is included in the statistics; if the pos in the BSM message of the V2X device remains unchanged during this period, it means that the device has no position change and no statistics are performed.

[0077] Furthermore, in order to accurately calculate whether there is GNSS spoofing, before step S30, it also includes: when the vehicle direction angle is less than a preset direction angle threshold, obtaining a preset direction angle gain value;

[0078] The vehicle direction angle is corrected according to the preset direction angle gain value, and the vehicle direction angle is a corrected vehicle direction angle.

[0079] It should be noted that the preset directional angle threshold may be 0, and the preset directional angle gain value may be 360.

[0080] In a specific implementation, if the vehicle direction angle is less than 0, the vehicle direction angle is added with a preset direction angle gain value 360 ​​as the corrected vehicle direction angle.

[0081] Step S30: determining whether GNSS spoofing exists based on the vehicle jump distance, the vehicle movement distance and the vehicle direction angle.

[0082] It should be noted that the determination of whether there is GNSS spoofing based on the vehicle jump distance, the vehicle moving distance and the vehicle direction angle may be when the vehicle jump distance is greater than the vehicle moving distance, indicating that the longitude and latitude of the vehicle equipment has an abnormal mutation within the sampling period and has exceeded the maximum moving distance between two points. At this time, it is determined that GNSS spoofing exists. When the vehicle jump distance is less than or equal to the vehicle moving distance, the direction angle corresponding to each BSM message collected within the sampling period is further determined based on the vehicle status information, and the direction angle average value within the sampling period is calculated based on the direction angle corresponding to each BSM message. When the difference between the vehicle direction angle and the direction angle average value is greater than or equal to a preset threshold, it is determined that there is GNSS spoofing. When the difference between the vehicle direction angle and the direction angle average value is less than the preset threshold, it is determined that there is no GNSS spoofing.

[0083] This embodiment obtains vehicle status information of the vehicle, the vehicle status information being information generated when the vehicle is traveling according to the received GNSS signal; determines the vehicle jump distance, vehicle moving distance and vehicle direction angle according to the vehicle status information; and determines whether GNSS spoofing exists based on the vehicle jump distance, vehicle moving distance and vehicle direction angle. This embodiment can determine the vehicle jump distance, vehicle moving distance and vehicle direction angle according to the vehicle status information, and then determine whether GNSS spoofing exists based on the vehicle jump distance, vehicle moving distance and vehicle direction angle, which can ensure the user's service quality requirements and improve the safety of vehicle-road collaboration.

[0084] refer to Figure 3 , Figure 3 FIG. 4 is a flow chart of a second embodiment of a fraud detection method according to the present invention.

[0085] Based on the above first embodiment, in this embodiment, step S30 includes:

[0086] Step S301: Determine whether the vehicle jump distance is greater than the vehicle moving distance.

[0087] It should be noted that if the vehicle jump distance is greater than the vehicle moving distance, the vehicle is judged to have GNSS spoofing. This means that the longitude and latitude of the vehicle have abnormal mutations within the sampling period and have exceeded the maximum moving distance between two points, and the vehicle V2X device is judged to have been GNSS-spoofed within the current sampling period T.

[0088] Step S302: If the vehicle jump distance is less than or equal to the vehicle moving distance, determine the average direction angle of the vehicle according to the vehicle state information.

[0089] It should be noted that the determination of the average vehicle direction angle according to the vehicle state information may be performed by using the following formula to determine the average vehicle direction angle:

[0090]

[0091] It should be noted that, n is used to represent the number of BSM messages collected during the sampling period, and heading i Used to represent the vehicle direction angle corresponding to the i-th BSM message.

[0092] Furthermore, in order to accurately determine whether there is GNSS spoofing, the step S302 may include: collecting the azimuth information in the vehicle status information according to a preset sampling period; determining the sampling quantity and the azimuth corresponding to each sampling data according to the azimuth information; and determining the average azimuth of the vehicle based on the azimuth and the sampling quantity.

[0093] It should be noted that the direction angle information may be vehicle direction angle information in multiple BSM messages collected within the sampling period.

