A method, device, equipment and storage medium for updating a vehicle driving state

Abstract

Embodiments of the present application provide a method, device and equipment for updating the driving state of a vehicle, and a storage medium. The application scenarios include, but are not limited to, map navigation, automatic driving, Internet of Vehicles, vehicle-road cooperation, intelligent transportation, etc. A plurality of visual perception images containing the same reference lane line are collected, and a first reference position point on the reference lane line is obtained from each visual perception image. The positional relationship between each first reference position point and the target vehicle satisfies a preset condition. Compared with a single visual perception image collected by a vehicle-mounted camera, the plurality of first reference position points obtained are not limited by the effective sensing distance of the vehicle-mounted camera, thereby ensuring the accuracy of the position information of the plurality of first reference position points. Therefore, when the target position information of the target vehicle in a map coordinate system is determined based on each first reference position point obtained, and the driving state of the target vehicle is updated, the accuracy of the driving state of the target vehicle can be improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, apparatus, device and storage medium for updating the driving status of a vehicle. Background Technology

[0002] With the rise of emerging industries and technological revolutions such as big data, cloud computing, and artificial intelligence, intelligent vehicles have become a complex integrating industries and disciplines such as mechanics, electrical engineering, computer science, information technology, artificial intelligence, and human factors engineering. In autonomous driving or assisted driving scenarios, how to update the driving status of intelligent vehicles in real time to improve driving safety has become a crucial technology.

[0003] Related technologies use onboard cameras to capture images of lane lines in front of the vehicle at a given moment, and then determine the positional relationship between the vehicle and the lane lines based on these images. However, the effective sensing distance of onboard cameras is relatively short, and the accuracy of the positional information corresponding to a point farther from the camera in the lane line image decreases. Therefore, the accuracy of determining the vehicle's driving status based on the lane line images captured by the onboard camera at a given moment is relatively low. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for updating the driving status of a vehicle, which improves the accuracy of updating the driving status of the target vehicle.

[0005] On one hand, embodiments of this application provide a method for updating the driving status of a vehicle, the method comprising:

[0006] Acquire N frames of visually perceived images captured by an image acquisition device, wherein the N frames of visually perceived images contain the same reference lane line, and N is a positive integer greater than 1;

[0007] From each frame of visually perceived image, a first reference position point on the reference lane line is obtained, wherein the positional relationship between each first reference position point and the target vehicle satisfies a preset condition;

[0008] Based on the obtained first reference points, the positional relationship between the first reference points and the target vehicle, and map data, the target position information of the target vehicle in the map coordinate system is determined.

[0009] The driving status of the target vehicle is updated based on the target location information to obtain a first driving status.

[0010] On one hand, embodiments of this application provide an apparatus for updating the driving status of a vehicle, the apparatus comprising:

[0011] The acquisition module is used to acquire N frames of visually perceived images acquired by the image acquisition device, wherein the N frames of visually perceived images contain the same reference lane line, and N is a positive integer greater than 1;

[0012] The extraction module is used to obtain a first reference position point on the reference lane line from each frame of visually perceived image, wherein the positional relationship between each first reference position point and the target vehicle satisfies a preset condition;

[0013] The positioning module is used to determine the target location information of the target vehicle in the map coordinate system based on the obtained first reference position points, the positional relationship between the first reference position points and the target vehicle, and map data.

[0014] The update module is used to update the driving status of the target vehicle based on the target location information to obtain a first driving status.

[0015] Optionally, the positioning module is specifically used for:

[0016] The obtained N first reference position points are subjected to curve fitting to obtain the target lane line curve;

[0017] The target lane curve is resampled to obtain M resampled first reference position points, where M is a positive integer greater than 1 and less than or equal to N;

[0018] Based on the M resampled first reference position points, the positional relationship between the M resampled first reference position points and the target vehicle, and map data, the target position information of the target vehicle in the map coordinate system is determined;

[0019] Optionally, the positioning module is specifically used for:

[0020] The M resampled first reference position points are matched with the map data to obtain the second reference position points corresponding to each of the M resampled first reference position points in the map data, as well as the reference position information of each second reference position point in the map coordinate system.

