Internet of vehicles self-vehicle data deduplication method and device, computer device and storage medium

Abstract

The application relates to a vehicle networking self-vehicle data deduplication method and device, computer equipment and a storage medium. Through distance matching and identification matching, the state data of a target vehicle in a basic state data set collected by a roadside sensing device is effectively removed, so that the authenticity and reliability of the basic state data set are ensured, the interference of collision warning calculation is reduced, the accuracy of collision warning prompts is improved, and due to comprehensive analysis of the distance matching result and the identification matching result, the optimal target matching result is obtained. When one of the identification matching algorithm and the distance matching fails, the other matching result can be intelligently switched to, the situation that a matching result cannot be obtained when a single matching method fails is avoided, and the universality, accuracy, stability and robustness of the matching algorithm are enhanced.

Description

Technical Field

[0001] This application relates to the field of vehicle networking technology, and in particular to a method, apparatus, computer equipment, and storage medium for deduplicating vehicle data in vehicle networking. Background Technology

[0002] The ability to effectively separate the real-time status information of the vehicle from the real-time status information of surrounding traffic participants is a crucial factor in achieving accurate collision warnings. However, roadside sensing devices (detection modules, edge computing units, V2X communication terminals, etc.) detect all traffic participants within their sensing range, including the connected vehicle itself. Therefore, before conducting collision warning analysis, the vehicle's information among the real-time traffic participants provided by the roadside sensing devices should be accurately removed.

[0003] In related technologies, the main approach is to calculate the distance between the center point of a traffic participant sensed by roadside sensing devices and the center point of a connected vehicle. By setting a distance threshold, if the calculated distance is less than or equal to the threshold, the traffic participant is considered a vehicle in the sensing data and is deleted. Since this method is primarily dependent on the threshold setting, setting the threshold too small will prevent the successful removal of vehicles from the sensing data, while setting the threshold too large will lead to the incorrect removal of vehicles next to the vehicle. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, device, computer equipment, and storage medium for deduplicating vehicle data in the Internet of Vehicles (IoV) that can accurately remove duplicate vehicle data from the IoV, addressing the aforementioned technical problems.

[0005] Firstly, this application provides a method for deduplicating vehicle data in a vehicle-to-everything (V2X) network, including:

[0006] Distance matching is performed on at least one frame of real-time state dataset acquired from the target vehicle and a subset of basic state data corresponding to each candidate vehicle in the basic state dataset collected by the roadside sensing device to obtain the distance matching result.

[0007] Each candidate vehicle is matched with the target vehicle to obtain the identification matching results;

[0008] Based on the distance matching result and the identifier matching result, determine the target matching result, and remove the basic state data subset in the basic state dataset corresponding to the target matching result based on the target matching result.

[0009] In one embodiment, the step of performing distance matching between at least one frame of real-time state dataset acquired by the target vehicle and a subset of basic state data corresponding to each candidate vehicle in the basic state dataset collected by the roadside sensing device to obtain a distance matching result includes:

[0010] Perform time matching on at least one frame of real-time state dataset and basic state dataset to obtain time matching results;

[0011] Distance matching is performed on the basic state data subset corresponding to each candidate vehicle in the time matching result and the real-time state dataset in the time matching result to obtain the distance matching result.

[0012] In one embodiment, the step of performing time matching on at least one frame of real-time state dataset and basic state dataset to obtain a time matching result includes:

[0013] For each time interval corresponding to the timestamp of the real-time state dataset, the time interval corresponding to the timestamp of each two adjacent frames of real-time state dataset is determined.

[0014] If the timestamp corresponding to the basic state dataset is at the midpoint of the time interval it is in, the basic state dataset and the real-time state dataset that is located after the timestamp corresponding to the basic state dataset in time are used as the time matching result.

[0015] If the timestamp corresponding to the basic state dataset is not at the midpoint of the time interval it is in, the basic state dataset and the real-time state dataset whose timestamp is closest to the timestamp corresponding to the basic state dataset will be used as the time matching result.

[0016] In one embodiment, the step of performing distance matching on the subset of basic state data corresponding to each candidate vehicle in the time matching result and the real-time state dataset in the time matching result to obtain the distance matching result includes:

[0017] For the real-time state dataset in the time matching results and the basic state data subset corresponding to each candidate vehicle, the first distance threshold is obtained based on the vehicle speed data of the target vehicle in the real-time state dataset.

[0018] Based on the vehicle coordinates in the subset of basic state data corresponding to the candidate vehicle and the vehicle coordinates of the target vehicle in the real-time state data, the straight-line distance, lateral distance, and longitudinal relative distance between the candidate vehicle and the target vehicle are obtained.

[0019] If the straight-line distance is less than or equal to the first distance threshold, the planar region where the candidate vehicle is located is determined; the planar region includes the actual distance region, the weighted distance region, and the out-of-range region;

[0020] When the candidate vehicle is located in the actual distance area or the weighted distance area, the first comprehensive distance between the candidate vehicle and the target vehicle is obtained based on the straight distance, the lateral distance, and the longitudinal relative distance.

[0021] The first comprehensive distance minimum value and the vehicle identifier of the candidate vehicle corresponding to the first comprehensive distance minimum value are used as the distance matching result.

[0022] In one embodiment, the step of determining the planar region where the candidate vehicle is located includes:

[0023] If the longitudinal relative distance is less than the second distance threshold, the lateral distance is less than the third distance threshold, and the lateral distance is greater than the fourth distance threshold, the candidate vehicle is determined to be located in the weighted distance region.

[0024] If the longitudinal relative distance is less than the second distance threshold and the lateral distance is less than or equal to the fourth distance threshold, the candidate vehicle is determined to be located in the actual distance area.

[0025] If the longitudinal relative distance is greater than or equal to the second distance threshold, or the lateral distance is greater than or equal to the third distance threshold, the candidate vehicle is determined to be located in the out-of-range area.

[0026] In one embodiment, the step of obtaining a first comprehensive distance between the candidate vehicle and the target vehicle based on straight-line distance, lateral distance, and longitudinal relative distance includes:

[0027] When the candidate vehicle is located in the actual distance area, the straight-line distance is used as the first comprehensive distance between the candidate vehicle and the target vehicle;

[0028] When the candidate vehicle is located in the weighted distance region, the first comprehensive distance between the candidate vehicle and the target vehicle is calculated based on the lateral distance and the longitudinal relative distance.

[0029] In one embodiment, the method further includes:

[0030] If the straight-line distance is greater than the first distance threshold, or if the candidate vehicle is located outside the range, the distance matching is determined to be unsuccessful.

[0031] In one embodiment, the step of matching the identifiers of each candidate vehicle with the target vehicle to obtain the identifier matching result includes:

[0032] Obtain the vehicle identifiers of candidate vehicles in the target matching results within the previous historical time period.

