Positioning method, vehicle-mounted device, and computer-readable storage medium

Abstract

The present application provides a positioning method, a vehicle-mounted device, and a computer-readable storage medium. The method includes: a positioning device performs forward and reverse calculations on sensor data and performs fusion filtering to obtain a first combined positioning result, wherein the first combined positioning result indicates the vehicle's motion state at multiple moments, wherein the motion state includes vehicle position, vehicle speed, and vehicle attitude; the positioning device obtains, based on the first combined positioning result, an estimated value of the vehicle's initial state variables, the vehicle's position information at multiple moments, and the difference between the vehicle's motion state increments; and the positioning device corrects the first combined positioning result based on the estimated value of the vehicle's initial state variables, the vehicle's position information at multiple moments, and the difference between the vehicle's motion state increments to obtain a target combined positioning result. Implementing the present application can improve post-processing positioning accuracy and meet the high-precision and high-reliability positioning requirements for vehicles when GNSS signals are interfered with.

Description

Technical Field

[0001] The present application relates to the field of intelligent connected vehicles (ICV), and in particular to a positioning method, a vehicle-mounted device, and a computer-readable storage medium. Background Art

[0002] With the development of autonomous driving technology, autonomous driving has become a crucial means of future mobility. The current autonomous driving system framework comprises core components such as the perception module, positioning module, planning and decision-making module, control module, high-precision map module, and cloud computing module. The perception module perceives the physical world through sensors. The sensors encode the physical world into specific data and transmit it to the perception module, which then uses relevant algorithms to extract a model of the physical world. The positioning module uses the sensor data from the perception module and, through a positioning algorithm, outputs the vehicle's motion state, including its position, velocity, and attitude relative to the world coordinate system. The planning and decision-making module integrates and makes decisions based on data from the perception module, positioning module, and high-precision map, ultimately sending the output to the control module, which then controls the vehicle's actuators. The cloud computing module also receives all vehicle data in real time. Once the cloud computing module determines the vehicle's location, it leverages the computing power and databases in the cloud to provide enhanced driving capabilities.

[0003] In autonomous driving systems, the positioning module is a core functional module. Its output is used for path planning and vehicle control, while also assisting the perception system to achieve more accurate detection and tracking results.

[0004] Most existing technology solutions use multi-sensor fusion positioning technology to determine vehicle motion status, such as position, speed, and attitude. Currently, this method, which uses a conventional inertial measurement unit (IMU) and global navigation satellite system (GNSS) fusion to determine vehicle motion status, cannot meet the high-precision and high-reliability positioning requirements of autonomous driving in typical urban road scenarios, such as urban canyons and GNSS signal obstruction. Summary of the Invention

[0005] The present application provides a positioning method, system and related devices, which can optimize the results of multi-sensor fusion processing by constructing a global variable model and obtaining a global optimal solution when the GNSS signal is interfered with, thereby improving positioning accuracy.

[0006] In a first aspect, a positioning method is provided, the method comprising: a positioning device acquiring a first combined positioning result, wherein the first combined positioning result indicates a vehicle motion state at multiple moments, the motion state including vehicle position, vehicle speed, and vehicle posture; the first combined positioning result is a combined positioning result obtained by fusing and filtering the second combined positioning result and the third combined positioning result, the second combined positioning result is a result obtained by forward solving data acquired by the vehicle's sensor, the third combined positioning result is a result obtained by reverse solving data acquired by the vehicle's sensor, the data including the position information of the vehicle acquired by the GNSS sensor at the multiple moments; the positioning device acquires a first combined positioning result based on the first combined positioning result. The combined positioning result obtains an estimated value of the vehicle's starting state variable, the position information of the vehicle at the multiple moments, and the difference between the vehicle's motion state increments, the estimated value of the vehicle's starting state variable is the motion state of the vehicle at the starting moment in the first combined positioning result, and the difference between the vehicle's motion state increments is the difference between the motion state increments between two adjacent moments in the multiple moments to be estimated and the motion state increments between the two adjacent moments in the first combined positioning result; the positioning device corrects the first combined positioning result according to the estimated value of the vehicle's starting state variable, the position information of the vehicle at the multiple moments, and the difference between the vehicle's motion state increments to obtain a target combined positioning result.

[0007] In implementing the embodiment of the present application, after obtaining the first combined positioning result through conventional post-processing, the vehicle's motion state variables, namely the vehicle's position, velocity, and posture, are modeled, and a residual model is constructed to achieve global optimization thereof. By performing multiple iterative solutions on the constructed model, the conventional post-processing results can be corrected and optimized, thereby improving the post-processing positioning accuracy, meeting the high-precision and high-reliability positioning requirements of the vehicle when the GNSS signal is interfered with, and ensuring the safety of the vehicle during driving.

[0008] In one possible implementation, the positioning device determines the combined positioning result that satisfies the target constraint condition as the target combined positioning result, wherein the target constraint condition is that the sum of the first difference, the second difference and the third difference is the minimum, the first difference is the difference between the starting state value of the vehicle to be estimated and the starting state value in the first combined positioning result, the second difference is the difference between the position information of the vehicle to be estimated and the position information of the vehicle obtained by the GNSS sensor, and the third difference is the difference between the motion state increment between the two adjacent moments to be estimated of the vehicle and the motion state change increment between the two adjacent moments in the first combined positioning result.

[0009] In implementing the embodiment of the present application, after the positioning device constructs a model for the global state variables, it obtains the optimal solution for all state variables, that is, obtains the state variables that meet the target constraints, thereby optimizing and correcting the first combined positioning result to obtain the target combined positioning result. This can effectively improve the post-processing positioning accuracy and ensure vehicle driving safety.

[0010] In another possible implementation, the motion state increment between the two adjacent moments to be estimated of the vehicle is the difference between the estimated motion state value of the vehicle at the jth moment and the estimated motion state value of the vehicle at the ith moment; the motion state increment between the two adjacent moments in the first combined positioning result is the difference between the motion state value of the vehicle at the jth moment and the motion state value of the vehicle at the ith moment; the i-th moment is earlier than the j-th moment by a preset time length.