[0021] Based on the obtained reference location information and the positional relationship between the M resampled first reference location points and the target vehicle, the target location information of the target vehicle in the map coordinate system is determined.

[0022] Optionally, the update module is specifically used for:

[0023] Obtain the first observation matrix and the first observation noise covariance corresponding to the image acquisition device;

[0024] Based on the first observation matrix and the target vehicle's last updated historical location information, the predicted location information of the target vehicle is determined;

[0025] Based on the historical driving state covariance of the target vehicle in the last update, the first observation matrix, and the first observation noise covariance, the first gain matrix is ​​determined.

[0026] The driving state of the target vehicle is updated based on the first gain matrix, the target position information, and the predicted position information to obtain a first driving state.

[0027] Optionally, the update module is further configured to:

[0028] The historical driving state covariance is updated based on the first gain matrix, the first observation matrix, and the first observation noise covariance.

[0029] Optionally, the update module is further configured to:

[0030] Based on the first gain matrix, the first observation matrix, and the first observation noise covariance, after updating the historical driving state covariance, the target positioning coordinates collected by the vehicle-mounted positioning device on the target vehicle, as well as the second observation matrix and the second observation noise covariance corresponding to the vehicle-mounted positioning device are obtained.

[0031] Based on the second observation matrix and the target vehicle's last updated historical positioning coordinates, the predicted positioning coordinates of the target vehicle are determined.

[0032] The second gain matrix is ​​determined based on the historical driving state covariance, the second observation matrix, and the second observation noise covariance.

[0033] The first driving state is updated based on the second gain matrix, the target positioning coordinates, and the predicted positioning coordinates to obtain the second driving state.

[0034] Optionally, the update module is further configured to:

[0035] The historical driving state covariance is updated based on the second gain matrix, the second observation matrix, and the second observation noise covariance.

[0036] Optionally, the update module is further configured to:

[0037] Based on the first gain matrix, the first observation matrix, and the first observation noise covariance, after updating the historical driving state covariance, the target speed information collected by the speed sensor on the target vehicle, as well as the third observation matrix and the third observation noise covariance corresponding to the speed sensor are obtained.

[0038] Based on the third observation matrix and the target vehicle's last updated historical speed information, the predicted speed information of the target vehicle is determined;

[0039] The third gain matrix is ​​determined based on the historical driving state covariance, the third observation matrix, and the third observation noise covariance.

[0040] The first driving state is updated based on the third gain matrix, the target speed information, and the predicted speed information to obtain the third driving state.

[0041] Optionally, the update module is further configured to:

[0042] The historical driving state covariance is updated based on the third gain matrix, the third observation matrix, and the third observation noise covariance.

[0043] Optionally, the update module is further configured to:

[0044] Based on the first gain matrix, the first observation matrix, and the first observation noise covariance, after updating the historical driving state covariance, the acceleration information collected by the vehicle-mounted inertial navigation device on the target vehicle and the fourth observation noise covariance corresponding to the vehicle-mounted inertial navigation device are obtained.

[0045] Based on the acceleration information, the previously updated historical state transition matrix, and the first driving state, the fourth driving state of the target vehicle is predicted.

[0046] The historical driving state covariance is updated based on the historical state transition matrix, the previously updated historical state transition covariance, and the fourth observation noise covariance.

[0047] This application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the method for updating the vehicle driving state described above.

[0048] This application provides a computer-readable storage medium storing a computer program executable by a computer device. When the program is run on the computer device, it causes the computer device to perform the steps of the method for updating the vehicle's driving state described above.

