Data processing method and device, electronic equipment and vehicle

Abstract

The present application relates to a kind of data processing method, device, electronic equipment and vehicle, it relates to data processing technical field.Data processing method, method includes: the observation data that perception sensor gathers is acquired.Based on observation data, the first coordinate position in the preset coordinate system of observation object is determined, and inverse conversion is carried out to the first coordinate position, obtain second coordinate position;Observation object is at least one in the observation object of any one of perception sensor.The first comparison result is obtained by comparing first coordinate position and second coordinate position, and first check failure message is generated in the case where first comparison result satisfies first preset condition.The driving safety of vehicle is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to the technical field of data processing in a vehicle, and more particularly to a data processing method and device, an electronic device and a vehicle. BACKGROUND

[0002] Automatic driving technology usually collects raw data of road conditions by using various sensors (such as laser radar, camera and millimeter wave radar), and fuses the raw data to obtain road condition data, so that the vehicle can perceive the road conditions in real time, and thus realize automatic driving of the vehicle.

[0003] However, due to the complexity of the fusion algorithm, even if each sensor is working normally, the algorithm itself may fail, resulting in unreasonable road condition data, thereby affecting the driving safety of the vehicle. Therefore, how to improve the driving safety of the vehicle is a technical problem to be solved at present. SUMMARY

[0004] The present application provides a data processing method and device, an electronic device and a vehicle to solve the technical problem of how to improve the driving safety of the vehicle in the prior art. The technical solution of the present application is as follows:

[0005] According to a first aspect of the present application, a data processing method is provided, which comprises: acquiring observation data collected by a perception sensor. Based on the observation data, a first coordinate position of an observation object in a preset coordinate system is determined, and the first coordinate position is inversely converted to obtain a second coordinate position; the observation object is any one of at least one object observed by the perception sensor. The first coordinate position and the second coordinate position are compared to obtain a first comparison result, and a first check failure message is generated when the first comparison result meets a first preset condition.

[0006] According to the above technical means, the input coordinate position and the coordinate position obtained after inverse conversion are compared and checked to obtain a first comparison result, and the coordinate conversion is determined to be abnormal when the first comparison result meets a first preset condition, thereby generating a first check failure message. In this way, the data is checked, and when unreasonable road condition data is obtained, a first check failure message is generated to prompt the driver, thereby improving the driving safety of the vehicle.

[0007] In one possible implementation, the method further includes: determining uniform motion data of the observed object based on observation data and a preset uniform motion algorithm; the uniform motion data includes a first longitudinal position, a first lateral position, a first longitudinal velocity, and a first lateral velocity. Determining uniform acceleration motion data of the observed object based on observation data and a preset uniform acceleration motion algorithm. The uniform acceleration motion data includes a second longitudinal position, a second lateral position, a second longitudinal velocity, and a second lateral velocity. Comparing the uniform motion data and the uniform acceleration motion data to obtain a second comparison result, and generating a second verification error message if the second comparison result meets a second preset condition.

[0008] Based on the aforementioned technical means, the electronic device synchronizes the time of the observed objects by each sensing sensor using both acceleration and uniform acceleration algorithms, and compares the results calculated by the two algorithm models. Furthermore, if the result deviation is significant, a time synchronization error is identified, and a verification error message is generated to alert the driver. Thus, verifying time synchronization improves driving safety.

[0009] In one possible implementation, the method further includes: determining the observed position and predicted position of the observed object. The observed position is the position observed by the sensing sensor at the current moment, and the predicted position is the position predicted at the current moment based on the historical position of the observed object. A third comparison result is obtained by comparing a first distance with a first distance threshold, and a third error message is generated if the third comparison result meets a third preset condition; the first distance is the distance between the observed position and the predicted position.

[0010] Based on the aforementioned technical means, the electronic device determines whether there is an error in the data fusion process based on the distance between the observed position and the predicted position. If the distance between the observed position and the predicted position is too large, an error message is generated to alert the driver, thereby improving driving safety.

[0011] In one possible implementation, if the third comparison result does not meet the third preset condition, and the sensing sensor is multiple sensing sensors, the method further includes: generating a matching identifier pair based on the observation object identifier corresponding to the same observation object, and generating a fourth error message if the observation object identifier in the matching identifier pair changes; the matching identifier pair includes multiple observation object identifiers, and one observation object identifier is used to identify that a sensing sensor observes an observation object.

[0012] Based on the aforementioned technical means, when the identifier of the observed object in the matching identifier pair changes, the electronic device determines that some of the multiple perception sensors are malfunctioning and generates a third error message to notify the driver of the autonomous driving abnormality, thereby improving vehicle driving safety.

[0013] In one possible implementation, if the third comparison result meets the third preset condition and the observed object identifier in the matched identifier pair remains unchanged, the method further includes: obtaining the position change and velocity change of the observed object per unit time. If the position change is greater than a second distance threshold and the velocity change is greater than a velocity threshold, a fifth error message is generated.

[0014] Based on the aforementioned technical methods, the system determines whether there are problems with data fusion by analyzing the changes in the observed object's position and speed within a unit of time. Furthermore, if a problem is detected, the electronic device generates a fifth error message to alert the driver, thereby improving vehicle safety.

[0015] In one possible implementation, if the third comparison result does not meet the third preset condition, the object identifier in the matched identifier pair changes, or the position change is greater than the second distance threshold and the velocity change is greater than the velocity threshold, the above method further includes: determining the topological change of the first distribution topological feature value and the second distribution topological feature value, and generating a sixth error message if the topological change is greater than the topological change threshold; the first distribution topological feature value is used to characterize the distribution characteristics of multiple observation locations, and the second distribution topological feature value is used to characterize the distribution characteristics of multiple prediction locations.

[0016] Based on the aforementioned technical methods, when the distribution characteristics of the observed object differ significantly from those of the predicted object, a data fusion error is identified, and a sixth error message is generated. This improves vehicle driving safety.

[0017] In one possible implementation, the first coordinate position includes a third lateral position and a third longitudinal position, and the second coordinate position includes a fourth lateral position and a fourth longitudinal position. The aforementioned "comparing the first coordinate position and the second coordinate position to obtain a first comparison result" includes: determining the absolute value of the difference between the third lateral position and the second lateral position to obtain a first absolute value, and comparing the first absolute value with a third distance threshold to obtain a lateral comparison result. Similarly, determining the absolute value of the difference between the fourth longitudinal position and the second longitudinal position to obtain a second absolute value, and comparing the second absolute value with a fourth distance threshold to obtain a longitudinal comparison result.

