Method, apparatus and electronic device for determining vehicle position and attitude data

Abstract

The present application discloses a method, apparatus, and electronic device for determining vehicle position and attitude data, which relates to the field of computer technology, and particularly to artificial intelligence fields such as autonomous driving, vehicle networking, intelligent cockpits, and computer vision. The specific implementation solution is as follows: Obtain the first position and attitude data of the vehicle at the first moment, the first predicted value of the first relative state of the vehicle at the second moment relative to the first moment, and the first error value of the error variable corresponding to the first relative state; wherein, the first moment is earlier than the second moment; Determine the first estimated value of the first relative state according to the first error value and the first predicted value; Determine the second position and attitude data of the vehicle at the second moment according to the first position and attitude data and the first estimated value. This method determines the position and attitude data of the vehicle at the second moment based on the error value of the error variable of the relative state between different moments, thereby being unaffected by map projection errors and improving the accuracy of the position and attitude data.

Description

Technical Field

[0001] The present application relates to the field of computer technology, in particular to artificial intelligence fields such as autonomous driving, vehicle networking, smart cockpits, computer vision, and more specifically to a method, device, and electronic device for determining vehicle position and posture data. Background Art

[0002] With the development of the modern automobile industry, intelligent driving systems have become a development trend in today's automobile industry. People have an increasingly high demand for intelligent driving systems. Most of these intelligent driving systems require feedback based on vehicle position and posture. The accuracy of vehicle position and posture plays a vital role in intelligent driving systems.

[0003] Therefore, how to improve the accuracy of the determined vehicle position and posture data is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present application provides a method, device and electronic device for determining vehicle position and posture data. The specific scheme is as follows:

[0005] According to one aspect of the present application, a method for determining vehicle position and posture data is provided, comprising:

[0006] Acquire first position and posture data of the vehicle at a first moment, a first predicted value of a first relative state of the vehicle at a second moment relative to the first moment, and a first error value of an error variable corresponding to the first relative state; wherein the first moment is earlier than the second moment;

[0007] Determining a first estimated value of a first relative state based on the first error value and the first predicted value;

[0008] Determine second position and posture data of the vehicle at a second moment based on the first position and posture data and the first estimated value.

[0009] According to another aspect of the present application, a device for determining vehicle position and posture data is provided, comprising:

[0010] An acquisition module, used to acquire first position and posture data of the vehicle at a first moment, a first predicted value of a first relative state of the vehicle at a second moment relative to the first moment, and a first error value of an error variable corresponding to the first relative state; wherein the first moment is earlier than the second moment;

[0011] A first determination module, configured to determine a first estimated value of a first relative state according to a first error value and a first prediction value;

[0012] The second determination module is used to determine second position and posture data of the vehicle at a second moment according to the first position and posture data and the first estimated value.

[0013] According to another aspect of the present application, there is provided an electronic device, including:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the method described in the above embodiments.

[0017] According to another aspect of the present application, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method described in the above embodiments.

[0018] According to another aspect of the present application, there is provided a computer program product including a computer program, and when the computer program is executed by a processor, the steps of the method described in the above embodiments are implemented.

[0019] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understandable through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings are used to better understand the solution and do not constitute a limitation to the present application. Among them:

[0021] Figure 1 is a schematic flowchart of a method for determining vehicle position and attitude data provided by an embodiment of the present application;

[0022] Figure 2 is a schematic diagram of the change of the vehicle body coordinate system corresponding to a vehicle movement provided by an embodiment of the present application;

[0023] Figure 3 is a schematic flowchart of a method for determining vehicle position and attitude data provided by another embodiment of the present application;

[0024] Figure 4 is a schematic flowchart of a method for determining vehicle position and attitude data provided by another embodiment of the present application;

[0025] Figure 5 is a schematic diagram of lane lines at different times provided by an embodiment of the present application;

[0026] Figure 6 is a schematic structural diagram of a device for determining vehicle position and attitude data provided by an embodiment of the present application;

[0027] Figure 7 It is a block diagram of an electronic device used to implement the method for determining the vehicle position and posture data of an embodiment of the present application. DETAILED DESCRIPTION

[0028] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0029] Artificial intelligence is a discipline that studies the use of computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.). It includes both hardware-level technical fields and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies include computer vision technology, speech recognition technology, natural language processing technology, as well as deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0030] Autonomous driving, also known as unmanned driving, computer driving or wheeled mobile robot, is a cutting-edge technology that relies on computers and artificial intelligence technology to complete complete, safe and effective driving without human control.

[0031] The concept of Internet of Vehicles originates from the Internet of Things, namely the Internet of Vehicles. It takes moving vehicles as information perception objects and uses the new generation of information and communication technology to realize network connection between vehicles and X (i.e. vehicles, people, roads, and service platforms), improve the overall intelligent driving level of vehicles, and provide users with safe, comfortable, intelligent, and efficient driving experience and transportation services. At the same time, it improves traffic operation efficiency and enhances the intelligence level of social transportation services.

[0032] A smart cockpit refers to the transformation of the vehicle's interior seating space to make the driving and riding experience more comfortable and intelligent.

[0033] Computer vision is a science that studies how to make machines "see". It refers to using cameras and computers to replace human eyes to identify, track and measure targets, and further perform graphic processing so that the computer processes the images into images that are more suitable for human eye observation or transmission to instruments for detection.

[0034] The following describes the method, device, electronic device and storage medium for determining vehicle position and posture data in embodiments of the present application with reference to the accompanying drawings.

[0035] Figure 1Schematic flowchart of a method for determining vehicle position and attitude data provided by an embodiment of the present application.

[0036] The method for determining vehicle position and attitude data according to the embodiments of the present application can be executed by the device for determining vehicle position and attitude data according to the embodiments of the present application. This device can be configured in an electronic device to determine the vehicle position and attitude data based on the error values of state variables between different moments.

