Vehicle data observation value correction method and device, terminal equipment and medium

Abstract

The invention is suitable for the technical field of automobiles, and provides a vehicle data observation value correction method and device, terminal equipment and a medium, and the method comprises the steps: obtaining a target vehicle data observation value of a vehicle at a current moment and a plurality of vehicle data observation values of the vehicle in a historical time period; determining a vehicle data prediction value of the vehicle at the current moment according to the plurality of vehicle data observation values; and correcting the target vehicle data observation value according to the plurality of vehicle data observation values and the vehicle data prediction value. Compared with the prior art in which the currently observed vehicle data is directly determined as the final observed value of the vehicle, the method combines the plurality of vehicle data observed values and the vehicle data predicted values in the historical time period to correct the target vehicle data observed value, so that the determination accuracy of the final vehicle data observed value is improved.

Description

Technical Field

[0001] This application belongs to the technical field of automobiles, and particularly relates to a method, device, terminal device and medium for correcting observed values of vehicle data. Background Art

[0002] At present, the observed values of vehicle data can assist observers in better understanding the running track of a vehicle. However, in the prior art, the currently observed vehicle data is usually directly determined as the final observed value of the vehicle, without comprehensive consideration, which reduces the accuracy of determining the observed value of vehicle data. Summary of the Invention

[0003] The embodiments of this application provide a method, device, terminal device and storage medium for correcting observed values of vehicle data, which improve the accuracy of determining the final observed value of vehicle data.

[0004] In a first aspect, the embodiments of this application provide a method for correcting observed values of vehicle data, including:

[0005] Obtaining the target vehicle data observed value of the vehicle at the current moment and multiple vehicle data observed values of the vehicle within a historical time period;

[0006] Determining the vehicle data predicted value of the vehicle at the current moment according to the multiple vehicle data observed values;

[0007] Correcting the target vehicle data observed value according to the multiple vehicle data observed values and the vehicle data predicted value.

[0008] Optionally, the multiple vehicle data observed values include the historical lateral speed and historical longitudinal speed at each historical moment within the historical time period, and the vehicle data predicted value includes the predicted course angle; the step of determining the vehicle data predicted value of the vehicle at the current moment according to the multiple vehicle data observed values includes:

[0009] Traversing all the historical moments, calculating each first difference of the historical lateral speeds at two adjacent historical moments, and each second difference of the historical longitudinal speeds at two adjacent historical moments;

[0010] Based on the arctangent function, solving each target difference set in turn to obtain multiple first radian values; where each target difference set is composed of any one of the first differences among the first differences and the second difference corresponding to the any one of the first differences among the second differences;

[0011] Converting the multiple first radian values into the first angles corresponding to the multiple first radian values;

[0012] Determine the mean value of each of the first angles as the predicted heading angle.

[0013] Optionally, the target vehicle data observation value includes the current heading angle, and the multiple vehicle data observation values further include the historical heading angles at various historical moments within the historical time period; the correcting the target vehicle data observation value according to the multiple vehicle data observation values and the vehicle data prediction value includes:

[0014] Calculate the first ratio between the number of the historical moments corresponding to each historical heading angle and the total number of all historical moments respectively.

[0015] If the maximum value among the first ratios is greater than a first threshold, the historical heading angle corresponding to the maximum value is the same as the predicted heading angle, and the predicted heading angle is different from the current heading angle, then correct the current heading angle to the predicted heading angle.

[0016] If the maximum value is greater than the first threshold, the historical heading angle corresponding to the maximum value is different from the predicted heading angle, and the current heading angle is different from both the predicted heading angle and the historical heading angle corresponding to the maximum value, then determine a target heading angle according to the historical lateral speed and historical longitudinal speed at each historical moment, and correct the current heading angle to the target heading angle.

[0017] Optionally, the multiple vehicle data observation values include the historical lateral speed and historical longitudinal speed at various historical moments within the historical time period, and the vehicle data prediction value includes a predicted direction; the determining the vehicle data prediction value of the vehicle at the current moment according to the multiple vehicle data observation values includes:

[0018] Calculate a third difference between the historical lateral speed at the first historical moment and the historical lateral speed at the last historical moment within the historical time period, and a fourth difference between the historical longitudinal speed at the first historical moment and the historical longitudinal speed at the last historical moment.

[0019] Solve the third difference and the fourth difference based on the arctangent function to obtain a second radian.

[0020] Convert the second radian into an angle to obtain a second angle corresponding to the second radian.

[0021] Determine the predicted direction according to the angle range where the second angle is located.

[0022] Optionally, the target vehicle data observation value includes the current orientation, and the multiple vehicle data observation values further include the historical orientations at each historical moment within the historical time period; the correcting the target vehicle data observation value according to the multiple vehicle data observation values and the vehicle data prediction value includes:

[0023] Calculating a second ratio between the number of the historical moments corresponding to each historical orientation and the total number of all historical moments respectively;

[0024] If the maximum value among the second ratios is greater than a second threshold, the historical orientation corresponding to the maximum value is the same as the predicted orientation, and the predicted orientation is different from the current orientation, then correcting the current orientation to the predicted orientation;

[0025] If the maximum value is greater than the second threshold, the historical orientation corresponding to the maximum value is different from the predicted orientation, and the current orientation is different from both the predicted orientation and the historical orientation corresponding to the maximum value, then determining a target orientation according to the historical lateral speed and historical longitudinal speed at each historical moment, and correcting the current orientation to the target orientation.

[0026] Optionally, the multiple vehicle data observation values include the historical vehicle types at each historical moment within the historical time period; the vehicle data prediction value includes a predicted vehicle type; the determining the vehicle data prediction value of the vehicle at the current moment according to the multiple vehicle data observation values includes:

[0027] Determining the historical vehicle type at the last historical moment within the historical time period as the predicted vehicle type.

[0028] Optionally, the target vehicle data observation value includes the current vehicle type and the current vehicle speed, and the multiple vehicle data observation values further include the historical vehicle speeds at each historical moment within the historical time period; the correcting the target vehicle data observation value according to the multiple vehicle data observation values and the vehicle data prediction value includes:

[0029] If the predicted vehicle type is different from the current vehicle type, then calculating a third ratio between the number of the historical moments corresponding to each historical vehicle type and the total number of all historical moments respectively;

[0030] If the maximum value among the third ratios is greater than a third threshold, then determining a fifth difference between the historical vehicle speed at the last historical moment and the current vehicle speed; if the fifth difference is less than a fourth threshold, then correcting the current vehicle type to the predicted vehicle type; wherein, the fourth threshold is positively correlated with the maximum value.