[0094] Step S303: Determine whether the vehicle average direction angle and the vehicle direction angle meet a preset deception condition.

[0095] It should be noted that the determination of whether the vehicle average direction angle and the vehicle direction angle meet the preset deception condition may be to determine whether the absolute value of the difference between the vehicle average direction angle and the vehicle direction angle is less than a preset direction angle threshold. If so, it is determined that the preset deception condition is not met. If the absolute value of the difference between the vehicle average direction angle and the vehicle direction angle is greater than or equal to the preset direction angle threshold, it is determined that the preset deception condition is met. The preset direction angle threshold can be customized according to the actual scenario. In this embodiment, the optional preset direction angle threshold is 20.

[0096] Further, if the vehicle average direction angle and the vehicle direction angle do not meet the preset deception condition, it can be said that the vehicle V2X device is not deceived by GNSS within the current sampling period T, but it cannot be said that the vehicle V2X device is not deceived by GNSS between the current sampling period and the next sampling period. Therefore, after step S303, it also includes: when the vehicle average direction angle and the vehicle direction angle do not meet the preset deception condition, collecting vehicle driving data within the next sampling period from the vehicle state information according to the preset sampling period;

[0097] Determine a target jump distance and a target moving distance according to the vehicle driving data;

[0098] Whether GNSS spoofing exists is determined according to the target jump distance and the target movement distance.

[0099] It should be noted that the next sampling period may be a sampling period continuous with the previous sampling period obtained in units of a sliding window in the order of message timestamps. The vehicle driving data may include n consecutive BSM messages corresponding to the next sampling period (hereinafter referred to as the present sampling period). The step of determining the target jump distance according to the vehicle driving data may refer to the step of determining the vehicle jump distance according to the vehicle position information in other embodiments of the present invention, which will not be repeated in this embodiment. The step of determining the target moving distance according to the vehicle driving data may be to calculate the target moving distance according to the vehicle driving data by the following formula:

[0100]

[0101] Among them, L o Used to characterize the target moving distance within the oth sampling period (i.e. the current sampling period), speedi It is used to represent the vehicle speed corresponding to the i-th BSM message, n is used to represent the number of BSM messages collected within the sampling period, and T is used to represent the sampling period.

[0102] It should be noted that the determination of whether GNSS spoofing exists according to the target jump distance and the target moving distance may be that when the target jump distance is greater than the target moving distance, it is determined that GNSS spoofing exists between the current cycle and the previous cycle. When the target jump distance is less than or equal to the target moving distance, it is determined that GNSS spoofing does not exist.

[0103] Furthermore, the above steps determine whether GNSS spoofing occurs in the current sampling period and between the current sampling period and the next sampling period. In order to accurately identify whether GNSS spoofing occurs in each sampling period and between two sampling periods. The step of determining whether GNSS spoofing exists based on the target jump distance and the target moving distance includes: determining whether the target jump distance is greater than the target moving distance; when the target jump distance is greater than the target moving distance, determining that GNSS spoofing exists; when the target jump distance is less than or equal to the target moving distance, returning to the step of determining the vehicle jump distance, vehicle moving distance and vehicle direction angle based on the vehicle status information.

[0104] It should be noted that when the target jump distance is less than or equal to the target moving distance, it can only be said that no GNSS spoofing has occurred between the current sampling period and the next sampling. In order to accurately identify whether GNSS spoofing has occurred between each sampling period and between two sampling periods. It is also necessary to use the vehicle driving data corresponding to the current sampling period as the vehicle status information, and then return to the step of determining the vehicle jump distance, vehicle moving distance and vehicle direction angle according to the vehicle status information. Based on the vehicle jump distance, the vehicle moving distance and the vehicle direction angle, it is determined whether GNSS spoofing exists in the current period.

[0105] Step S304: If the vehicle average direction angle and the vehicle direction angle meet a preset deception condition, it is determined that the vehicle has GNSS deception.

[0106] It should be noted that if the vehicle average direction angle and the vehicle direction angle meet the preset deception conditions, it means that the vehicle's direction angle has an abnormal mutation within the sampling period, and it is judged that the vehicle's V2X device is deceived by GNSS within the current sampling period T.