[0037] Among them, the electronic device can be any device with computing capabilities, such as a personal computer, a mobile terminal, a server, etc. The mobile terminal can be, for example, a vehicle-mounted device, a mobile phone, a tablet computer, a personal digital assistant, a wearable device, etc., which are hardware devices with various operating systems, touch screens, and / or display screens.

[0038] As Figure 1 shown, the method for determining vehicle position and attitude data includes:

[0039] Step 101, obtain the first position and attitude data of the vehicle at the first moment, the first predicted value of the first relative state of the vehicle at the second moment relative to the first moment, and the first error value of the error variable corresponding to the first relative state.

[0040] Among them, the first moment is earlier than the second moment. For example, the second moment can be the current moment, or the second moment can be the previous moment of the current moment.

[0041] In the present application, the first position and attitude data can be the position and attitude data of the vehicle in the world coordinate system at the first moment. The first predicted value of the first relative state can be obtained based on the prediction result of the target sensor on the vehicle at the second moment. For example, it can be obtained based on the predicted value of the relative state from the first moment to the previous moment and the measured value of the inertial measurement unit.

[0042] In the present application, the first error value of the error variable corresponding to the first relative state can be estimated by a filter. For example, it can be obtained by updating the vehicle speed observation acquisition filter, or it can also be obtained by updating the filter for lane line observation.

[0043] In practical applications, the frequency of measuring vehicle speed and the frequency of perceiving lane lines may be different. Therefore, in the present application, when the vehicle speed is obtained, the first error value can be updated according to the vehicle speed observation; when the lane line equation is obtained, the first error value can be updated according to the lane line observation.

[0044] Step 102, determine the first estimated value of the first relative state according to the first error value and the first predicted value.

[0045] In this application, the first predicted value can be corrected using the first error value to obtain the first estimated value of the first relative state.

[0046] For example, the first position and attitude data is the position and attitude data of the vehicle at the first moment k in the world coordinate system. The first position and attitude data of the vehicle at moment k can be expressed by the following formula:

[0047]

[0048] where is the position of the vehicle at moment k in the world coordinate system w, is the attitude of the vehicle at moment k in the world coordinate system w (represented by quaternion).

[0049] The relative motion of the vehicle at moment k + 1 relative to moment k can be expressed as:

[0050]

[0051] where are the relative displacement, relative velocity, and relative attitude of the vehicle at moment k + 1 relative to moment k respectively; b a and b g are the biases of the accelerometer and gyroscope respectively, is the gravity vector at moment k in the body coordinate system b k under. And define the error variable δx of as:

[0052] δx = [δp δv δθ δb a δb g δg] T (3)

[0053] where δp, δv, δθ, δb a , δb g , δg represent displacement error, velocity error, attitude error, accelerometer bias error, gyroscope bias error, and gravity vector error in the vehicle coordinate system respectively.

[0054] Based on δx and the predicted value obtained from the following formula

[0055]

[0056] where is the first estimated value of the first relative motion of the vehicle at moment k + 1 relative to moment k, is the first predicted value of the first relative motion, They are respectively the predicted relative displacement value, relative velocity value, relative attitude value, accelerometer bias prediction value, gyroscope bias prediction value of the vehicle at time k+1 relative to time k, and the predicted gravity vector value in the vehicle body coordinate system b k below.

[0057] For ease of understanding, Figure 2 FIG. is a schematic diagram of the change of the vehicle body coordinate system corresponding to the vehicle motion provided by the embodiment of the present application. Figure 2 In (a), the motion trajectory of the vehicle from time k to time k+1 is shown, Figure 2 In (b), the vehicle body coordinate system b at time k is shown k and the vehicle body coordinate system b at time k+1 k+1 . The vehicle body coordinate system is a three-dimensional coordinate system, and the three axes are respectively represented by x, y, and z.

[0058] Based on the above, the state equation can be:

[0059]

[0060] Among them, represents the predicted value of the error variable; F and G are as described in the following formulas (6) and (7); n is the noise of the accelerometer and gyroscope of the inertial measurement unit, as described in formula (8); based on formula (5), the prediction of the state variable can be performed.

[0061]

[0062]

[0063]

[0064] Among them, represents the rotation matrix of the attitude at time t relative to the vehicle body coordinate system b at time k k , a t represents the measured value of the accelerometer of the inertial measurement unit at time t (a t is 3D data); b a represents the measured value of the gyroscope of the inertial measurement unit at time t (b a is 3D data); n a , n g , n ba and n bg are all three-dimensional vectors, all of which are preset; n a represents the Gaussian noise of the accelerometer of the inertial measurement unit; n g represents the Gaussian noise of the gyroscope of the inertial measurement unit; n ba represents the random walk noise of the accelerometer of the inertial measurement unit; n bgRepresents the random walk noise of the gyroscope of the inertial measurement unit.

[0065] Step 103: Determine the second-position and -attitude data of the vehicle at the second moment according to the first-position and -attitude data and the first estimate.

[0066] In this application, after determining the first estimate of the first relative motion, the first-position and -attitude data of the vehicle at the first moment can be corrected using the first estimate to obtain the second-position and -attitude data of the vehicle at the second moment.

[0067] In this application, the first-position and -attitude data and the second-position and -attitude data can be position and attitude data in the same coordinate system. For example, if they are both position and attitude data in the world coordinate system, then based on the first-position and -attitude data of the vehicle in the world coordinate system at the previous moment and the first estimate of the first relative state of the vehicle at the current moment relative to the previous moment, the second-position and -attitude data of the vehicle in the world coordinate system at the current moment can be determined.

[0068] In an embodiment of this application, by obtaining the first error value of the error variable of the first relative state at the second moment relative to the first moment, and according to the first error value and the first prediction value of the first relative state, determining the first estimate of the first relative state, and according to the first estimate and the first-position and -attitude data of the vehicle at the first moment, determining the second-position and -attitude data of the vehicle at the second moment. Thus, by determining the position and attitude data of the vehicle at the second moment based on the error value of the error variable of the relative state between different moments, the influence of map projection error is eliminated, and the accuracy of the position and attitude data is improved.

