CN115407376A

CN115407376A - Vehicle positioning calibration method and device, computer equipment and storage medium

Info

Publication number: CN115407376A
Application number: CN202211029493.2A
Authority: CN
Inventors: 王瑞平
Original assignee: Shenzhen Guangtong Yuanchi Technology Co ltd
Current assignee: Shenzhen Guangtong Yuanchi Technology Co ltd
Priority date: 2022-08-25
Filing date: 2022-08-25
Publication date: 2022-11-29
Also published as: WO2024041156A1

Abstract

The present application relates to a vehicle positioning calibration method, apparatus, computer device, storage medium and computer program product. The method comprises the following steps: acquiring first positioning information and second positioning information acquired through a plurality of vehicle-mounted electronic devices of a target vehicle; acquiring an accumulated error of the first positioning information, and adjusting a confidence coefficient parameter according to the accumulated error; calculating the speed error of the target vehicle according to the second positioning information, and adjusting the weight parameter according to the speed error; and if the confidence coefficient parameter meets a first preset condition and the weight parameter meets a second preset condition, acquiring calibration positioning information according to the first positioning information and the second positioning information. By adopting the method, the confidence coefficient or the weight corresponding to each type of positioning information can be adjusted by calculating the error magnitude of the different types of positioning information, the effective positioning information is determined by combining the confidence coefficient and the weight, the calibration positioning information is obtained according to the effective positioning information, and the accuracy of vehicle positioning can be improved.

Description

Vehicle positioning calibration method and device, computer equipment and storage medium

Technical Field

The present application relates to the field of vehicle networking technologies, and in particular, to a vehicle positioning calibration method, apparatus, computer device, storage medium, and computer program product.

Background

When a vehicle runs in a place without a satellite positioning signal, such as an indoor parking lot, a remote mountain area, or a road scene with a weak positioning signal, such as a multi-tree shadow area, a multi-high building area, a multi-obstacle sheltered area, an overpass area and the like, accurate vehicle position information cannot be obtained, so that positioning-related applications on the vehicle cannot be used or errors are easy to occur; and if the vehicle runs in a bad navigation signal area for a long time, the position calculated by the GNSS + inertial navigation positioning system commonly applied to the vehicle at present gradually deviates from the actual position, and problems such as navigation errors are likely to occur.

The current vehicle positioning is influenced by different road scenes, and the positioning precision is not high.

Disclosure of Invention

In view of the above, it is necessary to provide a vehicle positioning calibration method, apparatus, computer device, computer readable storage medium and computer program product capable of improving the vehicle positioning accuracy.

In a first aspect, the present application provides a vehicle position calibration method. The method comprises the following steps:

acquiring first positioning information and second positioning information acquired through a plurality of vehicle-mounted electronic devices of a target vehicle;

acquiring an accumulated error of the first positioning information, and adjusting a confidence coefficient parameter according to the accumulated error;

calculating the speed error of the target vehicle according to the second positioning information, and adjusting the weight parameter according to the speed error;

and if the confidence coefficient parameter meets the first preset condition and the weight parameter meets the second preset condition, acquiring calibration positioning information according to the first positioning information and the second positioning information.

In one embodiment, the obtaining of the first positioning information and the second positioning information obtained by the plurality of onboard electronic devices of the target vehicle includes:

acquiring original positioning data of the target vehicle obtained by a satellite signal receiving device and six-axis data of the target vehicle obtained by an inertial measurement device;

calculating to obtain original three-dimensional position information of the target vehicle according to the six-axis data;

obtaining first positioning information according to the original three-dimensional position information and original positioning data;

and acquiring second positioning information of the target vehicle, which is obtained from the real-time dynamic measurement system through the network access device.

In one embodiment, acquiring an accumulated error of the first positioning information, and adjusting the confidence parameter according to the accumulated error includes:

and if the accumulated error is greater than or equal to the error threshold, reducing the confidence coefficient parameter.

In one embodiment, calculating a speed error of the target vehicle based on the second positioning information and adjusting the weight parameter based on the speed error comprises:

according to the second positioning information, positioning of the target vehicle at a plurality of continuous moments is obtained according to a preset time interval;

calculating to obtain the measuring speed of the target vehicle at the current continuous moment according to the positioning of the current continuous moment, the positioning of the previous continuous moment and a preset time interval;

if the measuring speed of the target vehicle at the current continuous moment is not within the range of the standard speed threshold, determining that the measuring speed of the target vehicle at the current continuous moment is an abnormal speed;

and if the frequency of continuously judging the abnormal speed exceeds the frequency threshold value, reducing the weight parameter.

In one embodiment, obtaining calibration positioning information according to the first positioning information and the second positioning information includes:

acquiring first three-dimensional position information from the first positioning information, wherein the first three-dimensional position information comprises a first longitude, a first latitude and a first altitude;

acquiring second three-dimensional positioning information from the second positioning information, wherein the second three-dimensional position information comprises a second longitude, a second latitude and a second altitude;

filtering the first longitude and the second longitude through the Federal Kalman filtering to obtain a fused longitude, filtering the first latitude and the second latitude to obtain a fused latitude, and filtering the first altitude and the second altitude to obtain a fused altitude;

and taking the fusion longitude, the fusion latitude and the fusion altitude as fusion three-dimensional position information, and replacing the fusion three-dimensional position information with the first three-dimensional position information in the first positioning information to obtain calibration positioning information.

In one embodiment, the method further comprises:

and acquiring third positioning information of at least three road side devices corresponding to the target vehicle.

In one embodiment, the method further comprises:

and if the confidence coefficient parameter meets the first preset condition and the weight parameter does not meet the second preset condition, acquiring calibration positioning information according to the first positioning information and the third positioning information.

In one embodiment, the method further comprises:

and if the confidence coefficient parameter does not meet the first preset condition and the weight parameter meets the second preset condition, acquiring calibration positioning information according to the second positioning information and the third positioning information.

In one embodiment, the method further comprises:

and if the confidence coefficient parameter does not meet the first preset condition and the weight parameter does not meet the second preset condition, taking the third positioning information as the calibration positioning information.