[0049] In this embodiment, multiple frames of visual perception images containing the same reference lane line are acquired. Then, a first reference position point on the reference lane line is obtained from each frame of visual perception image. The positional relationship between each first reference position point and the target vehicle satisfies preset conditions. Compared with a single frame of visual perception image acquired by a camera, the multiple first reference position points are not limited by the effective sensing distance of the camera, ensuring the accuracy of the position information of the multiple first reference position points. Therefore, when determining the target position information of the target vehicle in the map coordinate system based on the obtained first reference position points, and updating the driving status of the target vehicle based on the target position information, the accuracy of updating the driving status of the target vehicle can be effectively improved. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A schematic diagram of a system architecture provided for an embodiment of this application;

[0052] Figure 2 A flowchart illustrating a method for updating vehicle driving status provided in an embodiment of this application;

[0053] Figure 3 This is a schematic diagram of an image acquisition device provided in an embodiment of this application;

[0054] Figure 4 A schematic diagram of a first reference position point provided in an embodiment of this application;

[0055] Figure 5 A flowchart illustrating a method for caching a first reference location point provided in an embodiment of this application;

[0056] Figure 6 A schematic diagram of a map interface provided in an embodiment of this application;

[0057] Figure 7 A schematic diagram of a system architecture provided for an embodiment of this application;

[0058] Figure 8 A schematic diagram of a system architecture provided for an embodiment of this application;

[0059] Figure 9 A flowchart illustrating a method for updating vehicle driving status provided in an embodiment of this application;

[0060] Figure 10a A schematic diagram of a vehicle location provided for an embodiment of this application;

[0061] Figure 10b A schematic diagram of a vehicle location provided for an embodiment of this application;

[0062] Figure 11 A schematic diagram of a device for updating the driving status of a vehicle provided in an embodiment of this application;

[0063] Figure 12 A schematic diagram of the structure of a computer device provided in an embodiment of this application;

[0064] Figure 13 This application provides a schematic diagram of the structure of a distributed system according to an embodiment of the present application.

[0065] Figure 14 This is a schematic diagram of the structure of a block provided in an embodiment of this application. Detailed Implementation

[0066] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0067] For ease of understanding, the terms used in the embodiments of this invention are explained below.

[0068] Autonomous driving technology typically includes high-precision maps, environmental perception, behavior decision-making, path planning, motion control, and other technologies, and autonomous driving technology has broad application prospects.

[0069] Lane markings: These are used to indicate the direction vehicles should travel when entering an intersection. They are typically painted at busy intersections to clearly define driving directions, ensure vehicles stay in their lanes, and alleviate traffic congestion. Lane markings include white dashed lines, white solid lines, directional lines, and speed reduction warning lines.

[0070] Electronic horizon: Road geometry and attribute information at a certain distance in front of the vehicle.

[0071] Curve fitting: Given discrete data points, a data relationship (mathematical model) is established, and an optimization method is used to find a suitable parametric curve that approximates the given discrete data points as a whole. As long as the interval of the interpolation points is chosen properly, a smooth curve can be formed.

[0072] The design concept of the embodiments of this application will be introduced below.

[0073] In autonomous or assisted driving scenarios, real-time updates of a smart car's driving status to improve driving safety have become crucial technologies. Related technologies use onboard cameras to capture images of the lane lines ahead of the vehicle at any given moment, then determine the vehicle's position relative to the lane lines based on these images. However, onboard cameras have a relatively short effective sensing range, and the accuracy of the positional information corresponding to points farther from the camera in the lane line image decreases. Therefore, determining the vehicle's driving status based on lane line images captured by onboard cameras at any given moment is not very accurate.

[0074] Considering that the lane line images captured by vehicle cameras have higher accuracy at points closer to the vehicle, using lane line points closer to the vehicle from multiple frames of lane line images to represent lane lines will effectively improve the accuracy of lane line position information, thereby improving the accuracy of updating vehicle driving status.

[0075] In view of this, embodiments of this application provide a method for updating the driving state of a vehicle. In this method, firstly, N frames of visually perceived images are acquired by an image acquisition device, wherein the N frames of visually perceived images contain the same reference lane line, and N is a positive integer greater than 1. Then, from each frame of visually perceived images, a first reference position point on the reference lane line is obtained, wherein the positional relationship between each first reference position point and the target vehicle satisfies preset conditions. Based on the obtained first reference position points, the positional relationships between each first reference position point and the target vehicle, and map data, the target position information of the target vehicle in the map coordinate system is determined. Finally, the driving state of the target vehicle is updated based on the target position information to obtain a first driving state.

