Data Processing Method, Apparatus, Electronic Device, and Storage Medium

Abstract

The present disclosure provides a data processing method, apparatus, electronic device, and storage medium. The method includes: obtaining first positioning information obtained by a first positioning system for positioning a first vehicle and a first positioning error of the first positioning system; obtaining second positioning information obtained by a second positioning system for positioning a second vehicle and a second positioning error of the second positioning system; determining a third positioning error between the first positioning system and the second positioning system based on the first positioning error and the second positioning error; and determining a probability that the first vehicle and the second vehicle are the same vehicle based on the first positioning information, the second positioning information, and the third positioning error. Embodiments of the present disclosure can improve the application reference value of the positioning information of the vehicle.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent transportation, and particularly to a data processing method, apparatus, electronic device, and storage medium. Background Art

[0002] With the rapid development of all aspects of society, people's demand for intelligent transportation is increasing day by day. For example: when a user is driving vehicle A, it is desired that vehicle A can monitor the distance between other vehicles on the current road and vehicle A, and once the distance between other vehicles and vehicle A is too close or other conditions are met such that it is very likely that a collision will occur between other vehicles and vehicle A, vehicle A can give a collision warning to the user. In the prior art, during the collision warning process, vehicle A locates itself through its own positioning system to obtain first positioning information, and compares each second positioning information obtained by the cloud positioning system for positioning each vehicle on the current road with the first positioning information, so as to distinguish itself from other vehicles on the current road. However, due to the positioning error of the positioning system, errors will occur when vehicle A distinguishes itself from other vehicles, resulting in subsequent collision warnings also having errors. Summary of the Invention

[0003] An object of the present disclosure is to provide a data processing method, apparatus, electronic device, and storage medium, which can improve the application reference value of the positioning information of a vehicle.

[0004] According to an aspect of an embodiment of the present disclosure, a data processing method is disclosed, the method including:

[0005] Obtaining first positioning information obtained by a first positioning system for positioning a first vehicle and a first positioning error of the first positioning system;

[0006] Obtaining second positioning information obtained by a second positioning system for positioning a second vehicle and a second positioning error of the second positioning system;

[0007] Determining a third positioning error between the first positioning system and the second positioning system based on the first positioning error and the second positioning error;

[0008] Determining the probability that the first vehicle and the second vehicle are the same vehicle based on the first positioning information, the second positioning information, and the third positioning error.

[0009] According to an aspect of an embodiment of the present disclosure, a data processing apparatus is disclosed, characterized in that the apparatus includes:

[0010] A first obtaining module configured to obtain first positioning information obtained by a first positioning system for positioning a first vehicle and a first positioning error of the first positioning system;

[0011] A second acquisition module, configured to acquire second positioning information obtained by a second positioning system for positioning a second vehicle and a second positioning error of the second positioning system;

[0012] A first determination module, configured to determine a third positioning error between the first positioning system and the second positioning system based on the first positioning error and the second positioning error;

[0013] A second determination module, configured to determine the probability that the first vehicle and the second vehicle are the same vehicle based on the first positioning information, the second positioning information, and the third positioning error.

[0014] In an exemplary embodiment of the present disclosure, the apparatus is configured to:

[0015] Acquire a probability density function of a normal distribution with a mean of 0 and a variance of the third positioning error;

[0016] Determine an integration interval of the probability density function based on the first positioning information and the second positioning information;

[0017] Determine the probability that the first vehicle and the second vehicle are the same vehicle based on the integral value of the probability density function over the integration interval.

[0018] In an exemplary embodiment of the present disclosure, the apparatus is configured to:

[0019] Based on the first positioning information and the second positioning information, and according to the first vehicle length of the first vehicle and the second vehicle length of the second vehicle, determine a first integration interval for integrating the probability density function in the vehicle length direction;

[0020] Based on the first positioning information and the second positioning information, and according to the first vehicle width of the first vehicle and the second vehicle width of the second vehicle, determine a second integration interval for integrating the probability density function in the vehicle width direction;

[0021] Determine a first probability that the first vehicle and the second vehicle are the same vehicle based on the integral value of the probability density function over the first integration interval;

[0022] Determine a second probability that the first vehicle and the second vehicle are the same vehicle based on the integral value of the probability density function over the second integration interval;

[0023] Determine the probability that the first vehicle and the second vehicle are the same vehicle based on the first probability and the second probability.

[0024] In an exemplary embodiment of the present disclosure, the device is configured to:

[0025] Determine a first interval length of the first integration interval based on the first vehicle length;

[0026] Determine a first center distance between the first vehicle and the second vehicle in the vehicle length direction based on the first positioning information, the second positioning information, the first vehicle length, and the second vehicle length;

[0027] Determine a first left boundary of the first integration interval based on the first center distance;

[0028] Determine the first integration interval based on the first interval length and the first left boundary.

[0029] In an exemplary embodiment of the present disclosure, the device is configured to:

[0030] Determine a second interval length of the second integration interval based on the first vehicle width;

[0031] Determine a second center distance between the first vehicle and the second vehicle in the vehicle width direction based on the first positioning information, the second positioning information, the first vehicle width, and the second vehicle width;

[0032] Determine a second left boundary of the second integration interval based on the second center distance;

[0033] Determine the second integration interval based on the second interval length and the second left boundary.

[0034] In an exemplary embodiment of the present disclosure, the device is configured to:

[0035] Determine an interval length of the integration interval based on the first positioning information and the second positioning information, according to the first vehicle length of the first vehicle, the second vehicle length of the second vehicle, the first vehicle width of the first vehicle, and the second vehicle width of the second vehicle;

[0036] Determine a center distance between the first vehicle and the second vehicle based on the first positioning information and the second positioning information;

[0037] Determine a left boundary of the integration interval based on the center distance;

[0038] Determine the integration interval based on the interval length and the left boundary.

[0039] In an exemplary embodiment of the present disclosure, the device is configured to:

[0040] Obtain a preset interval length of the integration interval;

[0041] Determine the center distance between the first vehicle and the second vehicle based on the first positioning information and the second positioning information;

[0042] Determine the left boundary of the integration interval based on the center distance;

[0043] Determine the integration interval based on the interval length and the left boundary.

[0044] In an exemplary embodiment of the present disclosure, the device is configured to:

[0045] Determine the center distance between the first vehicle and the second vehicle based on the first positioning information and the second positioning information;

[0046] Determine the distance interval where the center distance is located based on the third positioning error;

[0047] Determine the probability that the first vehicle and the second vehicle are the same vehicle based on the position of a preset reference distance in the distance interval.

