WO2024041156A1 - Vehicle positioning calibration method and apparatus, computer device, and storage medium - Google Patents

Abstract

A vehicle positioning calibration method and apparatus, a computer device, and a storage medium. The method comprises: acquiring first positioning information and second positioning information obtained by means of a plurality of vehicle-mounted electronic devices of a target vehicle (202); acquiring an accumulated error of the first positioning information, and adjusting a confidence level parameter according to the accumulated error (204); calculating a speed error of the target vehicle according to the second positioning information, and adjusting a weight parameter according to the speed error (206); and if the confidence level parameter satisfies a first preset condition and the weight parameter satisfies a second preset condition, acquiring calibration positioning information according to the first positioning information and the second positioning information (208). By using the method, the confidence level or the weight corresponding to each type of positioning information can be adjusted by calculating errors of different types of positioning information, effective positioning information is determined by combining the confidence level and the weight, and calibration positioning information is obtained according to the effective positioning information.

Description

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

Related applications

This application claims priority to the Chinese patent application filed on August 25, 2022, with application number 202211029493.2 and titled "Vehicle Positioning Calibration Method, Device, Computer Equipment, Storage Medium", the full text of which is hereby incorporated by reference.

Technical field

The present application relates to the technical field of Internet of Vehicles, and in particular to a vehicle positioning calibration method, device, computer equipment, storage medium and computer program product.

Background technique

When the vehicle is driving in places without satellite positioning signals, such as indoor parking lots, remote mountainous areas, or road scenes with weak positioning signals, such as areas with many shade trees, many tall buildings, many obstacles, overpasses, etc., it is impossible to obtain accurate vehicle positions. Information, thus causing positioning-related applications on the vehicle to be unusable or prone to errors; and if the vehicle drives in an area with poor navigation signals for a long time, the position calculated by the GNSS+inertial navigation positioning system currently commonly used in vehicles will gradually deviate from the actual position. location, problems such as navigation errors are likely to occur.

Current vehicle positioning is affected by different road scenarios, and the positioning accuracy is not high.

Contents of the invention

In a first aspect, this application provides a vehicle positioning calibration method. The methods include:

Obtain first positioning information and second positioning information obtained through multiple on-board electronic devices of the target vehicle;

Obtain the cumulative error of the first positioning information, and adjust the confidence parameter based on the cumulative error;

Calculate the speed error of the target vehicle according to the second positioning information, and adjust the weight parameters according to the speed error;

If the confidence parameter meets the first preset condition and the weight parameter meets the second preset condition, the calibration positioning information is obtained according to the first positioning information and the second positioning information.

In one embodiment, the plurality of vehicle-mounted electronic devices at least include a satellite signal receiving device, an inertial measurement device and a network access device, and the first positioning information and the second positioning information obtained through the multiple vehicle-mounted electronic devices of the target vehicle are obtained, including :

Obtain the original positioning data of the target vehicle obtained through the satellite signal receiving device, and the six-axis data of the target vehicle obtained through the inertial measurement device;

The original three-dimensional position information of the target vehicle is calculated based on the six-axis data;

Obtain first positioning information based on the original three-dimensional position information and original positioning data;

Obtain the second positioning information of the target vehicle obtained from the real-time dynamic measurement system through the network access device.

In one embodiment, obtaining the cumulative error of the first positioning information, and adjusting the confidence parameter based on the cumulative error includes:

If the cumulative error is greater than or equal to the error threshold, reduce the confidence parameter.

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

According to the second positioning information, obtain the positioning of the target vehicle at multiple consecutive moments according to a preset time interval;

Based on the positioning at the current continuous moment, the positioning at the previous continuous moment and the preset time interval, the measured speed of the target vehicle at the current continuous moment is calculated;

If the measured speed of the target vehicle at the current continuous time is not within the standard speed threshold range, the measured speed of the target vehicle at the current continuous time is determined to be an abnormal speed;

If the number of consecutive abnormal speed determinations exceeds the number threshold, the weight parameter will be reduced.

In one embodiment, obtaining calibration positioning information based on the first positioning information and the second positioning information includes:

Obtain first three-dimensional position information from the first positioning information, where the first three-dimensional position information includes a first longitude, a first latitude, and a first altitude;

Obtain second three-dimensional positioning information from the second positioning information. The second three-dimensional position information includes a second longitude and a second latitude. and second elevation;

Filter the first longitude and the second longitude through the federated Kalman filter to obtain the fused longitude, filter the first latitude and the second latitude to obtain the fused latitude, and filter the first altitude and the second altitude to obtain the fused altitude;

The fused longitude, fused latitude and fused altitude are used as the fused three-dimensional position information, and the fused three-dimensional position information is replaced with the first three-dimensional position information in the first positioning information to obtain the calibrated positioning information.

In one embodiment, the method further includes:

Obtain third positioning information of at least three roadside devices corresponding to the target vehicle.

In one embodiment, the method further includes:

If the confidence parameter meets the first preset condition and the weight parameter does not meet the second preset condition, the calibration positioning information is obtained according to the first positioning information and the third positioning information.

In one embodiment, the method further includes:

If the confidence parameter does not meet the first preset condition and the weight parameter meets the second preset condition, the calibration positioning information is obtained according to the second positioning information and the third positioning information.

In one embodiment, the method further includes:

If the confidence parameter does not meet the first preset condition and the weight parameter does not meet the second preset condition, the third positioning information is used as the calibration positioning information.

In a second aspect, this application also provides a vehicle positioning calibration device. The device includes:

A positioning acquisition module, configured to acquire first positioning information and second positioning information obtained through multiple on-board electronic devices of the target vehicle;

The first adjustment module is used to obtain the cumulative error of the first positioning information and adjust the confidence parameter according to the cumulative error;

a second adjustment module, configured to calculate the speed error of the target vehicle based on the second positioning information, and adjust the weight parameters based on the speed error;

The positioning calibration module is used to obtain calibration positioning information according to the first positioning information and the second positioning information if the confidence parameter meets the first preset condition and the weight parameter meets the second preset condition.

In a third aspect, this application also provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

Obtain first positioning information and second positioning information obtained through multiple on-board electronic devices of the target vehicle;

Obtain the cumulative error of the first positioning information, and adjust the confidence parameter based on the cumulative error;

Calculate the speed error of the target vehicle according to the second positioning information, and adjust the weight parameters according to the speed error;

If the confidence parameter meets the first preset condition and the weight parameter meets the second preset condition, the calibration positioning information is obtained according to the first positioning information and the second positioning information.