[0076] In this embodiment, multiple frames of visual perception images containing the same reference lane line are acquired. Then, a first reference position point on the reference lane line is obtained from each frame of visual perception image. The positional relationship between each first reference position point and the target vehicle satisfies preset conditions. Compared with a single frame of visual perception image acquired by a camera, the multiple first reference position points are not limited by the effective sensing distance of the camera, ensuring the accuracy of the position information of the multiple first reference position points. Therefore, when determining the target position information of the target vehicle in the map coordinate system based on the obtained first reference position points, and updating the driving status of the target vehicle based on the target position information, the accuracy of updating the driving status of the target vehicle can be effectively improved.

[0077] refer to Figure 1 This is a system architecture diagram applicable to the embodiments of this application. The system architecture includes at least a terminal device 101 and a server 102.

[0078] Terminal device 101 is pre-installed with a target application for updating vehicle driving status. The target application may be a pre-installed client application, web application, mini-program, etc. Terminal device 101 may be a navigation device, autonomous driving device, smartphone, tablet, in-vehicle device, laptop, desktop computer, smart speaker, etc., but is not limited to these.

[0079] In one or more embodiments, the terminal device 101 is a device embedded in the vehicle, which may be a navigation device, an autonomous driving device, a smartphone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, etc., but is not limited thereto.

[0080] Server 102 is the backend server corresponding to the target application, providing services to the target application. Server 102 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms. Terminal device 101 and server 102 can be directly or indirectly connected via wired or wireless communication, which is not limited herein.

[0081] The method for updating the vehicle's driving status can be executed by the terminal device 101, by the server 102, or by the interaction between the terminal device 101 and the server 102.

[0082] In the first scenario, the method for updating the vehicle's driving status is executed by the terminal device 101.

[0083] Terminal device 101 acquires N frames of visually perceived images captured by an image acquisition device, wherein the N frames of visually perceived images contain the same reference lane line, and N is a positive integer greater than 1. Then, from each frame of visually perceived images, a first reference position point on the reference lane line is obtained, wherein the positional relationship between each first reference position point and the target vehicle satisfies preset conditions. Based on the obtained first reference position points, the positional relationships between each first reference position point and the target vehicle, and map data, the target vehicle's target position information in the map coordinate system is determined. The driving state of the target vehicle is then updated based on the target position information to obtain a first driving state. Terminal device 101 displays the first driving state of the target vehicle on a display interface.

[0084] In the second scenario, the method for updating the vehicle's driving status is executed by server 102.

[0085] Terminal device 101 acquires N frames of visually perceived images captured by an image acquisition device, wherein the N frames of visually perceived images contain the same reference lane line, and N is a positive integer greater than 1. Terminal device 101 sends the N frames of visually perceived images to server 102. Server 102 obtains a first reference position point on the reference lane line from each frame of visually perceived images, wherein the positional relationship between each first reference position point and the target vehicle satisfies preset conditions. Based on the obtained first reference position points, the positional relationship between each first reference position point and the target vehicle, and map data, the target position information of the target vehicle in the map coordinate system is determined. Then, the driving state of the target vehicle is updated based on the target position information to obtain a first driving state. Server 102 sends the first driving state of the target vehicle to vehicle terminal 101, and terminal device 101 displays the first driving state of the target vehicle on the display interface.

[0086] The method for updating the vehicle's driving status can also be executed interactively between the terminal device 101 and the server 102, which will not be elaborated here.

[0087] based on Figure 1 The system architecture diagram shown in this application illustrates the flowchart of a method for updating vehicle driving status. Figure 2 As shown, the process of this method can be executed by a computer device, which can be a terminal device 101 or a server 102, and includes the following steps:

[0088] Step S201: Acquire N frames of visual perception images captured by the image acquisition device.