[0048] According to one aspect of the embodiments of the present disclosure, a data processing electronic device is disclosed, including: a memory storing computer-readable instructions; a processor reading the computer-readable instructions stored in the memory to execute the method according to any one of the above claims.

[0049] According to one aspect of the embodiments of the present disclosure, a computer program medium is disclosed, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the method according to any one of the above claims.

[0050] By introducing the consideration of the third positioning error between the first positioning system and the second positioning system, the embodiments of the present disclosure determine the probability that the first vehicle positioned by the first positioning system and the second vehicle positioned by the second positioning system are the same vehicle, thereby improving the application reference value of the first positioning information of the first vehicle and the second positioning information of the second vehicle.

[0051] Other features and advantages of the present disclosure will become apparent through the following detailed description, or will be partially learned through the practice of the present disclosure.

[0052] It should be understood that the above general description and the following detailed description are only exemplary and do not limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] By referring to the accompanying drawings and describing its exemplary embodiments in detail, the above and other objectives, features, and advantages of the present disclosure will become more apparent.

[0054] Figure 1 Shows an architecture diagram according to an embodiment of the present disclosure.

[0055] Figure 2 Shows an architecture diagram according to an embodiment of the present disclosure.

[0056] Figure 3 Shows a flowchart of a data processing method according to an embodiment of the present disclosure.

[0057] Figure 4 Shows a schematic diagram of determining a first integration interval and a second integration interval according to an embodiment of the present disclosure.

[0058] Figure 5 Shows a schematic diagram of determining an integration interval according to an embodiment of the present disclosure.

[0059] Figure 6 Shows a block diagram of a data processing device according to an embodiment of the present disclosure.

[0060] Figure 7 Shows a hardware diagram of a data processing electronic device according to an embodiment of the present disclosure. Detailed implementation manners

[0061] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be more thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The accompanying drawings are schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted.

[0062] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more example embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the example embodiments of the present disclosure. However, those skilled in the art will realize that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or other methods, components, steps, etc. can be adopted. In other cases, well-known structures, methods, implementations, or operations are not shown or described in detail to avoid obscuring the various aspects of the present disclosure.

[0063] Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0064] Figure 1 The architecture composition of an embodiment of the present disclosure is shown: vehicle 10, server 20, and sensing terminal.

[0065] The vehicle 10 is equipped with an in-vehicle positioning system, which is mainly used to position the vehicle 10 and generate the in-vehicle positioning information of the vehicle 10.

[0066] The server 20 is equipped with a cloud positioning system, which is mainly used to position each vehicle traveling on the road and generate the cloud positioning information of each vehicle. Thus, the server 20 sends the cloud positioning information of each vehicle to the vehicle 10. Among them, the positioning performed by the in-vehicle positioning system is independent of the positioning performed by the cloud positioning system; due to the existence of positioning errors, for the same vehicle, the positioning information obtained by the in-vehicle positioning system in the vehicle for the vehicle may be inconsistent with the positioning information obtained by the cloud positioning system in the server for the vehicle.

[0067] The sensing terminal is mainly used to detect each vehicle traveling on the road and transmit the detection data to the server 20 for the cloud positioning system to generate the cloud positioning information of each vehicle.

[0068] After the vehicle 10 receives the cloud positioning information of each vehicle sent by the server 20, for a specific vehicle among each vehicle, the vehicle 10 determines the probability that itself and the specific vehicle are the same vehicle through the following method: The vehicle 10 determines the third positioning error between the in-vehicle positioning system and the cloud positioning system according to the first positioning error of the in-vehicle positioning system and the second positioning error of the cloud positioning system; and then determines the probability that itself and the specific vehicle are the same vehicle according to the in-vehicle positioning information and the cloud positioning information of the specific vehicle, in combination with the third positioning error.

[0069] Figure 2 The architecture composition of an embodiment of the present disclosure is shown: vehicle 10, server 20, sensing terminal, and third-party terminal 30.

[0070] The vehicle 10 is equipped with an in-vehicle positioning system, which is mainly used to position the vehicle 10 and generate the in-vehicle positioning information of the vehicle 10.

[0071] The server 20 is equipped with a cloud positioning system, which is mainly used to locate each vehicle traveling on the road, generate the cloud positioning information of each vehicle, and then the server 20 sends the cloud positioning information of each vehicle to the vehicle 10. Among them, the positioning performed by the in-vehicle positioning system is independent of the positioning performed by the cloud positioning system; due to the existence of positioning errors, for the same vehicle, the positioning information obtained by the in-vehicle positioning system in the vehicle for the vehicle may be inconsistent with the positioning information obtained by the cloud positioning system in the server for the vehicle.

[0072] The sensing terminal is mainly used to detect each vehicle traveling on the road and transmit the detection data to the server 20 for the cloud positioning system to generate the cloud positioning information of each vehicle.

[0073] The third-party terminal 30 can communicate with the vehicle 10 or the server 20, and is mainly used to receive the in-vehicle positioning information of the vehicle 10 sent by the vehicle 10 and receive the cloud positioning information of each vehicle sent by the server 20.

[0074] After the third-party terminal 30 receives the in-vehicle positioning information of the vehicle 10 sent to the vehicle 10 and the cloud positioning information of each vehicle sent by the server 20, for a specific vehicle among each vehicle, the third-party terminal 30 determines the probability that the vehicle 10 and the specific vehicle are the same vehicle through the following method: The third-party terminal 30 determines the third positioning error between the in-vehicle positioning system and the cloud positioning system according to the first positioning error of the in-vehicle positioning system and the second positioning error of the cloud positioning system; and then according to the in-vehicle positioning information of the vehicle 10 and the cloud positioning information of the specific vehicle, combined with the third positioning error, determines the probability that the vehicle 10 and the specific vehicle are the same vehicle.

[0075] It should be noted that Figure 1 and Figure 2 The embodiments shown are only exemplary descriptions and should not limit the functions and usage scope of the present disclosure. It can be understood that the first positioning system in the embodiments of the present disclosure can be the cloud positioning system in other servers, or a road base station positioning system or other positioning systems independent of the second positioning system; the second positioning system can be a road base station positioning system or other positioning systems independent of the first positioning system.

[0076] The following describes the specific implementation process of the embodiments of the present disclosure in detail.