In a fourth aspect, this application also provides a computer-readable storage medium. The computer-readable storage medium has a computer program stored thereon, and when the computer program is executed by the processor, the following steps are implemented:

Obtain the first positioning information and the second positioning information obtained through multiple on-board electronic devices of the target vehicle;

Obtain the cumulative error of the first positioning information, and adjust the confidence parameter based on the cumulative error;

Calculate the speed error of the target vehicle according to the second positioning information, and adjust the weight parameters according to the speed error;

If the confidence parameter meets the first preset condition and the weight parameter meets the second preset condition, the calibration positioning information is obtained according to the first positioning information and the second positioning information.

In a fifth aspect, this application also provides a computer program product. The computer program product includes a computer program that implements the following steps when executed by a processor:

Obtain first positioning information and second positioning information obtained through multiple on-board electronic devices of the target vehicle;

Obtain the cumulative error of the first positioning information, and adjust the confidence parameter based on the cumulative error;

Calculate the speed error of the target vehicle according to the second positioning information, and adjust the weight parameters according to the speed error;

If the confidence parameter meets the first preset condition and the weight parameter meets the second preset condition, the calibration positioning information is obtained according to the first positioning information and the second positioning information.

The above-mentioned vehicle positioning and calibration methods, devices, computer equipment, storage media and computer program products have been approved The first positioning information and the second positioning information obtained by multiple on-board electronic devices of the target vehicle; the cumulative error of the first positioning information is obtained, and the confidence parameter is adjusted based on the cumulative error; the speed error of the target vehicle is calculated based on the second positioning information, And adjust the weight parameter according to the speed error; if the confidence parameter meets the first preset condition, and the weight parameter meets the second preset condition, the calibration positioning information is obtained according to the first positioning information and the second positioning information. The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below. Other features, objects and advantages of the application will become apparent from the description, drawings and claims.

Description of drawings

In order to more clearly explain the technical solutions in the embodiments of the present application or the traditional technology, the drawings needed to be used in the description of the embodiments or the traditional technology will be briefly introduced below. Obviously, the drawings in the following description are only for the purpose of explaining the embodiments or the technical solutions of the traditional technology. For the embodiments of the application, those of ordinary skill in the art can also obtain other drawings based on the disclosed drawings without exerting creative efforts.

Figure 1 is an application environment diagram of the vehicle positioning calibration method in one embodiment;

Figure 2 is a schematic flow chart of a vehicle positioning calibration method in one embodiment;

Figure 3 is a logical schematic diagram of vehicle positioning calibration in one embodiment;

Figure 4 is a structural block diagram of a vehicle positioning calibration device in one embodiment;

Figure 5 is an internal structure diagram of a computer device in one embodiment.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

The vehicle positioning calibration method provided by the embodiment of the present application can be applied in the application environment as shown in Figure 1. Among them, the application processor is a component in the vehicle. It can be understood that the means of transportation can be vehicles, robotic equipment with driving functions, etc., and the vehicles can be trucks, off-road vehicles, dump trucks, tractors, special vehicles, passenger cars, cars, semi-trailers, etc. The satellite signal receiving device (GNSS receiving device), the inertial measurement unit (IMU, Inertial Measurement Unit), the network access device (NAD, Network Access Device), the communication device and the application processor are connected and communicated. The application processor can pass The network access device performs network communication with the real-time dynamic measurement system (RTK system), and the application processor can also perform wireless communication with multiple roadside devices through the communication device.

In one embodiment, as shown in Figure 2, a vehicle positioning calibration method is provided. The application of this method to the application processor in Figure 1 is used as an example to illustrate, including the following steps:

Step 202: Obtain first positioning information and second positioning information obtained through multiple vehicle-mounted electronic devices of the target vehicle.

Among them, the first positioning information can be vehicle dead reckoning positioning information, also known as DR (Dead Reckoning) positioning information; the second positioning information can be real-time dynamic measurement positioning information, also known as RTK (Real Time Kinematic) positioning information.

Optionally, the application processor is installed on the target vehicle and communicates with the GNSS receiving device and IMU on the target vehicle. The IMU mainly includes sensors such as gravity, geomagnetism, gyroscope, accelerometer, and electronic compass. The application processor uses the six-axis data of the IMU to correct the original positioning data received by the GNSS receiving device to obtain the DR positioning information of the target vehicle, and obtains the RTK positioning information, DR positioning information and RTK positioning information contains three-dimensional position information consisting of longitude, latitude, and altitude.

Step 204: Obtain the cumulative error of the first positioning information, and adjust the confidence parameter based on the cumulative error.

Optionally, the application processor continuously detects the cumulative error of the DR positioning information (first positioning information). When the cumulative error reaches a certain level, it is determined that the error of the DR positioning information is large and the confidence parameter is reduced.

Step 206: Calculate the speed error of the target vehicle according to the second positioning information, and adjust the weight parameters according to the speed error.

Optionally, the application processor determines multiple consecutive positioning positions according to the preset sampling frequency based on the RTK positioning information. position, calculate the displacement of the preset sampling frequency based on the two consecutive positioning positions, and further calculate the average speed of the preset sampling frequency, compare the average speed with the standard speed threshold, and obtain the speed error. According to the size of the speed error Adjust the weight parameters accordingly. For example, if the speed error is too large, it means that the currently calculated average speed of the preset sampling frequency is inaccurate. It may be that the RTK positioning information is inaccurate and the weight parameters are reduced.

Step 208: If the confidence parameter meets the first preset condition and the weight parameter meets the second preset condition, obtain calibration positioning information based on 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 parameter of the DR positioning information and the weight parameter of the RTK positioning information. When the confidence parameter meets the first preset condition and the weight parameter meets the second preset condition, determines the current If the DR positioning information and RTK positioning information are both valid positioning information, then the calibration positioning information is obtained based on the longitude, latitude, and altitude in the DR positioning information and RTK positioning information.

In the above vehicle positioning calibration method, the first positioning information and the second positioning information obtained through multiple vehicle-mounted electronic devices of the target vehicle are obtained; the cumulative error of the first positioning information is obtained, and the confidence parameter is adjusted according to the cumulative error; and the second positioning information is obtained according to the second positioning information. The positioning information calculates the speed error of the target vehicle and adjusts the weight parameter according to the speed error; if the confidence parameter meets the first preset condition and the weight parameter meets the second preset condition, the calibration is obtained based on the first positioning information and the second positioning information. Positioning information. By calculating the error size of different types of positioning information, adjusting the confidence or weight corresponding to each positioning information, and combining the confidence and weight to determine the effective positioning information, and obtaining the calibrated positioning information based on the effective positioning information, the accuracy of vehicle positioning can be improved. Accuracy.