[0033] If the vehicle identifier of a candidate vehicle in the current historical time period is consistent with the vehicle identifier of a candidate vehicle in the target matching result of the previous historical time period, the second comprehensive distance between the consistent candidate vehicle and the target vehicle in the current historical time period and the vehicle identifier of the consistent candidate vehicle are taken as the identifier matching result.

[0034] In one embodiment, the method further includes:

[0035] If there is no previous historical time period, or no target matching result in the previous historical time period, or no vehicle identifier of the candidate vehicle in the current historical time period is consistent with the vehicle identifier of the candidate vehicle in the target matching result in the previous historical time period, the identifier matching is determined to be unsuccessful.

[0036] In one embodiment, the step of determining the target matching result based on the distance matching result and the identifier matching result includes:

[0037] If the distance matching result and the identifier matching result are the same, the distance matching result or the identifier matching result will be used as the target matching result within the current historical time period, and the current cumulative matching count will be incremented by 1;

[0038] If the distance matching result and the identifier matching result are inconsistent, and the first comprehensive distance between the candidate vehicle and the target vehicle is less than or equal to the second comprehensive distance between the candidate vehicle and the target vehicle that are consistent in the current historical time period, or the current cumulative number of matching is less than or equal to the target number, the distance matching result will be used as the target matching result in the current historical time period, and the current cumulative number of matching will be cleared to zero.

[0039] If the first comprehensive distance is greater than the second comprehensive distance and the current cumulative number of matches is greater than the target number, the matching result will be marked as the target matching result within the current historical time period;

[0040] If no distance matching result is obtained but an identifier matching result is obtained, and the current cumulative number of matches is greater than the target number, the identifier matching result will be used as the target matching result for the current historical time period.

[0041] If a distance matching result is obtained but no identifier matching result is obtained, the third comprehensive distance between the candidate vehicle and the corresponding target vehicle in the target matching result in the previous historical time period is obtained. If the first comprehensive distance is less than or equal to the third comprehensive distance, or the current cumulative matching count is less than or equal to the target count, the distance matching result is taken as the target matching result in the current historical time period, and the current cumulative matching count is cleared to zero.

[0042] Secondly, this application also provides a vehicle-to-everything (V2X) data deduplication device, comprising:

[0043] The distance matching module is used to perform distance matching between at least one frame of real-time state dataset acquired by the target vehicle and the subset of basic state data corresponding to each candidate vehicle in the basic state dataset collected by the roadside sensing device, and to obtain the distance matching result.

[0044] The identifier matching module is used to match the identifiers of each candidate vehicle with the target vehicle and obtain the identifier matching results.

[0045] The hybrid matching module is used to determine the target matching result based on the distance matching result and the identifier matching result, and to remove the basic state data subset corresponding to the target matching result from the basic state dataset based on the target matching result.

[0046] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method steps of any one of the first aspects.

[0047] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method steps of any one of the first aspects.

[0048] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the method steps of any one of the first aspects.

[0049] The aforementioned vehicle-to-everything (V2X) deduplication methods, devices, computer equipment, storage media, and computer program products effectively remove target vehicle state data from the basic state dataset collected by roadside sensing devices by combining distance matching and identifier matching. This ensures the authenticity and reliability of the basic state dataset, reduces interference in collision warning calculations, and improves the accuracy of collision warning prompts. Furthermore, due to the comprehensive analysis of distance matching and identifier matching results, the optimal target matching result is obtained. When either the identifier matching algorithm or the distance matching algorithm fails, it can intelligently switch to the other matching result, avoiding the situation where no matching result can be obtained when a single matching method fails. This enhances the universality, accuracy, stability, and robustness of the matching algorithm. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is an application environment diagram of a vehicle-to-everything (V2X) data deduplication method in one embodiment;

[0052] Figure 2 This is a flowchart illustrating a method for deduplicating vehicle data in a vehicle-to-everything (V2X) system, as shown in one embodiment.

[0053] Figure 3This is a schematic diagram illustrating problems such as vehicle ghosting or incorrect collision warnings in one embodiment;

[0054] Figure 4 This is a flowchart illustrating the time matching steps in one embodiment;

[0055] Figure 5 This is a flowchart illustrating the distance matching step in one embodiment;

[0056] Figure 6 This is a flowchart illustrating the distance matching step in one embodiment;

[0057] Figure 7 This is a flowchart illustrating the identification matching step in one embodiment;

[0058] Figure 8 This is a flowchart illustrating the mixed matching steps in one embodiment;

[0059] Figure 9 This is a flowchart illustrating a method for deduplicating vehicle data in a vehicle-to-everything (V2X) system, as shown in one embodiment.

[0060] Figure 10 This is a structural block diagram of a vehicle-to-everything (V2X) data deduplication device in one embodiment.

[0061] Figure 11 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0063] The vehicle data deduplication method for the Internet of Vehicles provided in this application can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with roadside sensing device 104 via V2X communication. Terminal 102 and roadside sensing device 104 can be used individually or collaboratively to execute the vehicle-to-everything (V2X) data deduplication method of this application. Taking the individual execution by terminal 102 and roadside sensing device 104 as an example, terminal 102 or roadside sensing device 104 performs distance matching between at least one frame of real-time state dataset acquired by the target vehicle and the basic state data subsets corresponding to each candidate vehicle in the basic state dataset collected by the roadside sensing device, obtaining a distance matching result; performs identifier matching between each candidate vehicle and the target vehicle, obtaining an identifier matching result; determines a target matching result based on the distance matching result and the identifier matching result; and removes the basic state data subset corresponding to the target matching result from the basic state dataset based on the target matching result.

[0064] The terminal 102 may be, but is not limited to, an On-Board Unit (OBU). The OBU includes an antenna module, a GNSS module, a CAN (Controller Area Network) module, and a central processing module, used for broadcasting V2X messages, receiving V2X messages, and performing calculations. Specifically, the antenna module enhances communication signals and quality; the GNSS module acquires real-time vehicle status information and provides it to the central processing module, including but not limited to timestamps, speed, heading angle, latitude and longitude; the CAN module acquires the vehicle's real-time driving status and assists the OBU in providing driving advice to the vehicle; and the central processing module parses and calculates the received V2X messages (RSM, SSM, BSM, SPAT, MAP, etc.), generates collision warning prompts, and provides decision information.

[0065] The roadside sensing device 104 includes a roadside detection module (including but not limited to lidar, millimeter-wave radar, cameras, etc.), an edge computing unit, and a roadside V2X communication terminal (Road Side Unit, RSU). The roadside detection module communicates with the edge computing unit via a network port to provide the edge computing unit with raw data from the radar and cameras for processing. The edge computing unit communicates with the RSU via the network port to provide the RSU with processed data from the roadside detection module, i.e., basic status information of the sensed traffic participants, including but not limited to vehicle ID, timestamp, speed, heading angle, latitude and longitude, geometric dimensions, and vehicle type. The RSU can frame the sensing data provided by the edge computing unit into RSM / SSM frames and then send them to the OBU via PC5.