[0077] Figure 3 The data processing method of an embodiment of the present disclosure is shown. In this embodiment, the vehicle is exemplarily used as the execution subject, and the method includes:

[0078] Step S410: Obtain the first positioning information of the first vehicle obtained by the first positioning system and the first positioning error of the first positioning system;

[0079] Step S420: Obtain the second positioning information of the second vehicle obtained by the second positioning system and the second positioning error of the second positioning system;

[0080] Step S430: Determine the third positioning error between the first positioning system and the second positioning system based on the first positioning error and the second positioning error;

[0081] Step S440: Determine the probability that the first vehicle and the second vehicle are the same vehicle based on the first positioning information, the second positioning information, and the third positioning error.

[0082] In the embodiments of the present disclosure, by introducing the consideration of the third positioning error between the first positioning system and the second positioning system, the probability that the first vehicle located by the first positioning system and the second vehicle located by the second positioning system are the same vehicle is determined on this basis, thereby improving the application reference value of the first positioning information of the first vehicle and the second positioning information of the second vehicle.

[0083] It should be noted that in the following description of the embodiments of the present disclosure, exemplarily, a vehicle is used as the execution subject, the in-vehicle positioning system in the vehicle is used as the first positioning system, and the cloud positioning system in the server is used as the second positioning system. However, this does not mean that the embodiments of the present disclosure are limited to this situation only. Further, for the purpose of brief description, the vehicle serving as the execution subject is denoted as vehicle A, and the vehicle located by the cloud positioning system is denoted as vehicle B. It can be understood that due to the existence of positioning errors, vehicle A and vehicle B may be the same vehicle or two different vehicles.

[0084] In the embodiments of the present disclosure, the in-vehicle positioning system locates vehicle A to obtain the first positioning information, and the cloud positioning system locates vehicle B to obtain the second positioning information. Among them, vehicle A and vehicle B may be the same vehicle or two different vehicles.

[0085] Vehicle A obtains the first positioning information and the first positioning error of the in-vehicle positioning system, and obtains the second positioning information and the second positioning error of the cloud positioning system. Furthermore, vehicle A can determine the probability that vehicle A and vehicle B are the same vehicle on this basis.

[0086] It is understandable that the positioning error, as an important performance indicator of the positioning system, is usually fixed and public. Therefore, vehicle A can directly obtain the first positioning error of the in-vehicle positioning system and the second positioning error of the cloud positioning system by reading the performance information of the positioning system. For example, the first positioning error of the in-vehicle positioning system and the second positioning error of the cloud positioning system can be obtained by reading the technical manual of the positioning system.

[0087] It should be noted that according to the central limit theorem and the law of large numbers in statistics, for the scenario where the positioning system locates the position of an object extremely frequently, the distance between the position obtained by the positioning system for the vehicle and the actual position of the vehicle follows a normal distribution. Specifically, let the first positioning error of the in-vehicle positioning system be denoted as a, and the second positioning error of the cloud positioning system be denoted as b. Then, the distance between the position obtained by the in-vehicle positioning system for vehicle A (corresponding to the first positioning information) and the actual position of vehicle A follows a normal distribution with a mean of 0 and a variance of a, and the distance between the position obtained by the cloud positioning system for vehicle B (corresponding to the second positioning information) and the actual position of vehicle B follows a normal distribution with a mean of 0 and a variance of b.

[0088] In one embodiment, determining the third positioning error between the first positioning system and the second positioning system based on the first positioning error and the second positioning error includes: determining the sum of the first positioning error and the second positioning error as the third positioning error.

[0089] In this embodiment, after vehicle A obtains the first positioning error a of the in-vehicle positioning system and the second positioning error b of the cloud positioning system, it determines the sum of a and b as the third positioning error c between the in-vehicle positioning system and the cloud positioning system. It is understandable that according to the properties of the normal distribution in statistics, theoretically, the variance between two normal distributions is equal to the sum of the variances of these two normal distributions respectively. Therefore, this embodiment can be used as an implementation method for determining the third positioning error. For example, if vehicle A obtains the first positioning error of the in-vehicle positioning system as 15 meters and the second error of the cloud positioning system as 10 meters, then vehicle A determines the third positioning error between the in-vehicle positioning system and the cloud positioning system as 25 meters.

[0090] In one embodiment, determining the third positioning error between the first positioning system and the second positioning system based on the first positioning error and the second positioning error includes: determining the product of the sum of the first positioning error and the second positioning error and a preset error weight as the third positioning error.

[0091] In this embodiment, vehicle A obtains the sum of the first positioning error a of the in-vehicle positioning system and the second positioning error b of the cloud positioning system, and defines the product of the sum of a and b and the preset error weight r as the third positioning error c between the in-vehicle positioning system and the cloud positioning system, that is, (a + b) * r = c. Among them, the error weight r can be set according to specific application requirements. It should be noted that the error weight r can be between 0 and 1, or greater than 1.

[0092] For example: The preset error weight according to specific application requirements is 0.8. Vehicle A obtains the first positioning error of the in-vehicle positioning system as 15 meters, and the second error of the cloud positioning system as 10 meters. Then vehicle A determines the third positioning error between the in-vehicle positioning system and the cloud positioning system as (15 + 10) × 0.8 = 20 meters.

[0093] It should be noted that this embodiment is only an exemplary illustration and should not limit the functions and usage scope of the present disclosure.

[0094] Next, the specific implementation process of determining the probability that the first vehicle and the second vehicle are the same vehicle based on the first positioning information, the second positioning information, and the third positioning error after determining the third positioning error will be described in detail.

[0095] In one embodiment, based on the first positioning information, the second positioning information, and the third positioning error, determining the probability that the first vehicle and the second vehicle are the same vehicle includes:

[0096] Obtain the probability density function of the normal distribution with a mean of 0 and a variance of the third positioning error;

[0097] Based on the first positioning information and the second positioning information, determine the integration interval of the probability density function;

[0098] Based on the integral value of the probability density function over the integration interval, determine the probability that the first vehicle and the second vehicle are the same vehicle.

[0099] In this embodiment, vehicle A determines the probability that vehicle A and vehicle B are the same vehicle according to the integral of the probability density function of the normal distribution, where the normal distribution is a normal distribution with a mean of 0 and a variance of the third positioning error c.

[0100] Specifically, after vehicle A obtains the first positioning information, the second positioning information, and the third positioning error c, it obtains the probability density function of the normal distribution with a mean of 0 and a variance of the third positioning error c:

[0101]

[0102] Furthermore, based on the first positioning information and the second positioning information, determine the integration interval of f(x); furthermore, based on the integral value of f(x) over this integration interval, determine the probability that vehicle A and vehicle B are the same vehicle.