[0107] This embodiment determines whether the vehicle jump distance is greater than the vehicle moving distance; if the vehicle jump distance is less than or equal to the vehicle moving distance, the average vehicle direction angle is determined according to the vehicle status information; it is determined whether the vehicle average direction angle and the vehicle direction angle meet the preset deception condition; if the vehicle average direction angle and the vehicle direction angle meet the preset deception condition, it is determined that the vehicle has GNSS deception. This embodiment can determine whether GNSS deception exists based on the vehicle jump distance, the vehicle moving distance and the vehicle direction angle. Ensure the service quality requirements of C-V2X users and improve the safety of vehicle-road collaboration.

[0108] refer to Figure 4 , Figure 4 FIG. 4 is a flow chart of a third embodiment of a fraud detection method according to the present invention.

[0109] Based on the above embodiments, in this embodiment, step S20 includes:

[0110] Step 201: Collect vehicle position information and vehicle speed information in the vehicle status information based on a preset sampling period.

[0111] It should be noted that the preset sampling period can be a sampling period that is customized based on the BSM message sending frequency of the vehicle V2X device, as long as the number of BSM messages collected within the sampling period is not less than 2. In this embodiment, if the V2X device receives the GNSS signal normally and sends the BSM message at a frequency of 10 Hz, the preset sampling period can be 1 second. The vehicle position information and the vehicle speed information can be the vehicle speed and vehicle latitude and longitude information in the BSM message.

[0112] Step S202: Determine the vehicle jump distance and vehicle direction angle according to the vehicle position information.

[0113] It should be noted that the determination of the vehicle jump distance according to the vehicle position information may be based on the vehicle longitude and latitude corresponding to the first BSM message in the vehicle position information and the longitude and latitude corresponding to the last BSM message in the sampling period. The vehicle direction angle may be the offset direction of the vehicle position in any two adjacent BSM messages in the vehicle position information.

[0114] Furthermore, in order to accurately calculate the vehicle jump distance and vehicle direction angle within a preset sampling period, the step S202 may include: determining the initial latitude, initial longitude, target latitude and target longitude within the preset sampling period according to the vehicle position information;

[0115] Determining a vehicle jump distance according to the initial latitude, the initial longitude, the target latitude, the target longitude and the radius of the earth;

[0116] The vehicle direction angle is determined based on the vehicle position information using a preset direction angle algorithm.

[0117] It should be noted that the initial latitude and initial longitude may be the vehicle latitude and vehicle longitude corresponding to the first BSM message in the vehicle position information within the sampling period. The target latitude and target longitude may be the vehicle latitude and vehicle longitude corresponding to the last BSM message in the vehicle position information within the sampling period. The determining of the vehicle jump distance according to the initial latitude, initial longitude, target latitude, target longitude and earth radius may be calculating the vehicle jump distance according to the initial latitude, initial longitude, target latitude, target longitude and earth radius by the following formula:

[0118]

[0119] Among them, S is used to represent the vehicle jump distance, θ1 is used to represent the initial latitude, φ1 is used to represent the initial longitude, θ n Used to represent the target latitude, φ n It is used to represent the target longitude, R is used to represent the radius of the earth, and n is used to represent the number of data collected within the preset sampling period.

[0120] It should be noted that the determination of the vehicle direction angle through a preset direction angle algorithm based on the vehicle position information can be based on the vehicle position information Select the longitude and latitude of the vehicle in any two adjacent BSM messages, for example, the longitude and latitude of the vehicle in the zth BSM message is φz, θz, the longitude and latitude of the vehicle in the cth BSM message is φc, θc, z and c correspond to two adjacent BSM messages.

[0121] Determining the vehicle direction angle by a preset direction angle algorithm based on the vehicle position information may be performed by calculating the vehicle direction angle by the following formula based on the longitude and latitude of the vehicle in any two adjacent BSM messages:

[0122]

[0123] Among them, H is used to characterize the vehicle direction angle, φz, θz are used to characterize the longitude and latitude of the vehicle in the zth BSM message within the sampling period, φc, θc are used to characterize the longitude and latitude of the vehicle in the cth BSM message within the sampling period, and the zth BSM message and the cth BSM message are any two adjacent BSM messages within the sampling period.