In one embodiment, the plurality of vehicle-mounted electronic devices at least include a satellite signal receiving device, an inertial measurement device and a network access device, and obtaining the first positioning information and the second positioning information obtained through the multiple vehicle-mounted electronic devices of the target vehicle include: Obtain the original positioning data of the target vehicle obtained through the satellite signal receiving device, and the six-axis data of the target vehicle obtained through the inertial measurement device; calculate the original three-dimensional position information of the target vehicle based on the six-axis data; calculate the original three-dimensional position information of the target vehicle based on the original three-dimensional position information and the original The positioning data obtains the first positioning information; and the second positioning information of the target vehicle obtained from the real-time dynamic measurement system through the network access device is obtained.

Optionally, the GNSS receiving device receives in real time the unified original NMEA (National Marine Electronics Association, a standard format developed for marine electronic equipment) data sent by the GNSS (Global Navigation Satellite System) satellite group from the standard GNSS navigation equipment. as raw positioning data. In scenarios where signal occlusion exists, the original positioning data will have low data accuracy, invalid positioning data, and a large positioning error range. After the application processor obtains the original NMEA data, it obtains the six-axis data of the target vehicle from the IMU, and performs accuracy correction on the original NMEA data through a three-dimensional recursive algorithm to obtain the first positioning information (ie, DR positioning information).

Specifically, when the vehicle is stationary, the application processor records six-axis data from the IMU and converts it into Euler angles through the Mahony complementary filtering algorithm. According to the Euler angle and the acceleration information output by the accelerometer, the resume computer reference coordinate system is converted. After an integral operation, three velocity components are obtained, that is, three-dimensional velocity information. Through the second integration operation, three position components (longitude, latitude, altitude) are obtained, thereby obtaining the original three-dimensional position information. The original positioning data is corrected through the original three-dimensional position information to obtain the first positioning information.

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

In this embodiment, the original positioning data of the target vehicle obtained through the satellite signal receiving device and the six-axis data of the target vehicle obtained through the inertial measurement device are obtained; the original three-dimensional position information of the target vehicle is calculated based on the six-axis data; and the original three-dimensional position information of the target vehicle is obtained according to the original The three-dimensional position information and the original positioning data are used to obtain the first positioning information; and the second positioning information of the target vehicle obtained from the real-time dynamic measurement system through the network access device is obtained. Two different types of positioning information can be obtained to improve the accuracy of vehicle positioning.

In one embodiment, the cumulative error of the first positioning information is obtained, and the confidence parameter is adjusted according to the cumulative error, including: if the cumulative error is greater than or equal to the error threshold, reducing the confidence parameter.

Optionally, the application processor continuously detects the cumulative error of the DR positioning information. When the cumulative errors detected multiple times are greater than or equal to the error threshold, the confidence parameter is lowered, thereby reducing the number of pairs of DR positioning information with larger errors. The impact of final positioning. For example, the application processor continuously detects the cumulative error of the DR positioning information according to the preset detection period. When the cumulative error detected for 10 consecutive times is greater than or equal to the error threshold, the confidence parameter is lowered. This is because the zero bias of the gyroscope in the IMU drifts with time, so the original GNSS data needs to be corrected based on the accumulated error in the gyroscope. However, when the GNSS receiving device cannot receive the GNSS signal (original NMEA data), it cannot correct the IMU accumulated error, causing the error of the DR positioning information to accumulate over time, and there will also be accumulated errors.

Specifically, for calculating the confidence of DR positioning information, the frequency of GNSS signal is 10hz. The IMU update frequency is the standard frequency 30khz, and the ratio is 1:3000. That is, after the IMU outputs 3k times, the GNSS signal will be calibrated once. In the three-dimensional trajectory estimation of DR positioning information, it is necessary to continuously correct the initial heading angle in the recursive formula when GNSS is output, thereby updating t to 0 and reducing the accumulation of noise errors due to time. According to this method, if the IMU outputs 3k data continuously for 1 second, the GNSS data (10 times) will be in an invalid state and the accumulated error cannot be corrected. After the accumulated error exceeds the preset error threshold, the default confidence value of 1 will be reduced by 0.01.

In a feasible implementation, the application processor continuously detects the cumulative error of the DR positioning information. When the cumulative error detected multiple times is less than the error threshold, the confidence parameter is increased. The confidence parameter has a maximum value. value.

In this embodiment, if the cumulative error is greater than or equal to the error threshold, the confidence parameter is reduced. It can reduce the impact of large error first positioning information on the final positioning and improve the accuracy of vehicle positioning.

In one embodiment, calculating the speed error of the target vehicle based on the second positioning information and adjusting the weight parameter based on the speed error includes: obtaining the positioning of the target vehicle at multiple consecutive moments according to the second positioning information at preset time intervals; According to the positioning at the current continuous moment, the positioning at the previous continuous moment and the preset time interval, the measured speed of the target vehicle at the current continuous moment is calculated; if the measured speed of the target vehicle at the current continuous moment is not within the standard speed threshold range, it is determined that The measured speed of the target vehicle at the current continuous moment is abnormal speed; if the number of consecutive abnormal speed determinations exceeds the number threshold, the weight parameter is reduced.

Among them, the standard speed threshold range is used to characterize the reasonable speed range for normal driving of the vehicle.

Optionally, the application processor continues to obtain the positioning in the RTK positioning information, and uses the RTK positioning to calculate the vehicle speed at the current moment. The formula is as follows:
v＝||P _k，GNSS -P _k-1 ||/Δt,

Among them, Pk, GNSS and Pk-1 are the GNSS positioning at time k and the filtered positioning result at time k-1 respectively. Compare the two speeds. If v exceeds the standard speed threshold range (actual speed ± redundant value), it is considered that the RTK positioning error of this epoch is too large. After the above process has been abnormal for 5 consecutive times, the RTK positioning information is The weight parameters are reduced by 0.1.

In a feasible implementation, if the number of consecutive abnormal speed determinations does not exceed the number threshold, the weight parameter is increased and the weight parameter has a maximum value. For example, if RTK positioning is continuously used to calculate the vehicle speed at the current moment 5 times, and one of the times the speed is within the standard speed threshold range, the weight parameter of the RTK positioning information is weighted and the weight parameter is increased by 0.1.