[0031] In a second aspect, an embodiment of the present application provides a correction device for vehicle data observation values, including:

[0032] A first acquisition unit, configured to acquire a target vehicle data observation value of the vehicle at the current moment and a plurality of vehicle data observation values of the vehicle within a historical time period;

[0033] A predicted value determination unit, configured to determine a vehicle data predicted value of the vehicle at the current moment according to the plurality of vehicle data observation values;

[0034] A first correction unit, configured to correct the target vehicle data observation value according to the plurality of vehicle data observation values and the vehicle data predicted value.

[0035] In a third aspect, an embodiment of the present application provides a terminal device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the correction method for vehicle data observation values described in any one of the first aspects above is implemented.

[0036] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the correction method for vehicle data observation values described in any one of the first aspects above is implemented.

[0037] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device can execute the correction method for vehicle data observation values described in any one of the first aspects above.

[0038] The beneficial effects of the embodiments of the present application compared with the prior art are as follows:

[0039] A correction method for vehicle data observation values provided by an embodiment of the present application includes: acquiring a target vehicle data observation value of the vehicle at the current moment and a plurality of vehicle data observation values of the vehicle within a historical time period; determining a vehicle data predicted value of the vehicle at the current moment according to the plurality of vehicle data observation values; and correcting the target vehicle data observation value according to the plurality of vehicle data observation values and the vehicle data predicted value. Compared with the prior art that directly determines the currently observed vehicle data as the final observed value of the vehicle, this method corrects the target vehicle data observation value by combining a plurality of vehicle data observation values and the vehicle data predicted value within a historical time period, improving the determination accuracy of the final vehicle data observation value. Description of the Drawings

[0040] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the accompanying drawings required for use in the embodiments or the description of the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0041] Figure 1 is the implementation flowchart of the method for correcting vehicle data observation values provided by an embodiment of the present application;

[0042] Figure 2 is the implementation flowchart of the method for correcting vehicle data observation values provided by another embodiment of the present application;

[0043] Figure 3 is the implementation flowchart of the method for correcting vehicle data observation values provided by still another embodiment of the present application;

[0044] Figure 4 is the implementation flowchart of the method for correcting vehicle data observation values provided by yet another embodiment of the present application;

[0045] Figure 5 is the implementation flowchart of the method for correcting vehicle data observation values provided by yet another embodiment of the present application;

[0046] Figure 6 is the implementation flowchart of the method for correcting vehicle data observation values provided by yet another embodiment of the present application;

[0047] Figure 7 is the structural schematic diagram of the device for correcting vehicle data observation values provided by an embodiment of the present application;

[0048] Figure 8 is the structural schematic diagram of the terminal device provided by an embodiment of the present application. Detailed implementation manners

[0049] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are presented to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0050] It should be understood that when used in the specification of the present application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0051] It should also be understood that the term "and / or" used in the specification and appended claims of the present application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0052] As used in the specification and appended claims of the present application, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" depending on the context. Similarly, the phrases "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined", "in response to determining", "once [the described condition or event] is detected", or "in response to detecting [the described condition or event]" depending on the context.

[0053] In addition, in the description of the specification and appended claims of the present application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0054] Reference to "one embodiment" or "some embodiments" or the like described in the specification of the present application means that a specific feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of the present application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in another way. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized in another way.

[0055] Please refer to Figure 1 , Figure 1 is a flowchart of the implementation of a method for correcting vehicle data observation values provided by an embodiment of the present application. In the embodiment of the present application, the execution subject of the method for correcting vehicle data observation values is a terminal device. Among them, the terminal device includes, but is not limited to: electronic devices such as notebooks, desktop computers, and computers.

[0056] As Figure 1 shown, the method for correcting vehicle data observation values provided by an embodiment of the present application may include S101 to S103, which are described in detail as follows:

[0057] In S101, obtain the target vehicle data observation value of the vehicle at the current moment and a plurality of vehicle data observation values of the vehicle within a historical time period.

[0058] In practical applications, in order to obtain accurate vehicle data observation values, a user may send a data observation value correction request to a terminal device.

[0059] In an embodiment of the present application, the terminal device detecting a data observation value correction request may be: detecting a preset operation on the terminal device. Wherein, the preset operation can be set according to actual needs and is not limited herein. Exemplarily, the preset operation may be clicking a preset control of the terminal device. Based on this, when the terminal device detects that its preset control is clicked, it indicates that a preset operation on the terminal device is detected, that is, a data observation value correction request is detected.

[0060] After detecting the data observation value correction request, the terminal device may obtain the target vehicle data observation value of the vehicle at the current moment and multiple vehicle data observation values of the vehicle within a historical time period.

[0061] Wherein, the current moment specifically refers to the moment when the vehicle detects the data observation value correction request.

[0062] The end moment of the historical time period is the moment before the current moment, and the start moment of the historical time period can be determined according to actual needs and is not limited herein.

[0063] In an implementation manner of an embodiment of the present application, the terminal device may obtain the target vehicle data observation value of the vehicle at the current moment in real time through a camera device wirelessly connected thereto. Wherein, the camera device may be a camera.

[0064] In another implementation manner of an embodiment of the present application, the terminal device may obtain multiple vehicle data observation values of the vehicle within a historical time period through a server wirelessly connected thereto. Wherein, the server may be a cloud server or a computer device such as a desktop computer.

[0065] In S102, according to the multiple vehicle data observation values, determine the vehicle data prediction value of the vehicle at the current moment.

[0066] In an embodiment of the present application, after obtaining multiple vehicle data observation values of the vehicle within a historical time period, the terminal device may predict the vehicle data observation value at the current moment according to the multiple vehicle data observation values to obtain the vehicle data prediction value of the vehicle at the current moment.

[0067] Specifically, the terminal device may input the above multiple vehicle data observation values into a trained data observation value prediction model for processing to obtain the vehicle data prediction value of the vehicle at the current moment.

[0068] In the embodiments of the present application, the data observation value prediction model is used to predict the vehicle data observation values at each moment. The data observation value prediction model can be obtained by training a pre-constructed deep learning model based on a preset sample set. Each sample data in the preset sample set includes a set of sample data observation values within a set time period before each moment and the sample data observation value at each moment. When training the pre-constructed deep learning model, the set of vehicle data observation values within the set time period before each moment in each sample is used as the input of the deep learning model, and the sample data observation value at each moment in each sample is used as the output of the deep learning model. Through training, the deep learning model can learn the corresponding relationship between all possible sets of sample data observation values within the set time period before each moment and the sample data observation values at each moment, and the trained deep learning model is used as the data observation value prediction model.

[0069] In S103, the target vehicle data observation value is corrected according to the multiple vehicle data observation values and the vehicle data prediction value.

[0070] In the embodiments of the present application, after obtaining the vehicle data prediction value, the terminal device can compare the multiple vehicle data observation values, the vehicle data prediction value, and the target vehicle data observation value, and correct the target vehicle data observation value according to the comparison result.