[0011] In implementing the embodiment of the present application, after obtaining the first combined positioning result, the positioning device can obtain the motion state value of the vehicle at different times, and predict the motion state estimation value of the vehicle at different times, and then respectively obtain the increment of the motion state estimation value and the increment of the motion state value between two adjacent moments of the vehicle at different times, and finally obtain the difference between the motion state increments of the vehicle at two adjacent moments, and use the difference as a constraint condition in the constructed residual model to ensure that the constructed model can achieve global optimization of the first combined positioning result and improve positioning accuracy.

[0012] In another possible implementation, the motion state increment between two adjacent moments among the multiple moments to be estimated includes a position information increment to be estimated, and the position information increment to be estimated is the difference between the position difference to be estimated and the initial speed integral value, wherein the position information difference to be estimated is the difference between the position estimate of the vehicle at the jth moment and the position estimate of the vehicle at the i-th moment, and the initial speed integral value is the speed integral of the vehicle from the i-th moment to the j-th moment, and the i-th moment is earlier than the j-th moment by a preset time length.

[0013] When implementing the embodiments of the present application, when the GNSS signal is interfered with, the vehicle position information obtained by the sensor will not be accurate enough, which will affect the positioning accuracy. However, the vehicle speed is constrained by the wheel speed meter and other factors, and its accuracy is higher than that of the vehicle position. Therefore, the integral increment of the initial speed between two adjacent moments can be used as a position constraint, ultimately improving the post-processing positioning accuracy.

[0014] In another possible implementation, the motion state increment between two adjacent moments among the multiple moments to be estimated includes a position information increment to be estimated, and the position information increment to be estimated is the difference between the position difference to be estimated and the relative speed integral value, wherein the position information difference to be estimated is the difference between the position estimate of the vehicle at the jth moment and the position estimate of the vehicle at the i-th moment, and the relative speed integral value is the speed integral of the vehicle from the i-th moment to the j-th moment without considering the speed of the vehicle at the i-th moment, and the i-th moment is earlier than the j-th moment by a preset time length.

[0015] In implementing the embodiments of the present application, the positioning device utilizes the characteristics that the vehicle speed is constrained by the wheel speed meter and has high relative accuracy in a short period of time. When the GNSS signal is interfered with and the position constraint is inaccurate, the initial speed between two adjacent moments is further optimized and the integral increment of the relative speed between the two adjacent moments is used as the position constraint, thereby further improving the post-processing positioning accuracy.

[0016] In another possible implementation, the vehicle sensor includes at least one of an IMU, a wheel speed meter, and a laser radar.

[0017] In a second aspect, an embodiment of the present application provides a positioning device, which may include: an acquisition unit for acquiring a first combined positioning result, wherein the first combined positioning result indicates the vehicle motion state at multiple moments, and the motion state includes vehicle position, vehicle speed, and vehicle posture; the first combined positioning result is a combined positioning result obtained by fusion filtering the second combined positioning result and the third combined positioning result; the second combined positioning result is a structure obtained by forward solving the data obtained by the vehicle's sensor; the third combined positioning result is a result obtained by reverse solving the data obtained by the vehicle's sensor, and the data includes the position information of the vehicle obtained by the GNSS sensor at the multiple moments; an information determination unit, used to According to the first combined positioning result, an estimated value of the vehicle's initial state variable, the position information of the vehicle at the multiple moments, and the difference between the vehicle's motion state increments are obtained, the estimated value of the vehicle's initial state variable is the motion state of the vehicle at the initial moment in the first combined positioning result, and the difference between the vehicle's motion state increments is the difference between the motion state increments between two adjacent moments in the multiple moments to be estimated and the motion state increments between the two adjacent moments in the first combined positioning result; a correction unit is used to correct the first combined positioning result according to the estimated value of the vehicle's initial state variable, the position information of the vehicle at the multiple moments, and the difference between the vehicle's motion state increments, so as to obtain a target combined positioning result.

[0018] In one possible implementation, the correction unit is specifically used to: determine the combined positioning result that meets the target constraint condition as the target combined positioning result, wherein the target constraint condition is that the sum of the first difference, the second difference and the third difference is the minimum, the first difference is the difference between the starting state value of the vehicle to be estimated and the starting state value in the first combined positioning result, the second difference is the difference between the position information of the vehicle to be estimated and the position information of the vehicle obtained by the GNSS sensor, and the third difference is the difference between the motion state increment between the two adjacent moments to be estimated of the vehicle and the motion change increment between the two adjacent moments in the first combined positioning result.

[0019] In another possible implementation, the motion state increment between the two adjacent moments to be estimated of the vehicle is the difference between the estimated motion state value of the vehicle at the jth moment and the estimated motion state value of the vehicle at the ith moment; the motion state increment between the two adjacent moments in the first combined positioning result is the difference between the motion state value of the vehicle at the jth moment and the motion state value of the vehicle at the ith moment; the i-th moment is earlier than the j-th moment by a preset time length.

[0020] In another possible implementation, the motion state increment between two adjacent moments among the multiple moments to be estimated includes a position information increment to be estimated; the position information increment to be estimated is the difference between the position difference to be estimated and the initial speed integral value, wherein the position information difference to be estimated is the difference between the position estimate of the vehicle at the j moment and the position estimate of the vehicle at the i moment; the initial speed integral value is the speed integral of the vehicle from the i moment to the j moment; the i moment is earlier than the j moment by a preset time length.

[0021] In another possible implementation, the motion state increment between two adjacent moments among the multiple moments to be estimated includes a position information increment to be estimated; the position information increment to be estimated is the difference between the position difference to be estimated and the relative speed integral value, wherein the position information difference to be estimated is the difference between the position estimate of the vehicle at the jth moment and the position estimate of the vehicle at the i-th moment; the relative speed integral value is the speed integral of the vehicle from the i-th moment to the j-th moment without considering the speed of the vehicle at the i-th moment; the i-th moment is earlier than the j-th moment by a preset time length.

[0022] In another possible implementation, the sensor of the vehicle includes at least one of an inertial measurement unit (IMU), a wheel speed meter, and a laser radar.

[0023] In a third aspect, an embodiment of the present application further provides a positioning device, which may include a memory and a processor, wherein the memory is used to store a computer program, and the processor is configured to call the computer program so that the positioning device executes the method provided by the first aspect or any one of the implementations of the first aspect.