[0066] In one exemplary embodiment, such as Figure 2 As shown, a method for deduplicating vehicle data in the Internet of Vehicles (IoV) is provided, and this method is applied to... Figure 1 Taking terminal 102 as an example, the explanation includes the following steps 202 to 206. Wherein:

[0067] S202: Perform distance matching between at least one frame of real-time state dataset acquired by the target vehicle and the subset of basic state data corresponding to each candidate vehicle in the basic state dataset collected by the roadside sensing device, and obtain the distance matching result.

[0068] The real-time state dataset consists of the real-time state information of the target vehicle provided to the on-board unit by the Global Navigation Satellite System (GNSS) of the target vehicle. The basic state dataset consists of the roadside safety message (RSM) or sensor sharing message (SSM) provided to the OBU by the roadside perception device through the RSU. The basic state dataset includes a subset of basic state data corresponding to all candidate vehicles in the current traffic scenario.

[0069] When using the real-time status information of the target vehicle to perform collision detection analysis with the RSM / SSM, the RSM / SSM contains basic status information of the target vehicle, the roadside sensing equipment has detection errors, and there is a delay from sensing the vehicle to the OBU receiving the sensing data. This may cause the target vehicle in the RSM / SSM to fail to match the target vehicle information in the GNSS, resulting in... Figure 3 Issues such as ghosting of the target vehicle or incorrect collision warnings. Figure 3 In this context, SV represents the basic status information of the target vehicle provided by the GNSS for connected vehicles, RV1 represents the surrounding candidate vehicles provided by the roadside sensing device, and RSV represents the target vehicle among the candidate vehicles provided by the roadside sensing device.

[0070] Therefore, it is necessary to propose a method to accurately remove the vehicle status information in the aforementioned RSM / SSM without mistakenly removing the status information of other vehicles. Specifically, distance matching is performed by calculating a comprehensive distance. Different distance calculation methods are used for candidate vehicles in different areas. The candidate vehicle with the smallest comprehensive distance is selected as the target vehicle for the roadside sensing device, and the final distance matching result is obtained, namely the vehicle identifier of the candidate vehicle with the smallest comprehensive distance and the corresponding first comprehensive distance.

[0071] S204: Match the identifiers (IDs) of each candidate vehicle with the target vehicle to obtain the identifier matching results.

[0072] Since roadside sensing devices have detection errors, when the error is large, it will cause distance matching errors. Therefore, it is considered to add identifier matching to enhance the robustness of the matching mechanism. The vehicle identifier is a unique identifier (ID) assigned to the candidate vehicle by the roadside sensing device. Under normal circumstances, a vehicle has only one ID from entering the sensing area to leaving the sensing area.

[0073] Furthermore, the identifier matching involves matching the basic state data subset corresponding to each candidate vehicle with the real-time state dataset based on the previous target matching result. If there is a vehicle identifier in the previous target matching result that matches the current candidate vehicle, the current candidate vehicle is confirmed as a successfully matched vehicle. The resulting identifier matching result is the vehicle identifier of the successfully matched candidate vehicle and the corresponding second comprehensive distance.

[0074] S206: Determine the target matching result based on the distance matching result and the identifier matching result, and remove the basic state data subset corresponding to the target matching result from the basic state dataset based on the target matching result.

[0075] Distance matching is affected by the detection accuracy of roadside sensing devices; large detection errors can lead to mismatches. ID matching is affected by the tracking performance of roadside sensing devices; sudden changes in tracking can cause changes in the perceived vehicle IDs, resulting in ID matching failure. A comprehensive analysis of the distance and ID matching results determines the final target matching result, which includes the candidate vehicle IDs and the combined distance between the candidate and target vehicles.

[0076] Specifically, when the ID matching algorithm and the distance matching algorithm produce different results, the matching result is intelligently switched by comparing the comprehensive distance of the two matching results and the number of consecutive matchings of the same value, so as to obtain the optimal matching result. When either ID matching or distance matching fails, it can intelligently switch to the other matching result. When both distance matching and ID matching fail, the target matching result is empty.

[0077] Furthermore, based on the target matching result, the basic state data subset corresponding to the target matching result in the basic state dataset is removed. When the target matching result is not empty, the corresponding basic state data subset in the basic state dataset is removed based on the ID of the candidate vehicle in the target matching result. When the target matching result is empty, it means that there is no basic state data subset corresponding to the target vehicle in the basic state dataset, so no removal is performed.

[0078] In the aforementioned deduplication method for vehicle-to-everything (V2X) data, distance matching and identifier matching are used to effectively remove the target vehicle's state data from the basic state dataset collected by roadside sensing devices. This ensures the authenticity and reliability of the basic state dataset, reduces interference in collision warning calculations, and improves the accuracy of collision warning prompts. Furthermore, by comprehensively analyzing the distance matching and identifier matching results, the optimal target matching result is obtained. When either the identifier matching algorithm or the distance matching algorithm fails, the method avoids the situation where no matching result can be obtained when a single matching method fails, thus enhancing the universality, accuracy, stability, and robustness of the matching algorithm.

[0079] In an exemplary embodiment, the step of performing distance matching between at least one frame of real-time state dataset acquired by the target vehicle and a subset of basic state data corresponding to each candidate vehicle in the basic state dataset collected by the roadside sensing device to obtain a distance matching result includes:

[0080] Perform time matching on at least one frame of real-time state dataset and basic state dataset to obtain time matching results;

[0081] Distance matching is performed on the basic state data subset corresponding to each candidate vehicle in the time matching result and the real-time state dataset in the time matching result to obtain the distance matching result.

[0082] Because there is a time delay in the transmission of vehicle detection information from the roadside sensing device to the OBU, and a time delay in the transmission of the target vehicle's state information to the OBU, and these two delays are not equal, the basic state dataset received by the OBU at the current moment in the RSM / SSM is not the same as the real-time state dataset of the target vehicle. Therefore, before performing distance matching, time matching is performed on at least one frame of the real-time state dataset and the basic state dataset. The resulting time match includes the frame of the real-time state dataset whose timestamp is closest to that of the basic state dataset.

[0083] Furthermore, a basic state dataset includes multiple subsets of basic state data for candidate vehicles. By performing distance matching between the subsets of basic state data corresponding to each candidate vehicle in the time matching result and the real-time state dataset in the time matching result, the distance matching result is obtained, namely the vehicle identifier and corresponding comprehensive distance of the candidate vehicle with the smallest distance to the target vehicle.

[0084] In this embodiment, by time matching, the real-time state dataset frame that is closest to the timestamp of the basic state dataset is found, and subsequent distance matching is performed. This can effectively reduce the complexity of distance matching and thus ensure the accuracy of the distance matching results.