[0071] In an embodiment of the present application, when the terminal device detects that the multiple vehicle data observation values are the same as the vehicle data prediction value, and the target vehicle data observation value is different from both the multiple vehicle data observation values and the vehicle data prediction value, it indicates that the target vehicle data observation value is incorrect and needs to be corrected. Moreover, since the multiple vehicle data observation values are the same as the vehicle data prediction value, the terminal device can directly correct the vehicle data prediction value to the target vehicle data observation value.

[0072] In another embodiment of the present application, when the terminal device detects that the multiple vehicle data observation values are different from the vehicle data prediction value, and the target vehicle data observation value is different from both the multiple vehicle data observation values and the vehicle data prediction value, it indicates that the target vehicle data observation value is incorrect and needs to be corrected. Moreover, since the multiple vehicle data observation values are different from the vehicle data prediction value, the terminal device can calculate the target vehicle data value according to the lateral speed and longitudinal speed corresponding to each of the multiple vehicle data observation values, and correct the target vehicle data value to the target vehicle data observation value.

[0073] In yet another embodiment of the present application, when the terminal device detects that multiple vehicle data observation values are different from the vehicle data prediction value and the vehicle data prediction value is the same as the target vehicle data observation value, it indicates that the target vehicle data observation value is correct and there is no need to correct the target vehicle data observation value. Therefore, the terminal device can determine the target vehicle data observation value at the current moment as the final target vehicle data observation value.

[0074] As can be seen from the above, a method for correcting vehicle data observation values provided by an embodiment of the present application includes obtaining the target vehicle data observation value of the vehicle at the current moment and multiple vehicle data observation values of the vehicle within a historical time period; determining the vehicle data prediction value of the vehicle at the current moment according to the multiple vehicle data observation values; and correcting the target vehicle data observation value according to the multiple vehicle data observation values and the vehicle data prediction value. Compared with the prior art that directly determines the currently observed vehicle data as the final observed value of the vehicle, this method corrects the target vehicle data observation value by combining multiple vehicle data observation values and vehicle data prediction values within a historical time period, improving the determination accuracy of the final vehicle data observation value.

[0075] Please refer to Figure 2 , Figure 2 which is a flowchart of the implementation of the method for correcting vehicle data observation values provided by another embodiment of the present application. Compared with the Figure 1 corresponding embodiment, when the vehicle data prediction value includes a predicted heading angle and the multiple vehicle data observation values include historical lateral speeds and historical longitudinal speeds at each historical moment within a historical time period, in this embodiment, step S102 may specifically include S201 to S204, which are described in detail as follows:

[0076] In S201, traverse all the historical moments, and calculate each first difference of the historical lateral speeds at two adjacent historical moments, and each second difference of the historical longitudinal speeds at two adjacent historical moments.

[0077] In S202, based on the arctangent function, solve each target difference set in turn to obtain multiple first radians; where each target difference set is composed of any one of the first differences among the first differences and the second difference corresponding to the any one of the first differences among the second differences.

[0078] In S203, perform angle conversion on the multiple first radians to obtain the first angles corresponding to the multiple first radians respectively.

[0079] In S204, determine the mean value of each of the first angles as the predicted heading angle.

[0080] In this embodiment, after obtaining the historical lateral speed and historical longitudinal speed at each historical moment within the historical time period, the terminal device can successively calculate each first difference of the historical lateral speed between two adjacent historical moments, and each second difference of the historical longitudinal speed between two adjacent historical moments.

[0081] After that, the terminal device can successively form multiple target difference sets according to any one of the first differences and the second difference corresponding to the any one of the first differences among the respective first differences. That is to say, each target difference set only includes one first difference and the second difference corresponding to the first difference.

[0082] Exemplarily, assuming that the first difference is the difference between the historical lateral speed at time t1 and the historical lateral speed at time t2, then the second difference corresponding to the first difference is the difference between the historical longitudinal speed at time t1 and the historical longitudinal speed at time t2.

[0083] In this embodiment, after obtaining multiple target difference sets, the terminal device can successively solve each target difference set based on the arctangent function to obtain the first radian corresponding to each target difference set.

[0084] Specifically, the terminal device can calculate the first radian corresponding to each target difference set according to the following formula:

[0085] l i = arctan2(Δabs_vy i , Δabs_vx i );

[0086] Wherein, l i represents the first radian corresponding to the i-th target difference set, Δabs_vx i represents the first difference in the i-th target difference set, Δabs_vy i represents the second difference in the i-th target difference set, and arctan2(·) represents the arctangent function.

[0087] In this embodiment, after obtaining the first radian corresponding to each target difference set, the terminal device can successively perform angle conversion on each first radian according to the conversion formula between the radian and the angle to obtain the first angle corresponding to each first radian.

[0088] After obtaining multiple first angles, the terminal device can calculate the mean value of the multiple first angles and determine the mean value of the multiple first angles as the predicted heading angle.

[0089] As can be seen from the above, in the method for correcting the observed values of vehicle data provided in this embodiment, all historical moments are traversed, and each first difference of the historical lateral speed at two adjacent historical moments and each second difference of the historical longitudinal speed at two adjacent historical moments are calculated; based on the arctangent function, each target difference set is solved in turn to obtain a plurality of first radian values; wherein, each target difference set is composed of any one of the first differences and the second difference corresponding to any one of the first differences among the second differences; the plurality of first radian values are subjected to angle conversion to obtain a plurality of first angles corresponding to the respective first radian values; and the mean value of the respective first angles is determined as the predicted heading angle. The method provided in this embodiment improves the accuracy of determining the predicted heading angle.

[0090] Please refer to Figure 3 , Figure 3 which is a flowchart of the implementation of the method for correcting the observed values of vehicle data provided in another embodiment of the present application. Compared with Figure 2 the corresponding embodiment, when the target vehicle data observed value includes the current heading angle and the plurality of vehicle data observed values further include the historical heading angles at each historical moment within the historical time period, in this embodiment, step S103 may specifically include S301 to S303, which are described in detail as follows:

[0091] In S301, the first ratio between the number of historical moments corresponding to each of the historical heading angles and the total number of all the historical moments is calculated respectively.

[0092] In this embodiment, since one historical moment corresponds to one historical heading angle, and the historical heading angles at different historical moments may be the same or different, the terminal device can count the number of historical moments corresponding to each historical heading angle, that is, count the number of occurrences of each historical heading angle within the historical time period.

[0093] After that, the terminal device can calculate the first ratio of each historical heading angle within the historical time period according to the number of historical moments corresponding to each historical heading angle and the total number of multiple historical moments within the historical time period.