[0024] In a fourth aspect, an embodiment of the present application further provides a vehicle, comprising the positioning device described in the second aspect, any one of the implementations of the second aspect, the third aspect, or any one of the implementations of the third aspect.

[0025] In a fifth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer storage medium stores a computer program, and when the computer program is executed by a processor, the method of implementing the above-mentioned first aspect or any one of the implementation methods of the above-mentioned first aspect is implemented.

[0026] In a sixth aspect, an embodiment of the present application further provides a computer program, which, when executed by a processor, implements the positioning method provided by the first aspect or any one of the implementations of the first aspect.

[0027] Based on the implementation methods provided in the above aspects, this application can also be further combined to provide more implementation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1a A schematic diagram of the inventive concept provided in the embodiments of the present application;

[0029] Figure 1b A functional block diagram of a vehicle 100 provided in an embodiment of the present application;

[0030] Figure 2 A schematic structural diagram of a sensor subsystem provided in an embodiment of the present application;

[0031] Figure 3a A flowchart of a positioning method provided in an embodiment of the present application;

[0032] Figure 3b Result graphs of tests using the GNSS method, the bidirectional filtering method, and the method of the present application on a tree-lined road with severe GNSS signal obstruction, provided in an embodiment of the present application;

[0033] Figure 3c Result graphs of tests performed using the GNSS method, the bidirectional filtering method, and the method of the present application in a scenario without a GNSS signal, provided in an embodiment of the present application;

[0034] Figure 3dResult graphs of tests performed using the GNSS method, the bidirectional filtering method, and the method of the present application in a scenario without a GNSS signal, provided in an embodiment of the present application;

[0035] Figure 4 A schematic structural diagram of a positioning device provided in an embodiment of the present application;

[0036] Figure 5 A schematic structural diagram of another positioning device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application are clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0038] The terms "first" and "second" in the description and drawings of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects. In addition, the terms "including" and "having" and any variations thereof mentioned in the description of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices. It should be noted that in the embodiments of this application, words such as "exemplarily" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design method described in the embodiments of this application as "exemplarily" or "for example" should not be construed as being superior or more advantageous than other embodiments or design solutions. Specifically, the use of words such as "exemplarily" or "for example" is intended to present the relevant concepts in a concrete way. In the embodiments of this application, "A and / or B" means both A and B, or A or B. “A, and / or B, and / or C” means any one of A, B, and C, or any two of A, B, and C, or A, B, and C.

[0039] In order to better understand the technical solutions described in this application, the following first explains the relevant technical terms involved in the embodiments of this application:

[0040] The positioning method provided in the embodiments of the present application can be applied to the application scenario of vehicle positioning, and can also be applied to other application scenarios, such as vehicle navigation.

[0041] In some feasible embodiments, the vehicles involved in the embodiments of the present application may be autonomous vehicles or non-autonomous vehicles. Autonomous vehicles, also known as driverless cars, computer-driven cars, or wheeled mobile robots, are intelligent vehicles that are unmanned by computer systems. In practical applications, autonomous vehicles rely on artificial intelligence, visual computing, radar, monitoring devices, and global positioning systems to enable computer equipment to automatically and safely operate the motor vehicle without any active human intervention.

[0042] The execution subject of the positioning method provided in the embodiment of the present application can be an electronic device in a vehicle, or a positioning device in the above electronic device. For example, the above positioning device can be implemented by software and / or hardware.

[0043] The electronic devices mentioned above in the embodiments of the present application may include but are not limited to: a main control computer (or industrial computer) in a vehicle.

[0044] The inertial measurement unit (IMU) involved in the embodiments of this application is a device used to measure the three-axis attitude angle and acceleration of a carrier. Typically, an IMU may include, but is not limited to: three rate gyroscopes and three linear accelerometers; the gyroscopes and accelerometers are directly fixed to the carrier (such as a vehicle). Among them, the gyroscopes and accelerometers are used to measure the angular motion information and linear motion information of the carrier, respectively, so that the computer equipment can calculate the vehicle's heading, attitude, speed, and position information based on these measurement data.

[0045] Multi-sensor fusion positioning technology requires determining the vehicle's motion state, including its position, velocity, and attitude. Current methods, which use a conventional inertial measurement unit (IMU) and a global navigation satellite system (GNSS) to determine vehicle motion, struggle to meet the high-precision and high-reliability positioning requirements for autonomous driving in typical urban road scenarios, such as urban canyons and GNSS signal obstruction. Other common fusion positioning solutions also suffer from high computational burdens and limited engineering feasibility.

[0046] Based on the above problems, this application proposes a new positioning method. The concept of this method can be as follows Figure 1aAs shown. First, sensor data is acquired. For example, the sensor data sources may include data acquired by an IMU, data acquired by a GNSS, and data acquired by a WSS. Then, the acquired sensor data is forward solved, that is, the sensor data is processed in sequence according to a predetermined solution logic to obtain a second combined positioning result. The sensor data is reverse solved, that is, the sensor data is processed in the opposite order of the predetermined solution logic to obtain a third combined positioning result. Then, the second combined positioning result and the third combined positioning result are fused and filtered to obtain a first combined positioning result, wherein the first combined positioning result indicates the vehicle motion state at multiple moments. Finally, the first combined positioning result is optimized to obtain a target combined positioning result. For example, the optimization method may include: obtaining an estimated value of the vehicle's initial state variable, the difference between the vehicle's position information at multiple moments, and the vehicle's motion state increment based on the vehicle's motion state at multiple moments, so that the vehicle's motion state can be corrected by the estimated value of the vehicle's initial state variable, the difference between the vehicle's position information at multiple moments, and the vehicle's motion state increment. This implementation, building on conventional post-processing of multi-sensor data, further constructs a residual model for the vehicle's motion state variables to achieve global optimization, obtaining a globally optimal solution. This approach then corrects and optimizes the conventional post-processing results, improving post-processed positioning accuracy. This approach can meet the high-precision, high-reliability positioning requirements for autonomous driving in typical urban road scenarios, such as urban canyons and GNSS signal obstruction. Because this method can meet the high-precision, high-reliability positioning requirements for autonomous driving in typical urban road scenarios, such as urban canyons and GNSS signal obstruction, it facilitates the construction of high-precision maps. This improves driving safety when a vehicle navigates using these high-precision maps.