In a second aspect, the present application further provides a vehicle positioning calibration device. The device comprises:

the positioning acquisition module is used for acquiring first positioning information and second positioning information acquired by a plurality of vehicle-mounted electronic devices of a target vehicle;

the first adjusting module is used for acquiring the accumulated error of the first positioning information and adjusting the confidence coefficient parameter according to the accumulated error;

the second adjusting module is used for calculating the speed error of the target vehicle according to the second positioning information and adjusting the weight parameter according to the speed error;

and the positioning calibration module is used for acquiring calibration positioning information according to the first positioning information and the second positioning information if the confidence coefficient parameter meets a first preset condition and the weight parameter meets a second preset condition.

In a third aspect, the application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the following steps when executing the computer program:

and if the confidence coefficient parameter meets a first preset condition and the weight parameter meets a second preset condition, acquiring calibration positioning information according to the first positioning information and the second positioning information.

In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:

In a fifth aspect, the present application further provides a computer program product. The computer program product comprising a computer program which when executed by a processor performs the steps of:

The vehicle positioning calibration method, the vehicle positioning calibration device, the computer equipment, the storage medium and the computer program product acquire first positioning information and second positioning information acquired by a plurality of vehicle-mounted electronic equipment of a target vehicle; acquiring an accumulated error of the first positioning information, and adjusting a confidence coefficient parameter according to the accumulated error; calculating the speed error of the target vehicle according to the second positioning information, and adjusting the weight parameter according to the speed error; and if the confidence coefficient parameter meets a first preset condition and the weight parameter meets a second preset condition, acquiring calibration positioning information according to the first positioning information and the second positioning information. The confidence coefficient or the weight corresponding to each type of positioning information is adjusted by calculating the error magnitude of the different types of positioning information, effective positioning information is determined by combining the confidence coefficient and the weight, calibration positioning information is obtained according to the effective positioning information, and the precision of vehicle positioning can be improved.

Drawings

FIG. 1 is a diagram of an exemplary embodiment of a vehicle position calibration method;

FIG. 2 is a schematic flow chart diagram of a vehicle position calibration method in one embodiment;

FIG. 3 is a logic diagram of vehicle alignment calibration in one embodiment;

FIG. 4 is a block diagram of a vehicle alignment calibration apparatus according to an embodiment;

FIG. 5 is a diagram of the internal structure of a computer device in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

The vehicle positioning calibration method provided by the embodiment of the application can be applied to the application environment shown in fig. 1. Wherein the application processor is a component in the vehicle. It is understood that the vehicle may be a vehicle, a robot device having a traveling function, etc., and the vehicle may be a truck, an off-road vehicle, a dump truck, a tractor, a special purpose vehicle, a passenger car, a sedan, a semitrailer, etc. The system comprises a satellite signal receiving Device (GNSS receiving Device), an Inertial Measurement Unit (IMU), a Network Access Device (NAD), a communication Device and an application processor, wherein the communication Device is connected with the application processor for communication, the application processor can perform Network communication with a real-time kinematic (RTK) system through the Network Access Device, and the application processor can also perform wireless communication with a plurality of road side devices through the communication Device.

In one embodiment, as shown in fig. 2, a vehicle positioning calibration method is provided, which is exemplified by the application processor in fig. 1, and includes the following steps:

step 202, acquiring first positioning information and second positioning information acquired through a plurality of vehicle-mounted electronic devices of a target vehicle.

The first positioning information may be Dead Reckoning positioning information, which is also called DR (Dead Reckoning) positioning information; the second positioning information may be Real Time Kinematic (RTK) positioning information.

Optionally, the application processor is installed on the target vehicle and communicates with the GNSS receiver on the target vehicle and the IMU, which mainly includes sensors such as gravity, geomagnetism, gyroscope, accelerometer, and electronic compass. The application processor corrects the original positioning data received by the GNSS receiving device by adopting six-axis data of the IMU, so as to obtain DR positioning information of the target vehicle, and obtains RTK positioning information of the target vehicle through communication with the NAD on the target vehicle, wherein the DR positioning information and the RTK positioning information both comprise three-dimensional position information consisting of longitude, latitude and altitude.

Step 204, obtaining an accumulated error of the first positioning information, and adjusting the confidence coefficient parameter according to the accumulated error.

Optionally, the application processor continuously detects an accumulated error of the DR positioning information (first positioning information), and when the accumulated error reaches a certain degree, determines that the error of the DR positioning information is large, and reduces the confidence coefficient parameter.

And step 206, calculating the speed error of the target vehicle according to the second positioning information, and adjusting the weight parameter according to the speed error.

Optionally, the application processor determines a plurality of continuous positioning positions according to the RTK positioning information and the preset sampling frequency, calculates the displacement of the preset sampling frequency according to the front and rear two continuous positioning positions, further calculates the average speed of the preset sampling frequency, compares the average speed with the standard speed threshold to obtain a speed error, and adjusts the weight parameter according to the size of the speed error. For example, if the velocity error is too large, which indicates that the average velocity of the preset sampling frequency calculated at present is not accurate, it may be that the RTK positioning information is not accurate, and the weight parameter is reduced.

And 208, if the confidence coefficient parameter meets a first preset condition and the weight parameter meets a second preset condition, acquiring calibration positioning information according to the first positioning information and the second positioning information.

The first preset condition may be that the confidence parameter is not less than 50%, and the second preset condition may be that the weight parameter is not less than 0.6.

Optionally, the application processor continuously detects the confidence coefficient parameter of the DR positioning information and the weight parameter of the RTK positioning information, and determines that the current DR positioning information and the current RTK positioning information are both valid positioning information when the confidence coefficient parameter satisfies a first preset condition and the weight parameter satisfies a second preset condition, and then acquires the calibration positioning information according to the longitude, the latitude and the altitude in the DR positioning information and the RTK positioning information.