[0124] Step S203: determining the vehicle moving distance according to the preset sampling period and the vehicle speed information.

[0125] It should be noted that determining the vehicle moving distance based on the preset sampling period and the vehicle speed information may be to calculate the vehicle moving distance within the time corresponding to the preset sampling period by calculating the speed and time based on the vehicle speed in the vehicle speed information and the time corresponding to the preset sampling period.

[0126] Furthermore, in order to accurately calculate the vehicle moving distance, the step of determining the vehicle moving distance according to the preset sampling period and the vehicle speed information includes:

[0127] Determine the vehicle speed corresponding to each sampling data within a preset sampling period according to the vehicle speed information;

[0128] The vehicle moving distance is calculated according to the vehicle speed and the preset sampling period by the following formula:

[0129]

[0130] Among them, L is used to represent the vehicle's moving distance, speed i It is used to represent the vehicle speed corresponding to the i-th sampling data, T is used to represent the preset sampling period, and n is used to represent the number of data collected within the preset sampling period.

[0131] This embodiment collects the vehicle position information and vehicle speed information in the vehicle status information based on a preset sampling period; determines the vehicle jump distance and vehicle direction angle according to the vehicle position information; and determines the vehicle moving distance according to the preset sampling period and the vehicle speed information. This embodiment can accurately calculate the vehicle jump distance, vehicle moving distance and vehicle direction angle, and then determine whether there is GNSS spoofing based on the vehicle jump distance, vehicle moving distance and vehicle direction angle. Ensure the service quality requirements of C-V2X users and improve the safety of vehicle-road collaboration.

[0132] Reference Figure 5 , Figure 5 This is a structural block diagram of the first embodiment of the deception detection device of the present invention.

[0133] like Figure 5 As shown, the deception detection device proposed in the embodiment of the present invention includes:

[0134] An acquisition module 10 is used to acquire vehicle status information of the vehicle, wherein the vehicle status information is information generated when the vehicle is traveling according to the received GNSS signal;

[0135] A determination module 20, configured to determine a vehicle jump distance, a vehicle moving distance, and a vehicle direction angle according to the vehicle state information;

[0136] The judgment module 30 is used to judge whether GNSS spoofing exists based on the vehicle jump distance, the vehicle moving distance and the vehicle direction angle.

[0137] This embodiment obtains vehicle status information of the vehicle, the vehicle status information being information generated when the vehicle is traveling according to the received GNSS signal; determines the vehicle jump distance, vehicle moving distance and vehicle direction angle according to the vehicle status information; and determines whether GNSS spoofing exists based on the vehicle jump distance, vehicle moving distance and vehicle direction angle. This embodiment can determine the vehicle jump distance, vehicle moving distance and vehicle direction angle according to the vehicle status information, and then determine whether GNSS spoofing exists based on the vehicle jump distance, vehicle moving distance and vehicle direction angle, which can ensure the user's service quality requirements and improve the safety of vehicle-road collaboration.

[0138] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of them according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0139] In addition, for technical details not fully described in this embodiment, reference can be made to the deception detection method provided in any embodiment of the present invention, and will not be repeated here.

[0140] Based on the above-mentioned first embodiment of the deception detection device of the present invention, a second embodiment of the deception detection device of the present invention is proposed.

[0141] In this embodiment, the judging module 30 is further used to judge whether the vehicle jump distance is greater than the vehicle moving distance;

[0142] If the vehicle jump distance is less than or equal to the vehicle moving distance, determining the average direction angle of the vehicle according to the vehicle state information;

[0143] Determining whether the vehicle average direction angle and the vehicle direction angle meet a preset deception condition;

[0144] If the vehicle average direction angle and the vehicle direction angle meet a preset deception condition, it is determined that GNSS deception occurs in the vehicle.

[0145] Furthermore, the judgment module 30 is further configured to determine that GNSS spoofing occurs in the vehicle if the vehicle jump distance is greater than the vehicle moving distance.