[0094] In this embodiment, after the terminal device obtains the first ratio of each historical heading angle within the historical time period, it can compare the respective first ratios to obtain the maximum value among the respective first ratios, and compare the maximum value with a first threshold. The first threshold can be determined according to actual needs and is not limited here. Exemplarily, the first threshold can be 0.9.

[0095] In an implementation manner of the embodiment of the present application, after the terminal device detects that the above maximum value is greater than the first threshold, it can compare the historical heading angle corresponding to the maximum value, the predicted heading angle, and the current heading angle one by one.

[0096] In one embodiment of the present application, when the terminal device detects that the historical heading angle corresponding to the maximum value is the same as the predicted heading angle and the predicted heading angle is different from the current heading angle, step S302 may be executed.

[0097] In another embodiment of the present application, when the terminal device detects that the historical heading angle corresponding to the maximum value is different from the predicted heading angle and the current heading angle is different from both the predicted heading angle and the historical heading angle corresponding to the maximum value, step S303 may be executed.

[0098] In still another embodiment of the present application, when the terminal device detects that the historical heading angle corresponding to the maximum value is different from the predicted heading angle and the current heading angle is the same as the predicted heading angle, it indicates that the current heading angle is correct and there is no need to correct the current heading angle. Therefore, the terminal device may directly determine the current heading angle at the current moment as the final current heading angle.

[0099] In another implementation manner of the embodiments of the present application, after the terminal device detects that the above maximum value is less than or equal to the first threshold, it indicates that the historical heading angles in the historical time period often jump, indicating that the confidence levels of the historical heading angles in the historical time period are relatively low, that is, each historical heading angle is not credible. Since the predicted heading angle is calculated based on the historical lateral speed and historical longitudinal speed at each historical moment in the historical time period and has nothing to do with each historical heading angle, the terminal device may correct the current heading angle according to the predicted heading angle.

[0100] In this embodiment, the terminal device may detect a sixth difference between the predicted heading angle and the current heading angle and compare the sixth difference with a fifth threshold. The fifth threshold may be determined according to actual needs and is not limited herein.

[0101] In one embodiment of the present application, when the terminal device detects that the sixth difference is greater than the fifth threshold, it indicates that the difference between the predicted heading angle and the current heading angle is small, indicating that the current heading angle is correct and there is no need to correct the current heading angle. Therefore, the terminal device may directly determine the current heading angle at the current moment as the final current heading angle.

[0102] In another embodiment of the present application, when the terminal device detects that the sixth difference is less than or equal to the fifth threshold, it indicates that the difference between the predicted heading angle and the current heading angle is large, indicating that the current heading angle is incorrect and needs to be corrected. Therefore, the terminal device may correct the current heading angle to the predicted heading angle.

[0103] In S302, if the maximum value among the respective first ratios is greater than the first threshold, the historical heading angle corresponding to the maximum value is the same as the predicted heading angle, and the predicted heading angle is different from the current heading angle, then correct the current heading angle to the predicted heading angle.

[0104] In this embodiment, when the terminal device detects that the historical heading angle corresponding to the maximum value is the same as the predicted heading angle, and the predicted heading angle is different from the current heading angle, it indicates that the current heading angle is incorrect and needs to be corrected. Since the historical heading angle corresponding to the maximum value is the same as the predicted heading angle, the terminal device can correct the current heading angle to the predicted heading angle.

[0105] In S303, if the maximum value is greater than the first threshold, the historical heading angle corresponding to the maximum value is different from the predicted heading angle, and the current heading angle is different from both the predicted heading angle and the historical heading angle corresponding to the maximum value, then determine the target heading angle according to the historical lateral speed and historical longitudinal speed at each historical moment, and correct the current heading angle to the target heading angle.

[0106] In this embodiment, when the terminal device detects that the historical heading angle corresponding to the maximum value is different from the predicted heading angle, and the current heading angle is different from both the predicted heading angle and the historical heading angle corresponding to the maximum value, it indicates that the current heading angle is incorrect and needs to be corrected. Since the historical heading angle corresponding to the maximum value is also different from the predicted heading angle, the terminal device can calculate the final target heading angle according to the historical lateral speed and historical longitudinal speed at each historical moment, and correct the current heading angle to the target heading angle.

[0107] As can be seen above, the method for correcting the vehicle data observation value provided in this embodiment calculates the first ratio between the number of historical moments corresponding to each historical heading angle and the total number of all historical moments respectively; if the maximum value among the respective first ratios is greater than the first threshold, the historical heading angle corresponding to the maximum value is the same as the predicted heading angle, and the predicted heading angle is different from the current heading angle, then correct the current heading angle to the predicted heading angle; if the maximum value is greater than the first threshold, the historical heading angle corresponding to the maximum value is different from the predicted heading angle, and the current heading angle is different from both the predicted heading angle and the historical heading angle corresponding to the maximum value, then determine the target heading angle according to the historical lateral speed and historical longitudinal speed at each historical moment, and correct the current heading angle to the target heading angle. The method provided in this embodiment improves the determination accuracy of the current heading angle.

[0108] Please refer to Figure 4 , Figure 4 which is the implementation flowchart of the method for correcting the vehicle data observation value provided in another embodiment of this application. Relative to Figure 1For a corresponding embodiment, when the predicted vehicle data includes a predicted orientation, and the multiple vehicle data observations include the historical lateral speed and the historical longitudinal speed at each historical moment within a historical time period, in this embodiment, step S102 may specifically include S401 to S404, which are described in detail as follows:

[0109] In S401, calculate a third difference between the historical lateral speed at the first historical moment and the historical lateral speed at the last historical moment within the historical time period, and a fourth difference between the historical longitudinal speed at the first historical moment and the historical longitudinal speed at the last historical moment.

[0110] In S402, solve for the third difference and the fourth difference based on the arctangent function to obtain a second radian.

[0111] In S403, perform an angle conversion on the second radian to obtain a second angle corresponding to the second radian.

[0112] In S404, determine the predicted orientation according to the angle range in which the second angle is located.

[0113] In this embodiment, after the terminal device obtains the historical lateral speed and the historical longitudinal speed at each historical moment within the historical time period, it can calculate a third difference between the historical lateral speed at the first historical moment and the historical lateral speed at the last historical moment within the historical time period, and a fourth difference between the historical longitudinal speed at the first historical moment and the historical longitudinal speed at the last historical moment.

[0114] After that, the terminal device can solve for the third difference and the fourth difference based on the arctangent function to obtain a second radian.

[0115] Specifically, the terminal device can calculate the second radian according to the following formula:

[0116] l c = arctan2(Δabs_vy c , Δabs_vx c );

[0117] where l c represents the second radian, Δabs_vx c represents the third difference, Δabs_vy c represents the fourth difference, and arctan2(·) represents the arctangent function.