In the vehicle positioning calibration method, first positioning information and second positioning information obtained through a plurality of vehicle-mounted electronic devices of a target vehicle are obtained; acquiring an accumulated error of the first positioning information, and adjusting a confidence coefficient parameter according to the accumulated error; calculating the speed error of the target vehicle according to the second positioning information, and adjusting the weight parameter according to the speed error; and if the confidence coefficient parameter meets a first preset condition and the weight parameter meets a second preset condition, acquiring calibration positioning information according to the first positioning information and the second positioning information. The confidence coefficient or the weight corresponding to each type of positioning information is adjusted by calculating the error magnitude of the different types of positioning information, effective positioning information is determined by combining the confidence coefficient and the weight, calibration positioning information is obtained according to the effective positioning information, and the precision of vehicle positioning can be improved.

In one embodiment, the plurality of in-vehicle electronic devices includes at least a satellite signal receiving device, an inertial measurement device, and a network access device, and acquiring the first positioning information and the second positioning information obtained by the plurality of in-vehicle electronic devices of the target vehicle includes: acquiring original positioning data of the target vehicle obtained by a satellite signal receiving device and six-axis data of the target vehicle obtained by an inertial measurement device; calculating to obtain original three-dimensional position information of the target vehicle according to the six-axis data; obtaining first positioning information according to the original three-dimensional position information and the original positioning data; and acquiring second positioning information of the target vehicle, which is acquired from the real-time dynamic measurement system through the network access device.

Optionally, the GNSS receiving apparatus receives, in real time, raw NMEA (National Marine Electronics Association, standard format established by Marine electronic devices) data unified by a standard GNSS Navigation device from a GNSS (Global Navigation Satellite System) Satellite group as raw positioning data. In a scene with signal shielding, the original positioning data has the conditions of low data precision and invalid positioning data, and the positioning dotting error range is large. After the application processor obtains the original NMEA data, six-axis data of the target vehicle are obtained from the IMU, and the original NMEA data are subjected to precision correction through a three-dimensional recursion algorithm to obtain first positioning information (namely DR positioning information).

Specifically, the application processor records the six-axis data from the IMU while the vehicle is stationary, and converts the data to euler angles through a Mahony complementary filter algorithm. And converting the resume computer reference coordinate system together with the acceleration information output by the accelerometer according to the Euler angle, and performing integral operation once to obtain 3 velocity components, namely three-dimensional velocity information. And then, obtaining three position components (longitude, latitude and altitude) by second integral operation, thereby obtaining original three-dimensional position information. And correcting the original positioning data through the original three-dimensional position information to obtain first positioning information.

Further, the medical processor accesses an RTK system server of the positioning service provider through the NAD to acquire second positioning information (i.e., RTK positioning information) calculated by the RTK system for the current target vehicle. The RTK positioning information is NMEA data obtained by performing difference processing on original NMEA data sent by a GNSS satellite group by an RTK system through an inter-satellite double-difference model, and is equivalent to NMEA data corrected by RTK.

In the embodiment, the original positioning data of the target vehicle obtained by the satellite signal receiving device and the six-axis data of the target vehicle obtained by the inertial measurement device are obtained; calculating to obtain original three-dimensional position information of the target vehicle according to the six-axis data; obtaining first positioning information according to the original three-dimensional position information and the original positioning data; and acquiring second positioning information of the target vehicle, which is acquired from the real-time dynamic measurement system through the network access device. The positioning information of two different types can be acquired, and the accuracy of vehicle positioning is convenient to improve.

In one embodiment, obtaining an accumulated error of the first positioning information, and adjusting the confidence parameter according to the accumulated error includes: if the accumulated error is greater than or equal to the error threshold, the confidence parameter is decreased.

Optionally, the application processor continuously detects accumulated errors of the DR positioning information, and when the accumulated errors continuously detected for multiple times are all greater than or equal to an error threshold, the confidence coefficient parameter is reduced, so as to reduce the influence of DR positioning information with a large error on final positioning. For example, the application processor continuously detects the accumulated error of the DR positioning information according to a preset detection period, and when the accumulated error detected for 10 times is greater than or equal to an error threshold value, the confidence coefficient parameter is reduced. This is because the zero offset of the gyroscope in the IMU drifts with time, so the GNSS raw data needs to be corrected according to the accumulated error in the gyroscope. However, when the GNSS receiver cannot receive the GNSS signal (raw NMEA data), the error correction cannot be performed on the IMU accumulated error, and the error of the DR positioning information is accumulated over time, and an accumulated error may also exist.

Specifically, the GNSS signal is calculated to have a frequency of 10hz for the confidence of the DR positioning information. The IMU update frequency is 30khz at standard frequency, and the ratio is 1. In the three-dimensional track calculation of DR positioning information, the initial course angle in the recursion formula needs to be continuously corrected when GNSS is output, so that t is updated to be 0, and the noise error accumulation caused by time is reduced. According to the mode, if the IMU outputs 3k of data for 1 second continuously, the GNSS data (10 times) is in a failure state, the error correction of the accumulated error cannot be carried out, and after the accumulated error exceeds a preset error threshold, the confidence degree default value 1 is decreased by 0.01.

In one possible embodiment, the application processor continuously detects accumulated errors of the DR positioning information, and when the accumulated errors detected continuously for a plurality of times are smaller than an error threshold value, the confidence coefficient parameter is increased, and the confidence coefficient parameter has a maximum value.

In this embodiment, if the accumulated error is greater than or equal to the error threshold, the confidence parameter is decreased. The influence of the first positioning information with larger error on the final positioning can be reduced, and the precision of vehicle positioning is improved.

In one embodiment, calculating a speed error of the target vehicle based on the second positioning information and adjusting the weight parameter based on the speed error comprises: according to the second positioning information, positioning of the target vehicle at a plurality of continuous moments is obtained according to a preset time interval; calculating to obtain the measuring speed of the target vehicle at the current continuous moment according to the positioning of the current continuous moment, the positioning of the previous continuous moment and a preset time interval; if the measuring speed of the target vehicle at the current continuous moment is not within the range of the standard speed threshold, judging that the measuring speed of the target vehicle at the current continuous moment is an abnormal speed; and if the times of continuously judging the abnormal speed exceed the time threshold, reducing the weight parameter.