[0047] Figure 1b 1 is a functional block diagram of a vehicle 100 provided in an embodiment of the present application. In some embodiments, the vehicle 100 can be configured in a fully autonomous driving mode, a partially autonomous driving mode, or a manual driving mode.

[0048] In the embodiment of the present application, the vehicle 100 may include at least the following subsystems: a sensing subsystem 101, a decision subsystem 102, and an execution subsystem 103.

[0049] The sensing subsystem 101 may include at least one sensor. Specifically, the sensors may include internal sensors and external sensors. Internal sensors are used to monitor the vehicle's status and may include at least one of a vehicle speed sensor, an acceleration sensor, and an angular velocity sensor. External sensors are primarily used to monitor the external environment surrounding the vehicle and, for example, may include video sensors and radar sensors. Video sensors are used to acquire and monitor image data of the vehicle's surroundings. Radar sensors are used to acquire and monitor electromagnetic wave data of the vehicle's surroundings, primarily by emitting electromagnetic waves and then receiving electromagnetic waves reflected by surrounding objects to detect various data such as the distance between the vehicle and surrounding objects and their appearance.

[0050] For example, multiple radar sensors may be distributed throughout the exterior of the vehicle 100. A subset of the multiple radar sensors may be coupled to the front of the vehicle 100 to locate objects in front of the vehicle 100. One or more additional radar sensors may be located at the rear of the vehicle 100 to locate objects behind the vehicle 100 when the vehicle 100 is backing up. Other radar sensors may be located on the sides of the vehicle 100 to locate objects, such as other vehicles 100, that are approaching the vehicle 100 from the side. For example, a laser radar (light detection and ranging, LIDAR) sensor may be mounted on the vehicle 100, for example, by mounting the LIDAR sensor in a rotating structure mounted on top of the vehicle 100. The rotating LIDAR sensor may then transmit light signals around the vehicle 100 in a 360° pattern, thereby continuously mapping all objects around the vehicle 100 as the vehicle 100 moves.

[0051] For example, an imaging sensor, such as a camera, camcorder, or other similar image acquisition sensor, may be mounted on the vehicle 100 to capture images as the vehicle 100 moves. Multiple imaging sensors may be placed on all sides of the vehicle 100 to capture images around the vehicle 100 in a 360° pattern. The imaging sensor may capture images not only in the visible spectrum but also in the infrared spectrum.

[0052] For example, a Global Positioning System (GPS) sensor may be located on vehicle 100 to provide the controller with geographic coordinates and the time the coordinates were generated related to the location of vehicle 100. The GPS includes an antenna for receiving GPS satellite signals and a GPS receiver coupled to the antenna. For example, when an object is observed in an image or by another sensor, the GPS can provide the geographic coordinates and time of the object's discovery.

[0053] In some embodiments, as Figure 2As shown, the above-mentioned sensor subsystem 101 may include: an inertial measurement unit (IMU) 201, a global satellite navigation system (GNSS) 202, a laser radar (Lidar) 203, a wheel speed sensor (WSS) 204, a fusion positioning processing unit 205 and an antenna 206, wherein,

[0054] The inertial measurement unit (IMU) 201 can output the vehicle's angular velocity and acceleration at high frequency;

[0055] The global navigation satellite system (GNSS) 202 can output the position and velocity of the GNSS corresponding to the phase center of the antenna 206;

[0056] The laser radar 203 obtains a large number of point clouds by scanning the vehicle's surroundings with a laser beam. By matching it with a pre-recorded high-precision point cloud map, it can output the position and heading angle of the laser head installation location;

[0057] The wheel speed meter 204 outputs the forward speed of the tire at the contact point with the ground.

[0058] The processing unit of the above-mentioned sensor outputs data in real time, and the data is transmitted to the fusion positioning processing unit 205 (such as an embedded platform, etc.) via a wired method (such as a serial port, an Ethernet port, a controller area network (CAN) bus, etc.). The fusion positioning processing unit 205 obtains the target combined positioning result through the positioning method proposed in this application.

[0059] The decision-making subsystem 102 may include at least an electronic control unit (ECU), a map database, and an object database. Specifically, an ECU, also known as a "driving computer" or "onboard computer," is a microcomputer controller specifically designed for use in vehicles. An ECU consists of a microprocessor (MCU), memory (e.g., read-only memory (ROM) or random-access memory (RAM)), input / output interfaces, an analog-to-digital converter, and large-scale integrated circuits (ICs) such as those for shaping and driving. In some feasible embodiments, the decision-making subsystem 102 may also include a communication unit. The ECU is a computing device used to control the vehicle 100 and perform decision-making and control functions. For example, the ECU is connected to a bus and communicates with other devices via the bus. For example, the ECU can acquire information from internal and external sensors, the map database, and the HMI, and output corresponding information to the HMI and actuators. For example, the ECU loads programs stored in ROM into RAM, and the CPU executes the programs in RAM to implement autonomous driving functions. In practical applications, the decision-making subsystem 102 may include one or multiple ECUs. The ECU can identify static and / or dynamic objects around the vehicle, for example, by acquiring object monitoring results based on external sensors. The ECU monitors the speed, direction, and other attributes of surrounding objects. It also acquires information about the vehicle's own state, based on the output of internal sensors. Based on this information, the ECU plans the driving path and outputs corresponding control signals to the actuators, which then execute the corresponding lateral and longitudinal movements.

[0060] In the embodiment of the present application, the positioning device may include but is not limited to the above-mentioned ECU.

[0061] In an embodiment of the present application, the communication unit is used to perform V2X (vehicle to everything, i.e., Vehicle to X) communication. For example, data interaction can be performed with surrounding vehicles, roadside communication equipment, and cloud servers. For example, a radio coupled to an antenna may be located in the vehicle 100, thereby providing wireless communication for the system. The radio is used to operate any wireless communication technology or wireless standard, including but not limited to WiFi (IEEE 802.11), cellular (for example, Global System for Mobile Communications (GSM), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Long Term Evolution (LTE), New Radio) One or more. The radio may include multiple radios so that the controller can communicate through a wireless channel using multiple radio technologies.