[0018] Based on the aforementioned technical means, electronic devices obtain comparison results by judging the distance of observed objects in the same direction.

[0019] In one possible implementation, the first preset condition includes: a first absolute value is greater than a third distance threshold, or a second absolute value is greater than a fourth distance threshold.

[0020] According to a second aspect of the present invention, a data processing apparatus is provided, comprising: an acquisition unit, a determination unit, a processing unit, and a generation unit; the acquisition unit is used to acquire observation data collected by a sensing sensor. The determination unit is used to determine a first coordinate position of an observed object in a preset coordinate system based on the observation data, and to perform an inverse transformation on the first coordinate position to obtain a second coordinate position; the observed object is any one of at least one object observed by the sensing sensor. The processing unit is used to compare the first coordinate position and the second coordinate position to obtain a first comparison result. The generation unit is used to generate a first verification failure message if the first comparison result meets a first preset condition.

[0021] In one possible implementation, the determining unit is further configured to determine uniform motion data of the observed object based on the observation data and a preset uniform motion algorithm; the uniform motion data includes a first longitudinal position, a first lateral position, a first longitudinal velocity, and a first lateral velocity. The determining unit is further configured to determine uniformly accelerated motion data of the observed object based on the observation data and a preset uniformly accelerated motion algorithm. The uniformly accelerated motion data includes a second longitudinal position, a second lateral position, a second longitudinal velocity, and a second lateral velocity. The processing unit is further configured to compare the uniform motion data and the uniformly accelerated motion data to obtain a second comparison result. The generating unit is further configured to generate a second verification error message if the second comparison result meets a second preset condition.

[0022] In one possible implementation, the determining unit is further configured to determine the observed position and the predicted position of the observed object. The observed position is the position observed by the sensing sensor at the current moment, and the predicted position is the position predicted at the current moment based on the historical position of the observed object. The processing unit is further configured to compare a first distance with a first distance threshold to obtain a third comparison result. The generating unit is further configured to generate a third error message if the third comparison result meets a third preset condition; the first distance is the distance between the observed position and the predicted position.

[0023] In one possible implementation, if the third comparison result does not meet the third preset condition, and the sensing sensor is multiple sensing sensors, the generation unit is further configured to generate a matching identifier pair based on the observation object identifier corresponding to the same observation object. The generation unit is also configured to generate a fourth error message if the observation object identifier in the matching identifier pair changes; the matching identifier pair includes multiple observation object identifiers, and one observation object identifier is used to identify that one sensing sensor has observed one observation object.

[0024] In one possible implementation, if the third comparison result meets the third preset condition and the observed object identifier in the matched identifier pair remains unchanged, the acquisition unit is further configured to acquire the position change and velocity change of the observed object within a unit time. The generation unit is further configured to generate a fifth error message if the position change is greater than a second distance threshold and the velocity change is greater than a velocity threshold.

[0025] In one possible implementation, if the third comparison result does not meet the third preset condition, the observed object identifier in the matched identifier pair changes, or the position change is greater than the second distance threshold and the velocity change is greater than the velocity threshold, the determining unit is further configured to determine the topological change of the first distribution topological feature value and the second distribution topological feature value. The generating unit is further configured to generate a sixth error message if the topological change is greater than the topological change threshold; the first distribution topological feature value is used to characterize the distribution characteristics of multiple observation locations, and the second distribution topological feature value is used to characterize the distribution characteristics of multiple prediction locations.

[0026] In one possible implementation, the first coordinate position includes a third lateral position and a third longitudinal position, and the second coordinate position includes a fourth lateral position and a fourth longitudinal position. The processing unit is specifically configured to: determine the absolute value of the difference between the two third lateral positions to obtain a first absolute value, and compare the first absolute value with a third distance threshold to obtain a lateral comparison result; determine the absolute value of the difference between the two fourth longitudinal positions to obtain a second absolute value, and compare the second absolute value with a fourth distance threshold to obtain a longitudinal comparison result.

[0027] In one possible implementation, the first preset condition includes: a first absolute value is greater than a third distance threshold, or a second absolute value is greater than a fourth distance threshold.

[0028] According to a third aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement a method of any possible implementation of the first aspect described above.

[0029] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method of any possible implementation of the first aspect described above.

[0030] According to a fifth aspect of the present invention, a computer program product is provided, the computer program product including computer instructions that, when executed on an electronic device, cause the electronic device to perform any of the possible implementations of the first aspect described above.

[0031] According to a sixth aspect of the present invention, a vehicle is provided, including electronic equipment as described in the third aspect.

[0032] The data processing method provided by this invention brings the following beneficial effects: It compares and verifies the input coordinate position and the coordinate position obtained after inverse transformation to obtain a first comparison result. If the first comparison result meets a first preset condition, it determines that the coordinate transformation is abnormal, thereby generating a first verification failure message. In this way, by verifying the data and generating a first verification failure message when unreasonable road condition data is obtained, the driver is alerted, thus improving vehicle driving safety.

[0033] It should be noted that the technical effects of any of the implementation methods in aspects two through six can be found in the technical effects of the corresponding implementation methods in aspect one, and will not be repeated here.

[0034] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the structure of a data processing system provided in an embodiment of the present invention;

[0036] Figure 2 One of the flowcharts for the data processing method provided in the embodiments of the present invention;

[0037] Figure 3 A second flowchart of a data processing method provided in an embodiment of the present invention;

[0038] Figure 4 The third flowchart of the data processing method provided in the embodiments of the present invention;

[0039] Figure 5 The fourth flowchart of the data processing method provided in the embodiments of the present invention;

[0040] Figure 6 The fifth flowchart of the data processing method provided in the embodiments of the present invention;

[0041] Figure 7 One of the flowcharts illustrating the data processing method provided in this embodiment of the invention;

[0042] Figure 8 This is the second schematic flowchart of the data processing method provided in the embodiments of the present invention;

[0043] Figure 9 This is a schematic diagram of the structure of a data processing device provided in an embodiment of the present invention;

[0044] Figure 10This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0045] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0046] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0047] Before providing a detailed introduction to the transaction method provided by this invention, let me briefly introduce the relevant elements, application scenarios, and implementation environment involved in this invention.

[0048] First, a brief introduction to the relevant elements involved in this invention will be given.

[0049] Millimeter-wave radar is a special type of radar that uses short-wavelength electromagnetic waves and transmits signals with wavelengths on the order of millimeters.

[0050] A lidar (Light Detection and Ranging) system is a radar system that uses laser beams to detect the position, velocity, and other characteristics of a target. Its working principle involves emitting a detection signal (laser beam) towards the target, then comparing the received signal reflected back from the target (target echo) with the emitted signal. After appropriate processing, relevant information about the target can be obtained.