[0103] It should be noted that this embodiment is only an exemplary illustration and should not limit the functions and scope of use of the present disclosure.

[0104] In one embodiment, determining the integration interval of the probability density function based on the first positioning information and the second positioning information includes:

[0105] Based on the first positioning information and the second positioning information, according to the first vehicle length of the first vehicle and the second vehicle length of the second vehicle, determine the first integration interval for integrating the probability density function in the vehicle length direction;

[0106] Based on the first positioning information and the second positioning information, according to the first vehicle width of the first vehicle and the second vehicle width of the second vehicle, determine the second integration interval for integrating the probability density function in the vehicle width direction;

[0107] Determining the probability that the first vehicle and the second vehicle are the same vehicle based on the integral value of the probability density function over this integration interval includes:

[0108] Based on the integral value of the probability density function over the first integration interval, determine the first probability that the first vehicle and the second vehicle are the same vehicle;

[0109] Based on the integral value of the probability density function over the second integration interval, determine the second probability that the first vehicle and the second vehicle are the same vehicle;

[0110] Based on the first probability and the second probability, determine the probability that the first vehicle and the second vehicle are the same vehicle.

[0111] In this embodiment, when vehicle A determines whether it and vehicle B are the same vehicle, it is determined by the integral values of the probability density function in the vehicle length direction and the vehicle width direction respectively. Denote the first vehicle length of vehicle A as L1, the second vehicle length of vehicle B as L2, the first vehicle width of vehicle A as W1, and the second vehicle width of vehicle B as W2.

[0112] Specifically, vehicle A determines a first integration interval for integrating the probability density function in the vehicle length direction based on the first positioning information and the second positioning information according to L1 and L2; determines a second integration interval for integrating the probability density function in the vehicle width direction based on the first positioning information and the second positioning information according to W1 and W2; then determines a first probability P1 that vehicle A and vehicle B are the same vehicle based on the integral value of the probability density function in the first integration interval, and determines a second probability P2 that vehicle A and vehicle B are the same vehicle based on the integral value of the probability density function in the second integration interval; and then determines a probability P that vehicle A and vehicle B are the same vehicle according to P1 and P2.

[0113] In one embodiment, determining the first integration interval for integrating the probability density function in the vehicle length direction based on the first positioning information and the second positioning information according to the first vehicle length of the first vehicle and the second vehicle length of the second vehicle includes:

[0114] Determining a first interval length of the first integration interval based on the first vehicle length;

[0115] Determining a first center distance between the first vehicle and the second vehicle in the vehicle length direction based on the first positioning information, the second positioning information, the first vehicle length, and the second vehicle length;

[0116] Determining a first left boundary of the first integration interval based on the first center distance;

[0117] Determining the first integration interval based on the first interval length and the first left boundary.

[0118] In this embodiment, vehicle A determines the first interval length of the first integration interval based on the first vehicle length L1 of vehicle A. For example: vehicle A determines L1 as the first interval length of the first integration interval; or, vehicle A presets a vehicle length weight R1 and determines the product of L1 and R1 as the first interval length of the first integration interval.

[0119] Vehicle A determines the first left boundary of the first integration interval based on the first center distance between Vehicle A and Vehicle B in the vehicle length direction. Specifically, the first center distance between Vehicle A and Vehicle B in the vehicle length direction is obtained based on the first positioning information, the second positioning information, L1, and L2. For example: Vehicle A projects Vehicle B onto the vehicle length direction of Vehicle A based on the first positioning information and the second positioning information, and then determines the center distance between Vehicle A and the projected Vehicle B based on L1 and L2, that is, the first center distance between Vehicle A and Vehicle B in the vehicle length direction; or, Vehicle A projects Vehicle A onto the vehicle length direction of Vehicle B based on the first positioning information and the second positioning information, and then determines the center distance between the projected Vehicle A and Vehicle B based on L1 and L2, that is, the first center distance between Vehicle A and Vehicle B in the vehicle length direction. Then Vehicle A determines this first center distance as the first left boundary of the first integration interval; or, Vehicle A determines the product of this first center distance and a preset center distance weight as the first left boundary of the first integration interval, and this center distance weight can be preset according to application requirements.

[0120] Then Vehicle A determines the first integration interval based on this first interval length and this first left boundary.

[0121] It should be noted that this embodiment is only an exemplary illustration and should not limit the functions and usage scope of the present disclosure. It can be understood that Vehicle A can also determine the first interval length of the first integration interval according to the second vehicle length L2; Vehicle A can also determine max(first center distance - L2 / 2, 0) as the first left boundary of the first integration interval.

[0122] In one embodiment, based on the first positioning information and the second positioning information, according to the first vehicle width of the first vehicle and the second vehicle width of the second vehicle, determining the second integration interval for integrating the probability density function in the vehicle width direction includes:

[0123] Determine the second interval length of the second integration interval based on the first vehicle width;

[0124] Based on the first positioning information, the second positioning information, the first vehicle width, and the second vehicle width, determine the second center distance between the first vehicle and the second vehicle in the vehicle width direction;

[0125] Determine the second left boundary of the second integration interval based on the second center distance;

[0126] Determine the second integration interval based on the second interval length and the second left boundary.

[0127] In this embodiment, vehicle A determines the second interval length of the second integration interval based on the first vehicle width W1 of vehicle A; and then determines the second left boundary of the second integration interval based on the second center distance between vehicle A and vehicle B in the vehicle width direction. Specifically, the second center distance between vehicle A and vehicle B in the vehicle width direction is obtained based on the first positioning information, the second positioning information, W1, and W2. Then, vehicle A determines the second integration interval based on the second interval length and the second left boundary.

[0128] It can be understood that the specific implementation process of this embodiment is the same as that of determining the first integration interval, so it will not be elaborated here.

[0129] Figure 4 The figure shows a schematic diagram of determining the first integration interval and the second integration interval in an embodiment of the present disclosure.

[0130] In this embodiment, the first vehicle length L1 of vehicle A is directly determined as the first interval length of the first integration interval, the first vehicle width W1 of vehicle A is determined as the second interval length of the second integration interval, the difference between the first center distance between vehicle A and vehicle B in the vehicle length direction and half of the first vehicle length L1 is determined as the first left boundary of the first integration interval, and the difference between the second center distance between vehicle A and vehicle B in the vehicle width direction and half of the first vehicle width W1 is determined as the second left boundary of the second integration interval. Thus, the first integration interval [S*cosU - L1 / 2, S*cosU + L1 / 2] is obtained, and the second integration interval [S*sinU - W1 / 2, S*sinU + W1 / 2] is obtained.