[0062] In the embodiment of the present application, the object database may store content information or feature information of the corresponding object, such as the content of the marking line. It should be noted that the object database may be included in the map database and does not necessarily exist independently.

[0063] In an embodiment of the present application, a map database is used to store map information; in some feasible embodiments, a hard disk drive (HDD) can be used as a data storage device for the map database. It is understandable that the map database can contain rich location information; for example, the connection relationship between roads, the location of lane lines, the number of lane lines, and other objects around the road; for example, information about traffic signs (for example, the location and height of traffic lights, the content of the signs, such as speed limit signs, continuous bends, slow driving, etc.), trees around the road, building information, etc. The aforementioned information is all associated with the geographic location. In addition, map information can also be used for positioning and combined with sensor data. In some feasible embodiments, the stored map information can be two-dimensional information or three-dimensional information.

[0064] The actuator subsystem 103 may include at least one actuator, which is used to control the lateral and / or longitudinal movement of the vehicle 100. For example, the brake actuator controls the braking system and braking force based on control signals received from the ECU; the steering actuator controls the steering system based on control signals from the ECU. In some feasible embodiments, the steering system can be an electronic steering system or a mechanical steering system.

[0065] It should be noted that Figure 1b The elements of the systems in FIG. 5 are for illustrative purposes only, and other systems including more or fewer components may be used to perform any of the methods disclosed herein.

[0066] See also Figure 3a , Figure 3a A flowchart of a positioning method provided in an embodiment of the present application is provided. The method may include but is not limited to the following steps:

[0067] Step S301: The positioning device obtains a first combined positioning result.

[0068] Specifically, after acquiring the sensor data, the positioning device further processes the sensor data to obtain a first combined positioning result, wherein the first combined positioning result indicates the vehicle motion state at multiple moments, and the motion state includes vehicle position, vehicle speed and vehicle posture; the first combined positioning result is a combined positioning result obtained after fusion filtering of the second combined positioning result and the third combined positioning result; the second combined positioning result is a result obtained by forward solving the sensor data acquired by the vehicle; and the third combined positioning result is a result obtained by reverse solving the above sensor data.

[0069] In an embodiment of the present application, the sensor data may include but is not limited to data obtained through an inertial measurement unit (IMU), data obtained through a global satellite navigation system (GNSS), data obtained through a laser radar (Lidar), and data obtained through a wheel speed meter (WSS).

[0070] It should be noted that the above-mentioned sensors such as the inertial measurement unit (IMU), global satellite navigation system (GNSS), laser radar (Lidar), and wheel speedometer (WSS) are installed in different positions and postures, resulting in their output data usually not being based on the same reference datum. For example, the above-mentioned IMU-based inertial navigation solution uses the geometric center of the IMU as the reference datum for position and velocity solution, GNSS uses the phase center of the receiver antenna as the reference datum for positioning and constant speed, Lidar uses the geometric center of its laser emitting device as the reference datum for posture estimation, and WSS uses the contact point between the wheel and the ground as the reference datum for velocity output. In order to better determine the vehicle's motion state error variable, the outputs of sensors such as GNSS, Lidar, and WSS must be converted to a consistent and unified reference datum for the above-mentioned inertial navigation solution. This implementation method facilitates the subsequent improvement of the vehicle's positioning accuracy.

[0071] For example, the vehicle motion state can be expressed as shown in formula (1):

[0072] X=[PVA] (1)

[0073] Among them, P represents position; V represents velocity; and A represents attitude.

[0074] In the embodiment of the present application, the second combined positioning result and the third combined positioning result can be fused and filtered using Kalman filtering to obtain the first combined positioning result. For the specific implementation of how to fused and filter the second combined positioning result and the third combined positioning result using Kalman filtering, please refer to the prior art and will not be elaborated here.

[0075] Step S302: The positioning device calculates and obtains the estimated value of the vehicle's initial state variable, the increment of the vehicle's position information to be estimated between two adjacent moments, and the difference between the increment of the vehicle's motion state between two adjacent moments.

[0076] Specifically, after obtaining the first combined positioning result, the positioning device further calculates a combined positioning result based on the first combined positioning result to obtain a target constraint condition. Optionally, the target constraint condition may include minimizing the sum of a first difference, a second difference, and a third difference, wherein the first difference is the difference between the vehicle's starting state value to be estimated and the starting state value in the first combined positioning result, the second difference is the increment of the vehicle's position information to be estimated between two adjacent moments, and the third difference is the difference between the vehicle's motion state increment between two adjacent moments.

[0077] In the embodiment of the present application, the estimated value of the vehicle's initial state variable (i.e., the first difference) can be obtained by formula (2):

[0078]

[0079] in, Represents the prior information; X0 represents the motion state of the vehicle at the starting moment.

[0080] In the embodiment of the present application, the GNSS information of the vehicle (i.e., the second difference) can be obtained by formula (3):

[0081]

[0082] Where P represents the position information in the motion state to be estimated; P gnss Indicates the GNSS position obtained through GNSS; represents the posture matrix; l b Indicates the arm value of the antenna in the IMU coordinate system.

[0083] In the embodiment of the present application, the difference in the vehicle's motion state increment (i.e., the third difference) can be obtained by formula (4):

[0084] r pva_od =r1-r2 (4)

[0085] Here, r1 represents the motion state increment to be estimated, and r2 represents the motion state increment determined by the first combined positioning result.

[0086] In a feasible embodiment, the above r1 and r2 may satisfy the following formula:

[0087]

[0088] Among them, (X j -X i ) represents the difference between the estimated value of the vehicle's motion state at time j and the estimated value of the vehicle's motion state at time i; (X j_filter -X i_filter ) represents the difference between the motion state value of the vehicle at the jth moment and the motion state value of the vehicle at the i-th moment.

[0089] In a feasible embodiment, the position information in the above r1 and r2 may also satisfy the following formula:

[0090]

[0091] Among them, (P j -P i ) represents the difference between the estimated position value of the vehicle at the jth moment and the estimated position value of the vehicle at the ith moment; represents the velocity integral of the vehicle from the i-th moment to the j-th moment determined based on the first combined positioning result.