[0051] The world coordinate system is the system's absolute coordinate system. Before the user coordinate system is established, the coordinates of all points on the screen are determined by the origin of this coordinate system.

[0052] Autonomous driving levels: As shown in Table 1, autonomous driving is classified into 6 levels:

[0053] Table 1. Level-Element Table

[0054]

[0055]

[0056] In some embodiments, the present application is adapted to L3 and above intelligent driving.

[0057] Secondly, a brief introduction to the application scenarios involved in this invention will be given.

[0058] Autonomous driving technology typically uses a variety of sensors (such as lidar, cameras, and millimeter-wave radar) to collect raw road condition data and fuse the raw data to obtain road condition data, so that the vehicle can perceive the road conditions in real time and thus achieve autonomous driving.

[0059] Specifically, existing technologies for vehicle perception sensors mainly include cameras, millimeter-wave radar, ultrasonic radar, and lidar. Cameras, based on image recognition of target information, offer strong two-dimensional perception capabilities and are inexpensive, but their performance degrades in poor environmental conditions. Millimeter-wave radar boasts long detection range and robustness, but cannot accurately identify targets. Ultrasonic radar offers high data processing efficiency but has a limited measurement range. LiDAR provides high ranging accuracy and strong directionality, but is expensive. Therefore, autonomous driving technology typically utilizes multiple sensors to collect raw data from intersections.

[0060] However, due to the complexity of fusion algorithms, even when all sensors are functioning normally, the algorithm itself can fail, resulting in unreasonable road condition data and thus affecting vehicle driving safety. Therefore, improving vehicle driving safety is a pressing technical problem that needs to be solved.

[0061] To address the aforementioned problems, this invention provides a data processing method, comprising: acquiring observation data collected by a sensing sensor; determining a first coordinate position of the observed object in a preset coordinate system, and performing an inverse transformation on the first coordinate position to obtain a second coordinate position. The observed object is any one of at least one object observed by the sensing sensor. The first coordinate position and the second coordinate position are compared to obtain a first comparison result, and if the first comparison result meets a first preset condition, a first verification failure message is generated.

[0062] In this way, the input coordinate position and the coordinate position obtained after inverse transformation are compared and verified to obtain a first comparison result. If the first comparison result meets a first preset condition, the coordinate transformation is determined to be abnormal, thereby generating a first verification failure message. In this way, the data is verified, and if unreasonable road condition data is obtained, a first verification failure message is generated to alert the driver, thereby improving vehicle driving safety.

[0063] Finally, a brief introduction is given to the implementation environment (implementation architecture) involved in the method provided by this invention.

[0064] The data processing method provided in this embodiment of the invention can be applied to data processing systems. Figure 1 A schematic diagram of the data processing system is shown. For example... Figure 1As shown, the data processing system 10 includes sensing sensors 11, 12, and 13, and an electronic device 14. The electronic device 14 is connected to sensing sensors 11, 12, and 13 respectively. The electronic device 14 can be connected to each sensing sensor via a wired connection or a wireless connection; this embodiment of the invention does not limit the connection in this way.

[0065] In some embodiments, electronic device 14 may be a processing unit in a vehicle. Sensing sensor 11, sensing sensor 12, and sensing sensor 13 are different sensing sensors.

[0066] For example, sensor 11 is a camera, sensor 12 is a lidar, and sensor 13 is a millimeter-wave radar.

[0067] It needs to be explained that, Figure 1 This is merely a schematic diagram of a data processing system; in actual applications, there may be more or fewer sensing sensors. Electronic device 14, sensing sensor 11, sensing sensor 12, and sensing sensor 13 are located within the vehicle.

[0068] In some embodiments, the electronic device 14 is used to acquire observation data collected by the sensing sensor 11; determine the first coordinate position of the observed object in a preset coordinate system, and perform an inverse transformation on the first coordinate position to obtain a second coordinate position. The electronic device 14 is also used to identify any one of at least one object observed by the sensing sensor 11 as the observed object. The electronic device 14 is further used to compare the first coordinate position and the second coordinate position to obtain a first comparison result, and, if the first comparison result meets a first preset condition, generate a first verification failure message.

[0069] In some instances, electronic device 14 includes a parsing module, a preprocessing module, a synchronization module, an association module, and a fusion module.

[0070] Specifically, the parsing module is used to unify the format of object data observed by different sensors; the preprocessing module is used to filter and smooth the output data of the sensing sensors; the data synchronization module is used to unify the output data of different sensing sensors under the same timestamp and coordinate system; and the data fusion module is used to merge and output the data of the same object observed by different sensing sensors.

[0071] For ease of understanding, the data processing method provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0072] For vehicle driving safety, Figure 2 This is a flowchart illustrating a data processing method according to an exemplary embodiment. For example... Figure 2As shown, the data processing method includes the following steps: S201-S205.

[0073] S201. Electronic equipment acquires observation data collected by sensing sensors.

[0074] One possible approach is for the electronic device to acquire raw data from multiple sensing sensors and then use a parsing module to standardize the format of the observed objects from these sensors. Further, the electronic device uses a preprocessing module to filter and smooth the raw data from the multiple sensing sensors to obtain the observed data.

[0075] For example, consider a camera, LiDAR, and millimeter-wave radar as sensing sensors. The electronic device acquires data from the camera, LiDAR, and millimeter-wave radar respectively, obtaining multiple data sets. These multiple data sets are then formatted and filtered for smoothing to obtain the observation data.

[0076] It is understandable that different types of sensing sensors output data in different formats. In this embodiment, the format of the output data is standardized to facilitate subsequent fusion output.

[0077] S202. The electronic device determines the first coordinate position of the observed object in the preset coordinate system based on the observation data.

[0078] The observed object is any one of at least one objects observed by the sensing sensor.

[0079] As one possible approach, the electronic device, upon acquiring observation data, determines the first coordinate position of the observed object in a preset coordinate system, including a third horizontal position and a third vertical position, based on the observation data.

[0080] For example, taking pedestrian 1 as the object of observation, a camera as the sensing sensor, and the world coordinate system as the preset coordinate system, the electronic device, after acquiring the road condition image data captured by the camera, determines pedestrian 1 as the object of observation and determines the coordinate position of pedestrian 1 in the world coordinate system.