[0131] It should be noted that this embodiment is only an exemplary illustration and should not limit the functions and usage scope of the present disclosure.

[0132] In an embodiment, vehicle A determines the integral value of the probability density function over the first integration interval as the first probability P1 that the first vehicle and the second vehicle are the same vehicle.

[0133] It should be noted that this embodiment is only an exemplary illustration and should not limit the functions and usage scope of the present disclosure.

[0134] In an embodiment, vehicle A determines the product of the integral value of the probability density function over the first integration interval and a preset probability weight as the first probability P1 that the first vehicle and the second vehicle are the same vehicle. Wherein, the probability weight can be preset according to application requirements.

[0135] It should be noted that this embodiment is only an exemplary illustration and should not limit the functions and usage scope of the present disclosure.

[0136] In one embodiment, vehicle A determines the integral value of the probability density function over the second integral interval as the second probability P2 that the first vehicle and the second vehicle are the same vehicle.

[0137] It should be noted that this embodiment is only an exemplary illustration and should not limit the functions and scope of use of the present disclosure.

[0138] In one embodiment, vehicle A determines the product of the integral value of the probability density function over the second integral interval and a preset probability weight as the second probability P2 that the first vehicle and the second vehicle are the same vehicle. Wherein, the probability weight can be preset according to application requirements.

[0139] It should be noted that this embodiment is only an exemplary illustration and should not limit the functions and scope of use of the present disclosure.

[0140] In one embodiment, determining the probability that the first vehicle and the second vehicle are the same vehicle based on the first probability and the second probability includes: determining the sum of the first probability and the second probability as the probability that the first vehicle and the second vehicle are the same vehicle.

[0141] In this embodiment, vehicle A directly determines the sum of the first probability and the second probability as the probability that vehicle A and vehicle B are the same vehicle, and can then be used as a reference for subsequent applications based on this to improve the accuracy of subsequent applications.

[0142] In one embodiment, determining the probability that the first vehicle and the second vehicle are the same vehicle based on the first probability and the second probability includes: dividing the sum of the first probability and the second probability by a reference probability to obtain the probability that the first vehicle and the second vehicle are the same vehicle, where the reference probability is the sum of a first reference probability and a second reference probability, the first reference probability is the first probability obtained when the first left boundary is 0, and the second reference probability is the second probability obtained when the second left boundary is 0.

[0143] In this embodiment, vehicle A determines a reference probability used as a reference. The reference probability is the sum of a first reference probability P 01 and a second reference probability P 02 . Wherein, the main difference between the first reference probability P 01 and the first probability P1 is that the first left boundary of the first integral interval used to obtain the first probability P1 is based on the first center distance, while the left boundary of the integral interval used to obtain the first reference probability P 01 is 0; the second reference probability P 02The notable difference from the second probability P2 is that the second left boundary of the second integration interval used to obtain the second probability P2 is based on the second center distance, while the left boundary of the integration interval used to obtain the second reference probability P2 is 0.

[0144] Furthermore, vehicle A determines the value of (P1 + P2) / (P 01 + P 02 ) as the probability P that vehicle A and vehicle B are the same vehicle, that is, (P1 + P2) / (P 01 + P 02 ) = P.

[0145] For example: The distance between the center lines of vehicle A and vehicle B is denoted as S, and the angle between this center line and the driving direction of vehicle A is denoted as U (preferably taking the acute angle). If the first vehicle length L1 of vehicle A is directly determined as the first interval length of the first integration interval, the first center distance obtained by projecting vehicle B in the vehicle length direction of vehicle A is determined as the first left boundary of the first integration interval, the first vehicle width W1 of vehicle A is determined as the second interval length of the second integration interval, and the second center distance obtained by projecting vehicle B in the vehicle width direction of vehicle A is determined as the second left boundary of the second integration interval, then the first probability P1 is the integral value of the probability density function f(x) over the interval [max(S*cosU - L1 / 2, 0), S*cosU + L1 / 2], the second probability P2 is the integral value of the probability density function f(x) over the interval [max(S*sinU - W1 / 2, 0), S*sinU + W1 / 2], the first reference probability P 01 is the integral value of the probability density function f(x) over the interval [0, S*cosU + L1 / 2], and the second reference probability P 02 is the integral value of the probability density function f(x) over the interval [0, S*sinU + W1 / 2].

[0146] It should be noted that this embodiment is only an exemplary illustration and should not limit the functions and scope of use of the present disclosure.

[0147] In one embodiment, determining the integration interval of the probability density function based on the first positioning information and the second positioning information includes:

[0148] Based on the first positioning information and the second positioning information, determine the interval length of the integration interval according to the first vehicle length of the first vehicle, the second vehicle length of the second vehicle, the first vehicle width of the first vehicle, and the second vehicle width of the second vehicle;

[0149] Based on the first positioning information and the second positioning information, determine the center distance between the first vehicle and the second vehicle;

[0150] Determine the left boundary of the integration interval based on the center distance;

[0151] Determine the integration interval based on the interval length and the left boundary.

[0152] In this embodiment, vehicle A determines whether vehicle A and vehicle B are the same vehicle through the integral value of the probability density function in the direction of the center line connecting vehicle A and vehicle B; and determines the interval length of the integration interval according to the vehicle length and vehicle width. Denote the first vehicle length of vehicle A as L1, the second vehicle length of vehicle B as L2, the first vehicle width of vehicle A as W1, and the second vehicle width of vehicle B as W2.

[0153] Specifically, vehicle A determines the interval length of the integration interval based on the first positioning information and the second positioning information, according to the first vehicle length L1, the second vehicle length L2, the first vehicle width W1, and the second vehicle width W2.

[0154] Vehicle A determines the center distance between vehicle A and vehicle B based on the first positioning information and the second positioning information, and determines the left boundary of the integration interval based on the center distance; and then determines the integration interval based on the interval length and the left boundary.

[0155] Figure 5 Shows a schematic diagram of determining the integration interval in an embodiment of the present disclosure.

