Method and device for acquiring correction deviation value

Abstract

The invention provides a correction deviation value obtaining method and device. According to the method and the device, the situation that the vehicle presses the elevation change object such as a well lid, a pit or a deceleration strip on the ground under the condition that the vehicle does not have actual collision is considered, so that the pressing moment when the vehicle actually presses the elevation change object such as the well lid, the pit or the deceleration strip is obtained. And the correction deviation value is obtained based on the over-pressing moment, so that subsequent error correction is facilitated. According to the method, the first distance correction deviation value of the distance sensor, the first speed correction deviation value of the speed sensor and the first moment correction deviation value of the output moment when the distance sensor outputs the distance can be obtained under the condition that the vehicle is not subjected to actual collision, so that the safety of the vehicle is improved; and the precision of the obtained first distance correction deviation value, the first speed correction deviation value and the first moment correction deviation value is not reduced.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method and device for obtaining a corrected deviation. Background Art

[0002] With the rapid development of technology, technologies related to vehicle intelligence such as autonomous driving and assisted driving have developed rapidly.

[0003] In scenarios requiring high ranging accuracy, such as AEB (Autonomous Emergency Braking), emergency avoidance, and road preview, after sensors like cameras (monocular or binocular cameras) and lidar are installed on the vehicle, they can gradually experience slight changes in lens position and module structure due to factors such as prolonged vehicle vibration, seasonal thermal expansion and contraction, and temperature and humidity fluctuations. These changes can reduce ranging accuracy or even lead to failure. Summary of the Invention

[0004] This application shows a method and device for obtaining a corrected deviation amount.

[0005] In a first aspect, the present application provides a method for obtaining a corrected deviation, the method comprising:

[0006] Acquire multiple datasets of vehicles;

[0007] One data set includes: when the vehicle is traveling and the elevation change object is located ahead of the vehicle in the direction of travel, the estimated time when the front wheels of the vehicle pass over the elevation change object based on the vehicle's speed obtained by a speed sensor on the vehicle; and the actual time when the front wheels of the vehicle actually pass over the elevation change object during the subsequent travel of the vehicle in the direction of travel;

[0008] The estimated passing time is estimated based on the vehicle's speed and the distance between the vehicle and the elevation change object. The distance between the vehicle and the elevation change object is obtained based on the distance sensor on the vehicle when the vehicle's speed is obtained.

[0009] wherein, when the tire of the vehicle passes over the elevation change object, the elevation change object is used to change the elevation of the tire of the vehicle; at least one of the vehicle speed, the estimated passing time, and the actual passing time in different data sets is different;

[0010] A first distance correction offset of the distance sensor, a first speed correction offset of the speed sensor, and a first time correction offset of an output time when the distance sensor outputs a distance are obtained according to the plurality of data sets.

[0011] In a second aspect, the present application provides a device for obtaining a corrected deviation, the device comprising:

[0012] A first acquisition module is used to acquire multiple data sets of the vehicle;

[0013] One data set includes: when the vehicle is traveling and the elevation change object is located ahead of the vehicle in the direction of travel, the estimated time when the front wheels of the vehicle pass over the elevation change object based on the vehicle's speed obtained by a speed sensor on the vehicle; and the actual time when the front wheels of the vehicle actually pass over the elevation change object during the subsequent travel of the vehicle in the direction of travel;

[0014] The estimated passing time is estimated based on the vehicle's speed and the distance between the vehicle and the elevation change object. The distance between the vehicle and the elevation change object is obtained based on the distance sensor on the vehicle when the vehicle's speed is obtained.

[0015] wherein, when the tire of the vehicle passes over the elevation change object, the elevation change object is used to change the elevation of the tire of the vehicle; at least one of the vehicle speed, the estimated passing time, and the actual passing time in different data sets is different;

[0016] The second acquisition module is configured to acquire, based on the plurality of data sets, a first distance correction deviation of the distance sensor, a first speed correction deviation of the speed sensor, and a first time correction deviation of an output time when the distance sensor outputs a distance.

[0017] In a third aspect, the present application shows an electronic device, which includes: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the method described in any of the above aspects.

[0018] In a fourth aspect, the present application shows a non-temporary computer-readable storage medium, which, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to execute the method described in any of the above aspects.

[0019] In a fifth aspect, the present application illustrates a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to execute the method as described in any one of the above aspects.

[0020] The technical solution provided by this application may have the following beneficial effects:

[0021] In the present application, multiple data sets are acquired for a vehicle. One data set includes: the vehicle's speed, as acquired by a speed sensor on the vehicle, when the vehicle is traveling and an elevation change object is located ahead in the vehicle's direction of travel; the estimated time when the vehicle's front wheels pass over the elevation change object; and the actual time when the vehicle's front wheels actually pass over the elevation change object during subsequent travel in the same direction. The estimated time when the vehicle passes over the elevation change object is estimated based on the vehicle's speed and the distance between the vehicle and the elevation change object. The distance between the vehicle and the elevation change object is acquired based on the distance sensor on the vehicle when the vehicle's speed is acquired. For example, the "estimated time when the vehicle's front wheels pass over the elevation change object" and the "distance between the vehicle and the elevation change object" are acquired simultaneously. The elevation change object serves to change the elevation (or height) of the vehicle's tires during the process of the vehicle's tires passing over the elevation change object. Based on the multiple data sets, a first distance correction offset of the distance sensor, a first speed correction offset of the speed sensor, and a first time correction offset at the time the distance sensor outputs a distance are acquired.

[0022] It can be seen that the present application takes into account the use of milder and more common scenarios where the vehicle passes over objects with elevation changes such as manhole covers, potholes or speed bumps on the ground, in order to obtain the actual passing moment when the vehicle passes over objects with elevation changes such as manhole covers, potholes or speed bumps.

[0023] For example, in a driving scenario, without an actual collision occurring, the correction deviation can be obtained based on the "passing moment" when the vehicle passes over common elevation changes (with impact characteristics) such as manhole covers, potholes, or speed bumps on the ground, to facilitate subsequent error correction, for example, error correction of sensors on the vehicle such as monocular cameras, binocular cameras, lidar, and wheel speedometers.