In this embodiment, according to the second positioning information, the positioning of the target vehicle at multiple consecutive moments is obtained according to a preset time interval; based on the positioning at the current consecutive moment, the positioning at the previous consecutive moment and the preset time interval, the target vehicle is calculated The measured speed at the current continuous time; if the measured speed of the target vehicle at the current continuous time is not within the standard speed threshold range, the measured speed of the target vehicle at the current continuous time is determined to be an abnormal speed; if the number of consecutive abnormal speed determinations exceeds the number threshold , reduce the weight parameters. It can reduce the impact of the second positioning information with large errors on the final positioning and improve the accuracy of vehicle positioning.

In one embodiment, obtaining calibration positioning information based on the first positioning information and the second positioning information includes: obtaining first three-dimensional position information from the first positioning information, where the first three-dimensional position information includes a first longitude, a first latitude and the first altitude; obtain the second three-dimensional positioning information from the second positioning information, the second three-dimensional position information includes the second longitude, the second latitude and the second altitude; perform federal Kalman filtering on the first longitude and the second longitude. Filter to obtain the fused longitude, filter the first latitude and the second latitude to obtain the fused latitude, filter the first altitude and the second altitude to obtain the fused altitude; use the fused longitude, fused latitude and fused altitude as the fused three-dimensional position information, and The three-dimensional position information is fused to replace the first three-dimensional position information in the first positioning information to obtain calibration positioning information.

Optionally, the application processor obtains the first three-dimensional position information (first longitude, first latitude, and first altitude) in the DR positioning information, and the second three-dimensional position information (the second longitude, the first altitude) in the RTK positioning information. Second latitude and second altitude), perform a federal Kalman filter once for longitude, latitude and altitude respectively, that is, perform three federal Kalman filters.

The federated Kalman filter vector state is: δNk, δEk, δSk, They are northward and eastward position errors, step estimation errors and heading estimation errors respectively. Assuming that the step length and heading comply with the first-order Markov process, according to the DR principle, the system state equation is x _k =Ax _k-1 +Fω _k . in, xk-1 is the state at the previous moment; F and ωk are the system noise matrix and system process noise respectively; is the heading angle at the current moment; ΔT is the sampling interval; TS, is the relevant time.

The system observation quantity is the position difference between DR and RTK: Observation vector zk=[zn ze]T, zn and ze are the differences between the northward and eastward positions of DR and RTK respectively.

The system observation equation is z _k =Hx _k +v _k . in the formula vk is the observation noise.

After the three-dimensional federated Kalman filtering, the filtered fused longitude, fused dimension and fused altitude are respectively output to form the fused three-dimensional position information after federated Kalman filtering. Finally, the first three-dimensional position information in the DR positioning information is replaced with the fused three-dimensional position information feedback. The final fusion positioning result is output as the final three-dimensional position positioning.

In this embodiment, the first three-dimensional position information is obtained from the first positioning information. The first three-dimensional position information includes the first longitude, the first latitude and the first altitude; the second three-dimensional positioning information is obtained from the second positioning information. The two- and three-dimensional position information includes the second longitude, the second latitude and the second altitude; the first longitude and the second longitude are filtered through the federated Kalman filter to obtain the fused longitude, and the first latitude and the second latitude are filtered to obtain the fused latitude. , filter the first altitude and the second altitude to obtain the fused altitude; use the fused longitude, fused latitude and fused altitude as the fused three-dimensional position information, and replace the first three-dimensional position information in the first positioning information with the fused three-dimensional position information, we get Calibrate positioning information. Kalman filtering can be performed on the three-dimensional position information in the effective positioning information to obtain the filtered three-dimensional position information. The filtered three-dimensional position information is used to obtain the calibration positioning information, which improves the accuracy of vehicle positioning.

In one embodiment, the method further includes: obtaining third positioning information of at least three roadside devices corresponding to the target vehicle.

Among them, the roadside equipment refers to the roadside unit (RSU, Road Side Unit), which is in V2X (vehicle to everything, that is, the exchange of information between the vehicle and the outside world). It is installed on the roadside and uses cellular vehicle networking (C-V2X) technology. , a device that communicates with the on-board unit (OBU, On Board Unit) to realize vehicle identity recognition. Roadside devices can be vehicles or electronic devices installed on the roadside.

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

Specifically, the communication device can be a PC5 communication chip (V2X communication chip), and the application processor passes PC5 (ProSe5 proximity communication is a form of radio communication) communication, receiving high-precision positioning (latitude and longitude) in BSM (Basic Safety Message) basic safety information sent from other roadside devices (stationary V2X terminal vehicles or V2X roadside facilities) , altitude, 3-axis acceleration, confidence, etc.), according to geometric principles, selecting N+1 points can determine the N-dimensional space, so the 3D position information needs to be determined, and at least 3 additional V2X devices are required on the roadside in addition to positioning the vehicle. Three-dimensional location information. Each roadside three-dimensional location information includes longitude, latitude and altitude.

By calculating the distance between the target vehicle and the three RSUs, the target vehicle position is determined. The specific formula is as follows:

In the formula, X _a , Y _a , Z _a are the longitude, latitude and altitude coordinates (three _- dimensional position information) of _the target vehicle, X _{1 ,} Y ₁ , _{Z 1 ,} , Z ₃ are respectively the roadside three-dimensional position information of the three roadside devices. Obtain the distances d ₁ , d ₂ , and d ₃ between the three roadside devices and the target vehicle. The application processor calculates X _a , Y _a , and Z _a , which are the third positioning information.

In the V2X system, the application processor can obtain the real-time antenna signal RSSI value, antenna RF gain value, and V2X system real-time frequency through the network access layer interface API provided by the chip manufacturer, and use the theoretical model to calculate the speed and distance. You can also use the TDOA (Time Difference Of Arrival) measurement method to measure the signal arrival time, so that the distance measurement uses the above formula.

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 obtained based on the V2X system, which is convenient for obtaining the positioning of the target vehicle in scenarios where NAD cannot access the network and GNSS has no signal, improving the accuracy of vehicle positioning.

In one embodiment, the method further includes: if the confidence parameter meets the first preset condition and the weight parameter does not meet the second preset condition, obtaining calibration positioning information based on the first positioning information and the third positioning information.

If the confidence parameter does not meet the first preset condition and the weight parameter meets the second preset condition, the calibration positioning information is obtained according to the second positioning information and the third positioning information. If the confidence parameter does not meet the first preset condition and the weight parameter does not meet the second preset condition, the third positioning information is used as the calibration positioning information.