[0081] For example, taking pedestrian 1 as the object of observation, the sensing sensor and the lidar as the camera, and the preset coordinate system as the world coordinate system, the electronic device, after acquiring the road condition image data captured by the camera, determines pedestrian 1 as the object of observation and determines the coordinate position of pedestrian 1 in the world coordinate system; the electronic device, after acquiring the road condition data observed by the lidar, determines pedestrian 1 as the object of observation and determines the coordinate position of pedestrian 1 in the world coordinate system.

[0082] S203. The electronic device performs an inverse transformation on the first coordinate position to obtain the second coordinate position.

[0083] As one possible approach, the electronic device performs an inverse transformation on the third lateral position and the third longitudinal position in the first coordinate position to generate a fourth lateral position and a fourth longitudinal position, and obtains the second coordinate position based on the fourth lateral position and the fourth longitudinal position.

[0084] For example, taking the first coordinate position as the location of pedestrian 1 observed by the camera in the world coordinate system, the electronic device performs an inverse transformation on the first coordinate position to obtain the second coordinate position.

[0085] S204. The electronic device compares the first coordinate position and the second coordinate position to obtain the first comparison result.

[0086] As one possible approach, the electronic device determines the difference between the third lateral positions, obtains the absolute value of the first difference, and compares the first absolute value with a third distance threshold to obtain a lateral comparison result; it also determines the absolute value of the difference between the fourth longitudinal positions, obtains the second absolute value, and compares the second absolute value with a fourth distance threshold to obtain a longitudinal comparison result.

[0087] For example, taking a forward-facing camera as the sensing sensor, the electronic device acquires the original vertical coordinates (posX-C) and original horizontal coordinates (posY-C) output by the forward-facing camera, and performs an inverse transformation on the original vertical and horizontal coordinates to obtain the inversely transformed second position (posX-CV, posY-CV). Here, posX-CV is the inversely transformed vertical coordinate, and posY-CV is the inversely transformed horizontal coordinate. Further, the electronic device compares posX-C and posX-CV, and compares posY-C and posY-CV, to obtain a first absolute value |posX-C-posX-CV| and a second absolute value |posY-C-posY-CV|. Subsequently, the electronic device compares |posX-C-posX-CV| with a third distance threshold T3, and compares |posY-C-posY-CV| with a fourth distance threshold T4.

[0088] S205. If the first comparison result meets the first preset condition, the electronic device generates a first verification failure message.

[0089] As one possible implementation, if the first absolute value is greater than a third distance threshold, and / or the second absolute value is greater than a fourth distance threshold, the electronic device generates a first verification failure message and outputs it through the driver assistance system to alert the driver.

[0090] For example, under any of the following conditions, the first preset condition is: |posX-C-posX-CV|>T3, |posY-C-posY-CV|>T4. In the case of |posX-C-posX-CV|>T3, the electronic device generates a first verification failure message "coordinate transformation error" and plays it in voice form through the driver assistance system to prompt the driver.

[0091] It should be noted that the electronic device can verify whether the observed object observed by one sensing sensor has a coordinate transformation error, can verify whether the observed object observed by multiple sensing sensors has a coordinate transformation error, and can also verify whether a specific observed object (such as a vehicle or pedestrian) has a coordinate transformation error. This application does not limit the scope of the embodiments.

[0092] The data processing method provided in this application has the following beneficial effects: It compares and verifies the input coordinate position and the coordinate position obtained after inverse transformation to obtain a first comparison result. If the first comparison result meets a first preset condition, it determines that the coordinate transformation is abnormal, thereby generating a first verification failure message. In this way, by verifying the data and generating a first verification failure message when unreasonable road condition data is obtained, the driver is alerted, thus improving vehicle driving safety.

[0093] In one design, to improve vehicle driving safety, such as... Figure 3 As shown, the data processing method provided in this application embodiment further includes: S206-S209.

[0094] S206. The electronic device determines the uniform motion data of the observed object based on the observation data and the preset uniform motion algorithm.

[0095] The uniform motion data includes the first longitudinal position, the first lateral position, the first longitudinal velocity, and the first lateral velocity.

[0096] As one possible approach, the electronic device, upon acquiring the observation data at the current moment, determines the uniform motion data of the observed object based on the observation data at the current moment, historical observation data, and a preset uniform motion algorithm: the first longitudinal position posX-cst, the first lateral position posY-cst, the first longitudinal velocity Vx-cst, and the second lateral velocity Vy-cst.

[0097] It should be noted that the preset uniform motion algorithm is pre-set in the electronic equipment by the maintenance personnel.

[0098] S207. The electronic device determines the uniform acceleration motion data of the observed object based on the observation data and the preset uniform acceleration motion algorithm.

[0099] The uniformly accelerated motion data includes the second longitudinal position, the second lateral position, the second longitudinal velocity, and the second lateral velocity.

[0100] As one possible approach, the electronic device, upon acquiring the observation data at the current moment, determines the uniformly accelerated motion data of the observed object based on the observation data at the current moment, historical observation data, and a preset uniformly accelerated motion algorithm: the second longitudinal position posX-cacc, the second lateral position posY-cacc, the second longitudinal velocity Vx-cacc, and the second lateral velocity Vy-cacc.

[0101] It should be noted that the preset uniform acceleration motion algorithm is pre-set in the electronic equipment by the maintenance personnel.

[0102] S208. The electronic device compares the uniform motion data and the uniformly accelerated motion data to obtain a second comparison result.

[0103] As one possible implementation, the electronic device determines the absolute value of the difference between the first longitudinal position and the second longitudinal position, obtaining a fifth absolute value |posX-cst-posX-cacc|; determines the absolute value of the difference between the first lateral position and the second lateral position, obtaining a sixth absolute value |posY-cst-posY-cacc|; determines the absolute value of the difference between the first longitudinal velocity and the second longitudinal velocity, obtaining a seventh absolute value |Vx-cst-Vx-cacc|; and determines the absolute value of the difference between the first lateral velocity and the second lateral velocity, obtaining an eighth absolute value |Vy-cst-Vy-cacc|. Further, the electronic device compares the third absolute value with the fifth distance threshold T5, compares the third absolute value with the sixth distance threshold T6, compares the seventh absolute value with the first velocity threshold S1, and compares the eighth absolute value with the second velocity threshold S2, and obtains the comparison results.

[0104] S209. If the second comparison result meets the second preset condition, the electronic device generates a second verification error message.

[0105] As one possible implementation, the electronic device generates a second verification error message and broadcasts it through the vehicle audio system to alert the driver when |posX-cst-posX-cacc|>T5|||posY-cst-posY-cacc|>T6 and / or |Vx-cst-Vx-cacc|>S1|||Vy-cst-Vy-cacc|>S2.