[0069] Figure 3 It is a schematic flowchart of a method for determining vehicle position and attitude data provided in another embodiment of this application.

[0070] As Figure 3 shown, the method for determining vehicle position and attitude data includes:

[0071] Step 301: Obtain the first-position and -attitude data of the vehicle at the first moment and the first prediction value of the first relative state of the vehicle at the second moment relative to the first moment.

[0072] For the description of the first-position and -attitude data, reference can be made to the above embodiment, so it will not be elaborated here.

[0073] In this application, the second estimate of the second relative state of the vehicle at the first moment relative to the third moment and the measurement value of the inertial measurement unit at the second moment can be obtained, and the first prediction of the first relative state can be obtained according to the second estimate of the second relative state combined with the measurement value of the inertial measurement unit at the second moment.

[0074] Among them, the third moment is earlier than the first moment, and the first moment is earlier than the second moment. For example, if the first moment is k, then the third moment can be the moment k - 1, and the second moment is the moment k + 1. Then, the second estimated value of the second relative state of the moment k relative to the moment k - 1 and the measured value of the inertial measurement unit at the moment k + 1 can be obtained to get the first predicted value of the first relative state of the moment k + 1 relative to the moment k.

[0075] Thus, by according to the estimated value of the second relative state at the first moment and the measured value of the inertial measurement unit, the prediction of the relative state of the second moment relative to the first moment can be realized.

[0076] Step 302: Obtain the vehicle speed observation matrix at the second moment, the estimated value of the covariance matrix between the position and attitude of the vehicle at the first moment, and the second error value of the error variable corresponding to the second relative state of the vehicle at the first moment relative to the third moment.

[0077] Among them, the third moment is earlier than the first moment, and the first moment is earlier than the second moment.

[0078] Due to the characteristics of the lane line feature itself (parallel wire bundles), the motion along the direction of the lane line is unobservable. Thus, the observation of the longitudinal motion is introduced. In addition, considering the actual vehicle configuration, the vehicle usually has the observation of the vehicle speed. Therefore, the vehicle speed can be introduced as the longitudinal observation.

[0079] In this application, at the second moment, the first speed of the vehicle along the longitudinal direction in the body coordinate system at the second moment and the second speed of the first speed in the body coordinate system at the first moment can be measured by sensors. Since the first speed and the second speed are not in the same coordinate system, the vehicle speed observation matrix can be obtained according to the first vehicle speed, the second speed, and the rotation matrix from the second speed to the first speed. Thus, by according to the observed speed of the vehicle along the longitudinal direction in the body coordinate system, the vehicle speed observation matrix is obtained, and then the error value of the error variable can be updated based on the vehicle speed observation matrix to realize the estimation of the error variable.

[0080] For example, the first speed of the vehicle along the longitudinal direction in the body coordinate system at the moment k + 1 is V speed , and the running speed of the vehicle in the body coordinate system at the moment k + 1 can be expressed as:

[0081] V bk+1 =[V speed 0 0] (9)

[0082] Then there is:

[0083]

[0084] Among them, Denote the error in observing the vehicle body speed at time k + 1 in the vehicle body coordinate system b k+1 The following, Denote the rotation matrix from the second speed to the first speed, Denote the second speed of the first speed in the vehicle body coordinate system at the first moment. Wherein, It can be obtained based on the change amount of the vehicle attitude relative to time k + 1 at time k.

[0085] Then, the vehicle speed observation matrix S at time k + 1 is:

[0086]

[0087] Wherein, × represents an anti-symmetric matrix, that is, It is arranged according to the anti-symmetric matrix.

[0088] In this application, the second error value of the error variable corresponding to the second relative state of the vehicle at the first moment relative to the third moment can be updated by using a filter based on the vehicle speed observation matrix or the lane line observation matrix at the first moment.

[0089] Step 303, determine the predicted value of the covariance matrix at the second moment according to the estimated value of the covariance matrix at the first moment.

[0090] Assume that the first moment is k and the second moment is k + 1. The predicted value of the covariance matrix at time k + 1 can be determined by the following formula

[0091]

[0092] Wherein, P k Represents the estimated value of the covariance matrix at time k, I represents the identity matrix; F and G are matrices, which can be given by the following formulas (6) and (7); Q is the noise matrix, which can be given by the above formula (8); Δt represents the time interval (scalar), which can be considered as the measurement interval of the inertial measurement unit.

[0093] Step 304, update the second error value according to the vehicle speed observation matrix and the predicted value of the covariance matrix to determine the first error value.

[0094] In this application, the filtering gain matrix at the second moment can be determined first according to the vehicle speed observation matrix and the predicted value of the covariance matrix, and then the second error value is updated according to the filtering gain matrix at the second moment and the first speed of the vehicle along the longitudinal direction in the vehicle body coordinate system at the second moment to determine the first error value. Thus, by according to the vehicle speed observation matrix and the covariance matrix between the position and the attitude, the error value at the first moment can be updated to obtain the error value at the second moment.

[0095] Suppose the first moment is k, the second moment is k + 1, and the second error value of the error variable corresponding to the relative state at moment k is δx k , and the first error value of the error variable corresponding to the relative state at moment k + 1 is δx k+1 , for vehicle speed observation, error state Kalman filtering can be used for updating, and the update formula is as follows:

[0096]

[0097]

[0098]

[0099] where K k+1 represents the Kalman filter gain matrix at moment k + 1; S k+1 represents the vehicle speed observation matrix at moment k + 1; represents the predicted value of the rotation matrix from the second speed to the first speed, represents the predicted value of the second speed of the first speed in the body coordinate system at the first moment; R k+1 represents the measurement noise matrix, which can be set according to the confidence level of the measurement; P k+1 represents the estimated value of the covariance matrix at moment k + 1, which can be used to determine the predicted value of the covariance matrix at moment k + 2 for the next update.