[0146] Furthermore, the judgment module 30 is further used to collect the direction angle information in the vehicle state information according to a preset sampling period;

[0147] Determine the sampling quantity and the direction angle corresponding to each sampling data according to the direction angle information;

[0148] An average vehicle heading angle is determined based on the heading angle and the number of samples.

[0149] Furthermore, the judgment module 30 is further configured to collect vehicle driving data within a next sampling period from the vehicle state information according to a preset sampling period when the vehicle average direction angle and the vehicle direction angle do not satisfy a preset deception condition;

[0150] Determine a target jump distance and a target moving distance according to the vehicle driving data;

[0151] Whether GNSS spoofing exists is determined according to the target jump distance and the target movement distance.

[0152] Furthermore, the judging module 30 is further used to judge whether the target jump distance is greater than the target moving distance;

[0153] When the target jump distance is greater than the target moving distance, determining that GNSS spoofing exists;

[0154] When the target jump distance is less than or equal to the target moving distance, the step of determining the vehicle jump distance, the vehicle moving distance and the vehicle direction angle according to the vehicle state information is returned.

[0155] Furthermore, the determination module 20 is further configured to collect the vehicle position information and vehicle speed information in the vehicle status information based on a preset sampling period;

[0156] Determine the vehicle jump distance and the vehicle direction angle according to the vehicle position information;

[0157] The vehicle moving distance is determined according to the preset sampling period and the vehicle speed information.

[0158] Furthermore, the determination module 20 is further used to determine the initial latitude, initial longitude, target latitude and target longitude within a preset sampling period according to the vehicle position information;

[0159] Determining a vehicle jump distance according to the initial latitude, the initial longitude, the target latitude, the target longitude and the radius of the earth;

[0160] The vehicle direction angle is determined based on the vehicle position information using a preset direction angle algorithm.

[0161] Furthermore, the determination module 20 is further configured to calculate the vehicle jump distance according to the initial latitude, initial longitude, target latitude, target longitude and earth radius by the following formula:

[0162]

[0163] Among them, S is used to represent the vehicle jump distance, θ1 is used to represent the initial latitude, φ1 is used to represent the initial longitude, θ n Used to represent the target latitude, φ n It is used to represent the target longitude, R is used to represent the radius of the earth, and n is used to represent the number of data collected within the preset sampling period.

[0164] Furthermore, the determination module 20 is further used to determine the vehicle speed corresponding to each sampling data within a preset sampling period according to the vehicle speed information;

[0165] The vehicle moving distance is calculated according to the vehicle speed and the preset sampling period by the following formula:

[0166]

[0167] Among them, L is used to represent the vehicle's moving distance, speed i It is used to represent the vehicle speed corresponding to the i-th sampling data, T is used to represent the preset sampling period, and n is used to represent the number of data collected within the preset sampling period.

[0168] Furthermore, the determination module 20 is further configured to obtain a preset direction angle gain value when the vehicle direction angle is less than a preset direction angle threshold;

[0169] The vehicle direction angle is corrected according to the preset direction angle gain value, and the vehicle direction angle is a corrected vehicle direction angle.

[0170] Other embodiments or specific implementations of the deception detection device of the present invention can refer to the above-mentioned method embodiments and will not be described in detail here.

[0171] In addition, an embodiment of the present invention further provides a storage medium, on which a deception detection program is stored. When the deception detection program is executed by a processor, the steps of the deception detection method described above are implemented.

[0172] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0173] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0174] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0175] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

[0176] The present invention discloses A1, a deception detection method, the deception detection method comprising the following steps:

[0177] Acquiring vehicle status information of the vehicle, wherein the vehicle status information is information generated when the vehicle is traveling according to the received GNSS signal;

[0178] Determine the vehicle jump distance, vehicle movement distance and vehicle direction angle according to the vehicle state information;

[0179] Whether GNSS spoofing exists is determined based on the vehicle jump distance, the vehicle movement distance, and the vehicle direction angle.