[0106] For example, in the case where |posX-cst-posX-cacc|>T5|||posY-cst-posY-cacc|>T6 and |Vx-cst-Vx-cacc|<S1, the electronic device sends a second verification error message “time synchronization error” and broadcasts the second verification error message.

[0107] In another scenario, if the electronic device does not meet the second preset condition in the second comparison result, it continues to perform subsequent data fusion.

[0108] It should be noted that in the embodiments of this application, the data processing method can be to execute S201-S205 first, and then execute S206-S209; or it can be to execute S206-S209 first, and then execute S201-S205; or it can be to execute S201-S205 and S206-S209 simultaneously; the embodiments of this application do not limit this.

[0109] Understandably, electronic devices synchronize the time of each sensor's observations based on both acceleration and uniform acceleration algorithms, and compare the results calculated by the two algorithm models. Furthermore, if the results show a significant deviation, a time synchronization error is identified, and a verification error message is generated to alert the driver. Thus, verifying time synchronization improves driving safety.

[0110] In one design, to improve vehicle driving safety, such as... Figure 4 As shown, the data processing method provided in this application embodiment further includes: S210-S212.

[0111] S210. Electronic equipment determines the observation location and predicted location of the observed object.

[0112] The observed position is the position observed by the sensing sensor at the current moment, and the predicted position is the position predicted at the current moment based on the historical position of the observed object.

[0113] One possible approach is for the electronic device to acquire the observed object's current position based on the current observation data. Furthermore, the electronic device determines the predicted position of the observed object at the current moment based on historical observation data and a pre-defined motion algorithm.

[0114] In some embodiments, the electronic device associates observed objects and predicted objects through an association module. The predicted object is the object whose position is predicted after the observed object is analyzed. The steps for associating observed objects and predicted objects are as follows: S1. Based on the motion data of the observed object at the previous moment and a preset motion algorithm, predict the predicted position of the observed object at the current moment, and determine the object corresponding to the predicted position as the predicted object. S2. For each predicted object, calculate the observation distance between the predicted object and the observed object. Predicted objects with an observation distance less than a preset threshold are determined as candidate objects, and predicted objects with an observation distance greater than the preset threshold are deleted. S3. Based on the position, velocity, and identification information of the predicted object and the observed object at the current moment, and combined with the association relationship between the predicted object and the observed object at the previous moment, update the association relationship at the current moment in real time.

[0115] In other embodiments, the electronic device can associate observed objects and predicted objects using the Hungarian algorithm. The electronic device can also configure an observation identifier for each observed object and a prediction identifier for a corresponding predicted object, and associate the predicted objects with each other based on the identifiers.

[0116] S211. The electronic device compares the first distance with the first distance threshold to obtain the third comparison result.

[0117] The first distance is the distance between the observed location and the predicted location.

[0118] As one possible approach, the electronic device, having acquired the predicted and observed positions, determines a first distance between the observed and predicted positions, and compares this first distance with a first distance threshold to obtain a third comparison result.

[0119] For example, taking a first distance threshold of 3 meters as an example, when the electronic device acquires the observed position and predicted position of vehicle 1 observed by the laser type, it calculates the distance between the observed position and the predicted position to be 3.5 meters. Further, the electronic device compares 3.5 meters and 3 meters.

[0120] S212. If the third comparison result meets the third preset condition, the electronic device generates a third error message.

[0121] As one possible approach, the electronic device generates a third error message if the first distance is greater than a first distance threshold.

[0122] Understandably, electronic devices determine whether the data fusion process is flawed based on the distance between the observed and predicted locations, and generate error messages to alert the driver if the distance between the observed and predicted locations is too large, thereby improving driving safety.

[0123] In one design, to improve vehicle driving safety, such as... Figure 5 As shown, the third comparison result does not meet the third preset condition. The sensing sensor is a plurality of sensing sensors. The data processing method provided in this application embodiment also includes: S213-S214.

[0124] S213. Electronic devices generate matching identifier pairs based on the observer identifiers corresponding to the same observer.

[0125] The matching identifier pair includes multiple observation object identifiers, and one observation object identifier is used to identify a sensor that has observed an observation object.

[0126] For example, taking a sensing sensor including a camera, LiDAR, and millimeter-wave radar as an example, the camera, LiDAR, and millimeter-wave radar all observe vehicle 2, vehicle 3, and pedestrian 1. The electronic device acquires the observation data output by the camera, LiDAR, and millimeter-wave radar respectively, and matches vehicle 2, vehicle 3, and pedestrian 1. Further, the electronic device configures an observation object identifier CA1 for pedestrian 1 observed by the camera, an observation object identifier LA1 for pedestrian 1 observed by the LiDAR, and an observation object identifier MI1 for pedestrian 1 observed by the millimeter-wave radar, and generates a matching identifier pair based on CA1, LA1, and MI1.

[0127] S214. If the observed object identifier in the matching identifier pair is changed, the electronic device generates a fourth error message.

[0128] For example, consider a track_ID pair that includes CA1, LA1, and MI1. If the electronic device determines that CA1 in the track_ID pair has changed to CA2, it generates a fourth error message and displays it to alert the driver.

[0129] Understandably, in the event of a change in the observed object identifier in a matching identifier pair, the electronic device determines that some of the multiple perception sensors are malfunctioning and generates a third error message to notify the driver of the autonomous driving anomaly, thereby improving vehicle driving safety.

[0130] In one design, to improve vehicle driving safety, such as... Figure 6 As shown, if the third comparison result meets the third preset condition and the object identifier in the matched identifier pair has not changed, the data processing method provided in this application embodiment further includes: S215-S216.

[0131] S215. Electronic devices acquire the changes in position and velocity of the observed object per unit time.

[0132] As one possible approach, electronic devices acquire the changes in the horizontal axis of the observed object per unit time: delta-posX, the changes in the vertical axis: delta-posY, the velocity changes in the horizontal axis: delta-VX, and the velocity changes in the vertical axis: delta-VY.

[0133] S216. If the electronic device's position change is greater than the second distance threshold and its speed change is greater than the speed threshold, a fifth error message is generated.

[0134] As one possible implementation, after acquiring the changes in the horizontal axis (delta-posX), the vertical axis (delta-posY), the horizontal axis velocity change (delta-VX), and the vertical axis velocity change (delta-VY), the electronic device determines the magnitude of delta-posX relative to a second distance threshold T2; determines the magnitude of delta-posY relative to the second distance threshold T2; determines the magnitude of delta-VX relative to a third velocity threshold S3; and determines the magnitude of delta-VY relative to the third velocity threshold S3. Furthermore, if delta-posX > T2 and delta-VX > S3, the electronic device generates a fifth error message.