Optionally, if the confidence parameter meets the first preset condition and the weight parameter does not meet the second preset condition, it means that in the current road environment, NAD cannot access the network and cannot receive RTK system positioning. At this time, RTK positioning information The accuracy 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 the effective positioning information, and the calibration positioning information is obtained based on the first positioning information and the third positioning information. If the confidence parameter does not meet the first preset condition and the weight parameter meets the second preset condition, it means that there is no GNSS signal in the current road environment and the IMU cumulative error is large. At this time, the DR positioning information is accurate. The accuracy is low, so the DR positioning information cannot be used as effective positioning information. The RTK positioning information and the third positioning information are used as the effective positioning information, and the calibration positioning information is obtained based on the second positioning information and the third positioning information. If the confidence parameter does not meet the first preset condition and the weight parameter does not meet the second preset condition, it means that the current road environment is such that NAD cannot access the network and GNSS has no signal. It cannot receive RTK system positioning and the IMU has accumulated When the error is also large, the accuracy of both DR positioning information and RTK positioning information is low. Therefore, neither DR positioning information nor RTK positioning information can be used as effective positioning information. Only the third positioning information is used as effective positioning information. Directly The third positioning information is output as calibration positioning information.

In this embodiment, the error size of different types of positioning information is calculated, the confidence or weight corresponding to each positioning information is adjusted, and the effective positioning information is determined based on the confidence and weight, and the calibrated positioning information is obtained based on the effective positioning information. It can improve the accuracy of vehicle positioning.

In one embodiment, as shown in Figure 3, a vehicle positioning calibration method includes:

Obtain the original positioning data of the target vehicle obtained through the satellite signal receiving device, and the six-axis data of the target vehicle obtained through the inertial measurement device; calculate the original three-dimensional position information of the target vehicle based on the six-axis data; calculate the original three-dimensional position information of the target vehicle based on the original three-dimensional position information and the original The positioning data obtains the first positioning information; and the second positioning information of the target vehicle obtained from the real-time dynamic measurement system through the network access device is obtained. Obtain third positioning information of at least three roadside devices corresponding to the target vehicle.

Obtain the cumulative error of the first positioning information. If the cumulative error is greater than or equal to the error threshold, reduce the confidence parameter.

According to the second positioning information, the positioning of the target vehicle at multiple consecutive moments is obtained according to a preset time interval; based on the positioning at the current consecutive moment, the positioning at the previous consecutive moment and the preset time interval, the position of the target vehicle at the current consecutive moment is calculated. Measure the speed; if the measured speed of the target vehicle at the current continuous time is not within the standard speed threshold range, the measured speed of the target vehicle at the current continuous time is determined to be abnormal speed; if the number of consecutive abnormal speed determinations exceeds the threshold, the weight parameter is reduced.

If the confidence parameter satisfies the first preset condition and the weight parameter satisfies the second preset condition, obtain the first three-dimensional position information from the first positioning information. The first three-dimensional position information includes the first longitude, the first latitude and the third One altitude; obtain the second three-dimensional positioning information from the second positioning information, and the second three-dimensional position information includes the second longitude, the second latitude and the second altitude; filter the first longitude and the second longitude through the federal Kalman filter to obtain To fuse longitude, filter the first latitude and second latitude to obtain the fused latitude, and filter the first altitude and the second altitude to obtain the fused altitude; use the fused longitude, fused latitude and fused altitude as the fused three-dimensional position information, and fuse the three-dimensional The position information replaces the first three-dimensional position information in the first positioning information to obtain calibration positioning information.

If the confidence parameter meets the first preset condition and the weight parameter does not meet the second preset condition, obtain the first three-dimensional position information from the first positioning information. The first three-dimensional position information includes the first longitude, the first latitude and the first three-dimensional position information. The first altitude; obtain the third three-dimensional positioning information from the third positioning information, the third three-dimensional position information includes the third longitude, the third latitude and the third altitude; filter the first longitude and the third longitude through the federal Kalman filter Obtain the fused longitude, filter the first latitude and the third latitude to obtain the fused latitude, filter the first altitude and the third altitude to obtain the fused altitude; use the fused longitude, fused latitude and fused altitude as the fused three-dimensional position information, and fuse The three-dimensional position information replaces the first three-dimensional position information in the first positioning information to obtain calibration positioning information.

If the confidence parameter does not meet the first preset condition and the weight parameter meets the second preset condition, the third three-dimensional position information is obtained from the third positioning information, and the third three-dimensional position information includes the third longitude, the third Latitude and third altitude; obtain the second three-dimensional positioning information from the second positioning information, and the second three-dimensional position information includes the second longitude, the second latitude and the second altitude; perform federal Kalman filtering on the third longitude and the second longitude. Filter to obtain the fused longitude, filter the third latitude and the second latitude to obtain the fused latitude, filter the third altitude and the second altitude to obtain the fused altitude; use the fused longitude, fused latitude and fused altitude as the fused three-dimensional position information, and The three-dimensional position information is fused to replace the first three-dimensional position information in the first positioning information to obtain calibration positioning information.

If the confidence parameter does not meet the first preset condition and the weight parameter does not meet the second preset condition, the third positioning information is used as the calibration positioning information.

In this embodiment, in road scenarios where the status of different positioning data sources fails, different calculation methods can be used to calculate effective positioning data, and the vehicle can be positioned and calibrated to ensure the accuracy of the vehicle's positioning in different road scenarios.

It should be understood that although the steps in the flowcharts involved in the above-mentioned embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated in this article, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above embodiments may include multiple steps or stages. These steps or stages are not necessarily executed at the same time, but may be completed at different times. The execution order of these steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

Based on the same inventive concept, embodiments of the present application also provide a vehicle positioning calibration device for implementing the above-mentioned vehicle positioning calibration method. The solution to the problem provided by this device is the same as that recorded in the above method. The implementation solutions are similar, so the specific limitations in one or more vehicle positioning calibration device embodiments provided below can refer to the above limitations on the vehicle positioning calibration method, and will not be described again here.

In one embodiment, as shown in Figure 4, a vehicle positioning calibration device 400 is provided, including: a positioning acquisition module 401, a first adjustment module 402, a second adjustment module 403 and a positioning calibration module 404, wherein:

The positioning acquisition module 401 is used to obtain the first positioning information and the second positioning information obtained through multiple on-board electronic devices of the target vehicle;

The first adjustment module 402 is used to obtain the cumulative error of the first positioning information, and adjust the confidence parameter according to the cumulative error;

The second adjustment module 403 is used to calculate the speed error of the target vehicle according to the second positioning information, and adjust the weight parameters according to the speed error;

The positioning calibration module 404 is used to obtain calibration positioning information according to the first positioning information and the second positioning information if the confidence parameter meets the first preset condition and the weight parameter meets the second preset condition.