[0180] A2. The deception detection method as described in A1, wherein the step of determining whether GNSS deception exists based on the vehicle jump distance, the vehicle movement distance and the vehicle direction angle comprises:

[0181] Determining whether the vehicle jump distance is greater than the vehicle moving distance;

[0182] If the vehicle jump distance is less than or equal to the vehicle moving distance, determining the average direction angle of the vehicle according to the vehicle state information;

[0183] Determining whether the vehicle average direction angle and the vehicle direction angle meet a preset deception condition;

[0184] If the vehicle average direction angle and the vehicle direction angle meet a preset deception condition, it is determined that GNSS deception occurs in the vehicle.

[0185] A3. The deception detection method as described in A2, after the step of determining whether the vehicle jump distance is greater than the vehicle moving distance, further comprising:

[0186] If the vehicle jump distance is greater than the vehicle moving distance, it is determined that the vehicle has GNSS fraud.

[0187] A4. In the deception detection method as described in A2, the step of determining the average direction angle of the vehicle according to the vehicle state information comprises:

[0188] Collecting the direction angle information in the vehicle status information according to a preset sampling period;

[0189] Determine the sampling quantity and the direction angle corresponding to each sampling data according to the direction angle information;

[0190] An average vehicle heading angle is determined based on the heading angle and the number of samples.

[0191] A5. The deception detection method as described in A2, after the step of determining whether the vehicle average direction angle and the vehicle direction angle meet the preset deception condition, further comprising:

[0192] When the vehicle average direction angle and the vehicle direction angle do not satisfy a preset deception condition, collecting vehicle driving data within a next sampling period from the vehicle state information according to a preset sampling period;

[0193] Determine a target jump distance and a target moving distance according to the vehicle driving data;

[0194] Whether GNSS spoofing exists is determined according to the target jump distance and the target movement distance.

[0195] A6. The spoofing detection method as described in A5, wherein the step of determining whether GNSS spoofing exists according to the target jump distance and the target movement distance comprises:

[0196] Determining whether the target jump distance is greater than the target moving distance;

[0197] When the target jump distance is greater than the target moving distance, determining that GNSS spoofing exists;

[0198] When the target jump distance is less than or equal to the target moving distance, the method returns to the step of determining the vehicle jump distance, the vehicle moving distance and the vehicle direction angle according to the vehicle state information.

[0199] A7. In the deception detection method as described in any one of A1 to A6, the step of determining the vehicle jump distance, the vehicle movement distance and the vehicle direction angle according to the vehicle state information comprises:

[0200] Collecting the vehicle position information and vehicle speed information in the vehicle status information based on a preset sampling period;

[0201] Determine the vehicle jump distance and the vehicle direction angle according to the vehicle position information;

[0202] The vehicle moving distance is determined according to the preset sampling period and the vehicle speed information.

[0203] A8. The deception detection method as described in A7, wherein the step of determining the vehicle jump distance and the vehicle direction angle according to the vehicle position information comprises:

[0204] Determining an initial latitude, an initial longitude, a target latitude, and a target longitude within a preset sampling period according to the vehicle position information;

[0205] Determining a vehicle jump distance according to the initial latitude, the initial longitude, the target latitude, the target longitude and the radius of the earth;

[0206] The vehicle direction angle is determined based on the vehicle position information using a preset direction angle algorithm.

[0207] A9. The deception detection method as described in A8, wherein the step of determining the vehicle jump distance according to the initial latitude, initial longitude, target latitude, target longitude and earth radius comprises:

[0208] The vehicle jump distance is calculated according to the initial latitude, initial longitude, target latitude, target longitude and earth radius using the following formula:

[0209]

[0210] Among them, S is used to represent the vehicle jump distance, θ1 is used to represent the initial latitude, φ1 is used to represent the initial longitude, θ n Used to represent the target latitude, φ n It is used to represent the target longitude, R is used to represent the radius of the earth, and n is used to represent the number of data collected within the preset sampling period.