[0085] In one exemplary embodiment, such as Figure 4As shown, the steps for performing time matching on at least one frame of real-time state dataset and basic state dataset to obtain the time matching result include:

[0086] For each time interval corresponding to the timestamp of the real-time state dataset, the time interval corresponding to the timestamp of each two adjacent frames of real-time state dataset is determined.

[0087] If the timestamp corresponding to the basic state dataset is at the midpoint of the time interval it is in, the basic state dataset and the real-time state dataset that is located after the timestamp corresponding to the basic state dataset in time are used as the time matching result.

[0088] If the timestamp corresponding to the basic state dataset is not at the midpoint of the time interval it is in, the basic state dataset and the real-time state dataset whose timestamp is closest to the timestamp corresponding to the basic state dataset will be used as the time matching result.

[0089] The real-time state dataset is stored in ascending order of timestamps. The timestamps of every two adjacent frames of the real-time state dataset constitute a time interval. Based on the timestamps of the basic state dataset, the time interval of the current frame's basic state dataset is determined. Specifically, for example... Figure 4 As shown, Figure 4 In the middle, SV t0 and SV t2 To construct a real-time state dataset of two adjacent frames within a time interval, RV i Given a subset of basic state data for timestamp t1, determine the real-time state dataset that is closest to timestamp t1 based on the time segment in which timestamp t1 is located.

[0090] Among them, when the timestamp t1 is at the midpoint of the corresponding time interval, the real-time state dataset with the closest timestamp is SV. t0 and SV t2 At this point, the real-time state dataset located after the timestamp t1 is selected, i.e., SV. t2 If the t1 timestamp is not at the midpoint of the corresponding time interval, the real-time status dataset that is closest to the t1 timestamp is directly selected.

[0091] In this embodiment, by time matching, the real-time state dataset frame that is closest to the timestamp of the basic state dataset is found, which can effectively reduce the time difference between the real-time state dataset and the basic state dataset, thereby providing a basis for subsequent matching and ensuring the accuracy of the matching results.

[0092] In one exemplary embodiment, such as Figure 5As shown, the steps for obtaining distance matching results by performing distance matching on the subset of basic state data corresponding to each candidate vehicle in the time matching results and the real-time state dataset in the time matching results include:

[0093] For the real-time state dataset in the time matching results and the basic state data subset corresponding to each candidate vehicle, the first distance threshold is obtained based on the vehicle speed data of the target vehicle in the real-time state dataset.

[0094] Based on the vehicle coordinates in the subset of basic state data corresponding to the candidate vehicle and the vehicle coordinates of the target vehicle in the real-time state data, the straight-line distance, lateral distance, and longitudinal relative distance between the candidate vehicle and the target vehicle are obtained.

[0095] If the straight-line distance is less than or equal to the first distance threshold, the planar region where the candidate vehicle is located is determined; the planar region includes the actual distance region, the weighted distance region, and the out-of-range region;

[0096] When the candidate vehicle is located in the actual distance area or the weighted distance area, the first comprehensive distance between the candidate vehicle and the target vehicle is obtained based on the straight distance, the lateral distance, and the longitudinal relative distance.

[0097] The first comprehensive distance minimum value and the vehicle identifier of the candidate vehicle corresponding to the first comprehensive distance minimum value are used as the distance matching result.

[0098] The first distance threshold D is calculated based on the target vehicle's speed v in the time matching results. max The first distance threshold is calculated as follows:

[0099] D max =1+0.3*v

[0100] For each candidate vehicle, based on the vehicle coordinates (x, y, z) in the subset of basic state data corresponding to the candidate vehicle... k ,y k ) and the vehicle coordinates (x, y) of the target vehicle in the real-time status dataset h ,y h ), obtain the straight-line distance d between the candidate vehicle and the target vehicle. The straight-line distance is calculated as follows:

[0101]

[0102] In d <D max In the case where d ≥ D, it indicates that the candidate vehicle is close to the target vehicle and is likely to be the target vehicle detected by the roadside sensing device. Therefore, a first comprehensive distance is calculated based on the planar area where the candidate vehicle is located, and the candidate vehicle with the minimum first comprehensive distance is selected as the target vehicle detected by the roadside sensing device.max In cases where the distance between the candidate vehicle and the target vehicle is too far, it is confirmed that the candidate vehicle is not the target vehicle detected by the roadside sensing device.

[0103] In this embodiment, the first comprehensive distance is calculated by dividing the region, which can reduce the influence of lateral distance and avoid misidentifying left and right vehicles and the vehicle in front as the vehicle itself. Furthermore, the distance matching algorithm will perform a secondary screening on candidate vehicles that meet the distance conditions, and select the traffic participant with the smallest comprehensive distance as the distance matching result, thus avoiding the removal of other traffic participants.

[0104] In one exemplary embodiment, such as Figure 6 As shown, the steps for determining the planar region where the candidate vehicle is located include:

[0105] If the longitudinal relative distance is less than the second distance threshold, the lateral distance is less than the third distance threshold, and the lateral distance is greater than the fourth distance threshold, the candidate vehicle is determined to be located in the weighted distance region.

[0106] If the longitudinal relative distance is less than the second distance threshold and the lateral distance is less than or equal to the fourth distance threshold, the candidate vehicle is determined to be located in the actual distance area.

[0107] If the longitudinal relative distance is greater than or equal to the second distance threshold, or the lateral distance is greater than or equal to the third distance threshold, the candidate vehicle is determined to be located in the out-of-range area.

[0108] The schematic diagram of the planar region is as follows: Figure 6 As shown, Figure 6 HV is the target vehicle, and RV1, RV2, and RSV are vehicles that satisfy d. <D max The candidate vehicles have a second distance threshold of 3m, a fourth distance threshold determined based on the width of the target vehicle and the width of the candidate vehicles, and a third distance threshold greater than the fourth distance threshold.

[0109] Specifically, the weighted region is represented as follows:

[0110] d l <3m and (w) p +w s ) / 2 <d t <1+(w p +w s ) / 2

[0111] Where, d l d represents the longitudinal relative distance. l A value greater than 0 indicates that the candidate vehicle is in front of the target vehicle. l A value less than 0 indicates that the candidate vehicle is behind the target vehicle. t w represents the horizontal distance.p w represents the width of the candidate vehicle. s The width of the target vehicle.

[0112] The actual distance area is represented as:

[0113] d l <3m and d t ≤(w p +w s ) / 2

[0114] The out-of-range region is represented as:

[0115] d l ≥3m or d t ≥1+(w p +w s ) / 2

[0116] The planar schematic diagram of the weighted region is as follows: Figure 6 As shown in Figure ②, the planar schematic diagram of the actual distance area is as follows: Figure 6 As shown in ①, the planar schematic diagram of the out-of-range area is as follows: Figure 6 As shown in ③.