[0100] It should be noted that and the horizontal line above indicates that the value is a predicted value, and the above and in formula (10) only express the relationship of quantities.

[0101] Step 305, determine the first estimated value of the first relative state according to the first error value and the first predicted value.

[0102] After determining the first error value δx k+1 based on the above formula, δx k+1 can be added to the first predicted value to obtain the first estimated value of the first relative state at moment k + 1

[0103] Step 306, determine the second position and attitude data of the vehicle at the second moment according to the first position and attitude data and the first estimated value.

[0104] In this application, steps 305 - 306 are similar to the content described in the above embodiments, so they will not be elaborated here.

[0105] In the embodiments of the present application, by obtaining the vehicle speed observation matrix at the second moment and the estimated value of the covariance matrix between the position and attitude of the vehicle at the first moment, and determining the predicted value of the covariance matrix at the second moment according to the estimated value of the covariance matrix at the first moment, and updating the second error value according to the vehicle speed observation matrix and the predicted value of the covariance matrix to determine the first error value, and then determining the first estimated value of the first relative state according to the first error value and the first predicted value, and determining the position and attitude data of the vehicle at the second moment according to the first estimated value and the position and attitude data of the vehicle at the first moment. Since vehicle speed detection is usually available in practical applications, the vehicle speed is introduced as a longitudinal observation, and the error value of the error variable of the relative state is obtained by updating the error of the vehicle speed observation matrix, and then the position and attitude data of the vehicle are determined by using the error value, so the application range is wide.

[0106] Figure 4 It is a schematic flowchart of a method for determining vehicle position and attitude data provided in another embodiment of the present application.

[0107] As Figure 4 shown, the method for determining the vehicle position and attitude data includes:

[0108] Step 401, obtain the first position and attitude data of the vehicle at the first moment and the first predicted value of the first relative state of the vehicle at the second moment relative to the first moment.

[0109] In the present application, step 301 is similar to the content described in the above embodiments, so it will not be elaborated here.

[0110] Step 402, obtain the lane line observation matrix at the second moment, the estimated value of the covariance matrix between the position and attitude of the vehicle at the first moment, and the second error value of the error variable corresponding to the second relative state of the vehicle at the first moment relative to the third moment.

[0111] Among them, the third moment is earlier than the first moment, and the first moment is earlier than the second moment.

[0112] In the present application, the second error value of the error variable corresponding to the second relative state of the vehicle at the first moment relative to the third moment can be obtained by updating using a filter based on the vehicle speed observation matrix or the lane line observation matrix at the first moment.

[0113] In the present application, the first lane line equation at the second moment and the second lane line equation at the first moment output by the lane line perception sensor on the vehicle can be obtained, and the first lane line equation and the second lane line equation are sampled to obtain a plurality of first sampling points on the first lane line and a plurality of second sampling points on the second lane line. Among them, the sampling distances of the first lane line equation and the second lane line equation can be different or the same, and the present application does not limit this.

[0114] After that, two target second sampling points corresponding to each first sampling point can be determined from multiple second sampling points. The distances between these two target second sampling points and the first sampling point are the closest. Then, according to the first position corresponding to each first sampling point and the second positions of the two target second sampling points in the vehicle body coordinate system at the first moment, the observation matrix corresponding to each first sampling point can be determined. The observation matrices corresponding to multiple first sampling points can be combined to obtain the lane line observation matrix.

[0115] Thus, by introducing the lane line equation as an observation, the movement of the vehicle is constrained in the lateral and longitudinal directions, thereby improving the accuracy of the subsequently determined error value.

[0116] In practical applications, usually the lane line perception frequency is generally greater than 10 Hz (that is, the interval is less than 100 milliseconds), and the road surface is generally flat. Therefore, it can be considered that the lane lines in two frames of vehicle perception data are in the same plane, that is, it is considered that the perceived lane line equations of the current frame and the previous frame both describe the lane line entities in the same plane.

[0117] Assume that the first moment is k and the second moment is k + 1. The observation equation of the lane line can be defined as the following formula:

[0118]

[0119] The physical meaning of this lane line observation equation can be referred to Figure 5 , Figure 5 which is a schematic diagram of lane lines at different moments provided by the embodiments of this application.

[0120] Figure 5 In k , l k+1 is the first lane line corresponding to the first lane line equation, and l are the positions of two points on the first lane line at the k moment, represents the position of the i-th point on the second lane line l k+1 at the k + 1 moment with the position of rotated to the position in the vehicle body coordinate system b k at the k moment according to the corresponding state prediction value. The conversion formula is as follows:

[0121]

[0122] Among them, represents the rotation matrix of the position at the k + 1 moment relative to the vehicle body coordinate system b k ; represents the relative displacement of the vehicle at the k + 1 moment relative to k.

[0123] In this application, the lane line equation of l k+1 can be sampled at a preset interval d, and the lane line equation of l k can be sampled. Among them, d can be two meters or other values, and this application does not limit this.

[0124] After that, according to the above formula, the second sampling points obtained by sampling for l k+1 are transformed into the vehicle body coordinate system b k , and the golden section method is used to find a corresponding set of points for each sampling point at the position in the first sampling points of l k and and The residual calculation can be performed using the above formula (16).

[0125] If l k+1 has n sampling points, the corresponding vehicle speed observation matrix H can be obtained:

[0126] H = [H1…H i …H n T (18)

[0127]

[0128]

[0129]

[0130] Among them, H i represents the observation matrix of the i-th sampling point; represents the derivative of f (i.e., the above formula (16)) with respect to ; represents the derivative with respect to δx; J r (θ) represents the right Jacobian matrix.

[0131] Step 403: Determine the predicted value of the covariance matrix at the second moment according to the estimated value of the covariance matrix at the first moment.

[0132] In this application, step 403 is similar to the content described in the above embodiment, so it will not be repeated here.

[0133] Step 404: Update the second error value according to the lane line observation matrix and the predicted value of the covariance matrix to determine the first error value.