[0024] In this way, the present application can obtain the first distance correction deviation of the distance sensor, the first speed correction deviation of the speed sensor, and the first-moment correction deviation of the output moment when the distance sensor outputs the distance without an actual collision of the vehicle, thereby improving the safety of the vehicle without reducing the accuracy of the obtained first distance correction deviation of the distance sensor, the first speed correction deviation of the speed sensor, and the first-moment correction deviation of the output moment when the distance sensor outputs the distance. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flowchart of the steps of a method for obtaining a corrected deviation amount in the present application.

[0026] Figure 2 This is a flowchart of the steps of a method for obtaining a corrected deviation amount in the present application.

[0027] Figure 3 This is a flowchart of the steps of a method for obtaining a corrected deviation amount in the present application.

[0028] Figure 4 This is a flowchart of the steps of a method for obtaining a corrected deviation amount in the present application.

[0029] Figure 5 This is a flowchart of the steps of a method for obtaining a corrected deviation amount in the present application.

[0030] Figure 6 This is a structural block diagram of a device for obtaining a corrected deviation amount in the present application.

[0031] Figure 7 This is a block diagram of an electronic device of the present application.

[0032] Figure 8 This is a block diagram of an electronic device of the present application. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0034] To improve ranging accuracy, the sensor needs to be self-calibrated. This means that during vehicle operation, the sensor uses scene / laser point cloud data captured during use to self-correct sensor errors without relying on specific production line equipment and calibration boards / targets.

[0035] Self-calibration is divided into multiple dimensions, among which longitudinal ranging is generally one of the most concerned dimensions.

[0036] Regarding the self-calibration problem of longitudinal ranging, there are different calibration schemes for different sensors.

[0037] For example, for cameras, self-calibration methods include vanishing point / vanishing line methods, lane constraint methods, self-calibration methods based on multi-view geometry, and hand-eye calibration methods that combine multi-view geometry and self-motion. These methods all estimate the camera's own perspective through indirect external visual features. They rely on external features and are susceptible to interference (such as lane line misidentification, missed identification, feature point mismatching, and mismatching). Furthermore, by the end user (such as AEB), the estimated TTC (Time To Crash) still requires conversion between internal and external parameters, as well as errors in image sensor and data processing link system delays. These methods cannot be directly linked to application-layer metrics such as final ranging error and TTC time error. This results in unsatisfactory results or limited accuracy for the metrics of interest to the application layer.

[0038] To this end, this application proposes a method of correcting ranging errors through direct observation.

[0039] For example, specific markers on the road surface are measured through visual sensors, and then the actual impact moment when the vehicle "collides" with the specific markers on the road surface is identified based on sensors such as IMU (Inertial Measurement Unit) or suspension travel. According to the actual impact moments when the vehicle "collides" with different specific markers on the road surface at different vehicle speeds, the vehicle speed output by the sensor is corrected, the distance between the vehicle and the obstacle output by the sensor is corrected, and the output time when the sensor outputs the distance between the vehicle and the obstacle is corrected.

[0040] Secondly, this application can also simultaneously calibrate system delays (from camera observation to action execution). For example, changes in tire diameter due to tire pressure and temperature, which in turn lead to speed measurement errors, as well as image processing algorithm and data flow system delays, will ultimately be reflected in TTC deviations. This is particularly suitable for applications that require high hand-eye synchronization in the system, such as active safety functions that require TTC calculation, emergency braking, emergency avoidance, and road preview functions.

[0041] In this application, for self-calibration, zero error / zero distance feedback can be used as the true value. For example, in an intelligent driving scenario or an AEB scenario, TTC is calculated, the distance between the vehicle and the obstacle is observed, and the collision moment when the vehicle is expected to collide with the obstacle is obtained through the vehicle speed.

[0042] In this scenario, if a collision actually occurs, the collision moment when the vehicle actually collides with the obstacle can be obtained.

[0043] By correcting the error between the time when the vehicle is expected to collide with the obstacle and the time when the vehicle actually collides with the obstacle, it is foreseeable that the accuracy after correction will be better.

[0044] However, it is obvious that it is impossible to actually crash the vehicle into an obstacle while driving, as this will cause property damage and threaten personal safety.

[0045] To this end, the present application takes into account the use of milder and more common scenarios where the vehicle passes over objects with elevation changes such as manhole covers, potholes or speed bumps on the ground, in order to obtain the actual passing moment when the vehicle passes over objects with elevation changes such as manhole covers, potholes or speed bumps.

[0046] For example, in a driving scenario, error correction can be performed based on the "passing moment" when the vehicle passes over common elevation changes (with impact characteristics) on the ground, such as manhole covers, potholes, or speed bumps, without the vehicle actually hitting the ground. For example, error correction can be performed on sensors on the vehicle, such as monocular cameras, binocular cameras, lidar, and wheel speedometers.

[0047] In addition, IMU generates less data than sensors such as cameras and radars. For example, an IMU generally generates three accelerations per frame, while the number of pixels in an image is in the millions. Higher real-time performance can often be achieved through process priority allocation or the use of devices such as MCUs.

[0048] After time synchronization, the IMU's latency is generally between tens of microseconds and 100 microseconds, which is about 1,000 times slower than the image pipeline's latency (tens to 200 ms), making it almost negligible.

[0049] Therefore, the "passing time" when the vehicle passes over a manhole cover, pothole or speed bump on the ground can be analyzed through the IMU curve synchronized with the system time to correct the errors of the image, radar and other perception systems.

[0050] The errors in perception systems like imaging and radar include errors in time and distance, as well as speed. For example, a wheel speedometer determines vehicle speed based on wheel rotational speed, which is affected by wheel diameter. However, variations in tire equivalent diameter due to factors such as tire pressure, temperature, and load can lead to errors in the speed inferred from tire rotational speed.

[0051] For solutions that integrate wheel speed meters with other sensors (such as IMUs), other devices such as IMUs may also have problems such as temperature drift, which may lead to errors in speed estimation, thereby causing errors at the application level. For example, there may be deviations in the estimation of impact time in AEB applications.