In one embodiment, the plurality of vehicle-mounted electronic devices at least include a satellite signal receiving device, an inertial measurement device and a network access device. The positioning acquisition module 401 is also used to obtain the original positioning data of the target vehicle obtained through the satellite signal receiving device, and through The six-axis data of the target vehicle obtained by the inertial measurement device; the original three-dimensional position information of the target vehicle is calculated based on the six-axis data; the first positioning information is obtained based on the original three-dimensional position information and the original positioning data; the real-time dynamic measurement is obtained from the network access device The second positioning information of the target vehicle obtained in the system.

In one embodiment, the first adjustment module 402 is also configured to reduce the confidence parameter if the cumulative error is greater than or equal to the error threshold.

In one embodiment, the second adjustment module 403 is also configured to obtain the positioning of the target vehicle at multiple consecutive moments according to the second positioning information according to a preset time interval; according to the positioning at the current consecutive moment, the positioning at the previous consecutive moment and Preset time intervals, and calculate the measured speed of the target vehicle at the current continuous time; if the measured speed of the target vehicle at the current continuous time is not within the standard speed threshold range, it is determined that the measured speed of the target vehicle at the current continuous time is an abnormal speed; if If the number of consecutive abnormal speed determinations exceeds the threshold, the weight parameter will be reduced.

In one embodiment, the positioning calibration module 404 is also configured to obtain first three-dimensional position information from the first positioning information, where the first three-dimensional position information includes a first longitude, a first latitude and a first altitude; from the second positioning information Obtain the second three-dimensional positioning information, which includes the second longitude, the second latitude and the second altitude; filter the first longitude and the second longitude through the federal Kalman filter to obtain the fused longitude, and filter the first latitude and the second altitude. Filter the second latitude to obtain the fused latitude, filter the first altitude and the second altitude to obtain the fused altitude; use the fused longitude, fused latitude and fused altitude as the fused three-dimensional position information, and replace the fused three-dimensional position information in the first positioning information The first three-dimensional position information is obtained to obtain the calibration positioning information.

In one embodiment, the position acquisition module 401 is also used to obtain the third position information of at least three roadside devices corresponding to the target vehicle.

In one embodiment, the positioning calibration module 404 is also configured to obtain calibration positioning information based on the first positioning information and the third positioning information if the confidence parameter meets the first preset condition and the weight parameter does not meet the second preset condition.

In one embodiment, the positioning calibration module 404 is also used to obtain calibration based on the second positioning information and the third positioning information if the confidence parameter does not meet the first preset condition and the weight parameter meets the second preset condition. Positioning information.

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

Each module in the above-mentioned vehicle positioning calibration device can be implemented in whole or in part by software, hardware and combinations thereof. Each of the above modules may be embedded in or independent of the processor of the computer device in the form of hardware, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

In one embodiment, a computer device is provided. The computer device can be a vehicle-mounted T-BOX (Telematics BOX) or a vehicle-mounted terminal device with network functions and V2X functions. Its internal structure diagram can be shown in Figure 5 . The computer device includes a processor, a memory, an input/output interface (Input/Output, referred to as I/O), and a communication interface. Among them, the processor, memory and input/output interface are connected through the system bus, and the communication interface is connected through the input/output interface Connect to the system bus. Wherein, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes non-volatile storage media and internal memory. The non-volatile storage medium stores operating systems, computer programs and databases. This internal memory provides an environment for the execution of operating systems and computer programs in non-volatile storage media. The computer device's database is used to store positioning data. The input/output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. The computer program implements a vehicle positioning calibration method when executed by a processor.

Those skilled in the art can understand that the structure shown in Figure 5 is only a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer equipment to which the solution of the present application is applied. The specific computer equipment can May include more or fewer parts than shown, or combine certain parts, or have a different arrangement of parts.

In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, it implements the following steps: Obtaining the first data obtained through multiple vehicle-mounted electronic devices of the target vehicle. First positioning information and second positioning information; obtain the cumulative error of the first positioning information, and adjust the confidence parameter based on the cumulative error; calculate the speed error of the target vehicle based on the second positioning information, and adjust the weight parameter based on the speed error; if the confidence The parameter satisfies the first preset condition, and the weight parameter satisfies the second preset condition, and the calibration positioning information is obtained according to the first positioning information and the second positioning information.

In one embodiment, the plurality of vehicle-mounted electronic devices at least include a satellite signal receiving device, an inertial measurement device and a network access device. When the processor executes the computer program, the following steps are also implemented: Obtain the original positioning of the target vehicle obtained through the satellite signal receiving device. data, and the six-axis data of the target vehicle obtained through the inertial measurement device; calculate the original three-dimensional position information of the target vehicle based on the six-axis data; obtain the first positioning information based on the original three-dimensional position information and original positioning data; obtain the device through network access Second positioning information of the target vehicle obtained from the real-time dynamic measurement system.

In one embodiment, when the processor executes the computer program, the following steps are also implemented: if the cumulative error is greater than or equal to the error threshold, reduce the confidence parameter.

In one embodiment, when the processor executes the computer program, the following steps are also implemented: according to the second positioning information, obtain the positioning of the target vehicle at multiple consecutive moments according to a preset time interval; according to the positioning of the current consecutive moment, the previous consecutive moment Based on the positioning and preset time interval, the measured speed of the target vehicle at the current continuous time is calculated; if the measured speed of the target vehicle at the current continuous time is not within the standard speed threshold range, the measured speed of the target vehicle at the current continuous time is determined to be abnormal. Speed; if the number of consecutive abnormal speed determinations exceeds the threshold, the weight parameter will be reduced.

In one embodiment, when the processor executes the computer program, the processor further implements the following steps: obtaining first three-dimensional position information from the first positioning information, where the first three-dimensional position information includes a first longitude, a first latitude and a first altitude; from The second positioning information obtains the second three-dimensional positioning information. The second three-dimensional position information includes the second longitude, the second latitude and the second altitude; the first longitude and the second longitude are filtered through the federal Kalman filter to obtain the fused longitude. Filter the first latitude and the second latitude to obtain the fused latitude, filter the first altitude and the second altitude to obtain the fused altitude; use the fused longitude, fused latitude and fused altitude as the fused three-dimensional position information, and replace the fused three-dimensional position information with the fused three-dimensional position information. The first three-dimensional position information in a certain positioning information is used to obtain the calibration positioning information.