[0134] ​In this application, the lane line observation matrix corresponding to the j-th update and the third error value of the error variable of the first relative state obtained in the j-th update can be obtained. Where j is a natural number. When j is 0, the lane line observation matrix corresponding to the j-th update can be the lane line observation matrix obtained at the second moment, and the third error value of the error variable of the first relative state obtained in the j-th update can be the second error value of the error variable of the second relative state of the vehicle at the first moment relative to the third moment. Where the third moment is earlier than the first moment.

[0135] After that, according to the predicted value of the covariance matrix and the lane line observation matrix corresponding to the j-th update, the filtering gain matrix obtained in the j-th update is determined, and according to the filtering gain matrix obtained in the j-th update, the lane line observation matrix corresponding to the j-th update, and the third error value obtained in the j-th update, the update error corresponding to the (j + 1)-th update relative to the j-th update is determined, and the third error value obtained in the j-th update is corrected by using the update error corresponding to the (j + 1)-th update to determine the third error value obtained in the (j + 1)-th update.

[0136] After determining the third error value obtained in the (j + 1)-th update, the lane line observation value corresponding to the (j + 1)-th update is determined according to the above formula (2). If the lane line observation value corresponding to the (j + 1)-th update is less than the preset threshold, the third error value obtained in the (j + 1)-th update can be determined as the second error value. If the lane line observation value corresponding to the (j + 1)-th update is greater than or equal to the preset threshold, the third error value of the error variable of the first relative state is updated for the (j + 2)-th time according to the predicted value of the covariance matrix and the lane line observation matrix corresponding to the (j + 1)-th update, until the lane line observation value is less than the preset threshold, and the third error value obtained in the last update is used as the second error value.

[0137] Assume that the first moment is k, the second moment is k + 1, and the second error value of the error variable corresponding to the relative state at the k-th moment is δx k , and the first error value of the error variable corresponding to the relative state at the (k + 1)-th moment is δx k+1 , since the lane line equation is an observation with a relatively high degree of nonlinearity, the form of iterative error state Kalman filtering can be used for state estimation, which can improve its accuracy in strong nonlinear problems. The update formula is as follows:

[0138]

[0139]

[0140] δx k+1,j+1 = δx k+1,j + Δx j+1 (24)

[0141]

[0142] Among them, K k+1,j represents the Kalman filter gain matrix obtained from the j-th update; represents the predicted value of the covariance matrix at time k + 1; H k+1,j represents the lane line observation matrix obtained from the j-th update; R k+1,j represents the noise matrix obtained from the j-th update; Δx j+1 represents the update error of the (j + 1)-th update relative to the j-th update; δx k+1,j represents the third error value obtained from the j-th update; P k+1 represents the estimated value of the covariance matrix at time k + 1.

[0143] The above update formulas (23)-(24) can be repeated multiple times until the lane line observation value is less than the preset threshold, and the third error value obtained from the last update is used as the first error value.

[0144] In the above formula (25), the predicted value P of the covariance matrix at time k + 1 k+1 is obtained by using the predicted value of the covariance matrix at time k + 1 the Kalman filter gain matrix K obtained from the last update (the m-th update) k+1,m the lane line observation matrix H k+1,m and the noise matrix R k+1,m and P k+1 can be used to determine the predicted value of the covariance matrix at time k + 2 during the next update.

[0145] In addition, after the above update is completed, the state variables at the next moment can be re-initialized as:

[0146]

[0147] Among them:

[0148]

[0149]

[0150] Among them, represents the speed of the vehicle at time k + 1 in the vehicle body coordinate system at time k + 1; represents at time k + 1 in the vehicle body coordinate system b k+1 the gravity vector below.

[0151] Since the coordinate system is updated to the vehicle body coordinate system at time k + 1, the attitude of the vehicle can be determined as the unit quaternion q0, the position is 0, and the covariance matrix between the position and the attitude is re-initialized.

[0152] In this application, by using the lane line observation matrix, the error value is iteratively updated until the lane line observation value is less than a preset threshold, and the error value obtained from the last update is used as the first error value, which improves the accuracy of the first error value.

[0153] Step 405: Determine the first estimated value of the first relative state according to the first error value and the first predicted value.

[0154] Step 406: Determine the second position and attitude data of the vehicle at the second moment according to the first position and attitude data and the first estimated value.

[0155] In this application, steps 405-406 are similar to the content described in the above embodiments, so they will not be elaborated here.

[0156] In the embodiment of this application, by using the lane line observation matrix, the second error value of the error variable of the second relative state of the first moment relative to the third moment is updated to obtain the first error value of the error variable of the first relative state of the second moment relative to the first moment. Thus, by introducing the lane line equation as an observation, the movement of the vehicle is constrained in the lateral and longitudinal directions, and the error is updated based on the vehicle speed observation matrix to obtain the error value of the error variable of the relative state, so as to improve the accuracy of the error value, and further improve the accuracy of the determined vehicle position and attitude data.

[0157] To implement the above embodiments, the embodiment of this application also proposes a device for determining vehicle position and attitude data. Figure 6 This is a schematic structural diagram of a device for determining vehicle position and attitude data provided by an embodiment of this application.

[0158] As Figure 6 shown, the device 600 for determining vehicle position and attitude data includes:

[0159] An acquisition module 610, configured to acquire the first position and attitude data of the vehicle at the first moment, the first predicted value of the first relative state of the vehicle at the second moment relative to the first moment, and the first error value of the error variable corresponding to the first relative state; wherein, the first moment is earlier than the second moment;

[0160] A first determination module 620, configured to determine the first estimated value of the first relative state according to the first error value and the first predicted value;

[0161] A second determination module 630, configured to determine the second position and attitude data of the vehicle at the second moment according to the first position and attitude data and the first estimated value.