[0052] In summary, this application considers the following errors:

[0053] Distance estimation bias of image / radar sensors, time delay bias of image / radar sensors, and speed measurement error of speed sensors (including wheel speed meters or speed estimates based on multi-sensor fusion).

[0054] Specifically, refer to Figure 1 , shows a flowchart of a method for obtaining a corrected deviation amount of the present application, which can be applied to electronic devices, wherein the method specifically may include the following steps:

[0055] In step S101, multiple vehicle datasets are acquired. One dataset includes: the vehicle's speed, as determined by a speed sensor on the vehicle, when the vehicle is traveling and the elevation change object is located ahead of the vehicle in the direction of travel; the estimated time at which the vehicle's front wheels will pass over the elevation change object; and the actual time at which the vehicle's front wheels will pass over the elevation change object during subsequent travel in the same direction.

[0056] The estimated passing time is estimated based on the vehicle's speed and the distance between the vehicle and the elevation change. For example, the estimated passing time is calculated by calculating the ratio of the distance between the vehicle and the elevation change to the vehicle's speed.

[0057] The distance between the vehicle and the elevation change object is obtained based on a distance sensor on the vehicle when the vehicle's speed is obtained.

[0058] For example, the “estimated passing time when the front wheel of the vehicle passes over the elevation change object” and the “distance between the vehicle and the elevation change object” are obtained simultaneously.

[0059] In the process of the tire of the vehicle running over the elevation change object, the elevation change object is used to change the elevation (or height) of the tire of the vehicle.

[0060] In this application, objects such as speed bumps, potholes, manhole covers, or rocks that change the elevation of a vehicle's tires but do not affect the vehicle's normal passage are generally referred to as "elevation-changing objects." Regardless of whether the elevation-changing object is protruding (such as a speed bump), concave (such as a pothole), or flush with the ground but with a different structure (such as a manhole cover), the elevation-changing object changes the height of the tires, but the vehicle can still pass through it normally.

[0061] At least one of the vehicle's speed, estimated passing time, and actual passing time is different in different data sets.

[0062] The data in the multiple data sets may be data collected for the same vehicle.

[0063] The distance between the vehicle and the elevation change object can be obtained as follows:

[0064] During the driving process of the vehicle, an image of the road in front of the vehicle is collected by a camera on the vehicle, and the image is recognized by a neural network model to obtain elevation changes on the road in front of the vehicle. Alternatively, a point cloud or placeholder grid map of the road in front of the vehicle is collected and recognized to obtain elevation changes on the road in front of the vehicle.

[0065] Then, the distance between the vehicle and the elevation change object on the road ahead in the direction of travel of the vehicle is analyzed (the specific method of analyzing the distance can refer to the existing method, which is not limited in this application).

[0066] The multiple data sets may include more than 3 data sets, or more than 4 data sets, or more than 5 data sets, etc.

[0067] For any data set, during the process of obtaining the data set of the vehicle, the driving direction of the vehicle remains unchanged.

[0068] The distance sensor includes an image sensor or a radar sensor, and the speed sensor includes a wheel speed meter.

[0069] In step S102 , a first distance correction offset of the distance sensor, a first speed correction offset of the speed sensor, and a first time correction offset of the output time when the distance sensor outputs the distance are acquired based on the plurality of data sets.

[0070] In this application, the following equation can be used:

[0071] Tttc_true*(Vk+Voffset)=(Tttc_estimate+Toffset)*Vk+Doffset

[0072] Tttc_true is the actual passing moment when the front wheel of the vehicle passes over the elevation change object during the subsequent driving of the vehicle in the same driving direction.

[0073] Vk is the speed of the vehicle obtained by a speed sensor on the vehicle when the vehicle is traveling and the elevation change object is located in front of the vehicle in the traveling direction.

[0074] Tttc_estimate is the estimated time when the front wheel of the vehicle passes over the elevation change object.

[0075] Voffset is the first speed correction offset of the speed sensor.

[0076] Toffset is the first moment correction offset of the output time when the distance sensor outputs the distance.

[0077] Doffset is the first distance correction offset of the distance sensor.

[0078] The left side of the equation represents: actual passing time*corrected speed (eg, vehicle speed+first speed correction offset), ie, the actual distance.

[0079] The right side of the equation represents: the corrected estimated passing time (for example, the output time when the distance sensor outputs the distance + the first time correction deviation) * the vehicle speed + the first distance correction deviation, that is, the corrected actual distance obtained by the distance sensor.

[0080] Tttc true, Vk, and Tttc estimate are known quantities.

[0081] Doffset, Toffset, and Voffset are unknown quantities.

[0082] One data set includes Tttc_true, Vk, and Tttc_estimate, and at least one of Tttc_true, Vk, and Tttc_estimate is different in different data sets.

[0083] To this end, after having sufficient data sets, Tttc_true, Vk, and Tttc_estimate in each data set can be respectively substituted into the above equations, and Doffset, Toffset, and Voffset can be solved by solving the equations.

[0084] For example, in one example, assuming Vk is 10.5m / s, Tttc_true is 2.1s, and Tttc_estimate is 2.3s, substituting the above equal time amounts yields:

[0085] 2.1s*(10.5m / s+Voffset)=(2.3s+Toffset)*10.5m / s+Doffset

[0086] Assuming that Doffset, Toffset, and Voffset are x, y, and z respectively, the above equation can be changed into the form of a system of three linear equations:

[0087] 2.1*(10.5+x)=(2.3+y)*10.5+z

[0088] Rearranging the terms yields the simplified equation:

[0089] 2.1x-10.5yz=2.1.

[0090] When there are three or more data sets, three or more simplified equations can be obtained, and the coefficients of each simplified equation are different. The three or more simplified equations can then be solved using substitution, elimination, or matrix methods to obtain the values of x, y, and z, that is, Doffset, Toffset, and Voffset.

[0091] In this way, after the speed sensor on the vehicle subsequently obtains the vehicle's speed, a more accurate vehicle speed can be obtained based on the vehicle's speed obtained by the speed sensor and the first speed correction deviation of the speed sensor. For example, the sum of the vehicle's speed obtained by the speed sensor and the first speed correction deviation of the speed sensor is calculated to obtain a more accurate vehicle speed, so as to correct the vehicle's speed obtained by the speed sensor through the first speed correction deviation of the speed sensor.