Wherein the standard speed threshold range is used to characterize a reasonable speed range for normal vehicle travel.

Optionally, the processor is used to continuously obtain the position in the RTK positioning information, and calculate the vehicle speed at the current time by using the RTK positioning, where the formula is as follows:

v＝||P _k，GNSS -P _k-1 ||/Δt，

wherein, P _k,GNSS And P _k-1 Respectively positioning results of GNSS positioning at the time of k and filtered positioning results at the time of k-1. And comparing the speeds of the two, if v exceeds a standard speed threshold range (the actual speed +/-redundancy value), determining that the epoch RTK positioning error is overlarge, and after 5 times of continuous processes, performing weight reduction processing on the weight parameter of the RTK positioning information, wherein the weight parameter is reduced by 0.1.

In one possible embodiment, if the number of times of continuously determining the abnormal speed does not exceed the number threshold, the weight parameter is increased, and the weight parameter has a maximum value. For example, when the vehicle speed at the current moment is calculated for 5 times by continuously applying the RTK positioning, wherein the speed is within the range of the standard speed threshold for 1 time, the weighting parameter of the RTK positioning information is weighted and is increased by 0.1.

In the embodiment, the positioning of the target vehicle at a plurality of continuous moments is obtained according to the second positioning information and a preset time interval; calculating to obtain the measuring speed of the target vehicle at the current continuous moment according to the positioning of the current continuous moment, the positioning of the previous continuous moment and a preset time interval; if the measuring speed of the target vehicle at the current continuous moment is not within the range of the standard speed threshold, determining that the measuring speed of the target vehicle at the current continuous moment is an abnormal speed; and if the frequency of continuously judging the abnormal speed exceeds the frequency threshold value, reducing the weight parameter. The influence of the second positioning information with larger error on the final positioning can be reduced, and the precision of vehicle positioning is improved.

In one embodiment, obtaining calibration positioning information from the first positioning information and the second positioning information comprises: acquiring first three-dimensional position information from the first positioning information, wherein the first three-dimensional position information comprises a first longitude, a first latitude and a first altitude; acquiring second three-dimensional positioning information from the second positioning information, wherein the second three-dimensional position information comprises a second longitude, a second latitude and a second altitude; filtering the first longitude and the second longitude through the Federal Kalman filtering to obtain a fused longitude, filtering the first latitude and the second latitude to obtain a fused latitude, and filtering the first altitude and the second altitude to obtain a fused altitude; and taking the fusion longitude, the fusion latitude and the fusion altitude as fusion three-dimensional position information, and replacing the fusion three-dimensional position information with the first three-dimensional position information in the first positioning information to obtain calibration positioning information.

Optionally, the application processor obtains first three-dimensional position information (a first longitude, a first latitude and a first altitude) in the DR positioning information and second three-dimensional position information (a second longitude, a second latitude and a second altitude) in the RTK positioning information, and performs one federate kalman filtering, that is, three federate kalman filtering, on the longitude, the latitude and the altitude, respectively.

The state of the Federal Kalman filtering vector is as follows:

δNk、δEk、δSk、

the position error, step length estimation error and course estimation error are north and east. Assuming that the step size and heading are in accordance with a first-order Markov process, the system state is based on the DR principleThe equation is x _k ＝Ax _k-1 +Fω _k . Wherein, the first and the second end of the pipe are connected with each other,

x _k-1 the state of the last moment; f and ω _k Respectively a system noise matrix and a system process noise;

the current time course angle; Δ T is the sampling interval; t is a unit of _S 、

Is the correlation time.

The system observed quantity is DR and RTK position difference:

observation vector z _k ＝[z _n z _e ]T，z _n And z _e The difference between the DR and RTK north and east positions, respectively.

The system observation equation is z _k ＝Hx _k +v _k . In the formula

v _k To observe the noise.

And after the three times of Federal Kalman filtering, respectively outputting the fusion longitude, the fusion latitude and the fusion altitude after filtering processing to form the fusion three-dimensional position information after Federal Kalman filtering. And finally, replacing the first three-dimensional position information in the DR positioning information by fused three-dimensional position information feedback. And outputting the final fusion positioning result as the final three-dimensional position positioning.

In this embodiment, first three-dimensional position information is obtained from the first positioning information, where the first three-dimensional position information includes a first longitude, a first latitude, and a first altitude; acquiring second three-dimensional positioning information from the second positioning information, wherein the second three-dimensional position information comprises a second longitude, a second latitude and a second altitude; filtering the first longitude and the second longitude through the Federal Kalman filtering to obtain a fusion longitude, filtering the first latitude and the second latitude to obtain a fusion latitude, and filtering the first altitude and the second altitude to obtain a fusion altitude; and taking the fusion longitude, the fusion latitude and the fusion altitude as fusion three-dimensional position information, and replacing the first three-dimensional position information in the first positioning information with the fusion three-dimensional position information to obtain calibration positioning information. Kalman filtering can be performed on the three-dimensional position information in the effective positioning information to obtain filtered three-dimensional position information, and the filtered three-dimensional position information is adopted to obtain calibration positioning information, so that the vehicle positioning precision is improved.

In one embodiment, the method further comprises: and acquiring third positioning information of at least three road side devices corresponding to the target vehicle.

The roadside equipment is a roadside Unit (RSU, road Side Unit), is a device which is arranged at the roadside and is communicated with an On-Board Unit (OBU) by adopting a cellular internet of vehicles (C-V2X) technology and realizes vehicle identity recognition, and is V2X (vehicle to outside information exchange). The roadside apparatus may be a vehicle or an electronic apparatus installed at a roadside.

Optionally, the application processor obtains, through the communication device, roadside positioning information of three roadside devices corresponding to the target vehicle, and calculates third positioning information of the target vehicle according to the three roadside positioning information.