[0156] In this embodiment, vehicle A determines the coordinates of a1, a2, a3, and b1 based on the first positioning information and the second positioning information, according to L1, W1, L2, and W2, using geometric knowledge, where a1 is the center point of vehicle A and b1 is the center point of vehicle B. Vehicle A determines the interval length of the integration interval as the distance between a2 and a3, and determines the left boundary of the integration interval as the distance S1 between a2 and b1, thereby obtaining the integration interval [S1, S2], where the difference between S1 and S2, i.e., the interval length of the integration interval, is the distance between a2 and a3.

[0157] It should be noted that this embodiment is only an exemplary illustration and should not limit the functions and usage scope of the present disclosure.

[0158] In an embodiment, determining the integration interval of the probability density function based on the first positioning information and the second positioning information includes:

[0159] Obtain the preset interval length of the integration interval;

[0160] Determine the center distance between the first vehicle and the second vehicle based on the first positioning information and the second positioning information;

[0161] Determine the left boundary of the integration interval based on the center distance;

[0162] Determine the integration interval based on the interval length and the left boundary.

[0163] In this embodiment, vehicle A determines whether vehicle A and vehicle B are the same vehicle through the integral value of the probability density function in the direction of the center line connecting vehicle A and vehicle B; and the interval length of the integration interval is preset. Denote the first vehicle length of vehicle A as L1, the second vehicle length of vehicle B as L2, the first vehicle width of vehicle A as W1, and the second vehicle width of vehicle B as W2.

[0164] Specifically, vehicle A obtains the interval length of the preset integration interval; determines the center distance between vehicle A and vehicle B based on the first positioning information and the second positioning information, and determines the left boundary of the integration interval based on the center distance; and then determines the integration interval based on the interval length and the left boundary.

[0165] The advantage of this embodiment is that through this method, vehicle A does not need to obtain information on vehicle length and width, reducing the requirements for information collection and improving the operation speed.

[0166] It should be noted that this embodiment is only an exemplary illustration and should not limit the functions and usage scope of the present disclosure.

[0167] In one embodiment, determining the probability that the first vehicle and the second vehicle are the same vehicle based on the first positioning information, the second positioning information, and the third positioning error includes:

[0168] Determine the center distance between the first vehicle and the second vehicle based on the first positioning information and the second positioning information;

[0169] Determine the distance interval where the center distance is located based on the third positioning error;

[0170] Determine the probability that the first vehicle and the second vehicle are the same vehicle based on the position of the preset reference distance in the distance interval.

[0171] In this embodiment, vehicle A determines the range where the center distance between vehicle A and vehicle B is located through the third positioning error, and then measures the probability that vehicle A and vehicle B are the same vehicle according to the position of the reference distance in this range.

[0172] Specifically, vehicle A determines the center distance between vehicle A and vehicle B based on the first positioning information and the second positioning information; determines the distance interval where the center distance is located based on the third positioning error; and then determines the probability that vehicle A and vehicle B are the same vehicle based on the position of the preset reference distance in this range.

[0173] For example, the preset reference distance is 20 meters. Vehicle A determines that the center distance between Vehicle A and Vehicle B is 30 meters based on the first positioning information and the second positioning information. If the third positioning error is 25 meters, it can be determined that the center distance fluctuates by 25 meters left and right, that is, the distance interval where the center distance is located is [5, 55]. When it falls into the interval [5, 20], it can be regarded that Vehicle A and Vehicle B are the same vehicle. When it falls into the interval [20, 55], it can be regarded that Vehicle A and Vehicle B are different vehicles. Then the probability that Vehicle A and Vehicle B are the same vehicle can be determined as (20 - 5) / 50 = 0.30.

[0174] The advantage of this embodiment is that the distance interval is determined through the third positioning error, and then the probability that the first vehicle and the second vehicle are the same vehicle is determined according to the position of the reference distance in the distance interval, reducing the computing power consumption.

[0175] It should be noted that this embodiment is only an exemplary illustration and should not limit the functions and usage scope of the present disclosure.

[0176] The following table shows the evaluation results of the collision risks brought by each vehicle located by the cloud positioning system in the server to Vehicle A in an embodiment of the present disclosure.

[0177] Vehicle Risks to this vehicle Vehicle B The risk of collision with this vehicle is very high, with a probability of 0.91 being this vehicle Vehicle C The risk of collision with this vehicle is very high, with a probability of 0.87 being this vehicle Vehicle D The risk of collision with this vehicle is very high, with a probability of 0.71 being this vehicle Vehicle E The risk of collision with this vehicle is relatively low, with a probability of 0.51 being this vehicle Vehicle F The risk of collision with this vehicle is relatively low, with a probability of 0.41 being this vehicle Vehicle G The risk of collision with this vehicle is very low, with a probability of 0.21 being this vehicle Vehicle H The risk of collision with this vehicle is very low, with a probability of 0.11 being this vehicle

[0178] Table 1

[0179] In this embodiment, during the driving process of Vehicle A on the road, its built-in vehicle-mounted positioning system will position itself. At the same time, the cloud positioning system in the server will position each vehicle on the road. The server sends the positioning information of each vehicle located by the cloud positioning system (from the positioning information of Vehicle B to the positioning information of Vehicle H) to Vehicle A. On this basis, Vehicle A determines the collision risks between Vehicle B to Vehicle H and itself according to the built-in collision risk assessment logic, and also determines the probabilities that Vehicle B to Vehicle H are the same vehicle as itself according to the data processing method provided by the present disclosure, thereby obtaining this table.

[0180] If the probability threshold for determining the same vehicle is 0.95, then through this table, Vehicle A can determine that: although the probability that Vehicle B and the vehicle itself are the same vehicle is the highest but does not exceed 0.95, Vehicle B is not the vehicle itself, and a warning needs to be given for Vehicle B; Vehicles C and D, which have a high collision risk with the vehicle itself, are also not the vehicle itself, so warnings also need to be given for Vehicles C and D.

[0181] If the probability threshold for determining the same vehicle is 0.90, vehicle A can determine through this table that the probability that vehicle B is the same vehicle as itself is the highest and exceeds 0.90. Then vehicle B is itself. Although the collision risk between vehicle B and itself is very high, no warning needs to be issued for vehicle B. Vehicles C and D, which have a very high collision risk with itself, are not itself. Therefore, warnings need to be issued for vehicle C and vehicle D.

[0182] If the probability threshold for determining the same vehicle is 0.85, vehicle A can determine through this table that although the probability that vehicle C is the same vehicle as itself exceeds 0.85, it is not the highest. The vehicle with the highest probability of being the same vehicle as itself is vehicle B. And there cannot be two selves at the same time. Therefore, vehicle B is itself and vehicle C is not itself. Then although the collision risk between vehicle B and itself is very high, no warning needs to be issued for vehicle B. Vehicles C and D, which have a very high collision risk with itself, are not itself. Therefore, warnings need to be issued for vehicle C and vehicle D.