[0092] In a feasible embodiment, the position information in the above r1 and r2 may also satisfy the following formula:

[0093]

[0094] Among them, (P j -P i -V i *dt) represents the difference between the estimated position of the vehicle at time j and the estimated position of the vehicle at time i; represents the integral of the vehicle's velocity from time i to time j, ignoring the vehicle's velocity at time i. This approach prevents the influence of the vehicle's velocity error at time i on the determination of the integral of the vehicle's velocity from time i to time j using the first combined positioning result, further improving positioning accuracy.

[0095] Step S303: The positioning device corrects the vehicle's motion state based on the estimated value of the vehicle's initial state variable, the increment of the vehicle's position information to be estimated between two adjacent moments, and the difference between the vehicle's motion state increment between two adjacent moments to obtain a target combined positioning result.

[0096] In one feasible embodiment, after obtaining the estimated value of the vehicle's initial state variable, the difference between the vehicle's estimated position information increment between two adjacent moments, and the vehicle's motion state increment between two adjacent moments, the positioning device establishes a target constraint condition and determines the combined positioning result that satisfies the target constraint condition as the target combined positioning result. The target constraint condition is that the difference among the first difference, the second difference, and the third difference is the minimum.

[0097] In a feasible embodiment, the above target constraint condition can be expressed as:

[0098]

[0099] Among them, σ prior Represents the covariance information of the estimated value of the vehicle's initial state variables; σ gnss Represents the covariance information of the vehicle's GNSS information; σ pva_od Represents the covariance information of the vehicle's motion state increments.

[0100] It should be noted that the above target constraint condition can also be other variations or equivalent formulas of the above formula (5), and the embodiments of the present application do not specifically limit this.

[0101] In one feasible embodiment, a nonlinear optimization method (e.g., the least squares method) can be used to obtain a combined positioning result that satisfies the aforementioned target constraints. For information on how to obtain a combined positioning result that satisfies the aforementioned target constraints through nonlinear optimization, please refer to the prior art and will not be elaborated upon here. Of course, other methods can also be used to obtain a combined positioning result that satisfies the aforementioned target constraints, and this embodiment of the present application does not specifically limit this.

[0102] By implementing the embodiments of the present application, based on the use of multi-sensor bidirectional processing fusion, a residual model can be constructed using the vehicle's position, velocity, and attitude state variables to perform global optimization processing on the state variables when the GNSS signal is interfered with. Through multiple iterative solutions, the optimal solution for all state variables can be determined, and conventional post-processing results can be corrected and optimized, thereby improving post-processing positioning accuracy, meeting the vehicle's high-precision and high-reliability positioning requirements, and ensuring vehicle driving safety. Furthermore, the positioning device utilizes the high accuracy of constraints such as wheel speed meters to replace position with the integral of velocity for position constraint. It can also optimize the starting velocity between two adjacent moments and use the integral increment of relative velocity as a position constraint, further improving post-processing positioning accuracy.

[0103] The above embodiment focuses on how to optimize the first combined positioning result to obtain the target combined positioning result. The following describes the effects that can be achieved by the method proposed in this application with reference to specific examples:

[0104] When r1 and r2 satisfy In the case of Figure 3b As shown in the figure, the test results of the embodiment of the present invention using the GNSS method, the two-way filtering method and the above method of the present invention on a tree-lined road with severe GNSS signal obstruction are respectively obtained. Figure 3b It can be seen that the method proposed in this application can significantly reduce the impact of poor GNSS signals on positioning errors.

[0105] When r1 and r2 satisfy In the case of Figure 3c As shown in FIG, the test results of the embodiment of the present application are respectively tested using the GNSS method, the bidirectional filtering method and the above-mentioned method of the present application in a scenario without GNSS signal (for example, a simulated tunnel scenario). Figure 3c It can be seen that the method proposed in this application can significantly improve the positioning accuracy when there is no GNSS signal.

[0106] When r1 and r2 satisfy In the case of Figure 3d As shown in FIG, the test results of the embodiment of the present application are respectively tested using the GNSS method, the bidirectional filtering method and the above-mentioned method of the present application in a scenario without GNSS signal (for example, a simulated tunnel scenario). Figure 3d It can be seen that the method proposed in this application can significantly improve the positioning accuracy when there is no GNSS signal.

[0107] In summary, since the method proposed in this application can correct the vehicle's motion state through the difference between the vehicle's initial state variable estimate, the vehicle's estimated position information increment and the vehicle's motion state increment, it can meet the high-precision and high-reliability positioning requirements of unmanned driving in typical urban road scenarios such as urban canyons and GNSS signal obstruction, it provides convenience for vehicle driving and can also improve the safety of the vehicle during driving.

[0108] The above describes in detail the method of the embodiment of the present application. In order to facilitate better implementation of the above scheme of the embodiment of the present application, correspondingly, relevant devices for cooperating in implementing the above scheme are also provided below.

[0109] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of a positioning device provided in an embodiment of the present application. The positioning device may be the above-mentioned Figure 3a The execution subject in the method embodiment described above can execute Figure 3a The method and steps in the positioning method embodiment. Figure 4 As shown, the positioning device 400 may include an acquisition unit 410, an information determination unit 420, and a correction unit 430.

[0110] An acquisition unit 410 is configured to acquire a first combined positioning result, wherein the first combined positioning result indicates a vehicle motion state at multiple moments in time, the motion state including vehicle position, vehicle speed, and vehicle attitude; the first combined positioning result is a combined positioning result obtained by fusing and filtering the second combined positioning result and the third combined positioning result; the second combined positioning result is a structure obtained by forward solving data acquired by the vehicle's sensors; the third combined positioning result is a result obtained by reverse solving data acquired by the vehicle's sensors, the data including position information of the vehicle acquired by the GNSS sensor at the multiple moments in time;

[0111] An information determination unit 420 is configured to obtain, based on the first combined positioning result, an estimated value of a starting state variable of the vehicle, the position information of the vehicle at the multiple moments, and a difference between the motion state increments of the vehicle, wherein the estimated value of the starting state variable of the vehicle is the motion state of the vehicle at the starting moment in the first combined positioning result, and the difference between the motion state increments of the vehicle is the difference between the motion state increment between two adjacent moments in the multiple moments to be estimated and the motion state increment between the two adjacent moments in the first combined positioning result;

[0112] The correction unit 430 is used to correct the first combined positioning result according to the estimated value of the vehicle's initial state variable, the position information of the vehicle at the multiple moments, and the difference between the vehicle's motion state increments to obtain a target combined positioning result.