In one embodiment, when the processor executes the computer program, the processor also implements the following steps: obtaining third positioning information of at least three roadside devices corresponding to the target vehicle.

In one embodiment, the processor also implements the following steps when executing the computer program: if the confidence parameter meets the first preset condition and the weight parameter does not meet the second preset condition, obtain the information based on the first positioning information and the third positioning information. Calibrate positioning information.

In one embodiment, the processor also implements the following steps when executing the computer program: if the confidence parameter does not meet the first preset condition and the weight parameter meets the second preset condition, based on the second positioning information and the third Positioning information obtains calibration positioning information.

In one embodiment, the processor also implements the following steps when executing the computer program: if the confidence parameter does not meet the first preset condition and the weight parameter does not meet the second preset condition, use the third positioning information as the calibration Positioning information.

In one embodiment, a computer-readable storage medium is provided with a computer program stored thereon. When the computer program is executed by a processor, the following steps are implemented: obtaining first positioning information obtained through multiple on-board electronic devices of the target vehicle. and the second positioning information; obtain the cumulative error of the first positioning information, and adjust the confidence parameter based on the cumulative error; calculate the speed error of the target vehicle based on the second positioning information, and adjust the weight parameter based on the speed error; if the confidence parameter satisfies the A preset condition, and the weight parameter satisfies the second preset condition, and the calibration positioning information is obtained according to the first positioning information and the second positioning information.

In one embodiment, the plurality of vehicle-mounted electronic devices at least include a satellite signal receiving device, an inertial measurement device and a network access device. When the computer program is executed by the processor, the following steps are also implemented: Obtain the original data of the target vehicle obtained through the satellite signal receiving device. Positioning data, as well as the six-axis data of the target vehicle obtained through the inertial measurement device; the original three-dimensional position information of the target vehicle is calculated based on the six-axis data; the first positioning information is obtained based on the original three-dimensional position information and the original positioning data; accessed through the network The device obtains the second positioning information of the target vehicle from the real-time dynamic measurement system.

In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: if the cumulative error is greater than or equal to the error threshold, reduce the confidence parameter.

In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: according to the second positioning information, obtain the positioning of the target vehicle at multiple consecutive moments according to a preset time interval; according to the positioning at the current consecutive moment, the previous consecutive moment Moment positioning and preset time intervals are used to calculate the measured speed of the target vehicle at the current continuous time; if the measured speed of the target vehicle at the current continuous time is not within the standard speed threshold range, it is determined that the measured speed of the target vehicle at the current continuous time is Abnormal speed; if the number of consecutive abnormal speed determinations exceeds the threshold, the weight parameter will be reduced.

In one embodiment, when executed by the processor, the computer program also implements the following steps: obtaining first three-dimensional position information from the first positioning information, where the first three-dimensional position information includes a first longitude, a first latitude, and a first altitude; The second three-dimensional positioning information is obtained from the second positioning information. The second three-dimensional position information includes the second longitude, the second latitude and the second altitude; the first longitude and the second longitude are filtered through the federated Kalman filter to obtain the fused longitude, Filter the first latitude and the second latitude to obtain the fused latitude, filter the first altitude and the second altitude to obtain the fused altitude; use the fused longitude, fused latitude and fused altitude as the fused three-dimensional position information, and replace the fused three-dimensional position information Calibration positioning information is obtained from the first three-dimensional position information in the first positioning information.

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

In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: if the confidence parameter meets the first preset condition and the weight parameter does not meet the second preset condition, based on the first positioning information and the third positioning information Get calibration positioning information.

In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: if the confidence parameter does not meet the first preset condition and the weight parameter meets the second preset condition, based on the second positioning information and the third preset condition, 3. Positioning information to obtain calibration positioning information.

In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: if the confidence parameter does not meet the first preset condition and the weight parameter does not meet the second preset condition, use the third positioning information as Calibrate positioning information.

In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the following steps: acquiring first positioning information and second positioning obtained through a plurality of vehicle-mounted electronic devices of a target vehicle. information; obtain the cumulative error of the first positioning information, and adjust the confidence parameter based on the cumulative error; calculate the speed error of the target vehicle based on the second positioning information, and adjust the weight parameter based on the speed error; if the confidence parameter meets the first preset condition , and the weight parameter meets the second preset condition, and the calibration positioning information is obtained according to the first positioning information and the second positioning information.

In one embodiment, the plurality of vehicle-mounted electronic devices at least include a satellite signal receiving device, an inertial measurement device and a network access device. When the computer program is executed by the processor, the following steps are also implemented: Obtain the original data of the target vehicle obtained through the satellite signal receiving device. Positioning data, as well as six-axis data of the target vehicle obtained through an inertial measurement device; according to The six-axis data is calculated to obtain the original three-dimensional position information of the target vehicle; the first positioning information is obtained based on the original three-dimensional position information and the original positioning data; and the second positioning information of the target vehicle obtained from the real-time dynamic measurement system through the network access device is obtained.

In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: if the cumulative error is greater than or equal to the error threshold, reduce the confidence parameter.

In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: according to the second positioning information, obtain the positioning of the target vehicle at multiple consecutive moments according to a preset time interval; according to the positioning at the current consecutive moment, the previous consecutive moment Moment positioning and preset time intervals are used to calculate the measured speed of the target vehicle at the current continuous time; if the measured speed of the target vehicle at the current continuous time is not within the standard speed threshold range, it is determined that the measured speed of the target vehicle at the current continuous time is Abnormal speed; if the number of consecutive abnormal speed determinations exceeds the threshold, the weight parameter will be reduced.

In one embodiment, when executed by the processor, the computer program also implements the following steps: obtaining first three-dimensional position information from the first positioning information, where the first three-dimensional position information includes a first longitude, a first latitude, and a first altitude; The second three-dimensional positioning information is obtained from the second positioning information. The second three-dimensional position information includes the second longitude, the second latitude and the second altitude; the first longitude and the second longitude are filtered through the federated Kalman filter to obtain the fused longitude, Filter the first latitude and the second latitude to obtain the fused latitude, filter the first altitude and the second altitude to obtain the fused altitude; use the fused longitude, fused latitude and fused altitude as the fused three-dimensional position information, and replace the fused three-dimensional position information Calibration positioning information is obtained from the first three-dimensional position information in the first positioning information.