[0118] In this embodiment, after the terminal device obtains the second radian, it can perform an angle conversion on the second radian according to the conversion formula between the radian and the angle to obtain a second angle corresponding to the second radian.

[0119] It should be noted that the terminal device pre-stores multiple angular ranges, and each angular range corresponds to a certain orientation.

[0120] Specifically, the terminal device can set the orientation corresponding to the angular ranges of [315°, 360°] and [0, 45°] to be north, the orientation corresponding to the angular range of (45°, 135°] to be east, the orientation corresponding to the angular range of (135°, 225°] to be south, and the orientation corresponding to the angular range of (225°, 315°) to be west.

[0121] It should be noted that the above degrees increase gradually in the clockwise direction.

[0122] Based on this, the terminal device can determine the angular range where the second angle is located, and determine the predicted orientation according to the corresponding relationship between the above different angular ranges and orientations.

[0123] As can be seen from the above, in the method for correcting the vehicle data observation value provided in this embodiment, the third difference between the historical lateral speed at the first historical moment and the historical lateral speed at the last historical moment, and the fourth difference between the historical longitudinal speed at the first historical moment and the historical longitudinal speed at the last historical moment within the historical time period are calculated; the third difference and the fourth difference are solved based on the arctangent function to obtain the second radian; the second radian is subjected to angle conversion to obtain the second angle corresponding to the second radian; and the predicted orientation is determined according to the angular range where the second angle is located. The method provided in this embodiment improves the accuracy of determining the predicted orientation.

[0124] Please refer to Figure 5 , Figure 5 which is the implementation flowchart of the method for correcting the vehicle data observation value provided in another embodiment of this application. Compared with Figure 4 the corresponding embodiment, when the target vehicle data observation value includes the current orientation and the multiple vehicle data observation values further include the historical orientations at each historical moment within the historical time period, in this embodiment, step S103 may specifically include S501 to S503, which are described in detail as follows:

[0125] In S501, calculate the second proportion of the number of historical moments corresponding to each of the historical orientations to the total number of all historical moments respectively.

[0126] In this embodiment, since one historical moment corresponds to one historical orientation, and the historical orientations at different historical moments may be the same or different, the terminal device can count the number of historical moments corresponding to each historical orientation, that is, count the number of occurrences of each historical orientation within the historical time period.

[0127] After that, the terminal device can calculate the second proportion of each historical orientation in the historical time period according to the number of historical moments corresponding to each historical orientation and the total number of multiple historical moments in the historical time period.

[0128] In this embodiment, after the terminal device obtains the second proportion of each historical orientation in the historical time period, it can compare the respective second proportions to obtain the maximum value among the respective second proportions, and compare the maximum value with a second threshold. The second threshold can be determined according to actual needs and is not limited here. Exemplarily, the second threshold can be 0.9.

[0129] In an implementation manner of the embodiment of the present application, after the terminal device detects that the above maximum value is greater than the second threshold, it can compare the historical orientation, predicted orientation, and current orientation corresponding to the maximum value one by one.

[0130] In an embodiment of the present application, when the terminal device detects that the historical orientation corresponding to the maximum value is the same as the predicted orientation and the predicted orientation is different from the current orientation, it can execute step S502.

[0131] In another embodiment of the present application, when the terminal device detects that the historical orientation corresponding to the maximum value is different from the predicted orientation and the current orientation is different from both the predicted orientation and the historical orientation corresponding to the maximum value, it can execute step S503.

[0132] In still another embodiment of the present application, when the terminal device detects that the historical orientation corresponding to the maximum value is different from the predicted orientation and the current orientation is the same as the predicted orientation, it indicates that the current orientation is correct and there is no need to correct the current orientation. Therefore, the terminal device can directly determine the current orientation at the current moment as the final current orientation.

[0133] In another implementation manner of the embodiment of the present application, after the terminal device detects that the above maximum value is less than or equal to the second threshold, it indicates that the historical orientations in the historical time period often jump, indicating that the confidence levels of the historical orientations in the historical time period are relatively low, that is, the historical orientations are not credible. Since the predicted orientation is calculated based on the historical horizontal speed and historical vertical speed at each historical moment in the historical time period and has nothing to do with the historical orientations, the terminal device can correct the current orientation according to the predicted orientation.

[0134] In this embodiment, the terminal device can detect the seventh difference between the predicted orientation and the current orientation and compare the seventh difference with a sixth threshold. The sixth threshold can be determined according to actual needs and is not limited here.

[0135] In an embodiment of the present application, when the terminal device detects that the seventh difference is greater than the sixth threshold, it indicates that the difference between the predicted orientation and the current orientation is small, indicating that the current orientation is correct and there is no need to correct the current orientation. Therefore, the terminal device can directly determine the current orientation at the current moment as the final current orientation.

[0136] In another embodiment of the present application, when the terminal device detects that the seventh difference is less than or equal to the sixth threshold, it indicates that the difference between the predicted orientation and the current orientation is large, indicating that the current orientation is incorrect and needs to be corrected. Therefore, the terminal device can correct the current orientation to the predicted orientation.

[0137] In S502, if the maximum value among the second ratios is greater than the second threshold, the historical orientation corresponding to the maximum value is the same as the predicted orientation, and the predicted orientation is different from the current orientation, then the current orientation is corrected to the predicted orientation.

[0138] In this embodiment, when the terminal device detects that the historical orientation corresponding to the maximum value is the same as the predicted orientation, and the predicted orientation is different from the current orientation, it indicates that the current orientation is incorrect and needs to be corrected. Since the historical orientation corresponding to the maximum value is the same as the predicted orientation, the terminal device can correct the current orientation to the predicted orientation.

[0139] In S503, if the maximum value is greater than the second threshold, the historical orientation corresponding to the maximum value is different from the predicted orientation, and the current orientation is different from both the predicted orientation and the historical orientation corresponding to the maximum value, then the target orientation is determined according to the historical lateral velocity and historical longitudinal velocity at each historical moment, and the current orientation is corrected to the target orientation.

[0140] In this embodiment, when the terminal device detects that the historical orientation corresponding to the maximum value is different from the predicted orientation, and the current orientation is different from both the predicted orientation and the historical orientation corresponding to the maximum value, it indicates that the current orientation is incorrect and needs to be corrected. Since the historical orientation corresponding to the maximum value is also different from the predicted orientation, the terminal device can calculate the final target orientation according to the historical lateral velocity and historical longitudinal velocity at each historical moment, and correct the current orientation to the target orientation.