[0135] In some embodiments, the conditions for the electronic device to generate a fifth error message include any one or more of the following: delta-posX > T2 and delta-VX > S3, delta-posY > T2 and delta-VY > S3, delta-posX > T2, delta-posY > T2, delta-VX > S3 and delta-VY > S3, delta-posX > T2 and delta-VY > S3, and delta-posY > T2 and delta-VX > S3.

[0136] Understandably, the data fusion is assessed based on the changes in the observed object's position and velocity per unit time to determine if there are any issues. Furthermore, if a problem is detected, the electronic device generates a fifth error message to alert the driver, thereby improving vehicle safety.

[0137] In one design, to improve vehicle driving safety, the data processing method provided in this application embodiment further includes: S217-S218, in cases where the third comparison result does not meet the third preset condition, the observed object identifier in the matched identifier pair changes, or the position change is greater than the second distance threshold and the speed change is greater than the speed threshold.

[0138] S217. The electronic device determines the topological change of the first distribution topological characteristic value and the second distribution topological characteristic value.

[0139] The first distribution topological feature value is used to characterize the distribution characteristics of multiple observation locations, and the second distribution topological feature value is used to characterize the distribution characteristics of multiple prediction locations.

[0140] One possible approach is for the electronic device to acquire the distribution characteristics of multiple observation locations observed by multiple sensing sensors to obtain a first distribution topological feature value, and to acquire the distribution characteristics of multiple predicted locations to obtain a second distribution topological feature value. Further, based on the first and second distribution topological feature values, the electronic device determines the topological change amount of the first and second distribution topological feature values.

[0141] S218. When the amount of topology change is greater than the topology change threshold, the electronic device generates a sixth error message.

[0142] For example, taking a topology change of Δδ as an example, the electronic device generates a sixth error message when Δδ is greater than a preset topology change threshold TBD.

[0143] Understandably, when the distribution characteristics of the observed object differ significantly from those of the predicted object, a data fusion error is identified, and a sixth error message is generated. This improves vehicle driving safety.

[0144] In one design, the data processing method provided in this application embodiment further includes: S219-S222.

[0145] S219. The electronic device acquires target observation data from multiple sensing sensors.

[0146] S220: The electronic device weights multiple target observation data based on a preset weighting algorithm to obtain a first fusion object, and obtains a second fusion object based on the preset fusion algorithm and multiple target observation data.

[0147] In some embodiments, a preset weighting algorithm can configure different weights for the observed object based on the characteristics of the camera.

[0148] For example, taking a camera and a LiDAR as the sensing sensors: For objects observed at close range, a large weight is assigned to human body 1 observed by the camera, and a small weight is assigned to human body 2 observed by the LiDAR. For objects observed at long range, a small weight is assigned to vehicle 1 observed by the camera, and a large weight is assigned to vehicle 2 observed by the LiDAR.

[0149] It should be noted that the preset weighting algorithm and preset fusion algorithm are pre-set in the electronic equipment by the maintenance personnel.

[0150] S221. The electronic device obtains the first fusion object at the horizontal axis position: posX_fusa and the vertical axis position: posY_fusa, and obtains the second fusion object at the horizontal axis position: posX_fus and the vertical axis position: posY_fus.

[0151] S222. The electronic device generates a seventh error message if |posX_fusa-posX_fus| is greater than the seventh distance threshold T7, and / or |posY_fusa-posY_fus| is greater than the eighth distance threshold T8.

[0152] Understandably, electronic devices fuse observations of the same object from different sensors using different fusion algorithms, resulting in two fused objects. If the two fused objects are geographically distant, an error message is generated to alert the driver, thereby improving vehicle safety.

[0153] In one design, to obtain a first comparison result, the first coordinate position includes a third horizontal position and a third vertical position, and the second coordinate position includes a fourth horizontal position and a fourth vertical position. The S204 provided in this application embodiment specifically includes: S2041-S2042.

[0154] S2041. Determine the absolute value of the difference between the third lateral position and the third lateral position to obtain the first absolute value, and compare the first difference with the third distance threshold to obtain the lateral comparison result.

[0155] S2042. Determine the absolute value of the difference between the fourth longitudinal position and the fourth longitudinal position to obtain the second absolute value, and compare the second difference with the fourth distance threshold to obtain the longitudinal comparison result.

[0156] Understandably, electronic devices obtain comparison results by judging the distance of observed objects in the same direction.

[0157] To better illustrate the embodiments of this application, Table 2 shows the variables designed for the data processing method in the embodiments of this application.

[0158] Table 2 Variable Table

[0159] Variable Meaning Variable Meaning Unit track_ID Matching identification pair None posX_fusa First fusion object in horizontal axis position m posY_fusa First fusion object in vertical axis position m posX_fus Second fusion object in horizontal axis position m posY_fus Second fusion object in vertical axis position m Δδ Topological variation amount None delta-posX Horizontal axis variation amount m delta-posY Vertical axis variation amount m delta-VX Horizontal axis speed variation amount m / s delta-VY Vertical axis speed variation amount m / s posX-cst First longitudinal position m posY-cst First transversal position m Vx-cst First longitudinal speed m / s Vy-cst Second transversal speed m / s posX-cacc Second longitudinal position m posY-cacc Second transversal position m Vx-cacc Second longitudinal speed m / s Vy-cacc Second transversal speed m / s

[0160] like Figure 7 and Figure 8 As shown. In Figure 7The diagram illustrates a data fusion flowchart, which includes: acquiring data from multiple perception sensors and parsing the acquired data; further, preprocessing the parsed data to obtain observation data, and then performing synchronization processing on the observation data. This synchronization processing includes data synchronization, heterogeneous synchronization, and synchronization verification. Subsequently, data association and association monitoring are performed on the synchronized data. Finally, the associated data is fused, and the fused data is output to the autonomous driving module (e.g., an autonomous driving ADS module).

[0161] exist Figure 8 The diagram illustrates a synchronization verification flowchart. It describes acquiring the initial coordinates output by the sensing sensor and performing a coordinate transformation to obtain fused coordinates. Further, it describes an inverse transformation of the fused coordinates to obtain inverse-transformed coordinates. Subsequently, a coordinate verification module verifies the initial coordinates and the inverse-transformed coordinates to obtain a coordinate verification code.