[0117] In this embodiment, the planar area where the candidate vehicle is located is determined by the lateral distance and longitudinal relative distance between the candidate vehicle and the target vehicle. This reduces the influence of lateral distance and avoids misidentifying the vehicles on the left and right and the vehicle in front as the own vehicle, thereby ensuring the accuracy of the distance matching results.

[0118] In an exemplary embodiment, the step of obtaining a first comprehensive distance between a candidate vehicle and a target vehicle based on straight-line distance, lateral distance, and longitudinal relative distance includes:

[0119] When the candidate vehicle is located in the actual distance area, the straight-line distance is used as the first comprehensive distance between the candidate vehicle and the target vehicle;

[0120] When the candidate vehicle is located in the weighted distance region, the first comprehensive distance between the candidate vehicle and the target vehicle is calculated based on the lateral distance and the longitudinal relative distance.

[0121] In the case where the candidate vehicle is located within the actual distance zone, the straight-line distance is selected as the first comprehensive distance between the candidate vehicle and the target vehicle. When the candidate vehicle is located within the weighted distance zone, the first comprehensive distance D is calculated based on the lateral distance and the longitudinal relative distance. The calculation method for this first comprehensive distance D is as follows:

[0122]

[0123] In cases where a candidate vehicle is located outside the designated area, the candidate vehicle is confirmed to be a target vehicle not detected by the roadside sensing device, and no action is taken.

[0124] In this embodiment, the first comprehensive distance is obtained by calculating the distance by region, which can reduce the influence of the lateral distance and ensure the accuracy of the distance matching results.

[0125] In one exemplary embodiment, the method further includes:

[0126] If the straight-line distance is greater than the first distance threshold, or if the candidate vehicle is located outside the range, the distance matching is determined to be unsuccessful.

[0127] In cases where the straight-line distance is greater than the first distance threshold or the candidate vehicle is located outside the range, it indicates that the target vehicle is not among the candidate vehicles sensed by the roadside sensing device, and thus the distance matching is determined to be unsuccessful.

[0128] In this embodiment, by using distance matching, the target vehicle sensed by the roadside sensing device can be accurately obtained, thereby reducing interference in collision warning calculation and improving the accuracy of collision warning prompts.

[0129] In one exemplary embodiment, such as Figure 7 As shown, the identifiers of each candidate vehicle and the target vehicle are matched to obtain the identifier matching results, including:

[0130] Get the vehicle identifiers of candidate vehicles in the target matching results within the previous historical time period.

[0131] If the vehicle identifier of a candidate vehicle in the current historical time period is consistent with the vehicle identifier of a candidate vehicle in the target matching result of the previous historical time period, the second comprehensive distance between the consistent candidate vehicle and the target vehicle in the current historical time period and the vehicle identifier of the consistent candidate vehicle are taken as the identifier matching result.

[0132] Specifically, the vehicle identifiers of candidate vehicles in the target matching results of the previous historical time period are denoted as I1, and the vehicle identifiers of candidate vehicles in the current historical time period are denoted as I2. If there is a match between I1 and I2, the vehicle identifier of the corresponding candidate vehicle in I2 that matches I1 is selected as the identifier matching result.

[0133] In this embodiment, the candidate vehicles and target vehicles in the current historical time period are matched by the target matching results in the previous historical time period. This can avoid the situation where no matching result can be obtained when a single matching method fails, and enhance the universality, accuracy, stability and robustness of the matching algorithm.

[0134] In one exemplary embodiment, it remains as follows Figure 7As shown, the method also includes:

[0135] If there is no previous historical time period, or no target matching result in the previous historical time period, or no vehicle identifier of the candidate vehicle in the current historical time period is consistent with the vehicle identifier of the candidate vehicle in the target matching result in the previous historical time period, the identifier matching is determined to be unsuccessful.

[0136] In cases where there is no previous historical time period, no target matching result within the previous historical time period, or no case where I1 and I2 are consistent, the identifier matching is determined to be unsuccessful, meaning that the candidate vehicles sensed by the roadside sensing device in the current historical time period do not include the target vehicle.

[0137] In this embodiment, the candidate vehicles and target vehicles in the current historical time period are matched by the target matching results in the previous historical time period. This can avoid the situation where no matching result can be obtained when a single matching method fails, and enhance the universality, accuracy, stability and robustness of the matching algorithm.

[0138] In one exemplary embodiment, such as Figure 8 As shown, the steps for determining the target matching result based on the distance matching result and the identifier matching result include:

[0139] If the distance matching result and the identifier matching result are the same, the distance matching result or the identifier matching result will be used as the target matching result within the current historical time period, and the current cumulative matching count will be incremented by 1;

[0140] If the distance matching result and the identifier matching result are inconsistent, and the first comprehensive distance between the candidate vehicle and the target vehicle is less than or equal to the second comprehensive distance between the candidate vehicle and the target vehicle that are consistent in the current historical time period, or the current cumulative number of matching is less than or equal to the target number, the distance matching result will be used as the target matching result in the current historical time period, and the current cumulative number of matching will be cleared to zero.

[0141] If the first comprehensive distance is greater than the second comprehensive distance and the current cumulative number of matches is greater than the target number, the matching result will be marked as the target matching result within the current historical time period;

[0142] If no distance matching result is obtained but an identifier matching result is obtained, and the current cumulative number of matches is greater than the target number, the identifier matching result will be used as the target matching result for the current historical time period.

[0143] If a distance matching result is obtained but no identifier matching result is obtained, the third comprehensive distance between the candidate vehicle and the corresponding target vehicle in the target matching result in the previous historical time period is obtained. If the first comprehensive distance is less than or equal to the third comprehensive distance, or the current cumulative matching count is less than or equal to the target count, the distance matching result is taken as the target matching result in the current historical time period, and the current cumulative matching count is cleared to zero.

[0144] In this context, the candidate vehicle ID obtained by distance matching is denoted as ID1, and the corresponding first comprehensive distance is denoted as d1; the candidate vehicle ID obtained by identifier matching is denoted as ID2, and the corresponding second comprehensive distance is denoted as d2; the candidate vehicle ID of the target matching result in the previous historical time period is denoted as ID3, and the corresponding third comprehensive distance is denoted as d3; the number of times the same candidate vehicle is matched consecutively by ID matching and distance matching is denoted as c=0.

[0145] Specifically, if the distance matching result and the identifier matching result are the same (i.e., ID1 == ID2), the distance matching result ID1 or the identifier matching result ID2 is taken as the target matching result within the current historical time period, and the current cumulative matching count is incremented by 1 (i.e., c = c + 1).