[0113] As an embodiment, the correction unit 430 is specifically used to: determine the combined positioning result that meets the target constraint condition as the target combined positioning result, wherein the target constraint condition is that the sum of the first difference, the second difference and the third difference is the minimum, the first difference is the difference between the starting state value of the vehicle to be estimated and the starting state value in the first combined positioning result, the second difference is the difference between the position information of the vehicle to be estimated and the position information of the vehicle obtained by the GNSS sensor, and the third difference is the difference between the motion state increment between the two adjacent moments to be estimated of the vehicle and the motion change increment between the two adjacent moments in the first combined positioning result.

[0114] As an embodiment, the motion state increment between the two adjacent moments to be estimated of the vehicle is the difference between the estimated motion state value of the vehicle at the jth moment and the estimated motion state value of the vehicle at the i-th moment; the motion state increment between the two adjacent moments in the first combined positioning result is the difference between the motion state value of the vehicle at the jth moment and the motion state value of the vehicle at the i-th moment; the i-th moment is earlier than the j-th moment by a preset time length.

[0115] As an embodiment, the motion state increment between two adjacent moments in the multiple moments to be estimated includes a position information increment to be estimated; the position information increment to be estimated is the difference between the position difference to be estimated and the initial speed integral value, wherein the position information difference to be estimated is the difference between the position estimation value of the vehicle at the jth moment and the position estimation value of the vehicle at the i-th moment; the initial speed integral value is the speed integral of the vehicle from the i-th moment to the j-th moment; the i-th moment is earlier than the j-th moment by a preset time length.

[0116] As an embodiment, the motion state increment between two adjacent moments in the multiple moments to be estimated includes a position information increment to be estimated; the position information increment to be estimated is the difference between the position difference to be estimated and the relative speed integral value, wherein the position information difference to be estimated is the difference between the position estimation value of the vehicle at the jth moment and the position estimation value of the vehicle at the i-th moment; the relative speed integral value is the speed integral of the vehicle from the i-th moment to the j-th moment without considering the speed of the vehicle at the i-th moment; the i-th moment is earlier than the j-th moment by a preset time length.

[0117] As an embodiment, the sensor of the vehicle includes at least one of an inertial measurement unit (IMU), a wheel speed meter, and a laser radar.

[0118] It should be understood that the structure of the positioning device is only an example and should not constitute a specific limitation. The various units of the positioning device can be increased, reduced or combined as needed. In addition, the operation and / or function of each unit in the positioning device are respectively to achieve the above Figure 3a For the sake of brevity, the corresponding process of the described method will not be repeated here.

[0119] See also Figure 5 , Figure 5 This is a schematic diagram of the structure of another positioning device provided in an embodiment of the present application. Figure 5 As shown, the positioning device 500 includes: a processor 510, a communication interface 520 and a memory 530, which are interconnected via an internal bus 540. It should be understood that the positioning device 500 can be a terminal device or an in-vehicle device, and is applied to in-vehicle Ethernet.

[0120] The processor 510 may be composed of one or more general-purpose processors, such as a central processing unit (CPU), or a combination of a CPU and a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0121] The bus 540 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus 540 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0122] The memory 530 may include a volatile memory, such as a random access memory (RAM); the memory 530 may also include a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); the memory 530 may also include a combination of the above types.

[0123] It should be noted that the memory 530 of the positioning device 500 stores computer programs, and the processor 510 executes these computer programs, thereby enabling the positioning device 500 to perform the above Figure 3a The method in the embodiment shown.

[0124] The embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, which enables the positioning device to perform the above Figure 3a The method in the embodiment shown.

[0125] The embodiment of the present application also provides a computer program, which can be run to enable the electronic device to perform the above Figure 3a The method in the embodiment shown.

[0126] It is understood that those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the various embodiments of this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0127] Those skilled in the art will appreciate that the functions described in the various illustrative logic blocks, modules, and algorithm steps disclosed in the various embodiments of this application can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions described in the various illustrative logic blocks, modules, and steps can be stored or transmitted as one or more instructions or codes on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media can include computer-readable storage media, which corresponds to tangible media, such as data storage media, or communication media including any media that facilitates the transfer of computer programs from one place to another (e.g., according to a communication protocol). In this way, computer-readable media can generally correspond to (1) non-transitory tangible computer-readable storage media, or (2) communication media, such as signals or carrier waves. Data storage media can be any available media that can be accessed by one or more computers or one or more processors to retrieve instructions, codes, and / or data structures for implementing the technology described in this application. A computer program product can include computer-readable media.

[0128] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. On the other hand, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0129] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0130] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0131] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A positioning method, characterized in that: include: Obtaining a first combined positioning result; wherein the first combined positioning result indicates a vehicle motion state at multiple moments in time, the motion state including vehicle position, vehicle speed, and vehicle attitude; the first combined positioning result is a combined positioning result obtained by fusing and filtering the second combined positioning result and the third combined positioning result; the second combined positioning result is a result obtained by performing a forward solution on data acquired by the vehicle's sensors; the third combined positioning result is a result obtained by performing a reverse solution on data acquired by the vehicle's sensors, the data including position information of the vehicle at the multiple moments in time acquired by a Global Navigation Satellite System (GNSS) sensor; Obtaining, based on the first combined positioning result, an estimated value of the vehicle's initial state variable, the position information of the vehicle at the multiple moments, and a difference between the vehicle's motion state increments; the estimated value of the vehicle's initial state variable is the motion state of the vehicle at the initial moment in the first combined positioning result; and the difference between the vehicle's motion state increments is the difference between the motion state increments between two adjacent moments in the multiple moments to be estimated and the motion state increments between the two adjacent moments in the first combined positioning result; The first combined positioning result is corrected according to the estimated value of the initial state variable of the vehicle, the position information of the vehicle at the multiple moments and the difference between the motion state increments of the vehicle to obtain a target combined positioning result.