[0162] The foregoing primarily describes the solutions provided by the embodiments of the present invention from a methodological perspective. To achieve the above functions, the data processing device or electronic device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0163] According to the above method, exemplary data processing devices or electronic devices can be divided into functional modules. For example, the data processing device or electronic device may include functional modules corresponding to each functional division, or two or more functions may be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in the embodiments of the present invention is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0164] For example, such as Figure 9 As shown, this embodiment of the invention also provides a data processing device 30. The data processing device 30 includes an acquisition unit 301, a determination unit 302, a processing unit 303, and a generation unit 304.

[0165] The acquisition unit 301 is used to acquire the observation data collected by the sensing sensor.

[0166] The determining unit 302 is used to determine the first coordinate position of the observed object in the preset coordinate system based on the observation data.

[0167] Processing unit 303 is used to perform an inverse transformation on the first coordinate position to obtain the second coordinate position. The observed object is any one of at least one object observed by the sensing sensor.

[0168] The processing unit 303 is also used to compare the first coordinate position and the second coordinate position to obtain the first comparison result.

[0169] The generation unit 304 is used to generate a first verification failure message when the first comparison result meets the first preset condition.

[0170] Optionally, the determining unit 302 is also used to determine the uniform motion data of the observed object based on the observation data and the preset uniform motion algorithm; the uniform motion data includes the first longitudinal position, the first lateral position, the first longitudinal velocity, and the first lateral velocity.

[0171] The determining unit 302 is also used to determine the uniformly accelerated motion data of the observed object based on the observation data and a preset uniformly accelerated motion algorithm. The uniformly accelerated motion data includes a second longitudinal position, a second lateral position, a second longitudinal velocity, and a second lateral velocity.

[0172] The processing unit 303 is further configured to compare the uniform motion data and the uniformly accelerated motion data to obtain a second comparison result. The generation unit is further configured to generate a second verification error message if the second comparison result meets a second preset condition.

[0173] Optionally, the determining unit 302 is also used to determine the observed position and the predicted position of the observed object. The observed position is the position observed by the sensing sensor at the current moment, and the predicted position is the position predicted at the current moment based on the historical position of the observed object.

[0174] The processing unit 303 is also used to compare the first distance with the first distance threshold to obtain a third comparison result.

[0175] The generation unit 304 is also used to generate a third error message when the third comparison result meets the third preset condition; the first distance is the distance between the observation position and the predicted position.

[0176] Optionally, if the third comparison result does not meet the third preset condition, the sensing sensor is multiple sensing sensors, and the generation unit 304 is also used to generate matching identifier pairs based on the observation object identifiers corresponding to the same observation object.

[0177] The generation unit 304 is also configured to generate a fourth error message in the event of a change in the observed object identifier in the matching identifier pair; the matching identifier pair includes multiple observed object identifiers, and an observed object identifier is used to identify an observed object observed by a sensing sensor.

[0178] Optionally, if the third comparison result meets the third preset condition and the identification of the observed object in the matching identification pair has not changed, the acquisition unit 301 is also used to acquire the position change and velocity change of the observed object per unit time.

[0179] The generation unit 304 is also configured to generate a fifth error message when the position change is greater than a second distance threshold and the velocity change is greater than a velocity threshold.

[0180] Optionally, if the third comparison result does not meet the third preset condition, the identification of the observed object in the matching identification pair is changed, or the change in position is greater than the second distance threshold and the change in velocity is greater than the velocity threshold, the determining unit 302 is also used to determine the topological change of the first distribution topological feature value and the second distribution topological feature value.

[0181] The generation unit 304 is also used to generate a sixth error message when the amount of topological change is greater than the topological change threshold; the first distribution topological feature value is used to characterize the distribution characteristics of multiple observation locations, and the second distribution topological feature value is used to characterize the distribution characteristics of multiple prediction locations.

[0182] Optionally, the first coordinate position includes a third horizontal position and a third vertical position, and the second coordinate position includes a fourth horizontal position and a fourth vertical position. The processing unit 303 is specifically used to: determine the absolute value of the difference between the two third horizontal positions to obtain a first absolute value, and compare the first absolute value with a third distance threshold to obtain a horizontal comparison result; determine the absolute value of the difference between the two fourth vertical positions to obtain a second absolute value, and compare the second absolute value with a fourth distance threshold to obtain a vertical comparison result.

[0183] In one possible implementation, the first preset condition includes: a first absolute value is greater than a third distance threshold, or a second absolute value is greater than a fourth distance threshold.

[0184] In the case of implementing the functions of the integrated modules described above in hardware, this embodiment of the invention provides a possible structural schematic diagram of the electronic device involved in the above embodiments. For example... Figure 10 As shown, the electronic device 40 includes a processor 401, a memory 402, and a bus 403. The processor 401 and the memory 402 can be connected via the bus 403.

[0185] Processor 401 is the control center of the communication device. It can be a single processor or a collective term for multiple processing elements. For example, processor 401 can be a general-purpose central processing unit (CPU) or other general-purpose processors. Among them, the general-purpose processor can be a microprocessor or any conventional processor.

[0186] As one embodiment, processor 401 may include one or more CPUs, for example Figure 10 CPU 0 and CPU 1 are shown in the diagram.

[0187] The memory 402 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0188] In one possible implementation, the memory 402 can exist independently of the processor 401. The memory 402 can be connected to the processor 401 via a bus 403 and is used to store instructions or program code. When the processor 401 calls and executes the instructions or program code stored in the memory 402, it can implement the sensor determination method provided in this embodiment of the invention.

[0189] In another possible implementation, the memory 402 can also be integrated with the processor 401.

[0190] Bus 403 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0191] It should be pointed out that, Figure 10 The structure shown does not constitute a limitation on the electronic device 40. Except... Figure 10 In addition to the components shown, the electronic device 40 may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0192] Optionally, the electronic device 40 provided in this embodiment of the invention may further include a communication interface 404.

[0193] Communication interface 404 is used to connect with other devices via a communication network. This communication network can be Ethernet, a wireless access network, a wireless local area network (WLAN), etc. Communication interface 404 may include a receiving unit for receiving data and a transmitting unit for sending data.

[0194] In one design, the communication interface of the electronic device 40 provided in this embodiment of the invention may also be integrated into the processor.

[0195] In another hardware architecture of the server provided in this embodiment of the invention, the electronic device may include a processor and a communication interface. The processor is coupled to the communication interface.

[0196] The functions of the processor can be referred to in the processor description above. In addition, the processor also has storage functions, which can be referred to in the memory function description above.

[0197] The communication interface is used to provide data to the processor. This communication interface can be an internal interface of the communication device or an external interface of the communication device.