[0092] And, subsequently, after obtaining the output time when the distance sensor on the vehicle outputs the distance, a more accurate output time when the distance sensor on the vehicle outputs the distance can be obtained based on the output time when the distance sensor outputs the distance and the first-moment correction deviation of the output time when the distance sensor outputs the distance. For example, the sum of the output time when the distance sensor outputs the distance and the first-moment correction deviation of the output time when the distance sensor outputs the distance is calculated to obtain a more accurate output time when the distance sensor on the vehicle outputs the distance, so as to realize the correction of the output time when the distance sensor on the vehicle outputs the distance by the first-moment correction deviation of the output time when the distance sensor outputs the distance.

[0093] Furthermore, after the distance sensor on the vehicle subsequently obtains the distance between the vehicle and the object with elevation change, a more accurate distance between the vehicle and the object with elevation change can be obtained based on the distance between the vehicle and the object with elevation change obtained by the distance sensor and the first distance correction deviation of the distance sensor. For example, the sum of the distance between the vehicle and the object with elevation change obtained by the distance sensor and the first distance correction deviation of the distance sensor is calculated to obtain a more accurate distance between the vehicle and the object with elevation change, so as to correct the distance between the vehicle and the object with elevation change obtained by the distance sensor through the first distance correction deviation of the distance sensor.

[0094] It should be noted that, in the above method, a prerequisite is that the vehicle does not accelerate or decelerate rapidly. For example, the vehicle needs to be close to or move at a constant speed. Otherwise, the accuracy of at least one of the first distance correction deviation, the first speed correction deviation, and the first moment correction deviation obtained will be low.

[0095] For this purpose, in another embodiment of the present application, see Figure 2 , step S102 includes:

[0096] In step S201, for any one of the plurality of data sets, it is determined whether the vehicle maintains a constant speed during the process of acquiring the data set. If the vehicle does not maintain a constant speed, the data set is removed from the plurality of data sets.

[0097] Alternatively, retain the dataset across multiple datasets while the vehicle maintains a constant speed.

[0098] In the present application, the acquisition time when the speed of the vehicle in the data set is acquired can be acquired.

[0099] The vehicle speeds at multiple moments within a specific time period are respectively obtained. The specific time period includes a time period between the acquisition moment when the vehicle speed in the data set is obtained and the actual passing moment in the data set.

[0100] An average speed among the speeds of the vehicle traveling at multiple moments in the specific time period is obtained, and a maximum speed and / or a minimum speed among the speeds of the vehicle traveling at multiple moments in the specific time period is obtained.

[0101] When the difference between the maximum speed and the average speed is smaller than the preset difference and the difference between the minimum speed and the average speed is smaller than the preset difference, it is determined that the vehicle maintains a constant speed during the process of acquiring the data set.

[0102] Alternatively, when the difference between the maximum speed and the average speed is greater than or equal to a preset difference or the difference between the minimum speed and the average speed is greater than or equal to a preset difference, it is determined that the vehicle does not maintain a constant speed during the process of acquiring the data set.

[0103] The preset difference can be determined according to actual conditions and is not limited in this application.

[0104] In step S202 , a first distance correction offset of the distance sensor, a first speed correction offset of the speed sensor, and a first time correction offset of the output time when the distance sensor outputs the distance are acquired based on the remaining data sets.

[0105] For details of this step, please refer to the method of solving the equation in step S102, which will not be described in detail here.

[0106] In this way, it is possible to remove the data set obtained when the vehicle is in a state of rapid acceleration / sudden deceleration from multiple data sets, and subsequently use the data set obtained when the vehicle is in a state of uniform speed or near uniform speed to obtain the first distance correction deviation, the first speed correction deviation and the first moment correction deviation, which can improve the accuracy of the obtained first distance correction deviation, the first speed correction deviation and the first moment correction deviation.

[0107] Sometimes, a large number of data sets may be acquired during vehicle driving. For example, if 1,800 data sets are acquired, a total of 600 sets of equations can be established without reuse.

[0108] The speed change of a vehicle during driving is usually relatively gentle, and the difference between the vehicle speeds in consecutively acquired data sets is small, or the difference between the estimated passing times is small, or the difference between the actual passing times is small.

[0109] This causes the coefficients of x, y, or z in the equation group to be too small, which in turn may lead to large errors and low accuracy in the calculated Doffset, Toffset, and Voffset.

[0110] For example, if the speeds of vehicles in consecutively acquired data sets are close, the following set of equations may be obtained:

[0111] 2.10x-10.50yz=2.10

[0112] 2.12x-10.43yz=2.09

[0113] 2.13x-10.27yz=2.07

[0114] When solving equations using substitution, elimination, and matrix methods, you'll find that the coefficients of x, y, or z are very low. Subtracting equations 1 and 2 through elimination yields -0.02x - 0.07y = 0.01. This indicates that the coefficients of x and y are very small, potentially leading to significant errors in the calculations of x and y.

[0115] For another example, if the speeds of vehicles in consecutively acquired data sets are close, the following set of equations may be obtained:

[0116] 2.10x-10.50yz=2.10

[0117] 2.10x-10.50yz=2.10

[0118] 2.11x-10.28yz=2.09

[0119] Equation 1 and Equation 2 are the same equation and provide the same constraints, which makes it impossible to find a solution.

[0120] Of course, there will always be small noise differences in actual floating point representations, so there will always be an analytical solution, but this will result in significantly larger error values.

[0121] Therefore, from a statistical point of view, it is possible to solve the equation without using continuously acquired data sets.

[0122] The acquired multiple data sets may be reorganized, and the equation may be solved using at least three data sets in which the differences between the vehicle speeds included therein are large and / or the differences between the estimated passing times included therein are large.

[0123] Secondly, the same dataset can be reused to a certain extent to improve accuracy.