[0141] As can be seen from the above, in the method for correcting the observed value of vehicle data provided in this embodiment, the second ratio between the number of historical moments corresponding to each historical orientation and the total number of all historical moments is calculated respectively; if the maximum value among the second ratios is greater than the second threshold, and the historical orientation corresponding to the maximum value is the same as the predicted orientation, and the predicted orientation is different from the current orientation, then the current orientation is corrected to the predicted orientation; if the maximum value is greater than the second threshold, the historical orientation corresponding to the maximum value is different from the predicted orientation, and the current orientation is different from both the predicted orientation and the historical orientation corresponding to the maximum value, then the target orientation is determined according to the historical lateral speed and historical longitudinal speed at each historical moment, and the current orientation is corrected to the target orientation. The method provided in this embodiment improves the accuracy of determining the current orientation.

[0142] In one embodiment of the present application, when the predicted vehicle data includes the predicted vehicle type, and the multiple observed values of vehicle data include the historical vehicle types at each historical moment within the historical time period, the terminal device can directly determine the historical vehicle type at the last historical moment within the historical time period as the predicted vehicle type.

[0143] In one implementation manner of this embodiment, the terminal device can compare the predicted vehicle type with the current vehicle type.

[0144] In one embodiment of the present application, when the terminal device detects that the predicted vehicle type is the same as the current vehicle type, it indicates that the current vehicle type is correct and there is no need to correct the current vehicle type. Therefore, the terminal device can directly determine the current vehicle type at the current moment as the final current vehicle type.

[0145] In another embodiment of the present application, when the terminal device detects that the predicted vehicle type is different from the current vehicle type, it indicates that there is a jump in the vehicle type. Therefore, in order to improve the accuracy of determining the current vehicle type, the terminal device can specifically correct the current vehicle type through steps S601 - S602 as shown in Figure 6 shown.

[0146] Based on this, please refer to Figure 6 , Figure 6 which is a flowchart of the implementation of the method for correcting the observed value of vehicle data provided in another embodiment of the present application. When the target observed value of vehicle data includes the current vehicle speed and the current vehicle type, and the multiple observed values of vehicle data also include the historical vehicle speeds at each historical moment within the historical time period, in this embodiment, in order to achieve the accuracy of determining the current vehicle type, step S103 can specifically include S601 - S602, which are described in detail as follows:

[0147] In S601, if the predicted vehicle type is different from the current vehicle type, then calculate the third proportion between the number of the historical moments corresponding to each historical vehicle type and the total number of all historical moments respectively.

[0148] In this embodiment, when the terminal device detects that the predicted vehicle type is different from the current vehicle type, it indicates that there is a jump in the vehicle type. Therefore, in order to improve the accuracy of determining the current vehicle type, the terminal device can calculate the third proportion between the number of the historical moments corresponding to each historical vehicle type and the total number of all historical moments.

[0149] Since one historical moment corresponds to one historical vehicle type, and the historical vehicle types at different historical moments may be the same or different, the terminal device can count the number of historical moments corresponding to each historical vehicle type, that is, count the number of occurrences of each historical vehicle type in the historical time period.

[0150] After that, the terminal device can calculate the third proportion of each historical vehicle type in the historical time period according to the number of historical moments corresponding to each historical vehicle type and the total number of multiple historical moments in the historical time period.

[0151] In this embodiment, after the terminal device obtains the third proportion of each historical vehicle type in the historical time period, it can compare the third proportions to obtain the maximum value among the third proportions, and compare the maximum value with the third threshold. The third threshold can be determined according to actual needs and is not limited here. Exemplarily, the third threshold can be 0.6.

[0152] In an embodiment of the present application, after the terminal device detects that the maximum value among the third proportions is less than or equal to the third threshold, it indicates that the historical vehicle types in the historical time period often jump, indicating that the confidence levels of the historical vehicle types in the historical time period are relatively low, that is, the historical vehicle types are not credible. Since the predicted vehicle type is determined according to the historical vehicle type at the last historical moment in the historical time period, the terminal device does not need to correct the current vehicle type, that is, the terminal device can directly determine the current vehicle type at the current moment as the final current vehicle type.

[0153] In another embodiment of the present application, after the terminal device detects that the maximum value among the third proportions is greater than the third threshold, it can execute step S602.

[0154] In S602, if the maximum value among the respective third ratios is greater than the third threshold, determine a fifth difference between the historical vehicle speed at the last historical moment and the current vehicle speed; if the fifth difference is less than the fourth threshold, correct the current vehicle type to the predicted vehicle type; wherein, the fourth threshold is positively correlated with the maximum value.

[0155] In this embodiment, after the terminal device detects that the maximum value among the respective third ratios is greater than the third threshold, it indicates that the current vehicle type is incorrect and needs to be corrected. Therefore, the terminal device can calculate a fifth difference between the historical vehicle speed at the last historical moment and the current vehicle speed, and compare this fifth difference with the fourth threshold.

[0156] It should be noted that the fourth threshold is positively correlated with the maximum value among the third ratios. That is to say, the larger the maximum value among the third ratios, the larger the fourth threshold; the smaller the maximum value among the third ratios, the smaller the fourth threshold.

[0157] In an embodiment of the present application, when the terminal device detects that the fifth difference is less than the fourth threshold, it indicates that the difference between the historical vehicle speed at the last historical moment and the current vehicle speed is small, indicating that the current vehicle type is incorrect and needs to be corrected. Therefore, the terminal device can correct the current vehicle type to the predicted vehicle type.

[0158] In another embodiment of the present application, when the terminal device detects that the fifth difference is greater than the fourth threshold, it indicates that the difference between the historical vehicle speed at the last historical moment and the current vehicle speed is large, indicating that the vehicle observed at the current moment is different from the vehicle observed during the historical time. Therefore, the terminal device can output a prompt message for indicating that the observed target object at the current moment is different from the historical observed target object during the historical time period, so that relevant personnel can correct the observed target object.

[0159] As can be seen from the above, for the method for correcting vehicle data observation values provided in this embodiment, when the predicted vehicle type is different from the current vehicle type, calculate the third ratio between the number of historical moments corresponding to each historical vehicle type and the total number of all historical moments respectively; if the maximum value among the respective third ratios is greater than the third threshold, determine a fifth difference between the historical vehicle speed at the last historical moment and the current vehicle speed; if the fifth difference is less than the fourth threshold, correct the current vehicle type to the predicted vehicle type; wherein, the fourth threshold is positively correlated with the maximum value. The method provided in this embodiment improves the accuracy of determining the current vehicle type.

[0160] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not imply the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0161] Corresponding to a method for correcting vehicle data observation values described in the above embodiments, Figure 7 The schematic structural diagram of a device for correcting vehicle data observation values provided by an embodiment of the present application is shown. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown. Referring to Figure 7 The vehicle data observation value correction device 700 includes: a first acquisition unit 71, a predicted value determination unit 72, and a first correction unit 73.

[0162] Wherein:

[0163] The first acquisition unit 71 is configured to acquire the target vehicle data observation value of the vehicle at the current moment and a plurality of vehicle data observation values of the vehicle within a historical time period.