[0089] Specifically, the image acquisition device can be a camera, webcam, or other visual sensor, and can be installed at the front, parking space, or roof of the target vehicle. For example, such as... Figure 3 As shown, a camera 302 is installed at the front of vehicle 301. Image acquisition devices can also be directly embedded in the target vehicle, such as in-vehicle cameras or in-vehicle cameras.

[0090] A visually perceived image may include one or more lane lines. N frames of visually perceived images can be N frames continuously acquired by an image acquisition device, or N frames extracted from multiple images acquired by the same device. The extraction interval can be fixed or random, where N is a positive integer greater than 1. The N frames of visually perceived images contain the same reference lane line, which can be any lane line in the visually perceived image. A single frame of a visually perceived image may include one or more reference lane lines.

[0091] Step S202: Obtain a first reference position point on the reference lane line from each frame of visually perceived image.

[0092] Specifically, the positional relationship between the first reference position point and the target vehicle includes the lateral offset distance, longitudinal offset distance, and straight-line distance between the first reference position point and the target vehicle. The positional relationship between each first reference position point and the target vehicle satisfies preset conditions, such as minimum distance or distance less than a preset threshold.

[0093] The inherent characteristics of image acquisition equipment lead to uneven positional accuracy of reference lane lines in visually perceived images; that is, the farther the distance from the vehicle's front, the lower the positional accuracy. Therefore, if the positional relationship between the first reference point and the target vehicle satisfies the condition of minimizing the distance between the first reference point and the target vehicle, then the reference point with the highest positional accuracy on the reference lane line in each frame of the visually perceived image can be obtained. Utilizing the reference point with the highest positional accuracy in N frames of visually perceived images can effectively compensate for the uneven accuracy problem caused by different detection distances in the image acquisition equipment.

[0094] For example, such as Figure 4 As shown, the visual perception image includes lane line 1 and lane line 2. The closest point to the target vehicle on lane line 1 is designated as position point M, and the closest point to the target vehicle on lane line 2 is designated as position point N. Position point M is used as the first reference position point on lane line 1, and position point N is used as the first reference position point on lane line 2.

[0095] After acquiring each frame of visual perception image, the terminal device first determines the reference lane lines in the visual perception image. A frame of visual perception image may include one or more reference lane lines. For each reference lane line, the first reference position point closest to the target vehicle is obtained from that reference lane line, and then the first reference position point is cached. The first reference position points of the same reference lane line are cached accordingly.

[0096] The terminal device can also acquire multiple frames of visual perception images consecutively, obtain the first reference position points of the same reference lane line from each of the multiple frames, and then cache the corresponding first reference position points of the same reference lane line. When updating the driving status of the target vehicle, the N most recently cached first reference position points can be selected. Utilizing caching technology can significantly expand the effective range of visual perception data participating in each map matching process, improving matching accuracy.

[0097] For example, such as Figure 5As shown, the terminal device acquires a visual perception image A from the vehicle-mounted camera at the first moment. Visual perception image A includes lane line 1, lane line 2, and lane line 3. A first reference position point a1 is extracted from lane line 1, a2 from lane line 2, and a3 from lane line 3. The first reference position points a1, a2, and a3 are then cached.

[0098] The terminal device acquires a visual perception image B from the vehicle-mounted camera at a second moment. Visual perception image B includes lane line 1, lane line 2, and lane line 3. A first reference position point b1 is extracted from lane line 1, a first reference position point b2 is extracted from lane line 2, and a first reference position point b3 is extracted from lane line 3. The first reference position points a1 and b1 are cached accordingly, as are the first reference position points a2 and b2, and the first reference position points a3 and b3 are cached separately.

[0099] At the third moment, the terminal device acquires the visual perception image C captured by the vehicle-mounted camera. Visual perception image C includes lane line 1, lane line 2, and lane line 3. A first reference position point c1 is extracted from lane line 1, a first reference position point c2 from lane line 2, and a first reference position point c3 from lane line 3. The first reference position points a1, b1, and c1 are cached accordingly; the first reference position points a2, b2, and c2 are cached accordingly; and the first reference position points a3, b3, and c3 are cached accordingly. This process continues in sequence.