[0146] If the distance matching result and the identifier matching result are inconsistent (i.e., ID1≠ID2), and the first comprehensive distance between the candidate vehicle and the target vehicle is less than or equal to the second comprehensive distance between the candidate vehicle and the target vehicle that are consistent in the current historical time period, or the current cumulative number of matching is less than or equal to the target number (i.e., d1≤d2||c≤5), ID1 will be taken as the target matching result in the current historical time period, and the number of matching will be cleared to zero.

[0147] If the first comprehensive distance is greater than the second comprehensive distance and the current cumulative number of matches is greater than the target number (i.e., d1>d2 and c>5), then ID2 will be used as the target matching result within the current historical time period.

[0148] If no distance matching result is obtained but an identifier matching result is obtained (i.e., ID1 == -1 && ID2 != -1), and the current cumulative number of matches is greater than the target number (i.e., c > 5), then ID2 will be taken as the target matching result within the current historical time period.

[0149] If a distance matching result is obtained but no identifier matching result is obtained (i.e., ID1 != -1 && ID2 == -1), the third comprehensive distance between the candidate vehicle and the corresponding target vehicle in the target matching result in the previous historical time period is obtained. If the first comprehensive distance is less than or equal to the third comprehensive distance, or the current cumulative number of matching is less than or equal to the target number (i.e., d1 ≤ d3 || c ≤ 5), ID1 is taken as the target matching result in the current historical time period, and the number c is cleared to zero.

[0150] Otherwise, if the matching fails and the target matching result is empty, the candidate vehicles sensed by the roadside sensing device will not include the target vehicle.

[0151] In this embodiment, a vehicle matching scheme employing time matching, distance matching, identifier matching, and hybrid matching effectively removes a subset of the target vehicle's basic state data from the basic state dataset collected by roadside sensing devices. This ensures the authenticity and reliability of the basic state dataset, reduces interference in collision warning calculations, and improves the accuracy of collision warning prompts. This deduplication method combines multiple matching algorithms, and compared to other algorithms that deduplicatize based on time and location, it has stronger universality, higher accuracy, and better robustness.

[0152] In one exemplary embodiment, such as Figure 9 As shown, a method for deduplicating vehicle data in the Internet of Vehicles (IoV) is provided, which includes the following steps:

[0153] Time matching: For each time interval containing the timestamps of two adjacent real-time state datasets, the time interval containing the timestamp of the basic state dataset is determined. If the timestamp of the basic state dataset is at the midpoint of the corresponding time interval, the basic state dataset and the real-time state dataset that is time-wise after the timestamp of the basic state dataset are used as the time matching result. If the timestamp of the basic state dataset is not at the midpoint of the corresponding time interval, the basic state dataset and the real-time state dataset whose timestamp is closest to the timestamp of the basic state dataset are used as the time matching result.

[0154] Distance matching: Based on the real-time state dataset and the basic state data subsets corresponding to each candidate vehicle in the time matching results, a first distance threshold is obtained according to the vehicle speed data of the target vehicle in the real-time state dataset. Based on the vehicle coordinates in the basic state data subset corresponding to the candidate vehicle and the vehicle coordinates of the target vehicle in the real-time state dataset, the straight-line distance, lateral distance, and longitudinal relative distance between the candidate vehicle and the target vehicle are obtained. If the straight-line distance is less than or equal to the first distance threshold, and the longitudinal relative distance is less than the second distance threshold, the lateral distance is less than the third distance threshold, and the lateral distance is greater than the fourth distance threshold, the candidate vehicle is determined to be located in the weighted distance region. If the longitudinal relative distance is less than the second distance threshold and the lateral distance is less than or equal to the fourth distance threshold, the candidate vehicle is determined to be located in the actual distance region. If the longitudinal relative distance is greater than or equal to the second distance threshold, or the lateral distance is greater than or equal to the third distance threshold, the candidate vehicle is determined to be located in the out-of-range region.

[0155] When the candidate vehicle is located in the actual distance region or the weighted distance region, the straight-line distance is used as the first comprehensive distance between the candidate vehicle and the target vehicle when the candidate vehicle is located in the actual distance region; when the candidate vehicle is located in the weighted distance region, the first comprehensive distance between the candidate vehicle and the target vehicle is calculated based on the lateral distance and the longitudinal relative distance.

[0156] The first comprehensive distance minimum value and the vehicle identifier of the candidate vehicle corresponding to the first comprehensive distance minimum value are used as the distance matching result.

[0157] If the straight-line distance is greater than the first distance threshold, or if the candidate vehicle is located outside the range, the distance matching is determined to be unsuccessful.

[0158] Identifier (ID) matching: Obtain the vehicle identifiers of candidate vehicles in the target matching results of the previous historical time period; if the vehicle identifier of a candidate vehicle in the current historical time period is consistent with the vehicle identifier of a candidate vehicle in the target matching results of the previous historical time period, the second comprehensive distance between the consistent candidate vehicle and the target vehicle in the current historical time period and the vehicle identifier of the consistent candidate vehicle are used as the identifier matching result.

[0159] If there is no previous historical time period, or no target matching result in the previous historical time period, or no vehicle identifier of the candidate vehicle in the current historical time period is consistent with the vehicle identifier of the candidate vehicle in the target matching result in the previous historical time period, the identifier matching is determined to be unsuccessful.

[0160] Hybrid matching: If the distance matching result and the identifier matching result are the same, either the distance matching result or the identifier matching result is used as the target matching result for the current historical time period, and the current cumulative matching count is incremented by 1; if the distance matching result and the identifier matching result are inconsistent, and the first comprehensive distance between the candidate vehicle and the target vehicle is less than or equal to the second comprehensive distance between the matching candidate vehicle and the target vehicle for the current historical time period, or the current cumulative matching count is less than or equal to the target count, the distance matching result is used as the target matching result for the current historical time period, and the current cumulative matching count is reset to zero; if the first comprehensive distance is greater than the second comprehensive distance, and the current cumulative matching count is greater than the target count, then... In the following cases, the identifier matching result is used as the target matching result for the current historical time period; if no distance matching result is obtained but an identifier matching result is obtained, and the current cumulative matching count is greater than the target count, the identifier matching result is used as the target matching result for the current historical time period; if a distance matching result is obtained but no identifier matching result is obtained, the third comprehensive distance between the candidate vehicle and the corresponding target vehicle in the target matching result of the previous historical time period is obtained; if the first comprehensive distance is less than or equal to the third comprehensive distance, or the current cumulative matching count is less than or equal to the target count, the distance matching result is used as the target matching result for the current historical time period, and the current cumulative matching count is cleared to zero.

[0161] Remove the subset of basic state data corresponding to the target matching result from the basic state dataset based on the target matching result.

[0162] In this embodiment, ID matching and distance matching are fused through hybrid matching. By comprehensively analyzing the matching results of the two, one matching result is selected and trusted. When the results of ID matching and distance matching are different, the matching result is intelligently switched by comparing the comprehensive distance of the two matching results and the number of consecutive matchings of the same value, so as to obtain the optimal matching result. When one of the ID matching and distance matching fails, it can intelligently switch to the other matching result, thereby enhancing the universality, accuracy, stability and robustness of the matching.