[0164] The predicted value determination unit 72 is configured to determine the vehicle data predicted value of the vehicle at the current moment according to the plurality of vehicle data observation values.

[0165] The first correction unit 73 is configured to correct the target vehicle data observation value according to the plurality of vehicle data observation values and the vehicle data predicted value.

[0166] In an embodiment of the present application, the plurality of vehicle data observation values include the historical lateral speed and the historical longitudinal speed at each historical moment within the historical time period, and the vehicle data predicted value includes a predicted heading angle; the predicted value determination unit 72 specifically includes: a first calculation unit, a first solution unit, a first conversion unit, and a predicted heading angle determination unit. Wherein:

[0167] The first calculation unit is configured to traverse all the historical moments, calculate each first difference between the historical lateral speeds at two adjacent historical moments, and each second difference between the historical longitudinal speeds at two adjacent historical moments.

[0168] The first solution unit is configured to sequentially solve each target difference set based on the arctangent function to obtain a plurality of first radians; wherein, each target difference set is composed of any one of the first differences among the first differences and the second difference corresponding to the any one of the first differences among the second differences.

[0169] The first conversion unit is configured to perform angle conversion on the plurality of first radians to obtain the first angles corresponding to the plurality of first radians respectively.

[0170] The predicted heading angle determination unit is used to determine the mean value of each of the first angles as the predicted heading angle.

[0171] In an embodiment of the present application, the target vehicle data observation value includes the current heading angle, and the multiple vehicle data observation values further include the historical heading angles at each historical moment within the historical time period; the first correction unit 73 specifically includes: a second calculation unit, a second correction unit, and a third correction unit. Among them:

[0172] The second calculation unit is used to calculate the first ratio between the number of the historical moments corresponding to each historical heading angle and the total number of all historical moments respectively.

[0173] The second correction unit is used to, if the maximum value among the first ratios is greater than the first threshold, the historical heading angle corresponding to the maximum value is the same as the predicted heading angle, and the predicted heading angle is different from the current heading angle, then correct the current heading angle to the predicted heading angle.

[0174] The third correction unit is used to, if the maximum value is greater than the first threshold, the historical heading angle corresponding to the maximum value is different from the predicted heading angle, and the current heading angle is different from both the predicted heading angle and the historical heading angle corresponding to the maximum value, then determine the target heading angle according to the historical lateral speed and historical longitudinal speed at each historical moment, and correct the current heading angle to the target heading angle.

[0175] In an embodiment of the present application, the multiple vehicle data observation values include the historical lateral speed and historical longitudinal speed at each historical moment within the historical time period, and the vehicle data prediction value includes the predicted direction; the prediction value determination unit 72 specifically includes: a third calculation unit, a second solution unit, a second conversion unit, and a predicted direction determination unit. Among them:

[0176] The third calculation unit is used to calculate the third difference between the historical lateral speed at the first historical moment and the historical lateral speed at the last historical moment within the historical time period, and the fourth difference between the historical longitudinal speed at the first historical moment and the historical longitudinal speed at the last historical moment.

[0177] The second solution unit is used to solve the third difference and the fourth difference based on the arctangent function to obtain the second radian.

[0178] The second conversion unit is used to perform angle conversion on the second radian to obtain the second angle corresponding to the second radian.

[0179] The predicted direction determination unit is used to determine the predicted direction according to the angle range where the second angle is located.

[0180] In an embodiment of the present application, the target vehicle data observation value includes the current orientation, and the multiple vehicle data observation values further include the historical orientations at each historical moment within the historical time period; the first correction unit 73 specifically includes: a fourth calculation unit, a fourth correction unit, and a fifth correction unit. Wherein:

[0181] The fourth calculation unit is configured to calculate a second proportion between the number of the historical moments corresponding to each of the historical orientations and the total number of all the historical moments, respectively.

[0182] The fourth correction unit is configured to, if the maximum value among the second proportions is greater than a second threshold, the historical orientation corresponding to the maximum value is the same as the predicted orientation, and the predicted orientation is different from the current orientation, then correct the current orientation to the predicted orientation.

[0183] The fifth correction unit is configured to, if the maximum value is greater than the second threshold, the historical orientation corresponding to the maximum value is different from the predicted orientation, and the current orientation is different from both the predicted orientation and the historical orientation corresponding to the maximum value, then determine a target orientation according to the historical lateral speed and the historical longitudinal speed at each historical moment, and correct the current orientation to the target orientation.

[0184] In an embodiment of the present application, the multiple vehicle data observation values include the historical vehicle types at each historical moment within the historical time period; the vehicle data prediction value includes a predicted vehicle type; the prediction value determination unit 72 specifically includes: a type determination unit.

[0185] The type determination unit is configured to determine the historical vehicle type at the last historical moment within the historical time period as the predicted vehicle type.

[0186] In an embodiment of the present application, the target vehicle data observation value includes the current vehicle type and the current vehicle speed, and the multiple vehicle data observation values further include the historical vehicle speeds at each historical moment within the historical time period; the first correction unit 73 specifically includes: a fifth calculation unit and a sixth correction unit. Wherein:

[0187] The fifth calculation unit is configured to, if the predicted vehicle type is different from the current vehicle type, calculate a third proportion between the number of the historical moments corresponding to each of the historical vehicle types and the total number of all the historical moments, respectively.

[0188] The sixth correction unit is configured to determine a fifth difference between the historical vehicle speed at the last historical moment and the current vehicle speed if the maximum value among the respective third ratios is greater than a third threshold; and correct the current vehicle type to the predicted vehicle type if the fifth difference is less than a fourth threshold, where the fourth threshold is positively correlated with the maximum value.

[0189] It should be noted that for the information interaction, execution process, etc. between the above-mentioned devices / units, since they are based on the same concept as the method embodiments of the present application, their specific functions and the technical effects brought can be specifically referred to the method embodiment part, and will not be elaborated here.

[0190] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above-mentioned functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiments and will not be elaborated here.

[0191] Figure 8 It is a schematic structural diagram of a terminal device provided in an embodiment of the present application. As Figure 8 shown, the terminal device 8 in this embodiment includes: at least one processor 80 ( Figure 8 only one is shown in the figure), a memory 81, and a computer program 82 stored in the memory 81 and executable on the at least one processor 80. When the processor 80 executes the computer program 82, the steps in any of the above-mentioned method embodiments for correcting vehicle data observation values are implemented.