[0100] If lane line 1 is not present in the visual perception image subsequently acquired by the vehicle camera, then the caching of the first reference position point on lane line 1 will stop. Similarly, if lane line 2 or lane line 3 is not present in the visual perception image subsequently acquired by the vehicle camera, then the caching of the first reference position point on lane line 2 or lane line 3 will stop.

[0101] If lane line 4 is added to the visual perception images subsequently captured by the vehicle camera, the first reference position point on lane line 4 will be cached in the same way.

[0102] Step S203: Based on the obtained first reference position points, the positional relationship between each first reference position point and the target vehicle, and the map data, determine the target position information of the target vehicle in the map coordinate system.

[0103] Specifically, the obtained first reference position points can be used to represent reference lane lines. Combined with map data, the reference lane lines can be mapped to the map coordinate system. Then, based on the positional relationship between each first reference position point and the target vehicle, the target vehicle's target position information in the map coordinate system can be determined. The target position information can be represented as X = (x, y, theta), where x represents the position in the due east direction in the map coordinate system, y represents the position in the due north direction in the map coordinate system, and theta represents the heading angle in the map coordinate system, which represents the clockwise angle in the due north direction.

[0104] Step S204: Update the driving status of the target vehicle based on the target location information to obtain the first driving status.

[0105] Specifically, the target vehicle's historical driving state is updated based on its previous update, based on its location information, to obtain the first driving state. The target vehicle's driving state includes its location information, speed information, acceleration information, etc. For example, the target vehicle's driving state can be represented as X = (x, y, v) x v y (theta), where x represents the position due east in the map coordinate system, y represents the position due north in the map coordinate system, and v x v represents the velocity in the due east direction in the map coordinate system. y The value represents the speed in the due north direction in the map coordinate system, and theta represents the heading angle in the map coordinate system.

[0106] After obtaining the first driving status of the target vehicle, the terminal device overlays the first driving status onto the map for real-time rendering and displays the rendering result on the display interface.

[0107] For example, the terminal device determines the first driving state of the target vehicle as (x1, y1, v). x1 v y1 (theta1) The first driving state is overlaid onto the map for real-time rendering, and the resulting rendering result is as follows: Figure 6 As shown, this includes the target vehicle's position on the map, as well as its current speed and heading angle.

[0108] In this embodiment, multiple frames of visual perception images containing the same reference lane line are acquired. Then, a first reference position point on the reference lane line is obtained from each frame of visual perception image. The positional relationship between each first reference position point and the target vehicle satisfies preset conditions. Compared with a single frame of visual perception image acquired by a camera, the multiple first reference position points are not limited by the effective sensing distance of the camera, ensuring the accuracy of the position information of the multiple first reference position points. Therefore, when determining the target position information of the target vehicle in the map coordinate system based on the obtained first reference position points, and updating the driving status of the target vehicle based on the target position information, the accuracy of updating the driving status of the target vehicle can be effectively improved.

[0109] Optionally, in step S203 above, each obtained first reference position point is matched with map data to obtain the corresponding second reference position point in the map data for each first reference position point, as well as the reference position information of each second reference position point in the map coordinate system. Then, based on the obtained reference position information and the positional relationship between each first reference position point and the target vehicle, the target position information of the target vehicle in the map coordinate system is determined.

[0110] Specifically, each obtained first reference position point is a reference position point in the vehicle coordinate system, which represents a coordinate system with the vehicle as its origin. High-precision map data is acquired using an electronic horizon method. Based on the mapping relationship between the vehicle coordinate system and the map coordinate system, each first reference position point is matched with the map data to obtain the corresponding second reference position point in the map data for each first reference position point, as well as the reference position information of each second reference position point in the map coordinate system. The positional relationship between the first reference position point and the target vehicle includes, in the vehicle coordinate system, the lateral offset distance, longitudinal offset distance, and straight-line distance between the first reference position point and the target vehicle.