[0183] It should be noted that this embodiment is only an exemplary illustration and should not limit the functions and scope of use of the present disclosure. It can be understood that in addition to being applicable to collision risk assessment, the embodiments of the present disclosure can also be applied to other application scenarios that require differentiating vehicles from different positioning systems.

[0184] According to an embodiment of the present disclosure, as Figure 6 shown, there is also provided a data processing device, the device includes:

[0185] A first acquisition module 510, configured to acquire first positioning information obtained by a first positioning system for positioning a first vehicle and a first positioning error of the first positioning system;

[0186] A second acquisition module 520, configured to acquire second positioning information obtained by a second positioning system for positioning a second vehicle and a second positioning error of the second positioning system;

[0187] A first determination module 530, configured to determine a third positioning error between the first positioning system and the second positioning system based on the first positioning error and the second positioning error;

[0188] A second determination module 540, configured to determine the probability that the first vehicle and the second vehicle are the same vehicle based on the first positioning information, the second positioning information, and the third positioning error.

[0189] In an exemplary embodiment of the present disclosure, the device is configured to:

[0190] Acquire the probability density function of a normal distribution with a mean of 0 and a variance of the third positioning error;

[0191] Determine the integration interval of the probability density function based on the first positioning information and the second positioning information;

[0192] Determine the probability that the first vehicle and the second vehicle are the same vehicle based on the integral value of the probability density function over the integration interval.

[0193] In an exemplary embodiment of the present disclosure, the device is configured to:

[0194] Based on the first positioning information and the second positioning information, and according to the first vehicle length of the first vehicle and the second vehicle length of the second vehicle, determine the first integration interval for integrating the probability density function in the vehicle length direction;

[0195] Based on the first positioning information and the second positioning information, and according to the first vehicle width of the first vehicle and the second vehicle width of the second vehicle, determine the second integration interval for integrating the probability density function in the vehicle width direction;

[0196] Determine the first probability that the first vehicle and the second vehicle are the same vehicle based on the integral value of the probability density function over the first integration interval;

[0197] Determine the second probability that the first vehicle and the second vehicle are the same vehicle based on the integral value of the probability density function over the second integration interval;

[0198] Determine the probability that the first vehicle and the second vehicle are the same vehicle based on the first probability and the second probability.

[0199] In an exemplary embodiment of the present disclosure, the device is configured to:

[0200] Determine the first interval length of the first integration interval based on the first vehicle length;

[0201] Based on the first positioning information, the second positioning information, the first vehicle length, and the second vehicle length, determine the first center distance between the first vehicle and the second vehicle in the vehicle length direction;

[0202] Determine the first left boundary of the first integration interval based on the first center distance;

[0203] Determine the first integration interval based on the first interval length and the first left boundary.

[0204] In an exemplary embodiment of the present disclosure, the device is configured to:

[0205] Determine the second interval length of the second integration interval based on the first vehicle width;

[0206] Determine a second center distance between the first vehicle and the second vehicle in the vehicle width direction based on the first positioning information, the second positioning information, the first vehicle width, and the second vehicle width;

[0207] Determine a second left boundary of the second integration interval based on the second center distance;

[0208] Determine the second integration interval based on the second interval length and the second left boundary.

[0209] In an exemplary embodiment of the present disclosure, the apparatus is configured to:

[0210] Determine an interval length of the integration interval based on the first positioning information and the second positioning information according to a first vehicle length of the first vehicle, a second vehicle length of the second vehicle, a first vehicle width of the first vehicle, and a second vehicle width of the second vehicle;

[0211] Determine a center distance between the first vehicle and the second vehicle based on the first positioning information and the second positioning information;

[0212] Determine a left boundary of the integration interval based on the center distance;

[0213] Determine the integration interval based on the interval length and the left boundary.

[0214] In an exemplary embodiment of the present disclosure, the apparatus is configured to:

[0215] Obtain a preset interval length of the integration interval;

[0216] Determine a center distance between the first vehicle and the second vehicle based on the first positioning information and the second positioning information;

[0217] Determine a left boundary of the integration interval based on the center distance;

[0218] Determine the integration interval based on the interval length and the left boundary.

[0219] In an exemplary embodiment of the present disclosure, the apparatus is configured to:

[0220] Determine a center distance between the first vehicle and the second vehicle based on the first positioning information and the second positioning information;

[0221] Determine a distance interval where the center distance is located based on the third positioning error;

[0222] Determine a probability that the first vehicle and the second vehicle are the same vehicle based on a position of a preset reference distance in the distance interval.

[0223] Reference will now be made to Figure 7 describe the data processing electronic device 60 according to an embodiment of the present disclosure. Figure 7 The data processing electronic device 60 shown is merely an example and should not impose any limitation on the functions and scope of use of the embodiments of the present disclosure.

[0224] As Figure 7 shown, the data processing electronic device 60 is presented in the form of a general-purpose computing device. The components of the data processing electronic device 60 may include, but are not limited to: at least one of the above-mentioned processing units 610, at least one of the above-mentioned storage units 620, and a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610).

[0225] Among them, the storage unit stores program code, and the program code can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present invention described in the description part of the above exemplary method of this specification. For example, the processing unit 610 can execute each step as shown in Figure 3 shown.

[0226] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only storage unit (ROM) 6203.

[0227] The storage unit 620 may further include a program / utility 6204 having a set (at least one) of program modules 6205. Such program modules 6205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0228] The bus 630 may represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any bus structure in a variety of bus structures.

[0229] The data processing electronic device 60 can also communicate with one or more external devices 700 (such as a keyboard, a pointing device, a Bluetooth device, etc.), and can also communicate with one or more devices that enable a user to interact with the data processing electronic device 60, and / or communicate with any device that enables the data processing electronic device 60 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 650. The input / output (I / O) interface 650 is connected to the display unit 640. Also, the data processing electronic device 60 can communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 660. As shown in the figure, the network adapter 660 communicates with other modules of the data processing electronic device 60 through the bus 630. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in combination with the data processing electronic device 60, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0230] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software, or can be implemented by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, and the software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0231] In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a computer, the computer is enabled to execute the method described in the method embodiment part above.