[0124] Specifically, in another embodiment of the present application, see Figure 3 , step S102 includes:

[0125] In step S301, at least one data group is determined among multiple data sets, and one data group includes at least three data sets, and the difference between the vehicle speeds in each two of the at least three data sets is greater than a preset speed difference, and / or the difference between the estimated passing moments in each two of the at least three data sets is greater than a preset moment difference.

[0126] The preset speed difference and the preset time difference can be determined according to actual conditions, and this application does not impose any limitation on this.

[0127] At least one data set in different data groups is different.

[0128] For example, set a retry counter with an initial value of 0.

[0129] For each of the plurality of datasets, two datasets are randomly selected from the remaining 1799 datasets.

[0130] Determine whether a difference between vehicle speeds in each of the three data sets is greater than a preset speed difference, and / or calculate whether a difference between estimated passing times in each of the three data sets is greater than a preset time difference.

[0131] If the difference between the vehicle speeds in any two of the three data sets is greater than a preset speed difference, and / or the difference between the estimated passing times in any two of the three data sets is greater than a preset time difference, the three data sets are combined into one data group and the loop is exited.

[0132] Secondly, a retry mechanism can be added: if a dataset that meets the conditions is still not found after 20 attempts, the current dataset is abandoned and the next dataset is processed.

[0133] In step S302 , a first distance correction offset of the distance sensor, a first speed correction offset of the speed sensor, and a first time correction offset of the output time when the distance sensor outputs the distance are obtained based on at least three data sets in at least one data group.

[0134] For details of this step, please refer to the method of solving the equation in step S102, which will not be described in detail here.

[0135] In this way, it can be ensured that different data groups have sufficient independence, and thus the equation groups solved based on different data groups have sufficient independence, thereby improving the accuracy of the calculated first distance correction deviation, first speed correction deviation and first moment correction deviation.

[0136] Through the above embodiment, 1600 data groups can be obtained from 1800 data sets (one data group has three data sets). Then, according to the above three-variable linear equation method, 1600 sets of Doffset, Toffset and Voffset can be solved.

[0137] However, even after data reorganization, there may still be some abnormal data sets. These data sets may be caused by sensor noise, external interference, recognition errors, and interference caused by abnormal vibrations of the car body due to non-recognized targets in advance (such as potholes other than speed bumps).

[0138] In order to further improve the accuracy of the calculated first distance correction deviation, first speed correction deviation and first moment correction deviation, it is necessary to perform statistical filtering on the reorganized data (data group) to exclude abnormal data that may cause errors.

[0139] Among them, the basic idea of statistical filtering is to identify and exclude outliers by calculating statistics (such as mean, standard deviation, etc.) based on the distribution characteristics of the data.

[0140] In the field of autonomous driving, common statistical filtering methods include but are not limited to mean filtering, median filtering, Gaussian filtering, etc. Median filtering is used as an example for illustration, but the scope of protection of this application should not be limited to median filtering.

[0141] Specifically, in another embodiment of the present application, the determined data group is multiple. Figure 4 , step S302 includes:

[0142] In step S401, for each of the multiple data groups, a second distance correction deviation of the distance sensor, a second speed correction deviation of the speed sensor, and a second time correction deviation of the output time when the distance sensor outputs the distance are obtained based on at least three data sets in the data group.

[0143] The method for obtaining the second distance correction deviation of the distance sensor, the second speed correction deviation of the speed sensor, and the second moment correction deviation of the output moment when the distance sensor outputs the distance based on at least three data sets in any data group can be referred to the method of solving the equation in step S102 and will not be described in detail here.

[0144] Through this step, there are several sets of solutions that can be obtained if there are several data sets.

[0145] In step S402, a first distance-corrected deviation is obtained based on the second distance-corrected deviations obtained for at least some of the multiple data sets. A first speed-corrected deviation is obtained based on the second speed-corrected deviations obtained for at least some of the multiple data sets. A first moment-corrected deviation is obtained based on the second moment-corrected deviations obtained for at least some of the multiple data sets.

[0146] In the present application, for the distance-corrected deviation, speed-corrected deviation, and time-corrected deviation, a statistical value of the second distance-corrected deviation obtained from each data group is calculated, and the statistical value includes a mean, a standard deviation, or a median. For any data group in the multiple data groups, if the difference between the second distance-corrected deviation obtained from the data group and the statistical value of the second distance-corrected deviation is greater than a preset distance difference, the second distance-corrected deviation, the second time-corrected deviation, and the second speed-corrected deviation obtained from the data group are discarded. The same is true for each of the other data groups in the multiple data groups. The average value of the remaining second distance-corrected deviations is calculated and used as the first distance-corrected deviation.

[0147] The same is done for the distance correction deviation, the speed correction deviation, and the speed correction deviation among the time correction deviations, thereby obtaining a first speed correction deviation.

[0148] The same is done for the distance correction offset, the speed correction offset, and the time correction offset, thereby obtaining a first time correction offset.

[0149] The preset distance difference can be determined according to actual conditions and is not limited in this application.

[0150] In another embodiment of the present application, see Figure 5The process of obtaining the actual passing time of the front wheel of a vehicle passing over the elevation change object in a data set during the subsequent driving of the vehicle in the driving direction includes:

[0151] In step S501 , during the subsequent driving of the vehicle in the driving direction, the acceleration in the driving direction collected by the acceleration sensor on the vehicle at multiple time points is acquired.

[0152] The acceleration sensor on the vehicle (such as IMU) can be sampled at a rate of 200Hz, which means that 200 vehicle accelerations can be obtained per second. This high sampling rate can capture the rapid and short-term acceleration changes caused by the vehicle passing over an object with a change in elevation.

[0153] Multi-axis measurement: An IMU typically provides three-axis acceleration data (X, Y, and Z). For a vehicle, speed bumps, bumps, and potholes convert the energy in the vehicle's forward direction into movement in the height direction due to the undulations of the ground. As speed decreases, the vehicle's height changes. Therefore, data from the Z axis (perpendicular to the ground) and the X axis (the vehicle's forward direction) best reflect the dynamics of passing over speed bumps.

[0154] In one embodiment of the present application, this step can be implemented through the following process, including:

[0155] 5011. Obtain a candidate time period based on the estimated passing time in the data set, where the estimated passing time in the data set is within the candidate time period.