[0111] In this embodiment, the first reference position points of the reference lane line are obtained from multiple frames of visually perceived images containing the same reference lane line. Then, multiple first reference position points are used to represent the reference lane line. Compared with the reference lane line in a single frame of visually perceived image, the reference lane line represented by multiple first reference position points is not limited by the effective sensing distance of the camera, thereby improving the accuracy of the reference lane line's position. Therefore, when the target vehicle's target position information in the map coordinate system is determined by matching the obtained multiple first reference position points with map data, the accuracy of locating the target vehicle's target position information in the map coordinate system can be effectively improved.

[0112] Optionally, since the multiple first reference position points are extracted from multiple frames of visual perception images respectively, and the image acquisition device may have positional deviations or jitters when acquiring different visual perception images, the multiple first reference position points obtained are not smooth enough and have burrs.

[0113] Therefore, in this embodiment, the obtained N first reference position points are curve-fitted to obtain the target lane line curve. Then, the target lane line curve is resampled to obtain M resampled first reference position points, where M is a positive integer greater than 1 and less than or equal to N. Based on the M resampled first reference position points, the positional relationship between the M resampled first reference position points and the target vehicle, and map data, the target position information of the target vehicle in the map coordinate system is determined.

[0114] Specifically, Fresnel curves, Bezier curves, and other curves can be used to fit the obtained N first reference position points to obtain the target lane line curve. Resampling represents the discretization of the target lane line curve, and the resampling interval can be set according to actual needs. Then, M resampled first reference position points are matched with map data to obtain the corresponding second reference position points in the map data for each of the M resampled first reference position points, as well as the reference position information of each second reference position point in the map coordinate system. Based on the obtained reference position information and the positional relationship between the M resampled first reference position points and the target vehicle, the target position information of the target vehicle in the map coordinate system is determined.

[0115] In this embodiment, curve fitting technology is used to smooth and filter the first reference position points extracted from multiple frames of visually perceived images, removing spurious points, thereby improving the accuracy of matching map data and locating the target vehicle's position information in the map coordinate system.

[0116] Optionally, in step S204 above, Kalman filtering or extended Kalman filtering can be used to update the driving state of the target vehicle and obtain the first driving state.

[0117] Specifically, the first observation matrix and the first observation noise covariance corresponding to the image acquisition device are obtained. Then, based on the first observation matrix and the target vehicle's last updated historical position information, the predicted position information of the target vehicle is determined. Next, based on the target vehicle's last updated historical driving state covariance, the first observation matrix, and the first observation noise covariance, a first gain matrix is ​​determined. Based on the first gain matrix, the target position information, and the predicted position information, the target vehicle's driving state is updated to obtain the first driving state. The first observation matrix can be the observation Jacobian matrix.

[0118] Specifically, the first observation matrix and the first observation noise covariance are related to the characteristics of the image acquisition device itself. Based on the historical driving state covariance of the target vehicle in the last update, the first observation matrix, and the first observation noise covariance, the first gain matrix is ​​determined as shown in the following formula (1):

[0119] K1 = P1 k-1 *C′1 / (C1*P1 k-1 *C′1+R1)……………….(1)

[0120] Where K1 represents the first gain matrix, P1 k-1 Let C1 represent the historical driving state covariance of the target vehicle in the last update, C′1 represent the first observation matrix, C′1 represent the transpose of the first observation matrix, and R1 represent the first observation noise covariance.

[0121] Based on the first gain matrix, target position information, and predicted position information, the driving state of the target vehicle is updated to obtain the first driving state, as shown in the following formula (2):

[0122] X1′ k =X1′ k-1 +K1*(Y1 k -Y1)………………….(2)

[0123] Where K1 represents the first gain matrix, X1′ k Indicates the first driving state, X1′ k-1 Y1 represents the historical driving status of the target vehicle in the last update. k Y1 represents the target location information, and Y2 represents the predicted location information.