[0211] A10. In the deception detection method described in A7, the step of determining the vehicle moving distance according to the preset sampling period and the vehicle speed information comprises:

[0212] Determine the vehicle speed corresponding to each sampling data within a preset sampling period according to the vehicle speed information;

[0213] The vehicle moving distance is calculated according to the vehicle speed and the preset sampling period by the following formula:

[0214]

[0215] Among them, L is used to represent the vehicle's moving distance, speed i It is used to represent the vehicle speed corresponding to the i-th sampling data, T is used to represent the preset sampling period, and n is used to represent the number of data collected within the preset sampling period.

[0216] A11. The deception detection method as described in any one of A1 to A6, before the step of determining whether there is GNSS deception based on the vehicle jump distance, the vehicle movement distance and the vehicle direction angle, further comprising:

[0217] When the vehicle direction angle is less than a preset direction angle threshold, obtaining a preset direction angle gain value;

[0218] The vehicle direction angle is corrected according to the preset direction angle gain value, and the vehicle direction angle is a corrected vehicle direction angle.

[0219] The present invention discloses B12, a deception detection device, the deception detection device comprising:

[0220] An acquisition module, used to acquire vehicle status information of the vehicle, wherein the vehicle status information is information generated when the vehicle is traveling according to the received GNSS signal;

[0221] A determination module, used to determine a vehicle jump distance, a vehicle moving distance and a vehicle direction angle according to the vehicle state information;

[0222] The judgment module is used to judge whether GNSS spoofing exists based on the vehicle jump distance, the vehicle movement distance and the vehicle direction angle.

[0223] B13. The deception detection device as described in B12, wherein the judgment module is further used to judge whether the vehicle jump distance is greater than the vehicle moving distance;

[0224] If the vehicle jump distance is less than or equal to the vehicle moving distance, determining the average direction angle of the vehicle according to the vehicle state information;

[0225] Determining whether the vehicle average direction angle and the vehicle direction angle meet a preset deception condition;

[0226] If the vehicle average direction angle and the vehicle direction angle meet a preset deception condition, it is determined that GNSS deception occurs in the vehicle.

[0227] B14. The deception detection device as described in B13, wherein the judgment module is further used to collect the direction angle information in the vehicle status information according to a preset sampling period;

[0228] Determine the sampling quantity and the direction angle corresponding to each sampling data according to the direction angle information;

[0229] An average vehicle heading angle is determined based on the heading angle and the number of samples.

[0230] B15. The deception detection device as described in B13, wherein the judgment module is further used to collect vehicle driving data within a next sampling period from the vehicle state information according to a preset sampling period when the vehicle average direction angle and the vehicle direction angle do not meet the preset deception condition;

[0231] Determine a target jump distance and a target moving distance according to the vehicle driving data;

[0232] Whether GNSS spoofing exists is determined according to the target jump distance and the target movement distance.

[0233] B16. The deception detection device as described in B15, wherein the judgment module is further used to judge whether the target jump distance is greater than the target movement distance;

[0234] When the target jump distance is greater than the target moving distance, determining that GNSS spoofing exists;

[0235] When the target jump distance is less than or equal to the target moving distance, the step of determining the vehicle jump distance, the vehicle moving distance and the vehicle direction angle according to the vehicle state information is returned.

[0236] B17. The deception detection device as described in any one of B12-B16, wherein the determination module is further used to collect vehicle position information and vehicle speed information in the vehicle status information based on a preset sampling period;

[0237] Determine the vehicle jump distance and the vehicle direction angle according to the vehicle position information;

[0238] The vehicle moving distance is determined according to the preset sampling period and the vehicle speed information.

[0239] B18. The deception detection device as described in B17, wherein the determination module is further used to determine the initial latitude, initial longitude, target latitude and target longitude within a preset sampling period according to the vehicle position information;

[0240] Determining a vehicle jump distance according to the initial latitude, the initial longitude, the target latitude, the target longitude and the radius of the earth;

[0241] The vehicle direction angle is determined based on the vehicle position information using a preset direction angle algorithm.

[0242] The present invention discloses C19, a deception detection device, which includes: a memory, a processor, and a deception detection program stored in the memory and executable on the processor, wherein the deception detection program is configured to implement the steps of a deception detection method as described in any one of A1 to A11.