[0156] For example, the starting time of the candidate time period is 1.5 seconds or 2 seconds before the estimated passing time in the data set, and the ending time of the candidate time period is 1.5 seconds or 2 seconds after the estimated passing time in the data set.

[0157] 5012. During subsequent travel of the vehicle in the travel direction, obtain acceleration in the travel direction collected by an acceleration sensor on the vehicle at multiple moments in the candidate time period.

[0158] When a vehicle's front wheels pass over an elevation change, the impact of the object on the front wheels causes significant peaks and valleys in the acceleration along the X-axis of the IMU curve. These peaks and valleys reflect the impact on the front wheels. These peaks and valleys can be detected by setting thresholds. Any acceleration exceeding the threshold can be considered a potential speed bump event.

[0159] Through this embodiment, the acceleration in the driving direction collected by the acceleration sensor on the vehicle at multiple moments in the candidate time period is obtained, and there is no need to obtain more accelerations, which can save system resources and save time for subsequent acceleration screening.

[0160] In step S502 , among the acquired accelerations, the earliest acquired acceleration that is smaller than a preset acceleration is screened.

[0161] Acceleration sensors (such as IMU) can measure the acceleration of the vehicle in three axes. When the front wheels of the vehicle press over an elevation change object, the elevation change object will give the front wheels of the vehicle a sudden vertical upward impact. The acceleration sensor will detect an obvious increase in acceleration in the vertical upward direction and an obvious decrease in acceleration in the direction of vehicle movement (the acceleration decrease is a negative value, that is, it provides the direction of vehicle movement). This change can be identified by analyzing the acceleration curve.

[0162] The preset acceleration is a negative value, which can be determined according to actual conditions and is not limited in this application.

[0163] However, sometimes the acceleration less than the preset acceleration may not be caused by the front wheel of the vehicle running over an object with a height change, for example, it may be caused by sudden braking or sudden acceleration of the vehicle.

[0164] In order to avoid interference caused by sudden braking or acceleration of the vehicle, in another embodiment of the present application, before filtering the earliest acquired acceleration among the accelerations that are less than the preset acceleration, the method further includes:

[0165] The accelerations in the driving direction acquired at multiple times are arranged in chronological order.

[0166] If the arranged accelerations include a first acceleration and a second acceleration, and the time interval between the time when the acceleration sensor collects the first acceleration and the time when the acceleration sensor collects the second acceleration is greater than a first preset time interval and less than a second preset time interval, then the earliest collected acceleration that is less than the preset acceleration is selected from the obtained accelerations. The first acceleration is less than the preset acceleration, and the second acceleration is less than the preset acceleration.

[0167] Since the time it takes for a vehicle to pass over an object with an elevation change is usually very short, the time intervals between accelerations that are less than the preset acceleration are also short. Therefore, the solution of this embodiment avoids misjudging the acceleration caused by sudden braking or acceleration of the vehicle as acceleration caused by the vehicle's front wheels passing over an object with an elevation change.

[0168] In step S503, the collection time when the acceleration sensor collects the filtered acceleration is obtained.

[0169] In this application, when an acceleration sensor (such as an IMU, etc.) collects acceleration, it will synchronously record the collection time when the acceleration is collected, and record the mapping relationship between the collected acceleration and the collection time when the acceleration is collected. In this way, the collection time when the acceleration sensor collects the filtered acceleration can be obtained through the recorded mapping relationship.

[0170] In step S504, the actual passing time when the front wheel of the vehicle passes over the elevation change object is obtained based on the acquisition time.

[0171] For example, the acquisition time is determined as the actual passing time when the front wheel of the vehicle actually passes over the elevation change object.

[0172] It should be noted that for the method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions involved are not necessarily required by this application.

[0173] Reference Figure 6 , shows a structural block diagram of a device for obtaining a corrected deviation amount of the present application, the device comprising:

[0174] A first acquisition module 11 is used to acquire multiple data sets of the vehicle;

[0175] One data set includes: when the vehicle is traveling and the elevation change object is located ahead of the vehicle in the direction of travel, the estimated time when the front wheels of the vehicle pass over the elevation change object based on the vehicle's speed obtained by a speed sensor on the vehicle; and the actual time when the front wheels of the vehicle actually pass over the elevation change object during the subsequent travel of the vehicle in the direction of travel;

[0176] The estimated passing time is estimated based on the vehicle's speed and the distance between the vehicle and the elevation change object. The distance between the vehicle and the elevation change object is obtained based on the distance sensor on the vehicle when the vehicle's speed is obtained.

[0177] wherein, when the tire of the vehicle passes over the elevation change object, the elevation change object is used to change the elevation of the tire of the vehicle; at least one of the vehicle speed, the estimated passing time, and the actual passing time in different data sets is different;

[0178] The second acquisition module 12 is configured to acquire, based on the plurality of data sets, a first distance correction deviation of the distance sensor, a first speed correction deviation of the speed sensor, and a first time correction deviation of the output time when the distance sensor outputs the distance.

[0179] In an optional implementation, the second acquisition module includes:

[0180] a determining unit configured to determine, for any one of the plurality of data sets, whether the vehicle maintains a constant speed during the process of acquiring the data set; and a removing unit configured to remove the data set from the plurality of data sets if the vehicle does not maintain a constant speed.

[0181] The first acquiring unit is configured to acquire, based on the remaining data sets, a first distance correction deviation of the distance sensor, a first speed correction deviation of the speed sensor, and a first time correction deviation of the output time when the distance sensor outputs the distance.