[0163] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0164] Based on the same inventive concept, this application also provides a vehicle-to-everything (V2X) data deduplication device for implementing the aforementioned V2X data deduplication method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more V2X data deduplication device embodiments provided below can be found in the limitations of the V2X data deduplication method described above, and will not be repeated here.

[0165] In one exemplary embodiment, such as Figure 10 As shown, a vehicle-to-everything (V2X) data deduplication device is provided, comprising: a distance matching module 10, an identifier matching module 20, and a hybrid matching module 30, wherein:

[0166] The distance matching module 10 is used to perform distance matching between at least one frame of real-time state dataset acquired by the target vehicle and the subset of basic state data corresponding to each candidate vehicle in the basic state dataset collected by the roadside sensing device, and to obtain the distance matching result.

[0167] The identifier matching module 20 is used to match the identifiers of each candidate vehicle with the target vehicle to obtain the identifier matching result.

[0168] The hybrid matching module 30 is used to determine the target matching result based on the distance matching result and the identifier matching result, and to remove the basic state data subset corresponding to the target matching result from the basic state dataset based on the target matching result.

[0169] In an exemplary embodiment, the distance matching module 10 is further configured to perform time matching on at least one frame of real-time state dataset and basic state dataset to obtain a time matching result; and to perform distance matching on the basic state data subset corresponding to each candidate vehicle in the time matching result and the real-time state dataset in the time matching result to obtain a distance matching result.

[0170] In an exemplary embodiment, the distance matching module 10 is further configured to determine the time interval of the timestamp corresponding to the basic state dataset for the time interval of the timestamp corresponding to each pair of adjacent real-time state datasets; if the timestamp corresponding to the basic state dataset is at the midpoint of the time interval, the basic state dataset and the real-time state dataset that is time-wise after the timestamp corresponding to the basic state dataset are used as the time matching result; if the timestamp corresponding to the basic state dataset is not at the midpoint of the time interval, the basic state dataset and the real-time state dataset whose timestamp is closest to the timestamp corresponding to the basic state dataset are used as the time matching result.

[0171] In an exemplary embodiment, the distance matching module 10 is further configured to: obtain a first distance threshold based on the vehicle speed data of the target vehicle in the real-time state dataset and the basic state data subset corresponding to each candidate vehicle in the time matching result; obtain the straight-line distance, lateral distance, and longitudinal relative distance between the candidate vehicle and the target vehicle based on the vehicle coordinates in the basic state data subset corresponding to the candidate vehicle and the vehicle coordinates in the real-time state dataset; determine the planar region where the candidate vehicle is located if the straight-line distance is less than or equal to the first distance threshold; the planar region includes the actual distance region, the weighted distance region, and the out-of-range region; obtain the first comprehensive distance between the candidate vehicle and the target vehicle based on the straight-line distance, lateral distance, and longitudinal relative distance if the candidate vehicle is located in the actual distance region or the weighted distance region; and use the minimum value of the first comprehensive distance and the vehicle identifier of the candidate vehicle corresponding to the minimum value of the first comprehensive distance as the distance matching result.

[0172] In an exemplary embodiment, the distance matching module 10 is further configured to determine that the candidate vehicle is located in the weighted distance region when the longitudinal relative distance is less than the second distance threshold, the lateral distance is less than the third distance threshold, and the lateral distance is greater than the fourth distance threshold; determine that the candidate vehicle is located in the actual distance region when the longitudinal relative distance is less than the second distance threshold and the lateral distance is less than or equal to the fourth distance threshold; and determine that the candidate vehicle is located in the out-of-range region when the longitudinal relative distance is greater than or equal to the second distance threshold or the lateral distance is greater than or equal to the third distance threshold.

[0173] In an exemplary embodiment, the distance matching module 10 is further configured to use the straight-line distance as the first comprehensive distance between the candidate vehicle and the target vehicle when the candidate vehicle is located in the actual distance area; and to calculate the first comprehensive distance between the candidate vehicle and the target vehicle based on the lateral distance and the longitudinal relative distance when the candidate vehicle is located in the weighted distance area.

[0174] In an exemplary embodiment, the distance matching module 10 is further configured to determine that the distance matching has failed if the straight-line distance is greater than a first distance threshold or if the candidate vehicle is located in an out-of-range area.

[0175] In an exemplary embodiment, the identifier matching module 20 is further configured to obtain the vehicle identifiers of candidate vehicles in the target matching results of the previous historical time period; if there is a case where the vehicle identifier of a candidate vehicle in the current historical time period is consistent with the vehicle identifier of a candidate vehicle in the target matching results of the previous historical time period, the second comprehensive distance between the consistent candidate vehicle and the target vehicle in the current historical time period and the vehicle identifier of the consistent candidate vehicle are used as the identifier matching result.

[0176] In an exemplary embodiment, the identifier matching module 20 is further configured to determine that identifier matching has failed if there is no previous historical time period, or no target matching result in the previous historical time period, or no vehicle identifier of the candidate vehicle in the current historical time period is consistent with the vehicle identifier of the candidate vehicle in the target matching result in the previous historical time period.

[0177] In an exemplary embodiment, the hybrid matching module 30 is further configured to, when the distance matching result and the identifier matching result are consistent, use the distance matching result or the identifier matching result as the target matching result within the current historical time period and increment the current cumulative matching count by 1; when the distance matching result and the identifier matching result are inconsistent, and the first comprehensive distance between the candidate vehicle and the target vehicle is less than or equal to the second comprehensive distance between the consistent candidate vehicle and the target vehicle within the current historical time period, or the current cumulative matching count is less than or equal to the target count, use the distance matching result as the target matching result within the current historical time period and reset the current cumulative matching count to zero; when the first comprehensive distance is greater than the second comprehensive distance and the current cumulative matching count is less than or equal to the target count, use the distance matching result as the target matching result within the current historical time period and reset the current cumulative matching count to zero; when the first comprehensive distance is greater than the second comprehensive distance and the current cumulative matching count is less than or equal to the target count, use the distance matching result as the target matching result within the current historical time period and reset the current cumulative matching count to zero. If the number of matches exceeds the target number, the identified matching result will be used as the target matching result for the current historical time period. If no distance matching result is obtained but an identified matching result is obtained, and the current cumulative number of matches exceeds the target number, the identified matching result will be used as the target matching result for the current historical time period. If a distance matching result is obtained but no identified matching result is obtained, the third comprehensive distance between the candidate vehicle and the corresponding target vehicle in the target matching result of the previous historical time period will be obtained. If the first comprehensive distance is less than or equal to the third comprehensive distance, or the current cumulative number of matches is less than or equal to the target number, the distance matching result will be used as the target matching result for the current historical time period, and the current cumulative number of matches will be cleared to zero.