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

In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: if the confidence parameter meets the first preset condition and the weight parameter does not meet the second preset condition, based on the first positioning information and the third positioning information Get calibration positioning information.

In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: if the confidence parameter does not meet the first preset condition and the weight parameter meets the second preset condition, based on the second positioning information and the third preset condition, 3. Positioning information to obtain calibration positioning information.

In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: if the confidence parameter does not meet the first preset condition and the weight parameter does not meet the second preset condition, use the third positioning information as Calibrate positioning information.

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 used for analysis, stored data, displayed data, etc.) involved in this application are all It is information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with the relevant laws, regulations and standards of relevant countries and regions.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage. In the media, when executed, the computer program may include the processes of the above method embodiments. Any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetic variable memory (Magnetoresistive memory) Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory may include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM). The databases involved in the various embodiments provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include blockchain-based distributed databases, etc., but are not limited thereto. The processors involved in the various embodiments provided in this application may be general-purpose processors, central processing units Processors, graphics processors, digital signal processors, programmable logic devices, data processing logic devices based on quantum computing, etc., are not limited to these.

The technical features of the above-described embodiments can be combined in any way. To simplify the description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, All should be considered to be within the scope of this manual.

The above-described embodiments only express several implementation modes of the present application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent application. It should be noted that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all fall within the protection scope of the present application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims (15)

1. A vehicle positioning calibration method, characterized in that the method includes:

   Obtaining first positioning information and second positioning information, the first positioning information and the second positioning information being obtained through multiple vehicle-mounted electronic devices of the target vehicle;

   Obtain the cumulative error of the first positioning information, and adjust the confidence parameter according to the cumulative error;

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

   When the confidence parameter satisfies the first preset condition and the weight parameter satisfies the second preset condition, calibration positioning information is obtained according to the first positioning information and the second positioning information.
2. The method according to claim 1, wherein the plurality of vehicle-mounted electronic devices at least include a satellite signal receiving device, an inertial measurement device and a network access device, and the obtaining the first positioning information and the second positioning information includes:

   Obtain the original positioning data and six-axis data of the target vehicle, the original positioning data is obtained through the satellite signal receiving device, and the six-axis data is obtained through the inertial measurement device;

   Calculate the original three-dimensional position information of the target vehicle based on the six-axis data;

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

   The second positioning information of the target vehicle is obtained, and the second positioning information is obtained from the real-time dynamic measurement system through the network access device.
3. The method of claim 1, wherein obtaining the cumulative error of the first positioning information and adjusting the confidence parameter according to the cumulative error includes:

   If the cumulative error is greater than or equal to the error threshold, the confidence parameter is reduced.
4. The method of claim 1, wherein calculating the speed error of the target vehicle based on the second positioning information, and adjusting weight parameters based on the speed error includes:

   According to the second positioning information, obtain the positioning of the target vehicle at multiple consecutive moments according to a preset time interval;

   Calculate the measured speed of the target vehicle at the current continuous time based on the positioning at the current continuous time, the positioning at the previous continuous time and the preset time interval;

   When the measured speed of the target vehicle at the current continuous time is not within the standard speed threshold range, determine that the measured speed of the target vehicle at the current continuous time is an abnormal speed;

   When the number of consecutive abnormal speed determinations exceeds the number threshold, the weight parameter is reduced.
5. The method according to claim 1, characterized in that, obtaining calibration positioning information according to the first positioning information and the second positioning information includes:

   Obtain first three-dimensional position information from the first positioning information, where the first three-dimensional position information includes a first longitude, a first latitude and a first altitude;

   Obtain second three-dimensional positioning information from the second positioning information, where the second three-dimensional position information includes a second longitude, a second latitude, and a second altitude;

   The first longitude and the second longitude are filtered through federated Kalman filtering to obtain a fused longitude, the first latitude and the second latitude are filtered to obtain a fused latitude, and the first altitude and the second longitude are filtered to obtain a fused latitude. Filter the second altitude to obtain the fused altitude;

   The fused longitude, fused latitude and fused altitude are used as fused three-dimensional position information, and the first three-dimensional position information in the first positioning information is replaced with the fused three-dimensional position information to obtain the calibrated positioning information.
6. The method of claim 1, further comprising:

   Obtain third positioning information of at least three roadside devices corresponding to the target vehicle.
7. The method of claim 6, further comprising:

   When the confidence parameter meets the first preset condition and the weight parameter does not meet the second preset condition, calibration positioning information is obtained according to the first positioning information and the third positioning information.
8. The method of claim 6, further comprising:

   When the confidence parameter does not meet the first preset condition and the weight parameter meets the second preset condition, calibration positioning information is obtained according to the second positioning information and the third positioning information.
9. The method of claim 6, further comprising:

   When the confidence parameter does not meet the first preset condition and the weight parameter does not meet the second preset condition, the third positioning information is used as calibration positioning information.
10. A vehicle positioning calibration device, characterized in that the device includes:

    A positioning acquisition module, configured to obtain first positioning information and second positioning information, the first positioning information and the second positioning information being obtained through multiple vehicle-mounted electronic devices of the target vehicle;

    A first adjustment module, configured to obtain the cumulative error of the first positioning information and adjust the confidence parameter according to the cumulative error;

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

    A positioning calibration module, configured to obtain calibration positioning information according to the first positioning information and the second positioning information if the confidence parameter satisfies a first preset condition and the weight parameter satisfies a second preset condition.
11. The device according to claim 10, characterized in that the positioning acquisition module is also used to obtain the third positioning information of at least three roadside devices corresponding to the target vehicle.
12. The device according to claim 11, wherein the positioning calibration module is further configured to: when the confidence parameter meets a first preset condition and the weight parameter does not meet a second preset condition, Obtain calibration positioning information according to the first positioning information and the third positioning information;

    The positioning calibration module is also configured to, when the confidence parameter does not meet the first preset condition and the weight parameter meets the second preset condition, calculate the positioning information based on the second positioning information and the third positioning condition. Information acquisition calibration positioning information;

    The positioning calibration module is also configured to use the third positioning information as calibration positioning information when the confidence parameter does not meet the first preset condition and the weight parameter does not meet the second preset condition.
13. A computer device includes a memory and a processor, the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method described in any one of claims 1 to 9 are implemented.
14. A computer-readable storage medium with a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 9 are implemented.
15. A computer program product, comprising a computer program, characterized in that, when executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 9.