[0182] In an optional implementation, the determining unit includes:

[0183] A first acquisition subunit is used to acquire the acquisition time when the speed of the vehicle in the data set is acquired;

[0184] a second acquisition subunit, configured to respectively acquire the vehicle speed at a plurality of moments within a specific time period; the specific time period including the time period between the acquisition moment when the vehicle speed in the data set is acquired and the actual passing moment in the data set;

[0185] a third obtaining subunit, configured to obtain an average speed of the vehicle's speeds at a plurality of moments in a specific time period, and to obtain a maximum speed and / or a minimum speed among the vehicle's speeds at a plurality of moments in the specific time period;

[0186] a first determining subunit, configured to determine that the vehicle maintains a constant speed during the process of acquiring the data set if a difference between the maximum speed and the average speed is less than a preset difference and a difference between the minimum speed and the average speed is less than a preset difference;

[0187] or,

[0188] The second determining subunit is configured to determine that the vehicle does not maintain a constant speed during the process of acquiring the data set when the difference between the maximum speed and the average speed is greater than or equal to a preset difference or the difference between the minimum speed and the average speed is greater than or equal to a preset difference.

[0189] In an optional implementation, the second acquisition module includes:

[0190] a determining unit configured to determine, from among the plurality of data sets, at least one data set, wherein one data set includes at least three data sets, wherein a difference between vehicle speeds in each two of the at least three data sets is greater than a preset speed difference, and / or a difference between estimated pass-through times in each two of the at least three data sets is greater than a preset time difference;

[0191] The second acquiring unit is configured to acquire, based on at least three data sets in at least one data group, a first distance correction deviation of the distance sensor, a first speed correction deviation of the speed sensor, and a first time correction deviation of an output time when the distance sensor outputs a distance.

[0192] In an optional implementation, the determined data group is multiple;

[0193] The second acquiring unit includes:

[0194] a fourth acquiring subunit, configured to acquire, for each of the plurality of data groups, a second distance correction offset of the distance sensor, a second speed correction offset of the speed sensor, and a second time correction offset of an output time when the distance sensor outputs the distance based on at least three data sets in the data group;

[0195] The fifth acquisition subunit is used to obtain the first distance correction deviation based on the second distance correction deviation obtained from at least some of the data groups in the multiple data groups; to obtain the first speed correction deviation based on the second speed correction deviation obtained from at least some of the data groups in the multiple data groups; and to obtain the first moment correction deviation based on the second moment correction deviation obtained from at least some of the data groups in the multiple data groups.

[0196] In an optional implementation, the first acquisition module includes:

[0197] a third acquiring unit, configured to acquire, during a subsequent process of the vehicle traveling in the traveling direction, accelerations in the traveling direction acquired by an acceleration sensor on the vehicle at multiple moments;

[0198] A screening unit, configured to screen the earliest acquired acceleration from among the acquired accelerations that are smaller than a preset acceleration;

[0199] a fourth acquiring unit, configured to acquire a collection time when the acceleration sensor collects the filtered acceleration;

[0200] The fifth acquisition unit is used to acquire the actual passing time when the front wheel of the vehicle actually passes over the elevation change object according to the acquisition time.

[0201] In an optional implementation, the third acquiring unit includes:

[0202] a sixth acquiring subunit, configured to acquire a candidate time period according to the estimated passing time in the one data set, the estimated passing time in the one data set being within the candidate time period;

[0203] The seventh acquisition subunit is configured to acquire, during subsequent driving of the vehicle in the driving direction, accelerations in the driving direction collected by an acceleration sensor on the vehicle at multiple moments in the candidate time period.

[0204] In an optional implementation, the first acquisition module further includes:

[0205] an arranging unit, configured to arrange the accelerations in the driving direction collected at multiple moments in chronological order before filtering the earliest collected acceleration among the acquired accelerations that are less than a preset acceleration;

[0206] The screening unit is further configured to: if there is a first acceleration and a second acceleration in the arranged accelerations, and the distance between the time when the acceleration sensor collects the first acceleration and the time when the acceleration sensor collects the second acceleration is greater than a first preset time length and less than a second preset time length, then screen the earliest collected acceleration among the acquired accelerations that are less than the preset acceleration, where the first acceleration is less than the preset acceleration and the second acceleration is less than the preset acceleration.

[0207] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0208] Optionally, an embodiment of the present application also provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and runnable on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned method embodiment are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0209] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the various processes of the above-described method embodiments are implemented and the same technical effects are achieved. To avoid repetition, the details are not described here. The computer-readable storage medium may be, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0210] FIG7 is a block diagram of an electronic device 800 shown in the present application. For example, the electronic device 800 can be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0211] Reference Figure 7 , the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0212] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 802 may include one or more modules to facilitate interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

[0213] The memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, images, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0214] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0215] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensor can not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0216] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), which is configured to receive external audio signals when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0217] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0218] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the device 800, the relative positioning of components, such as the display and keypad of the electronic device 800. The sensor assembly 814 can also detect changes in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and temperature changes of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0219] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, an operator network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast operation information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0220] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0221] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, and the instructions can be executed by the processor 820 of the electronic device 800 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0222] Figure 8 1 is a block diagram of an electronic device 1900 shown in the present application. For example, the electronic device 1900 can be provided as a server.

[0223] Reference Figure 8 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute the instructions to perform the above-described method.

[0224] The electronic device 1900 may further include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or the like.

[0225] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0226] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0227] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

[0228] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0229] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0230] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0231] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0232] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0233] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0234] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for obtaining a corrected deviation, characterized in that: The method comprises: Acquire multiple datasets of vehicles; A data set includes: an estimated passing time when the front wheels of the vehicle pass over the elevation change object, based on the vehicle's speed obtained by a speed sensor on the vehicle, when the vehicle is traveling and the elevation change object is located ahead of the vehicle in the direction of travel; and an actual passing time when the front wheels of the vehicle actually pass over the elevation change object during subsequent travel of the vehicle in the direction of travel; the estimated passing time is estimated based on the vehicle's speed and the distance between the vehicle and the elevation change object; the distance between the vehicle and the elevation change object is obtained based on the distance sensor on the vehicle when the vehicle's speed is obtained; wherein, when the tire of the vehicle passes over the elevation change object, the elevation change object is used to change the elevation of the tire of the vehicle; at least one of the vehicle speed, the estimated passing time, and the actual passing time in different data sets is different; A first distance correction offset of the distance sensor, a first speed correction offset of the speed sensor, and a first time correction offset of an output time when the distance sensor outputs a distance are obtained according to the plurality of data sets.