[0178] The various modules in the aforementioned vehicle-to-everything (V2X) data deduplication device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0179] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 11 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for deduplicating vehicle data in a vehicle-to-everything (V2X) network. The display unit is used to create a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0180] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0181] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0182] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0183] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0184] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0185] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0186] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for deduplicating vehicle data in a vehicle-to-everything (V2X) network, characterized in that, The method includes: Time matching is performed on at least one frame of real-time state dataset acquired by the target vehicle and the basic state dataset collected by the roadside sensing device to obtain the time matching result; For the real-time state dataset and the basic state data subset corresponding to each candidate vehicle in the time matching result, a first distance threshold is obtained based on the vehicle speed data of the target vehicle in the real-time state dataset. Based on the vehicle coordinates in the basic state data subset corresponding to the candidate vehicle and the vehicle coordinates of the target vehicle in the real-time state data set, the straight-line distance, lateral distance, and longitudinal relative distance between the candidate vehicle and the target vehicle are obtained. If the straight-line distance is less than or equal to the first distance threshold, and if the longitudinal relative distance is less than the second distance threshold, the lateral distance is less than the third distance threshold, and the lateral distance is greater than the fourth distance threshold, then the candidate vehicle is determined to be located in the weighted distance region. If the longitudinal relative distance is less than the second distance threshold and the lateral distance is less than or equal to the fourth distance threshold, the candidate vehicle is determined to be located in the actual distance region. If the longitudinal relative distance is greater than or equal to the second distance threshold, or the lateral distance is greater than or equal to the third distance threshold, the candidate vehicle is determined to be located in the out-of-range area. When the candidate vehicle is located in the actual distance region or the weighted distance region, a first comprehensive distance between the candidate vehicle and the target vehicle is obtained based on the straight-line distance, the lateral distance, and the longitudinal relative distance. The minimum comprehensive distance and the vehicle identifier of the candidate vehicle corresponding to the minimum comprehensive distance are used as the distance matching result; The identifiers of each candidate vehicle and the target vehicle are matched to obtain the identifier matching results. Based on the distance matching result and the identifier matching result, a target matching result is determined, and a subset of basic state data corresponding to the target matching result is removed from the basic state dataset based on the target matching result.

2. The method according to claim 1, characterized in that, The step of performing time matching on the at least one frame of real-time state dataset and the basic state dataset to obtain a time matching result includes: For each time interval corresponding to the timestamp of the real-time state dataset, the time interval corresponding to the timestamp of each two adjacent frames of real-time state dataset is determined; If the timestamp corresponding to the basic state dataset is at the midpoint of the time interval, the basic state dataset and the real-time state dataset that is located after the timestamp corresponding to the basic state dataset in time are taken as the time matching result. If the timestamp corresponding to the basic state dataset is not at the midpoint of the time interval it is in, the basic state dataset and the real-time state dataset whose timestamp is closest to the timestamp corresponding to the basic state dataset are taken as the time matching result.

3. The method according to claim 1, characterized in that, The step of obtaining the first comprehensive distance between the candidate vehicle and the target vehicle based on the straight-line distance, the lateral distance, and the longitudinal relative distance includes: When the candidate vehicle is located within the actual distance area, the straight-line distance is used as the first comprehensive distance between the candidate vehicle and the target vehicle; When the candidate vehicle is located in the weighted distance region, the first comprehensive distance between the candidate vehicle and the target vehicle is calculated based on the lateral distance and the longitudinal relative distance.

4. The method according to claim 1, characterized in that, The method further includes: If the straight-line distance is greater than the first distance threshold, or if the candidate vehicle is located in the out-of-range area, the distance matching is determined to be unsuccessful.

5. The method according to claim 1, characterized in that, The step of performing identifier matching between the basic state data subset corresponding to each candidate vehicle and the real-time state dataset to obtain the identifier matching result includes: Get the vehicle identifiers of candidate vehicles in the target matching results within the previous historical time period. If the vehicle identifier of a candidate vehicle in the current historical time period is consistent with the vehicle identifier of a candidate vehicle in the target matching result in the previous historical time period, the second comprehensive distance between the consistent candidate vehicle and the target vehicle in the current historical time period and the vehicle identifier of the consistent candidate vehicle are taken as the identifier matching result.

6. The method according to claim 5, characterized in that, The method further includes: If there is no previous historical time period, or no target matching result in the previous historical time period, or no vehicle identifier of the candidate vehicle in the current historical time period is consistent with the vehicle identifier of the candidate vehicle in the target matching result in the previous historical time period, the identifier matching is determined to be unsuccessful.

7. The method according to claim 1, characterized in that, The step of determining the target matching result based on the distance matching result and the identifier matching result includes: If the distance matching result and the identifier matching result are consistent, the distance matching result or the identifier matching result shall be used as the target matching result within the current historical time period, and the current cumulative matching count shall be incremented by 1. If the distance matching result and the identifier matching result are inconsistent, and the first comprehensive distance between the candidate vehicle and the target vehicle is less than or equal to the second comprehensive distance between the candidate vehicle and the target vehicle that are consistent in the current historical time period, or the current cumulative matching number is less than or equal to the target number, the distance matching result will be taken as the target matching result in the current historical time period, and the current cumulative matching number will be cleared to zero. If the first comprehensive distance is greater than the second comprehensive distance and the current cumulative number of matches is greater than the target number, the identifier matching result will be used as the target matching result within the current historical time period. If no distance matching result is obtained but an identifier matching result is obtained, and the current cumulative number of matching results is greater than the target number of matching results, the identifier matching result will be used as the target matching result for the current historical time period. If a distance matching result is obtained but no identifier matching result is obtained, the third comprehensive distance between the candidate vehicle and the corresponding target vehicle in the target matching result in the previous historical time period is obtained. If the first comprehensive distance is less than or equal to the third comprehensive distance, or the current cumulative matching count is less than or equal to the target count, the distance matching result is taken as the target matching result in the current historical time period, and the current cumulative matching count is cleared to zero.

8. A vehicle-to-everything (V2X) data deduplication device, characterized in that, The apparatus is applied to the vehicle-to-everything (V2X) data deduplication method according to any one of claims 1 to 7; the apparatus comprises: The distance matching module is used to perform distance matching between at least one frame of real-time state dataset acquired by the target vehicle and the subset of basic state data corresponding to each candidate vehicle in the basic state dataset collected by the roadside sensing device, and to obtain the distance matching result. The identifier matching module is used to perform identifier matching between each candidate vehicle and the target vehicle to obtain the identifier matching result; The hybrid matching module is used to determine the target matching result based on the distance matching result and the identifier matching result, and to remove the basic state data subset corresponding to the target matching result from the basic state dataset based on the target matching result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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