[0198] It should be noted that the above-mentioned alternative hardware structure does not constitute a limitation on the server. In addition to the above-mentioned alternative hardware component, the server may include more or fewer components, or combine certain components, or have different component arrangements.

[0199] When the functions of the integrated modules described above are implemented in hardware, the present invention provides a structural diagram of the middleware involved in the above embodiments, which can be referred to the structural diagram of the execution machine described above.

[0200] This invention also provides a computer-readable storage medium storing instructions. When a computer executes these instructions, the computer performs each step of the data processing method flow shown in the above-described method embodiments.

[0201] This invention also provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the data processing method described in the above method embodiments.

[0202] This invention also provides a vehicle comprising the above-described components. Figure 10 Electronic devices in the system.

[0203] The computer-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 thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), registers, hard disks, optical fibers, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing, or any other form of computer-readable storage medium in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may reside in an application-specific integrated circuit (ASIC). In embodiments of the present invention, a computer-readable storage medium may be any tangible medium that contains or stores a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0204] Since the server, user equipment, computer-readable storage medium, and computer program product in the embodiments of the present invention can be applied to the above methods, the technical effects that can be obtained can also be referred to the above method embodiments. The embodiments of the present invention will not be described again here.

[0205] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present invention should be covered within the scope of protection of the present invention.

Claims

1. A data processing method, characterized in that, The method includes: Acquire observation data collected by sensing sensors; Based on the observation data, the first coordinate position of the observed object in the preset coordinate system is determined, and the first coordinate position is inversely transformed to obtain the second coordinate position; the observed object is any one of at least one object observed by the sensing sensor; The first coordinate position and the second coordinate position are compared to obtain a first comparison result, and a first verification failure message is generated if the first comparison result meets the first preset condition. The uniform motion data of the observed object is determined based on the observation data and a preset uniform motion algorithm; the uniform motion data includes a first longitudinal position, a first lateral position, a first longitudinal velocity, and a first lateral velocity; The uniform acceleration motion data of the observed object is determined based on the observation data and a preset uniform acceleration motion algorithm; the uniform acceleration motion data includes a second longitudinal position, a second lateral position, a second longitudinal velocity, and a second lateral velocity; The uniform motion data and the uniformly accelerated motion data are compared to obtain a second comparison result. If the second comparison result meets the second preset condition, a second verification error message is generated.

2. The data processing method according to claim 1, characterized in that, The method further includes: Determine the observation position and predicted position of the observed object; the observation position is the position observed by the sensing sensor at the current moment, and the predicted position is the position predicted at the current moment based on the historical position of the observed object; A third comparison result is obtained by comparing the first distance with a first distance threshold, and a third error message is generated if the third comparison result meets a third preset condition; the first distance is the distance between the observed position and the predicted position.

3. The data processing method according to claim 2, characterized in that, If the third comparison result does not meet the third preset condition, and the sensing sensor comprises multiple sensing sensors, the method further includes: A matching identifier pair is generated based on the observation object identifier corresponding to the same observation object, and a fourth error message is generated if the observation object identifier in the matching identifier pair changes; the matching identifier pair includes multiple observation object identifiers, and one observation object identifier is used to identify that a sensing sensor observes an observation object.

4. The data processing method according to claim 3, characterized in that, The method further includes: The third comparison result satisfies the third preset condition and the observed object identifier in the matched identifier pair remains unchanged. Obtain the change in position and change in velocity of the observed object per unit time; If the change in position is greater than a second distance threshold and the change in velocity is greater than a velocity threshold, a fifth error message is generated.

5. The data processing method according to claim 4, characterized in that, The method further includes cases where the third comparison result does not meet the third preset condition, the observed object identifier in the matched identifier pair changes, the position change is greater than the second distance threshold, and the velocity change is greater than the velocity threshold. The topological change of the first distribution topological feature value and the second distribution topological feature value is determined, and a sixth error message is generated if the topological change is greater than the topological change threshold; the first distribution topological feature value is used to characterize the distribution characteristics of multiple observation locations, and the second distribution topological feature value is used to characterize the distribution characteristics of multiple prediction locations.

6. The data processing method according to any one of claims 1-5, characterized in that, The first coordinate position includes a third horizontal position and a third vertical position, and the second coordinate position includes a fourth horizontal position and a fourth vertical position. The comparison of the first coordinate position and the second coordinate position to obtain a first comparison result includes: Determine the absolute value of the difference between the third lateral position and the third lateral position to obtain the first absolute value, and compare the first absolute value with the third distance threshold to obtain the lateral comparison result; Determine the absolute value of the difference between the fourth longitudinal position and the fourth longitudinal position to obtain the second absolute value, and compare the second absolute value with the fourth distance threshold to obtain the longitudinal comparison result.

7. The data processing method according to claim 6, characterized in that, The first preset condition includes: the first absolute value is greater than the third distance threshold, or the second absolute value is greater than the fourth distance threshold.

8. A data processing apparatus, characterized in that, The device includes: an acquisition unit, a determination unit, a processing unit, and a generation unit; The acquisition unit is used to acquire observation data collected by the sensing sensor; The determining unit is used to determine the first coordinate position of the observed object in a preset coordinate system based on the observation data, and to perform an inverse transformation on the first coordinate position to obtain a second coordinate position; the observed object is any one of at least one object observed by the sensing sensor; The processing unit is used to compare the first coordinate position and the second coordinate position to obtain a first comparison result; The generation unit is used to generate a first verification failure message when the first comparison result meets the first preset condition; The determining unit is further configured to determine the uniform motion data of the observed object based on the observation data and a preset uniform motion algorithm; the uniform motion data includes a first longitudinal position, a first lateral position, a first longitudinal velocity, and a first lateral velocity; The determining unit is further configured to determine the uniform acceleration motion data of the observed object based on the observation data and a preset uniform acceleration motion algorithm; the uniform acceleration motion data includes a second longitudinal position, a second lateral position, a second longitudinal velocity, and a second lateral velocity; The processing unit is further configured to compare the uniform motion data and the uniformly accelerated motion data to obtain a second comparison result. The generation unit is further configured to generate a second verification error message when the second comparison result meets a second preset condition.

9. An electronic device, characterized in that, Deployed in vehicles, including memory and processor; The memory and the processor are coupled; The memory is used to store computer program code, which includes computer instructions; When the processor executes the computer instructions, the electronic device performs the data processing method as described in any one of claims 1-7.

10. A computer-readable storage medium storing instructions, characterized in that, When the instruction is executed on the control unit, it causes the control unit to perform the data processing method as described in any one of claims 1-7.

11. A vehicle, characterized in that, Including the electronic device as described in claim 9.

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