Specifically, the communication device may be a PC5 communication chip (V2X communication chip), the application processor receives high-precision positioning (longitude and latitude, altitude, 3-axis acceleration, confidence, etc.) in basic security information of BSM (basic safety message) sent from other roadside devices (stationary V2X terminal vehicles or V2X roadside facilities) through PC5 (ProSe 5 proximity communication is a communication form of radio), and selects N +1 points to determine an N-dimensional space according to a geometric principle, so that 3D position information needs to be determined, roadside three-dimensional position information of at least 3 additional V2X devices other than the positioned vehicle needs to be determined, and each piece of three-dimensional position information includes longitude, latitude and altitude.

The position of the target vehicle is determined by calculating the distance between the target vehicle and the 3 RSUs, and the specific formula is as follows:

in the formula, X _a 、Y _a 、Z _a As longitude, latitude and altitude coordinates (three-dimensional position information) of the target vehicle, X ₁ 、Y ₁ 、Z ₁ ，X ₂ 、Y ₂ 、Z ₂ And X ₃ 、Y ₃ 、Z ₃ Three-dimensional roadside position information of three roadside devices respectively. Obtaining the distances d between the three road side devices and the target vehicle ₁ 、d ₂ 、d ₃ . Calculating to obtain X by using a processor _a 、Y _a 、Z _a I.e. the third positioning information.

In the V2X system, the application processor can obtain the RSSI value of a real-time antenna signal, the radio frequency gain value of the antenna and the real-time frequency of the V2X system through a network access layer interface API provided by a chip manufacturer, and the speed and the distance are calculated by using a theoretical model. The TDOA (Time Difference Of Arrival) measurement method may also be used to measure the signal Arrival Time, so that the ranging uses the above equation.

In this embodiment, third positioning information of at least three roadside devices corresponding to the target vehicle is obtained. The positioning information of the target vehicle can be acquired based on the V2X system, so that the positioning of the target vehicle can be acquired conveniently in the scene that the NAD can not access the network and the GNSS has no signal, and the accuracy of vehicle positioning is improved.

In one embodiment, the method further comprises: and if the confidence coefficient parameter meets the first preset condition and the weight parameter does not meet the second preset condition, acquiring calibration positioning information according to the first positioning information and the third positioning information.

And if the confidence coefficient parameter does not meet the first preset condition and the weight parameter meets the second preset condition, acquiring calibration positioning information according to the second positioning information and the third positioning information. And if the confidence coefficient parameter does not meet the first preset condition and the weight parameter does not meet the second preset condition, taking the third positioning information as the calibration positioning information.

Optionally, if the confidence coefficient parameter meets the first preset condition and the weight parameter does not meet the second preset condition, it indicates that the NAD cannot access the network and cannot receive the positioning of the RTK system in the current road environment, and at this time, the accuracy of the RTK positioning information is low, so the RTK positioning information cannot be used as effective positioning information, the DR positioning information and the third positioning information are used as effective positioning information, and the calibration positioning information is obtained according to the first positioning information and the third positioning information. If the confidence coefficient parameter does not meet the first preset condition and the weight parameter meets the second preset condition, it is indicated that the current road environment has a situation that GNSS is free of signals and the accumulated error of the IMU is large, and the accuracy of DR positioning information is low at this time, so that the DR positioning information cannot be used as effective positioning information, RTK positioning information and third positioning information are used as effective positioning information, and calibration positioning information is obtained according to the second positioning information and the third positioning information. If the confidence coefficient parameter does not meet the first preset condition and the weight parameter does not meet the second preset condition, it is indicated that the current road environment has the condition that the NAD cannot access the network and the GNSS has no signal, the RTK system cannot be received for positioning, and the IMU accumulated error is also large, at this time, the accuracy of the DR positioning information and the RTK positioning information is low, so that the DR positioning information and the RTK positioning information cannot be used as effective positioning information, only the third positioning information is used as effective positioning information, and the third positioning information is directly output as calibration positioning information.

In this embodiment, the accuracy of vehicle positioning can be improved by calculating the error magnitudes of different types of positioning information, adjusting the confidence or weight corresponding to each type of positioning information, determining effective positioning information by combining the confidence and the weight, and obtaining calibration positioning information according to the effective positioning information.

In one embodiment, as shown in fig. 3, a vehicle position calibration method includes:

acquiring original positioning data of the target vehicle obtained by the satellite signal receiving device and six-axis data of the target vehicle obtained by the inertial measurement device; calculating to obtain original three-dimensional position information of the target vehicle according to the six-axis data; obtaining first positioning information according to the original three-dimensional position information and the original positioning data; and acquiring second positioning information of the target vehicle, which is acquired from the real-time dynamic measurement system through the network access device. And acquiring third positioning information of at least three road side devices corresponding to the target vehicle.

And acquiring an accumulated error of the first positioning information, and if the accumulated error is greater than or equal to an error threshold, reducing the confidence coefficient parameter.

According to the second positioning information, positioning of the target vehicle at a plurality of continuous moments is obtained according to a preset time interval; calculating to obtain the measuring speed of the target vehicle at the current continuous moment according to the positioning of the current continuous moment, the positioning of the previous continuous moment and a preset time interval; if the measuring speed of the target vehicle at the current continuous moment is not within the range of the standard speed threshold, determining that the measuring speed of the target vehicle at the current continuous moment is an abnormal speed; and if the times of continuously judging the abnormal speed exceed the time threshold, reducing the weight parameter.

If the confidence coefficient parameter meets a first preset condition and the weight parameter meets a second preset condition, acquiring first three-dimensional position information from the first positioning information, wherein the first three-dimensional position information comprises a first longitude, a first latitude and a first altitude; acquiring second three-dimensional positioning information from the second positioning information, wherein the second three-dimensional position information comprises a second longitude, a second latitude and a second altitude; filtering the first longitude and the second longitude through the Federal Kalman filtering to obtain a fused longitude, filtering the first latitude and the second latitude to obtain a fused latitude, and filtering the first altitude and the second altitude to obtain a fused altitude; and taking the fusion longitude, the fusion latitude and the fusion altitude as fusion three-dimensional position information, and replacing the fusion three-dimensional position information with the first three-dimensional position information in the first positioning information to obtain calibration positioning information.