[0162] In a possible implementation manner of the embodiment of this application, the acquisition module 610 includes:

[0163] A first acquisition unit, configured to acquire a vehicle speed observation matrix at a second moment, an estimated value of a covariance matrix between the position and attitude of a vehicle at a first moment, and a second error value of an error variable corresponding to a second relative state of the vehicle at the first moment relative to a third moment; wherein, the third moment is earlier than the first moment;

[0164] A first determination unit, configured to determine a predicted value of the covariance matrix at the second moment according to the estimated value of the covariance matrix at the first moment;

[0165] A first update unit, configured to update the second error value according to the vehicle speed observation matrix and the predicted value of the covariance matrix to determine a first error value.

[0166] In a possible implementation manner of the embodiment of the present application, the first update unit is configured to:

[0167] Determine a filtering gain matrix at the second moment according to the vehicle speed observation matrix and the predicted value of the covariance matrix;

[0168] Update the second error value according to the filtering gain matrix and a first speed of the vehicle in the longitudinal direction in the vehicle body coordinate system at the second moment to determine a first error value.

[0169] In a possible implementation manner of the embodiment of the present application, the first acquisition unit is configured to:

[0170] Acquire a first speed of the vehicle in the longitudinal direction in the vehicle body coordinate system at the second moment, and a second speed of the first speed in the vehicle body coordinate system at the first moment;

[0171] Acquire the vehicle speed observation matrix according to the first vehicle speed, the second speed, and a rotation matrix from the second speed to the first speed.

[0172] In a possible implementation manner of the embodiment of the present application, the acquisition module 610 includes:

[0173] A second acquisition unit, configured to acquire a lane line observation matrix at the second moment, an estimated value of a covariance matrix between the position and attitude of a vehicle at the first moment, and a second error value of an error variable corresponding to a second relative state of the vehicle at the first moment relative to a third moment; wherein, the third moment is earlier than the first moment;

[0174] A second determination unit, configured to determine a predicted value of the covariance matrix at the second moment according to the estimated value of the covariance matrix at the first moment;

[0175] A second update unit, configured to update the second error value according to the lane line observation matrix and the predicted value of the covariance matrix to determine a first error value.

[0176] In a possible implementation manner of the embodiment of the present application, the second update unit is configured to:

[0177] Obtain the lane line observation matrix corresponding to the j-th update and the third error value of the error variable of the first relative state obtained in the j-th update; where j is a natural number;

[0178] Determine the filtering gain matrix obtained in the j-th update according to the predicted value of the covariance matrix and the lane line observation matrix corresponding to the j-th update;

[0179] Determine the update error of the (j + 1)-th update relative to the j-th update according to the filtering gain matrix obtained in the j-th update, the lane line observation matrix corresponding to the j-th update, and the third error value obtained in the j-th update;

[0180] Determine the third error value obtained in the (j + 1)-th update according to the update error corresponding to the (j + 1)-th update and the third error value corresponding to the j-th update;

[0181] Determine the lane line observation value corresponding to the (j + 1)-th update;

[0182] In the case where the lane line observation value corresponding to the (j + 1)-th update is less than the preset threshold, determine the third error value obtained in the (j + 1)-th update as the second error value;

[0183] In the case where the lane line observation value corresponding to the (j + 1)-th update is greater than or equal to the preset threshold, perform the (j + 2)-th update on the third error value of the error variable of the first relative state according to the predicted value of the covariance matrix and the lane line observation matrix corresponding to the (j + 1)-th update until the lane line observation value is less than the preset threshold, and use the updated third error value as the second error value.

[0184] In a possible implementation manner of the embodiment of the present application, the second obtaining unit is configured to:

[0185] Obtain the first lane line equation at the second moment and the second lane line equation at the first moment;

[0186] Sample the first lane line equation and the second lane line equation respectively to obtain a plurality of first sampling points on the first lane line and a plurality of second sampling points on the second lane line;

[0187] Determine two target second sampling points corresponding to each first sampling point from the plurality of second sampling points;

[0188] Determine the first position of each first sampling point in the vehicle body coordinate system at the first moment;

[0189] Determine the observation matrix corresponding to each first sampling point according to the first position corresponding to each first sampling point and the second positions of the two target second sampling points in the vehicle body coordinate system at the first moment;

[0190] Determine a lane line observation matrix based on the observation matrices corresponding to multiple first sampling points.

[0191] In a possible implementation manner of the embodiment of the present application, an acquisition module 610 is configured to:

[0192] Obtain a second estimated value of the second relative state of the vehicle at a first moment relative to a third moment, and a measurement value of the inertial measurement unit at a second moment; wherein, the third moment is earlier than the first moment;

[0193] Obtain a first predicted value of the first relative state according to the second estimated value and the measurement value of the second relative state.

[0194] It should be noted that the explanations of the embodiments of the method for determining the vehicle position and attitude data also apply to the device for determining the vehicle position and attitude data in this embodiment, so details will not be repeated here.

[0195] In the embodiment of the present application, by obtaining a first error value of the error variable of the first relative state of the second moment relative to the first moment, and according to the first error value and the first predicted value of the first relative state, determining a first estimated value of the first relative state, and according to the first estimated value and the first position and attitude data of the vehicle at the first moment, determining the second position and attitude data of the vehicle at the second moment. Thus, by determining the position and attitude data of the vehicle at the second moment based on the error value of the error variable of the relative state between different moments, the influence of map projection error is eliminated, and the accuracy of the position and attitude data is improved.

[0196] According to the embodiments of the present application, the present application also provides an electronic device, a readable storage medium, and a computer program product.

[0197] Figure 7 FIG. shows a schematic block diagram of an exemplary electronic device 700 that can be used to implement the embodiments of the present application. The electronic device is intended to represent various forms of digital computers, such as, for example, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described herein and / or claimed.

[0198] As Figure 7As shown, device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to computer programs stored in a ROM (Read-Only Memory) 702 or computer programs loaded from a storage unit 708 into a RAM (Random Access Memory) 703. In the RAM 703, various programs and data required for the operation of device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An I / O (Input / Output) interface 705 is also connected to the bus 704.