[0243] The present invention discloses D20, a storage medium, on which a deception detection program is stored, and when the deception detection program is executed by a processor, the steps of the deception detection method as described in any one of A1 to A11 are implemented.

Claims

1. A method for detecting fraud, characterized in that: The deception detection method comprises the following steps: Acquiring vehicle status information of the vehicle, wherein the vehicle status information is information generated when the vehicle is traveling according to the received GNSS signal; Determine the vehicle jump distance, vehicle movement distance and vehicle direction angle according to the vehicle state information; Whether GNSS spoofing exists is determined based on the vehicle jump distance, the vehicle movement distance, and the vehicle direction angle.

2. The fraud detection method according to claim 1, characterized in that: The step of judging whether there is GNSS spoofing based on the vehicle jump distance, the vehicle moving distance and the vehicle direction angle comprises: Determining whether the vehicle jump distance is greater than the vehicle moving distance; If the vehicle jump distance is less than or equal to the vehicle moving distance, determining the average direction angle of the vehicle according to the vehicle state information; Determining whether the vehicle average direction angle and the vehicle direction angle meet a preset deception condition; If the vehicle average direction angle and the vehicle direction angle meet a preset deception condition, it is determined that GNSS deception occurs in the vehicle.

3. The fraud detection method according to claim 2, characterized in that: After the step of determining whether the vehicle jump distance is greater than the vehicle moving distance, the method further includes: If the vehicle jump distance is greater than the vehicle moving distance, it is determined that the vehicle has GNSS fraud.

4. The fraud detection method according to claim 2, characterized in that: The step of determining the average direction angle of the vehicle according to the vehicle state information comprises: Collecting the direction angle information in the vehicle status information according to a preset sampling period; Determine the sampling quantity and the direction angle corresponding to each sampling data according to the direction angle information; An average vehicle heading angle is determined based on the heading angle and the number of samples.

5. The fraud detection method according to claim 2, characterized in that: After the step of determining whether the vehicle average direction angle and the vehicle direction angle meet the preset deception condition, the method further includes: When the vehicle average direction angle and the vehicle direction angle do not satisfy a preset deception condition, collecting vehicle driving data within a next sampling period from the vehicle state information according to a preset sampling period; Determine a target jump distance and a target moving distance according to the vehicle driving data; Whether GNSS spoofing exists is determined according to the target jump distance and the target movement distance.

6. The fraud detection method according to claim 5, characterized in that: The step of judging whether there is GNSS spoofing according to the target jump distance and the target moving distance comprises: Determining whether the target jump distance is greater than the target moving distance; When the target jump distance is greater than the target moving distance, determining that GNSS spoofing exists; When the target jump distance is less than or equal to the target moving distance, the method returns to the step of determining the vehicle jump distance, the vehicle moving distance and the vehicle direction angle according to the vehicle state information.

7. The fraud detection method according to any one of claims 1 to 6, characterized in that: The step of determining the vehicle jump distance, the vehicle moving distance and the vehicle direction angle according to the vehicle state information comprises: Collecting the vehicle position information and vehicle speed information in the vehicle status information based on a preset sampling period; Determine the vehicle jump distance and vehicle direction angle according to the vehicle position information; The vehicle moving distance is determined according to the preset sampling period and the vehicle speed information.

8. A fraud detection device, characterized in that: The deception detection device comprises: An acquisition module, used to acquire vehicle status information of the vehicle, wherein the vehicle status information is information generated when the vehicle is traveling according to the received GNSS signal; A determination module, used to determine a vehicle jump distance, a vehicle moving distance and a vehicle direction angle according to the vehicle state information; The judgment module is used to judge whether GNSS spoofing exists based on the vehicle jump distance, the vehicle movement distance and the vehicle direction angle.

9. A fraud detection device, characterized in that: The device comprises: a memory, a processor, and a fraud detection program stored in the memory and executable on the processor, wherein the fraud detection program is configured to implement the steps of the fraud detection method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a deception detection program, and when the deception detection program is executed by the processor, the steps of the deception detection method according to any one of claims 1 to 7 are implemented.

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