[0232] According to an embodiment of the present disclosure, there is also provided a program product for implementing the method in the above method embodiment. It can adopt a portable compact disc read-only memory (CD-ROM) and include program code, and can run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, apparatus, or device.

[0233] The program product may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0234] The computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium may also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0235] The program code contained on the readable medium may be transmitted by any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the above.

[0236] The program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages such as JAVA, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., by connecting through the Internet using an Internet service provider).

[0237] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, such a division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of the two or more modules or units described above may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above may be further divided and embodied by multiple modules or units.

[0238] In addition, although the various steps of the methods in the present disclosure are described in a specific order in the accompanying drawings, this does not require or imply that these steps must be performed in that specific order, or that all of the steps shown must be performed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc.

[0239] From the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the methods according to the embodiments of the present disclosure.

[0240] After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily conceive of other embodiments of the present disclosure. This application is intended to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include known common knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the present disclosure are pointed out by the appended claims.

Claims

1. A data processing method, characterized in that, The method includes: Obtaining first positioning information obtained by a first positioning system for positioning a first vehicle and a first positioning error of the first positioning system; Obtaining second positioning information obtained by a second positioning system for positioning a second vehicle and a second positioning error of the second positioning system; Determining a third positioning error between the first positioning system and the second positioning system based on the first positioning error and the second positioning error; Determining the probability that the first vehicle and the second vehicle are the same vehicle based on the first positioning information, the second positioning information, and the third positioning error; Determining the probability that the first vehicle and the second vehicle are the same vehicle based on the first positioning information, the second positioning information, and the third positioning error, including: Obtaining a probability density function of a normal distribution with a mean of 0 and a variance of the third positioning error; Determining an integration interval of the probability density function based on the first positioning information and the second positioning information; Determining the probability that the first vehicle and the second vehicle are the same vehicle based on the integral value of the probability density function over the integration interval.

2. The method according to claim 1, wherein Determining the integration interval of the probability density function based on the first positioning information and the second positioning information, including: Determining a first integration interval for integrating the probability density function in the vehicle length direction based on the first positioning information and the second positioning information, according to a first vehicle length of the first vehicle and a second vehicle length of the second vehicle; Determining a second integration interval for integrating the probability density function in the vehicle width direction based on the first positioning information and the second positioning information, according to a first vehicle width of the first vehicle and a second vehicle width of the second vehicle; Determining the probability that the first vehicle and the second vehicle are the same vehicle based on the integral value of the probability density function over the integration interval, including: Determining a first probability that the first vehicle and the second vehicle are the same vehicle based on the integral value of the probability density function over the first integration interval; Determining a second probability that the first vehicle and the second vehicle are the same vehicle based on the integral value of the probability density function over the second integration interval; Determining the probability that the first vehicle and the second vehicle are the same vehicle based on the first probability and the second probability.

3. The method according to claim 2, wherein Determining a first integration interval for integrating the probability density function in the vehicle length direction based on the first positioning information and the second positioning information, according to a first vehicle length of the first vehicle and a second vehicle length of the second vehicle, including: Determining a first interval length of the first integration interval based on the first vehicle length; Determining a first center distance between the first vehicle and the second vehicle in the vehicle length direction based on the first positioning information, the second positioning information, the first vehicle length, and the second vehicle length; Determining a first left boundary of the first integration interval based on the first center distance; Determining the first integration interval based on the first interval length and the first left boundary.

4. The method according to claim 2, characterized in that, Based on the first positioning information and the second positioning information, and according to the first vehicle width of the first vehicle and the second vehicle width of the second vehicle, determining a second integration interval for integrating the probability density function in the vehicle width direction, includes: Determining a second interval length of the second integration interval based on the first vehicle width; Determining a second center distance between the first vehicle and the second vehicle in the vehicle width direction based on the first positioning information, the second positioning information, the first vehicle width, and the second vehicle width; Determining a second left boundary of the second integration interval based on the second center distance; Determining the second integration interval based on the second interval length and the second left boundary.

5. The method according to claim 1, wherein Determining an integration interval of the probability density function based on the first positioning information and the second positioning information, includes: Determining an interval length of the integration interval according to the first vehicle length of the first vehicle, the second vehicle length of the second vehicle, the first vehicle width of the first vehicle, and the second vehicle width of the second vehicle based on the first positioning information and the second positioning information; Determining a center distance between the first vehicle and the second vehicle based on the first positioning information and the second positioning information; Determining a left boundary of the integration interval based on the center distance; Determining the integration interval based on the interval length and the left boundary.

6. The method according to claim 2, wherein Determining an integration interval of the probability density function based on the first positioning information and the second positioning information, includes: Obtaining a preset interval length of the integration interval; Determining a center distance between the first vehicle and the second vehicle based on the first positioning information and the second positioning information; Determining a left boundary of the integration interval based on the center distance; Determining the integration interval based on the interval length and the left boundary.

7. The method according to claim 1, characterized in that Determining a probability that the first vehicle and the second vehicle are the same vehicle based on the first positioning information, the second positioning information, and the third positioning error, includes: Determining a center distance between the first vehicle and the second vehicle based on the first positioning information and the second positioning information; Determining a distance interval where the center distance is located based on the third positioning error; Determining a probability that the first vehicle and the second vehicle are the same vehicle based on a position of a preset reference distance in the distance interval.

8. A data processing device, characterized in that, The device includes: A first acquisition module configured to acquire first positioning information obtained by a first positioning system for positioning a first vehicle and a first positioning error of the first positioning system; A second acquisition module configured to acquire second positioning information obtained by a second positioning system for positioning a second vehicle and a second positioning error of the second positioning system; A first determination module configured to determine a third positioning error between the first positioning system and the second positioning system based on the first positioning error and the second positioning error; A second determination module configured to determine a probability that the first vehicle and the second vehicle are the same vehicle based on the first positioning information, the second positioning information, and the third positioning error; The device is configured to: Obtain the probability density function of a normal distribution with a mean of 0 and a variance of the third positioning error; determine the integration interval of the probability density function based on the first positioning information and the second positioning information; determine the probability that the first vehicle and the second vehicle are the same vehicle based on the integral value of the probability density function over the integration interval.

9. A data processing electronic device, characterized in that, Comprising: A memory storing computer-readable instructions; A processor that reads the computer-readable instructions stored in the memory to execute the method according to any one of claims 1-7.

10. A computer program medium, characterized in that, Stored thereon are computer-readable instructions that, when executed by a processor of a computer, cause the computer to execute the method according to any one of claims 1-7.

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