2. The method according to claim 1, characterized in that The step of obtaining, based on the plurality of data sets, a first distance correction deviation of the distance sensor, a first speed correction deviation of the speed sensor, and a first time correction deviation of the output time when the distance sensor outputs the distance includes: For any one of the multiple data sets, determining whether the vehicle maintains a constant speed during the process of acquiring the data set; if the vehicle does not maintain a constant speed, removing the data set from the multiple data sets; According to the remaining data sets, a first distance correction deviation of the distance sensor, a first speed correction deviation of the speed sensor, and a first time correction deviation of the output time when the distance sensor outputs the distance are obtained.

3. The method according to claim 2, characterized in that The determining whether the vehicle maintains a constant speed during the process of acquiring the data set includes: Obtaining the acquisition time when the speed of the vehicle in the data set is obtained; respectively obtaining the vehicle speed at a plurality of moments within a specific time period; the specific time period including the time period between the acquisition moment when the vehicle speed in the data set is obtained and the actual passing moment in the data set; Obtaining an average speed among the speeds of the vehicle at multiple moments in a specific time period, and obtaining a maximum speed and / or a minimum speed among the speeds of the vehicle at multiple moments in the specific time period; If the difference between the maximum speed and the average speed is less than a preset difference and the difference between the minimum speed and the average speed is less than a preset difference, determining that the vehicle maintains a constant speed during the process of acquiring the data set; or, When the difference between the maximum speed and the average speed is greater than or equal to the preset difference or the difference between the minimum speed and the average speed is greater than or equal to the preset difference, it is determined that the vehicle does not maintain a constant speed during the process of acquiring the data set.

4. The method according to claim 1, wherein The step of obtaining, based on the plurality of data sets, a first distance correction deviation of the distance sensor, a first speed correction deviation of the speed sensor, and a first time correction deviation of the output time when the distance sensor outputs the distance includes: Determining at least one data group among the plurality of data sets, wherein one data group includes at least three data sets, wherein a difference between vehicle speeds in each two of the at least three data sets is greater than a preset speed difference, and / or a difference between estimated rollover times in each two of the at least three data sets is greater than a preset time difference; A first distance correction offset of the distance sensor, a first speed correction offset of the speed sensor, and a first time correction offset of an output time when the distance sensor outputs the distance are obtained according to at least three data sets in the at least one data group.

5. The method according to claim 4, characterized in that There are multiple data groups determined; The obtaining, based on at least three data sets in the at least one data group, a first distance correction deviation of the distance sensor, a first speed correction deviation of the speed sensor, and a first time correction deviation of the output time when the distance sensor outputs the distance includes: For each of the plurality of data groups, obtaining, based on at least three data sets in the data group, a second distance correction offset of the distance sensor, a second speed correction offset of the speed sensor, and a second time correction offset of the output time when the distance sensor outputs the distance; Based on the second distance correction deviation obtained from at least some of the data groups in the multiple data groups, a first distance correction deviation is obtained; based on the second speed correction deviation obtained from at least some of the data groups in the multiple data groups, a first speed correction deviation is obtained; based on the second moment correction deviation obtained from at least some of the data groups in the multiple data groups, a first moment correction deviation is obtained.

6. The method according to claim 1, characterized in that in, The process of obtaining the actual passing time when the front wheel of the vehicle actually passes over the elevation change object during subsequent driving of the vehicle in the driving direction in a data set includes: During subsequent travel of the vehicle in the travel direction, acquiring accelerations in the travel direction collected by an acceleration sensor on the vehicle at multiple times; Among the acquired accelerations, the earliest acquired acceleration that is smaller than a preset acceleration is selected; Obtaining the acquisition time when the acceleration sensor acquires the filtered acceleration; According to the acquisition time, the actual passing time when the front wheel of the vehicle actually passes over the elevation change object is obtained.

7. The method according to claim 6, characterized in that The step of obtaining the acceleration in the driving direction collected by an acceleration sensor on the vehicle at multiple times during the subsequent driving of the vehicle in the driving direction includes: obtaining a candidate time period according to the estimated passing time in the one data set, wherein the estimated passing time in the one data set is within the candidate time period; During subsequent driving of the vehicle in the driving direction, accelerations in the driving direction respectively acquired by an acceleration sensor on the vehicle at multiple time points in the candidate time period are acquired.

8. The method according to claim 6, characterized in that The method further comprises: Arranging the accelerations in the driving direction collected at multiple moments in chronological order before filtering the earliest collected acceleration among the accelerations that are less than the preset acceleration; If a first acceleration and a second acceleration exist in the arranged accelerations, and the distance between the time when the acceleration sensor collects the first acceleration and the time when the acceleration sensor collects the second acceleration is greater than a first preset time length and less than a second preset time length, then the earliest collected acceleration among the acquired accelerations that are less than the preset acceleration is filtered, and the first acceleration is less than the preset acceleration and the second acceleration is less than the preset acceleration.

9. A device for obtaining a corrected deviation, characterized in that: The device comprises: A first acquisition module is used to acquire multiple data sets of the vehicle; One data set includes: when the vehicle is traveling and the elevation change object is located ahead of the vehicle in the direction of travel, the estimated time when the front wheels of the vehicle pass over the elevation change object based on the vehicle's speed obtained by a speed sensor on the vehicle; and the actual time when the front wheels of the vehicle actually pass over the elevation change object during the subsequent travel of the vehicle in the direction of travel; The estimated passing time is estimated based on the vehicle's speed and the distance between the vehicle and the elevation change object. The distance between the vehicle and the elevation change object is obtained based on the distance sensor on the vehicle when the vehicle's speed is obtained. wherein, when the tire of the vehicle passes over the elevation change object, the elevation change object is used to change the elevation of the tire of the vehicle; at least one of the vehicle speed, the estimated passing time, and the actual passing time in different data sets is different; The second acquisition module is configured to acquire, based on the plurality of data sets, a first distance correction deviation of the distance sensor, a first speed correction deviation of the speed sensor, and a first time correction deviation of an output time when the distance sensor outputs a distance.

10. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the method according to any one of claims 1 to 8 when executed by the processor.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which implements the method according to any one of claims 1 to 8 when executed by a processor.

Images