[0192] The terminal device may include, but is not limited to, a processor 80 and a memory 81. Those skilled in the art can understand that Figure 8 this is only an example of the terminal device 8 and does not constitute a limitation on the terminal device 8. It may include more or fewer components than shown in the figure, or combine some components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0193] The so-called processor 80 may be a Central Processing Unit (CPU), and the processor 80 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0194] In some embodiments, the memory 81 may be an internal storage unit of the terminal device 8, such as the memory of the terminal device 8. In other embodiments, the memory 81 may also be an external storage device of the terminal device 8, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc., equipped on the terminal device 8. Further, the memory 81 may also include both the internal storage unit and the external storage device of the terminal device 8. The memory 81 is used to store an operating system, application programs, a BootLoader, data, and other programs, such as the program code of the computer program. The memory 81 may also be used to temporarily store data that has been output or will be output.

[0195] An embodiment of the present application further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps in the above various method embodiments can be implemented.

[0196] An embodiment of the present application provides a computer program product, and when the computer program product runs on a terminal device, the terminal device can implement the steps in the above various method embodiments when executed.

[0197] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned embodiment methods of this application, a computer program can be used to instruct the relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0198] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0199] The above-mentioned embodiments are only used to illustrate the technical solutions of this application, rather than to limit it; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of this application, and should all be included in the protection scope of this application.

Claims

1. A method for correcting vehicle data observation values, characterized in that, it includes: Obtaining the target vehicle data observation value of the vehicle at the current moment and multiple vehicle data observation values of the vehicle within a historical time period; Determining the vehicle data prediction value of the vehicle at the current moment according to the multiple vehicle data observation values; Correcting the target vehicle data observation value according to the multiple vehicle data observation values and the vehicle data prediction value.

2. The method according to claim 1, characterized in that, the multiple vehicle data observation values include the historical lateral speed and historical longitudinal speed at each historical moment within the historical time period, and the vehicle data prediction value includes a predicted heading angle; the determining of the vehicle data prediction value of the vehicle at the current moment according to the multiple vehicle data observation values includes: Traversing all the historical moments, calculating each first difference of the historical lateral speeds at two adjacent historical moments, and each second difference of the historical longitudinal speeds at two adjacent historical moments; Successively solving each target difference set based on the arctangent function to obtain a plurality of first radians; wherein, each target difference set is composed of any one of the first differences among the first differences and the second difference corresponding to the any one of the first differences among the second differences; Performing angle conversion on the plurality of first radians to obtain the first angles corresponding to the plurality of first radians respectively; Determining the average value of each of the first angles as the predicted heading angle.

3. The method according to claim 2, characterized in that, the target vehicle data observation value includes the current heading angle, and the multiple vehicle data observation values further include the historical heading angles at each historical moment within the historical time period; the correcting of the target vehicle data observation value according to the multiple vehicle data observation values and the vehicle data prediction value includes: Calculating respectively the first proportion between the number of the historical moments corresponding to each historical heading angle and the total number of all the historical moments; If the maximum value among the first proportions is greater than a first threshold, the historical heading angle corresponding to the maximum value is the same as the predicted heading angle, and the predicted heading angle is different from the current heading angle, then correcting the current heading angle to the predicted heading angle; If the maximum value is greater than the first threshold, the historical heading angle corresponding to the maximum value is different from the predicted heading angle, and the current heading angle is different from both the predicted heading angle and the historical heading angle corresponding to the maximum value, then determining a target heading angle according to the historical lateral speed and historical longitudinal speed at each historical moment, and correcting the current heading angle to the target heading angle.

4. The method according to claim 1, characterized in that, the multiple vehicle data observation values include the historical lateral speed and historical longitudinal speed at each historical moment within the historical time period, and the vehicle data prediction value includes a predicted orientation; the determining of the vehicle data prediction value of the vehicle at the current moment according to the multiple vehicle data observation values includes: Calculate a third difference between the historical lateral speed at the first historical moment and the historical lateral speed at the last historical moment within the historical time period, and a fourth difference between the historical longitudinal speed at the first historical moment and the historical longitudinal speed at the last historical moment; Solve for the third difference and the fourth difference based on the arctangent function to obtain a second radian; Perform an angle conversion on the second radian to obtain a second angle corresponding to the second radian; Determine the predicted orientation according to the angle range in which the second angle is located.

5. The method according to claim 4, wherein, the target vehicle data observation value includes the current orientation, and the multiple vehicle data observation values further include the historical orientations at each historical moment within the historical time period; the correcting the target vehicle data observation value according to the multiple vehicle data observation values and the vehicle data prediction value includes: Calculate a second proportion between the number of historical moments corresponding to each historical orientation and the total number of all historical moments respectively; If the maximum value among all the second proportions is greater than a second threshold, the historical orientation corresponding to the maximum value is the same as the predicted orientation, and the predicted orientation is different from the current orientation, then correct the current orientation to the predicted orientation; If the maximum value is greater than the second threshold, the historical orientation corresponding to the maximum value is different from the predicted orientation, and the current orientation is different from both the predicted orientation and the historical orientation corresponding to the maximum value, then determine a target orientation according to the historical lateral speed and historical longitudinal speed at each historical moment, and correct the current orientation to the target orientation.

6. The method according to claim 1, wherein, the multiple vehicle data observation values include the historical vehicle types at each historical moment within the historical time period; the vehicle data prediction value includes a predicted vehicle type; the determining the vehicle data prediction value of the vehicle at the current moment according to the multiple vehicle data observation values includes: Determine the historical vehicle type at the last historical moment within the historical time period as the predicted vehicle type.

7. The method according to claim 6, wherein, the target vehicle data observation value includes the current vehicle type and the current vehicle speed, and the multiple vehicle data observation values further include the historical vehicle speeds at each historical moment within the historical time period; the correcting the target vehicle data observation value according to the multiple vehicle data observation values and the vehicle data prediction value includes: If the predicted vehicle type is different from the current vehicle type, then calculate a third proportion between the number of historical moments corresponding to each historical vehicle type and the total number of all historical moments respectively; If the maximum value among the respective third ratios is greater than the third threshold, determine a fifth difference between the historical vehicle speed at the last historical moment and the current vehicle speed; if the fifth difference is less than the fourth threshold, correct the current vehicle type to the predicted vehicle type; wherein, the fourth threshold is positively correlated with the maximum value.

8. A correction device for vehicle data observation values, characterized in that, comprising: A first acquisition unit, configured to acquire the target vehicle data observation value of the vehicle at the current moment and a plurality of vehicle data observation values of the vehicle within a historical time period; A predicted value determination unit, configured to determine the predicted value of the vehicle data of the vehicle at the current moment according to the plurality of vehicle data observation values; A first correction unit, configured to correct the target vehicle data observation value according to the plurality of vehicle data observation values and the predicted value of the vehicle data.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the correction method for vehicle data observation values according to any one of claims 1 to 7.

10. A computer-readable storage medium, the computer-readable storage medium stores a computer program, characterized in that, when the computer program is executed by a processor, it implements the correction method for vehicle data observation values according to any one of claims 1 to 7.

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