2. The method according to claim 1, wherein The step of correcting the first combined positioning result based on the estimated value of the vehicle's initial state variable, the position information of the vehicle at the multiple moments, and the difference between the vehicle's motion state increments to obtain a target combined positioning result includes: The combined positioning result that meets the target constraint condition is determined as the target combined positioning result; wherein, the target constraint condition is that the sum of the first difference, the second difference and the third difference is the smallest, the first difference is the difference between the starting state value of the vehicle to be estimated and the starting state value in the first combined positioning result, the second difference is the difference between the position information of the vehicle to be estimated and the position information of the vehicle acquired by the GNSS sensor, and the third difference is the difference between the motion state increment between the two adjacent moments to be estimated of the vehicle and the motion state change increment between the two adjacent moments in the first combined positioning result.

3. In the method as claimed in claim 2, the motion state increment between the two adjacent moments to be estimated of the vehicle is the difference between the estimated motion state value of the vehicle at the jth moment and the estimated motion state value of the vehicle at the i-th moment; the motion state increment between the two adjacent moments in the first combined positioning result is the difference between the motion state value of the vehicle at the jth moment and the motion state value of the vehicle at the i-th moment; the i-th moment is earlier than the j-th moment by a preset time length.

4. The method according to any one of claims 1 to 2, wherein the motion state increment between two adjacent moments in the plurality of moments to be estimated comprises a position information increment to be estimated; the position information increment to be estimated is the difference between the position difference to be estimated and the initial velocity integral value, wherein: The position information difference to be estimated is the difference between the position estimate of the vehicle at the jth moment and the position estimate of the vehicle at the ith moment; the initial speed integral value is the speed integral of the vehicle from the ith moment to the jth moment; the ith moment is earlier than the jth moment by a preset time.

5. The method according to any one of claims 1 to 2, wherein the motion state increment between two adjacent moments in the plurality of moments to be estimated comprises a position information increment to be estimated; the position information increment to be estimated is the difference between the position difference to be estimated and the relative velocity integral value, wherein: The difference value of the position information to be estimated is the difference between the estimated position value of the vehicle at the jth moment and the estimated position value of the vehicle at the i-th moment; the relative speed integral value is the speed integral of the vehicle from the i-th moment to the j-th moment without considering the speed of the vehicle at the i-th moment; the i-th moment is earlier than the j-th moment by a preset time.

6. The method according to claim 1 or 2, wherein: The sensors of the vehicle include at least one of an inertial measurement unit (IMU), a wheel speed meter, and a laser radar.

7. A positioning device, characterized in that: include: an acquisition unit, configured to acquire a first combined positioning result, wherein the first combined positioning result indicates a vehicle motion state at multiple moments in time, the motion state including vehicle position, vehicle speed, and vehicle attitude; the first combined positioning result is a combined positioning result obtained by fusing and filtering the second combined positioning result and the third combined positioning result; the second combined positioning result is a structure obtained by forward solving data obtained by the vehicle's sensors; the third combined positioning result is a result obtained by reverse solving data obtained by the vehicle's sensors, the data including position information of the vehicle obtained by the GNSS sensor at the multiple moments in time; an information determination unit, configured to obtain, based on the first combined positioning result, an estimated value of a starting state variable of the vehicle, position information of the vehicle at the multiple moments, and a difference between an increment of motion state of the vehicle, wherein the estimated value of the starting state variable of the vehicle is the motion state of the vehicle at the starting moment in the first combined positioning result, and the difference between the increment of motion state of the vehicle is a difference between an increment of motion state between two adjacent moments in the multiple moments to be estimated and an increment of motion state between the two adjacent moments in the first combined positioning result; The correction unit is used to correct the first combined positioning result according to the estimated value of the initial state variable of the vehicle, the position information of the vehicle at the multiple moments and the difference between the motion state increments of the vehicle to obtain a target combined positioning result.

8. The device according to claim 7, wherein The correction unit is specifically used to: The combined positioning result that meets the target constraint condition is determined as the target combined positioning result, wherein the target constraint condition is that the sum of the first difference, the second difference and the third difference is the minimum, the first difference is the difference between the starting state value of the vehicle to be estimated and the starting state value in the first combined positioning result, the second difference is the difference between the position information of the vehicle to be estimated and the position information of the vehicle obtained by the GNSS sensor, and the third difference is the difference between the motion state increment between the two adjacent moments to be estimated of the vehicle and the motion change increment between the two adjacent moments in the first combined positioning result.

9. The device according to claim 8, wherein The motion state increment between the two adjacent moments to be estimated of the vehicle is the difference between the estimated motion state value of the vehicle at the jth moment and the estimated motion state value of the vehicle at the ith moment; the motion state increment between the two adjacent moments in the first combined positioning result is the difference between the motion state value of the vehicle at the jth moment and the motion state value of the vehicle at the ith moment; the i-th moment is earlier than the j-th moment by a preset time length.

10. The device according to claim 7 or 8, characterized in that The motion state increment between two adjacent moments among the multiple moments to be estimated includes a position information increment to be estimated; the position information increment to be estimated is the difference between the position difference to be estimated and the initial speed integral value, wherein the position information difference to be estimated is the difference between the position estimate of the vehicle at the j moment and the position estimate of the vehicle at the i moment; the initial speed integral value is the speed integral of the vehicle from the i moment to the j moment; the i moment is earlier than the j moment by a preset time length.

11. The device according to claim 7 or 8, characterized in that The motion state increment between two adjacent moments among the multiple moments to be estimated includes a position information increment to be estimated; the position information increment to be estimated is the difference between the position difference to be estimated and the relative speed integral value, wherein the position information difference to be estimated is the difference between the position estimate of the vehicle at the j moment and the position estimate of the vehicle at the i moment; the relative speed integral value is the speed integral of the vehicle from the i moment to the j moment without considering the speed of the vehicle at the i moment; the i moment is earlier than the j moment by a preset time length.

12. The device according to claim 7 or 8, characterized in that The vehicle's sensors include at least one of an inertial measurement unit (IMU), a wheel speed meter, and a laser radar.

13. A positioning device, characterized in that: The positioning device includes a memory and a processor, and the processor executes computer instructions stored in the memory, so that the positioning device performs the method according to any one of claims 1 to 6.

14. A vehicle, characterized in that: The vehicle comprises a positioning device according to any one of claims 7 to 13.

15. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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