If the confidence coefficient parameter meets a first preset condition and the weight parameter does not meet a second preset condition, acquiring first three-dimensional position information from the first positioning information, wherein the first three-dimensional position information comprises a first longitude, a first latitude and a first altitude; acquiring third three-dimensional positioning information from the third positioning information, wherein the third three-dimensional position information comprises a third longitude, a third latitude and a third altitude; filtering the first longitude and the third longitude through the Federal Kalman filtering to obtain a fused longitude, filtering the first latitude and the third latitude to obtain a fused latitude, and filtering the first altitude and the third altitude to obtain a fused altitude; and taking the fusion longitude, the fusion latitude and the fusion altitude as fusion three-dimensional position information, and replacing the fusion three-dimensional position information with the first three-dimensional position information in the first positioning information to obtain calibration positioning information.

If the confidence coefficient parameter does not meet the first preset condition and the weight parameter meets the second preset condition, acquiring third three-dimensional position information from the third positioning information, wherein the third three-dimensional position information comprises a third longitude, a third latitude and a third altitude; acquiring second three-dimensional positioning information from the second positioning information, wherein the second three-dimensional position information comprises a second longitude, a second latitude and a second altitude; filtering the third longitude and the second longitude through the Federal Kalman filtering to obtain a fused longitude, filtering the third latitude and the second latitude to obtain a fused latitude, and filtering the third altitude and the second altitude to obtain a fused altitude; and taking the fusion longitude, the fusion latitude and the fusion altitude as fusion three-dimensional position information, and replacing the fusion three-dimensional position information with the first three-dimensional position information in the first positioning information to obtain calibration positioning information.

The effective positioning data can be calculated by adopting different calculation methods under the condition that the states of different positioning data sources are invalid, the vehicle is positioned and calibrated, and the positioning accuracy of the vehicle in different road scenes is ensured.

It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.

Based on the same inventive concept, the embodiment of the application also provides a vehicle positioning calibration device for realizing the vehicle positioning calibration method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so specific limitations in one or more embodiments of the vehicle positioning calibration device provided below can be referred to the limitations in the vehicle positioning calibration method above, and details are not repeated here.

In one embodiment, as shown in FIG. 4, there is provided a vehicle position calibration apparatus 400 comprising: a positioning acquisition module 401, a first adjustment module 402, a second adjustment module 403, and a positioning calibration module 404, wherein:

a positioning acquisition module 401, configured to acquire first positioning information and second positioning information obtained through a plurality of in-vehicle electronic devices of a target vehicle;

a first adjusting module 402, configured to obtain an accumulated error of the first positioning information, and adjust the confidence parameter according to the accumulated error;

a second adjusting module 403, configured to calculate a speed error of the target vehicle according to the second positioning information, and adjust the weight parameter according to the speed error;

and a positioning calibration module 404, configured to obtain calibration positioning information according to the first positioning information and the second positioning information if the confidence coefficient parameter meets a first preset condition and the weight parameter meets a second preset condition.

In one embodiment, the plurality of vehicle-mounted electronic devices at least comprise a satellite signal receiving device, an inertial measurement device and a network access device, and the positioning acquisition module 401 is further configured to acquire raw positioning data of the target vehicle obtained by the satellite signal receiving device and six-axis data of the target vehicle obtained by the inertial measurement device; calculating to obtain original three-dimensional position information of the target vehicle according to the six-axis data; obtaining first positioning information according to the original three-dimensional position information and original positioning data; and acquiring second positioning information of the target vehicle, which is obtained from the real-time dynamic measurement system through the network access device.

In one embodiment, the first adjustment module 402 is further configured to decrease the confidence parameter if the accumulated error is greater than or equal to the error threshold.

In one embodiment, the second adjusting module 403 is further configured to obtain, according to the second positioning information, the positioning of the target vehicle at multiple consecutive times at preset time intervals; calculating to obtain the measuring speed of the target vehicle at the current continuous moment according to the positioning of the current continuous moment, the positioning of the previous continuous moment and a preset time interval; if the measuring speed of the target vehicle at the current continuous moment is not within the range of the standard speed threshold, determining that the measuring speed of the target vehicle at the current continuous moment is an abnormal speed; and if the frequency of continuously judging the abnormal speed exceeds the frequency threshold value, reducing the weight parameter.

In one embodiment, the positioning calibration module 404 is further configured to obtain first three-dimensional position information from the first positioning information, the first three-dimensional position information including a first longitude, a first latitude, and a first altitude; acquiring second three-dimensional positioning information from the second positioning information, wherein the second three-dimensional position information comprises a second longitude, a second latitude and a second altitude; filtering the first longitude and the second longitude through the Federal Kalman filtering to obtain a fused longitude, filtering the first latitude and the second latitude to obtain a fused latitude, and filtering the first altitude and the second altitude to obtain a fused altitude; and taking the fusion longitude, the fusion latitude and the fusion altitude as fusion three-dimensional position information, and replacing the first three-dimensional position information in the first positioning information with the fusion three-dimensional position information to obtain calibration positioning information.

In one embodiment, the location obtaining module 401 is further configured to obtain third location information of at least three roadside devices corresponding to the target vehicle.

In an embodiment, the positioning calibration module 404 is further configured to obtain calibration positioning information according to the first positioning information and the third positioning information if the confidence parameter meets a first preset condition and the weight parameter does not meet a second preset condition.

In an embodiment, the positioning calibration module 404 is further configured to obtain calibration positioning information according to the second positioning information and the third positioning information if the confidence parameter does not satisfy the first preset condition and the weight parameter satisfies the second preset condition.

In one embodiment, the positioning calibration module 404 is further configured to use the third positioning information as the calibration positioning information if the confidence parameter does not satisfy the first preset condition and the weight parameter does not satisfy the second preset condition.

The various modules in the vehicle positioning calibration device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a vehicle-mounted T-BOX (Telematics BOX) or a vehicle-mounted terminal device with a network function and a V2X function, and its internal structure diagram may be as shown in fig. 5. The computer device includes a processor, a memory, an Input/Output interface (I/O for short), and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing positioning data. The input/output interface of the computer device is used for exchanging information between the processor and an external device. The communication interface of the computer device is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to implement a vehicle position calibration method.

Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program: acquiring first positioning information and second positioning information acquired through a plurality of vehicle-mounted electronic devices of a target vehicle; acquiring an accumulated error of the first positioning information, and adjusting a confidence coefficient parameter according to the accumulated error; calculating the speed error of the target vehicle according to the second positioning information, and adjusting the weight parameter according to the speed error; and if the confidence coefficient parameter meets a first preset condition and the weight parameter meets a second preset condition, acquiring calibration positioning information according to the first positioning information and the second positioning information.

In one embodiment, the plurality of in-vehicle electronic devices includes at least a satellite signal receiving means, an inertial measurement unit, and a network access unit, and the processor when executing the computer program further performs the steps of: acquiring original positioning data of the target vehicle obtained by the satellite signal receiving device and six-axis data of the target vehicle obtained by the inertial measurement device; calculating to obtain original three-dimensional position information of the target vehicle according to the six-axis data; obtaining first positioning information according to the original three-dimensional position information and original positioning data; and acquiring second positioning information of the target vehicle, which is acquired from the real-time dynamic measurement system through the network access device.

In one embodiment, the processor when executing the computer program further performs the steps of: if the accumulated error is greater than or equal to the error threshold, the confidence parameter is decreased.

In one embodiment, the processor, when executing the computer program, further performs the steps of: according to the second positioning information, positioning of the target vehicle at a plurality of continuous moments is obtained according to a preset time interval; calculating to obtain the measuring speed of the target vehicle at the current continuous moment according to the positioning of the current continuous moment, the positioning of the previous continuous moment and a preset time interval; if the measuring speed of the target vehicle at the current continuous moment is not within the range of the standard speed threshold, determining that the measuring speed of the target vehicle at the current continuous moment is an abnormal speed; and if the frequency of continuously judging the abnormal speed exceeds the frequency threshold value, reducing the weight parameter.

In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring first three-dimensional position information from the first positioning information, wherein the first three-dimensional position information comprises a first longitude, a first latitude and a first altitude; acquiring second three-dimensional positioning information from the second positioning information, wherein the second three-dimensional position information comprises a second longitude, a second latitude and a second altitude; filtering the first longitude and the second longitude through the Federal Kalman filtering to obtain a fusion longitude, filtering the first latitude and the second latitude to obtain a fusion latitude, and filtering the first altitude and the second altitude to obtain a fusion altitude; and taking the fusion longitude, the fusion latitude and the fusion altitude as fusion three-dimensional position information, and replacing the first three-dimensional position information in the first positioning information with the fusion three-dimensional position information to obtain calibration positioning information.

In one embodiment, the processor, when executing the computer program, further performs the steps of: and acquiring third positioning information of at least three road side devices corresponding to the target vehicle.

In one embodiment, the processor when executing the computer program further performs the steps of: and if the confidence coefficient parameter meets the first preset condition and the weight parameter does not meet the second preset condition, acquiring calibration positioning information according to the first positioning information and the third positioning information.

In one embodiment, the processor when executing the computer program further performs the steps of: and if the confidence coefficient parameter does not meet the first preset condition and the weight parameter meets the second preset condition, acquiring calibration positioning information according to the second positioning information and the third positioning information.

In one embodiment, the processor, when executing the computer program, further performs the steps of: and if the confidence coefficient parameter does not meet the first preset condition and the weight parameter does not meet the second preset condition, taking the third positioning information as the calibration positioning information.

In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring first positioning information and second positioning information acquired through a plurality of vehicle-mounted electronic devices of a target vehicle; acquiring an accumulated error of the first positioning information, and adjusting a confidence coefficient parameter according to the accumulated error; calculating the speed error of the target vehicle according to the second positioning information, and adjusting the weight parameter according to the speed error; and if the confidence coefficient parameter meets a first preset condition and the weight parameter meets a second preset condition, acquiring calibration positioning information according to the first positioning information and the second positioning information.

In one embodiment, the plurality of in-vehicle electronic devices comprises at least satellite signal receiving means, inertial measurement means and network access means, the computer program when executed by the processor further implementing the steps of: acquiring original positioning data of the target vehicle obtained by the satellite signal receiving device and six-axis data of the target vehicle obtained by the inertial measurement device; calculating to obtain original three-dimensional position information of the target vehicle according to the six-axis data; obtaining first positioning information according to the original three-dimensional position information and original positioning data; and acquiring second positioning information of the target vehicle, which is acquired from the real-time dynamic measurement system through the network access device.

In one embodiment, the computer program when executed by the processor further performs the steps of: if the accumulated error is greater than or equal to the error threshold, the confidence parameter is decreased.

In one embodiment, the computer program when executed by the processor further performs the steps of: according to the second positioning information, positioning of the target vehicle at a plurality of continuous moments is obtained according to a preset time interval; calculating to obtain the measuring speed of the target vehicle at the current continuous moment according to the positioning of the current continuous moment, the positioning of the previous continuous moment and a preset time interval; if the measuring speed of the target vehicle at the current continuous moment is not within the range of the standard speed threshold, determining that the measuring speed of the target vehicle at the current continuous moment is an abnormal speed; and if the frequency of continuously judging the abnormal speed exceeds the frequency threshold value, reducing the weight parameter.

In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring first three-dimensional position information from the first positioning information, wherein the first three-dimensional position information comprises a first longitude, a first latitude and a first altitude; acquiring second three-dimensional positioning information from the second positioning information, wherein the second three-dimensional position information comprises a second longitude, a second latitude and a second altitude; filtering the first longitude and the second longitude through the Federal Kalman filtering to obtain a fused longitude, filtering the first latitude and the second latitude to obtain a fused latitude, and filtering the first altitude and the second altitude to obtain a fused altitude; and taking the fusion longitude, the fusion latitude and the fusion altitude as fusion three-dimensional position information, and replacing the fusion three-dimensional position information with the first three-dimensional position information in the first positioning information to obtain calibration positioning information.