[0199] Multiple components in device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a disk, an optical disc, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0200] The computing unit 701 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include but are not limited to a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 701 executes the various methods and processes described above, such as the method for determining vehicle position and attitude data. For example, in some embodiments, the method for determining vehicle position and attitude data can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed onto device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the method for determining vehicle position and attitude data described above can be executed. Alternatively, in other embodiments, the computing unit 701 can be configured to execute the method for determining vehicle position and attitude data in any other appropriate manner (e.g., by means of firmware).

[0201] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chip), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which may be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0202] The program code for implementing the methods of this application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that the program codes, when executed by the processor or controller, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on a remote machine or server.

[0203] In the context of this application, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory), or a flash memory, an optical fiber, a CD-ROM (Compact Disc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0204] To provide for interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0205] The systems and techniques described herein can be implemented in a computing system that includes backend components (such as, for example, a data server), or a computing system that includes middleware components (such as, for example, an application server), or a computing system that includes frontend components (such as, for example, a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (such as, for example, a communication network). Examples of communication networks include: a LAN (Local Area Network), a WAN (Wide Area Network), the Internet, and a blockchain network.

[0206] A computer system may include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The client-server relationship is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services (Virtual Private Server). The server may also be a server of a distributed system or a server combined with a blockchain.

[0207] According to an embodiment of the present application, the present application also provides a computer program product, which, when executed by an instruction processor in the computer program product, executes the method for determining vehicle position and attitude data proposed in the above embodiments of the present application.

[0208] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in the present application can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present application can be achieved, and no limitation is made herein.

[0209] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present application shall be included within the protection scope of the present application.

Claims

1. A method for determining vehicle position and attitude data, comprising: Obtaining the first position and attitude data of the vehicle at the first moment, the first predicted value of the first relative state of the vehicle at the second moment relative to the first moment, and the first error value of the error variable corresponding to the first relative state; wherein, the first moment is earlier than the second moment, the first predicted value is obtained based on the predicted value of the relative state of the first moment relative to the third moment and the measured value of the inertial measurement unit, the third moment is earlier than the first moment, and the first error value is obtained by updating the vehicle speed observation acquisition filter or the lane line observation acquisition filter; Determining a first estimated value of the first relative state according to the first error value and the first predicted value; Determining the second position and attitude data of the vehicle at the second moment according to the first position and attitude data and the first estimated value.

2. The method according to claim 1, wherein, Updating the vehicle speed observation acquisition filter to obtain the first error value of the error variable corresponding to the first relative state, including: Obtaining the vehicle speed observation matrix at the second moment, the estimated value of the covariance matrix between the position and attitude of the vehicle at the first moment, and the second error value of the error variable corresponding to the second relative state of the vehicle at the first moment relative to the third moment; Determining the predicted value of the covariance matrix at the second moment according to the estimated value of the covariance matrix at the first moment; Updating the second error value according to the vehicle speed observation matrix and the predicted value of the covariance matrix to determine the first error value.

3. The method according to claim 2, wherein, The updating the second error value according to the vehicle speed observation matrix and the predicted value of the covariance matrix to determine the first error value includes: Determining the filtering gain matrix at the second moment according to the vehicle speed observation matrix and the predicted value of the covariance matrix; Updating the second error value according to the filtering gain matrix and the first speed of the vehicle in the longitudinal direction in the body coordinate system at the second moment to determine the first error value.

4. The method according to claim 2, wherein, The obtaining the vehicle speed observation matrix at the second moment includes: Obtaining the first speed of the vehicle in the longitudinal direction in the body coordinate system at the second moment, and the second speed of the first speed in the body coordinate system at the first moment; Obtaining the vehicle speed observation matrix according to the first speed, the second speed, and the rotation matrix from the second speed to the first speed.

5. The method according to claim 1, wherein, Updating the lane line observation acquisition filter to obtain the first error value of the error variable corresponding to the first relative state, including: Obtaining the lane line observation matrix at the second moment, the estimated value of the covariance matrix between the position and attitude of the vehicle at the first moment, and the second error value of the error variable corresponding to the second relative state of the vehicle at the first moment relative to the third moment; Determining the predicted value of the covariance matrix at the second moment according to the estimated value of the covariance matrix at the first moment; Updating the second error value according to the lane line observation matrix and the predicted value of the covariance matrix to determine the first error value.

6. The method according to claim 5, wherein, Updating the second error value according to the predicted values of the lane line observation matrix and the covariance matrix to determine the first error value, includes: Obtaining the lane line observation matrix corresponding to the j-th update and the third error value of the error variable of the first relative state obtained in the j-th update; where j is a natural number; Determining the filtering gain matrix obtained in the j-th update according to the predicted value of the covariance matrix and the lane line observation matrix corresponding to the j-th update; Determining the update error of the (j + 1)-th update relative to the j-th update according to the filtering gain matrix obtained in the j-th update, the lane line observation matrix corresponding to the j-th update, and the third error value obtained in the j-th update; Determining the third error value obtained in the (j + 1)-th update according to the update error corresponding to the (j + 1)-th update and the third error value corresponding to the j-th update; Determining the lane line observation value corresponding to the (j + 1)-th update; When the lane line observation value corresponding to the (j + 1)-th update is less than the preset threshold, determining the third error value obtained in the (j + 1)-th update as the second error value; When the lane line observation value corresponding to the (j + 1)-th update is greater than or equal to the preset threshold, performing the (j + 2)-th update on the third error value of the error variable of the first relative state according to the predicted value of the covariance matrix and the lane line observation matrix corresponding to the (j + 1)-th update, until the lane line observation value is less than the preset threshold, and taking the updated third error value as the second error value.