In one embodiment, the computer program when executed by the processor further performs the steps of: and acquiring third positioning information of at least three road side devices corresponding to the target vehicle.

In one embodiment, the computer program when executed by the processor further performs the steps of: and if the confidence coefficient parameter meets the first preset condition and the weight parameter does not meet the second preset condition, acquiring calibration positioning information according to the first positioning information and the third positioning information.

In one embodiment, the computer program when executed by the processor further performs the steps of: and if the confidence coefficient parameter does not meet the first preset condition and the weight parameter meets the second preset condition, acquiring calibration positioning information according to the second positioning information and the third positioning information.

In one embodiment, the computer program when executed by the processor further performs the steps of: and if the confidence coefficient parameter does not meet the first preset condition and the weight parameter does not meet the second preset condition, taking the third positioning information as the calibration positioning information.

In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, performs the steps of: acquiring first positioning information and second positioning information acquired through a plurality of vehicle-mounted electronic devices of a target vehicle; acquiring an accumulated error of the first positioning information, and adjusting a confidence coefficient parameter according to the accumulated error; calculating the speed error of the target vehicle according to the second positioning information, and adjusting the weight parameter according to the speed error; and if the confidence coefficient parameter meets the first preset condition and the weight parameter meets the second preset condition, acquiring calibration positioning information according to the first positioning information and the second positioning information.

In one embodiment, the plurality of in-vehicle electronic devices includes at least satellite signal receiving means, inertial measurement means and network access means, the computer program when executed by the processor further implementing the steps of: acquiring original positioning data of the target vehicle obtained by the satellite signal receiving device and six-axis data of the target vehicle obtained by the inertial measurement device; calculating to obtain original three-dimensional position information of the target vehicle according to the six-axis data; obtaining first positioning information according to the original three-dimensional position information and original positioning data; and acquiring second positioning information of the target vehicle, which is obtained from the real-time dynamic measurement system through the network access device.

In one embodiment, the computer program when executed by the processor further performs the steps of: and if the accumulated error is greater than or equal to the error threshold, reducing the confidence coefficient parameter.

In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring first three-dimensional position information from the first positioning information, wherein the first three-dimensional position information comprises a first longitude, a first latitude and a first altitude; acquiring second three-dimensional positioning information from the second positioning information, wherein the second three-dimensional position information comprises a second longitude, a second latitude and a second altitude; filtering the first longitude and the second longitude through the Federal Kalman filtering to obtain a fusion longitude, filtering the first latitude and the second latitude to obtain a fusion latitude, and filtering the first altitude and the second altitude to obtain a fusion altitude; and taking the fusion longitude, the fusion latitude and the fusion altitude as fusion three-dimensional position information, and replacing the fusion three-dimensional position information with the first three-dimensional position information in the first positioning information to obtain calibration positioning information.

In one embodiment, the computer program when executed by the processor further performs the steps of: and acquiring third positioning information of at least three roadside devices corresponding to the target vehicle.

It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the relevant laws and regulations and standards of the relevant country and region.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, databases, or other media used in the embodiments provided herein can include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), for example. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A vehicle positioning calibration method, the method comprising:

acquiring first positioning information and second positioning information which are acquired through a plurality of vehicle-mounted electronic devices of a target vehicle;

calculating the speed error of the target vehicle according to the second positioning information, and adjusting a weight parameter according to the speed error;

2. The method of claim 1, wherein the plurality of in-vehicle electronic devices includes at least a satellite signal receiving device, an inertial measurement device, and a network access device, and wherein the obtaining the first positioning information and the second positioning information obtained by the plurality of in-vehicle electronic devices of the target vehicle comprises:

acquiring original positioning data of the target vehicle obtained by the satellite signal receiving device and six-axis data of the target vehicle obtained by the inertial measurement device;

obtaining the first positioning information according to the original three-dimensional position information and the original positioning data;

and acquiring second positioning information of the target vehicle, which is obtained from a real-time dynamic measurement system through the network access device.

3. The method of claim 1, wherein obtaining an accumulated error of the first positioning information and adjusting the confidence parameter according to the accumulated error comprises:

and if the accumulated error is greater than or equal to an error threshold, reducing the confidence coefficient parameter.

4. The method of claim 1, wherein said calculating a speed error of the target vehicle from the second positioning information and adjusting a weight parameter according to the speed error comprises:

calculating to obtain the measurement speed of the target vehicle at the current continuous moment according to the positioning of the current continuous moment, the positioning of the previous continuous moment and a preset time interval;

and if the frequency of continuously judging the abnormal speed exceeds a frequency threshold value, reducing the weight parameter.

5. The method of claim 1, wherein obtaining calibration positioning information based on the first positioning information and the second positioning information comprises:

filtering the first longitude and the second longitude through federal Kalman filtering to obtain a fused longitude, filtering the first latitude and the second latitude to obtain a fused latitude, and filtering the first altitude and the second altitude to obtain a fused altitude;

and taking the fusion longitude, the fusion latitude and the fusion altitude as fusion three-dimensional position information, and replacing the first three-dimensional position information in the first positioning information with the fusion three-dimensional position information to obtain the calibration positioning information.

6. The method of claim 1, further comprising:

7. The method of claim 6, further comprising:

and if the confidence coefficient parameter meets a first preset condition and the weight parameter does not meet a second preset condition, acquiring calibration positioning information according to the first positioning information and the third positioning information.

8. The method of claim 6, further comprising:

and if the confidence coefficient parameter does not meet a first preset condition and the weight parameter meets a second preset condition, acquiring calibration positioning information according to the second positioning information and the third positioning information.

9. The method of claim 6, further comprising:

and if the confidence coefficient parameter does not meet a first preset condition and the weight parameter does not meet a second preset condition, taking the third positioning information as calibration positioning information.

10. A vehicle positioning calibration device, the device comprising:

the second adjusting module is used for calculating the speed error of the target vehicle according to the second positioning information and adjusting a weight parameter according to the speed error;

11. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any one of claims 1 to 9 when executing the computer program.

12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 9.

13. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 9.