7. The method according to claim 5, wherein, The obtaining the lane line observation matrix at the second moment includes: Obtaining the first lane line equation at the second moment and the second lane line equation at the first moment; Sampling the first lane line equation and the second lane line equation respectively to obtain a plurality of first sampling points on the first lane line and a plurality of second sampling points on the second lane line; Determining two target second sampling points corresponding to each first sampling point from the plurality of second sampling points; Determining the first position of each first sampling point in the vehicle body coordinate system at the first moment; Determining the observation matrix corresponding to each first sampling point according to the first position corresponding to each first sampling point and the second positions of the two target second sampling points in the vehicle body coordinate system at the first moment; Determining the lane line observation matrix according to the observation matrices corresponding to the plurality of first sampling points respectively; 8. The method according to any one of claims 1-7, wherein, The obtaining the first predicted value of the first relative state of the vehicle at the second moment relative to the first moment includes: Obtaining the second estimated value of the second relative state of the vehicle at the first moment relative to the third moment and the measurement value of the inertial measurement unit at the second moment; obtaining the first predicted value of the first relative state according to the second estimated value of the second relative state and the measurement value; 9. A device for determining vehicle position and attitude data, includes: An acquisition module, configured to acquire the first position and attitude data of the vehicle at the first moment, the first predicted value of the first relative state of the vehicle at the second moment relative to the first moment, and the first error value of the error variable corresponding to the first relative state; wherein, the first moment is earlier than the second moment; the first predicted value is obtained based on the predicted value of the relative state of the first moment relative to the third moment and the measured value of the inertial measurement unit, the third moment is earlier than the first moment, and the first error value is obtained by updating a vehicle speed observation acquisition filter or a lane line observation acquisition filter; A first determination module, configured to determine a first estimated value of the first relative state according to the first error value and the first predicted value; A second determination module, configured to determine the second position and attitude data of the vehicle at the second moment according to the first position and attitude data and the first estimated value.

10. The device according to claim 9, wherein, The acquisition module includes: A first acquisition unit, configured to acquire the vehicle speed observation matrix at the second moment, the estimated value of the covariance matrix between the position and attitude of the vehicle at the first moment, and the second error value of the error variable corresponding to the second relative state of the vehicle at the first moment relative to the third moment; A first determination unit, configured to determine the predicted value of the covariance matrix at the second moment according to the estimated value of the covariance matrix at the first moment; A first update unit, configured to update the second error value according to the vehicle speed observation matrix and the predicted value of the covariance matrix to determine the first error value.

11. The apparatus according to claim 10, wherein, The first update unit is configured to: Determine the filtering gain matrix at the second moment according to the vehicle speed observation matrix and the predicted value of the covariance matrix; Update the second error value according to the filtering gain matrix and the first speed of the vehicle in the longitudinal direction in the body coordinate system at the second moment to determine the first error value.

12. The device according to claim 10, wherein, The first acquisition unit is configured to: Acquire the first speed of the vehicle in the longitudinal direction in the body coordinate system at the second moment, and the second speed of the first speed in the body coordinate system at the first moment; Acquire the vehicle speed observation matrix according to the first speed, the second speed, and the rotation matrix from the second speed to the first speed.

13. The device according to claim 9, wherein The acquisition module includes: A second acquisition unit, configured to acquire the lane line observation matrix at the second moment, the estimated value of the covariance matrix between the position and attitude of the vehicle at the first moment, and the second error value of the error variable corresponding to the second relative state of the vehicle at the first moment relative to the third moment; A second determination unit, configured to determine the predicted value of the covariance matrix at the second moment according to the estimated value of the covariance matrix at the first moment; A second update unit, configured to update the second error value according to the lane line observation matrix and the predicted value of the covariance matrix to determine the first error value.

14. The apparatus according to claim 13, wherein, The second update unit is configured to: Acquire the lane line observation matrix corresponding to the jth update, and the third error value of the error variable of the first relative state obtained by the jth update; wherein, j is a natural number; Determine the filtering gain matrix obtained by the j-th update according to the predicted value of the covariance matrix and the lane line observation matrix corresponding to the j-th update; Determine the update error corresponding to the (j + 1)-th update relative to the j-th update according to the filtering gain matrix obtained by the j-th update, the lane line observation matrix corresponding to the j-th update, and the third error value obtained by the j-th update; Determine the third error value obtained by the (j + 1)-th update according to the update error corresponding to the (j + 1)-th update and the third error value corresponding to the j-th update; Determine the lane line observation value corresponding to the (j + 1)-th update; In the case where the lane line observation value corresponding to the (j + 1)-th update is less than the preset threshold, determine the third error value obtained by the (j + 1)-th update as the second error value; In the case where the lane line observation value corresponding to the (j + 1)-th update is greater than or equal to the preset threshold, perform the (j + 2)-th update on the third error value of the error variable of the first relative state according to the predicted value of the covariance matrix and the lane line observation matrix corresponding to the (j + 1)-th update until the lane line observation value is less than the preset threshold, and use the updated third error value as the second error value.

15. The device according to claim 13, wherein, The second obtaining unit is configured to: Obtain the first lane line equation at the second moment and the second lane line equation at the first moment; Sample the first lane line equation and the second lane line equation respectively to obtain a plurality of first sampling points on the first lane line and a plurality of second sampling points on the second lane line; Determine two target second sampling points corresponding to each of the first sampling points from the plurality of second sampling points; Determine the first position of each of the first sampling points in the vehicle body coordinate system at the first moment; Determine the observation matrix corresponding to each of the first sampling points according to the first position corresponding to each of the first sampling points and the second positions of the two target second sampling points in the vehicle body coordinate system at the first moment; Determine the lane line observation matrix according to the observation matrices corresponding to the plurality of first sampling points respectively.

16. The device according to any one of claims 9-15, wherein, The obtaining module is configured to: Obtain the second estimated value of the second relative state of the vehicle at the first moment relative to the third moment and the measurement value of the inertial measurement unit at the second moment; Obtain the first predicted value of the first relative state according to the second estimated value of the second relative state and the measurement value.

17. An electronic device, comprising: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1-8.

18. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to execute the method according to any one of claims 1-8.

19. A computer program product, comprising a computer program, wherein the computer program implements the steps of the method according to any one of claims 1-8 when executed by a processor.

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