[0124] Optionally, after updating the driving state of the target vehicle and obtaining the first driving state, the historical driving state covariance is updated based on the first gain matrix, the first observation matrix, and the first observation noise covariance, as shown in the following formula (3):

[0125] P1 k =(I-K1*C1)P1 k-1 *(I-K1*C1)′+K1*R1*K′1………….(3)

[0126] Among them, P1 k K represents the updated historical driving state covariance, K1 represents the first gain matrix, and K′1 represents the transpose of the first gain matrix.

[0127] In this embodiment, Kalman filtering or extended Kalman filtering is used to update the driving status of the target vehicle in real time, thereby improving the efficiency and accuracy of vehicle driving status updates.

[0128] Optionally, the driving state of the target vehicle is updated to obtain a first driving state. After updating the historical driving state covariance based on the first gain matrix, the first observation matrix, and the first observation noise covariance, in this embodiment, the target vehicle's position information in the map coordinate system at the next moment can be determined based on N first reference position points obtained at the next moment. The first driving state of the target vehicle is then updated based on this target position information. In this embodiment, data collected by other positioning data acquisition devices can also be used to update the first driving state, specifically including the following implementation methods:

[0129] Implementation Method 1: Obtain the target positioning coordinates collected by the vehicle-mounted positioning device on the target vehicle, as well as the corresponding second observation matrix and second observation noise covariance. Then, based on the second observation matrix and the target vehicle's last updated historical positioning coordinates, determine the predicted positioning coordinates of the target vehicle. Next, based on the historical driving state covariance, the second observation matrix, and the second observation noise covariance, determine the second gain matrix. Finally, based on the second gain matrix, the target positioning coordinates, and the predicted positioning coordinates, update the first driving state to obtain the second driving state.

[0130] Specifically, the vehicle-mounted positioning device can be a Global Navigation Satellite System (GNSS), such as the Global Positioning System (GPS), BeiDou Navigation Satellite System, or GLONASS. The vehicle-mounted positioning device can be installed on the roof of the target vehicle to receive satellite signals in real time and output the target's positioning coordinates. The target positioning coordinates can be represented as W = (x, y), where x represents the position due east in the map coordinate system, and y represents the position due north in the map coordinate system. The second observation matrix and the second observation noise covariance are related to the positioning performance of the vehicle-mounted positioning device.

[0131] Based on the historical driving state covariance, the second observation matrix, and the second observation noise covariance, the second gain matrix is ​​determined as shown in the following formula (4):

[0132] K2 = P1 k *C′2 / (C2*P1 k *C′2+R2)……………….(4)

[0133] Where K2 represents the second gain matrix, P1 k C1 represents the historical driving state covariance of the target vehicle in the last update, C2 represents the second observation matrix, C′2 represents the transpose of the second observation matrix, and R2 represents the second observation noise covariance.

[0134] Based on the second gain matrix, the target positioning coordinates, and the predicted positioning coordinates, the first driving state is updated to obtain the second driving state, as shown in the following formula (5):

[0135] X2′ k =X1′ k +K2*(Y2 k -Y2)………………….(5)

[0136] Where K2 represents the second gain matrix, X1′ k Indicates the first driving state, X2′ k Y2 represents the second driving state of the target vehicle. k Y1 represents the target location coordinates, and Y2 represents the predicted location coordinates.

[0137] Optionally, the first driving state is updated based on the second gain matrix, the target positioning coordinates, and the predicted positioning coordinates. After obtaining the second driving state, the historical driving state covariance is updated based on the second gain matrix, the second observation matrix, and the second observation noise covariance, as shown in the following formula (6):

[0138] P2 k =(I-K2*C2)P1 k *(I-K2*C2)′+K2*R2*K′2………….(6)

[0139] Among them, P2 k K represents the updated historical driving state covariance, K2 represents the second gain matrix, and K′2 represents the transpose of the second gain matrix.

[0140] Implementation Method 2: Obtain the target speed information collected by the speed sensor on the target vehicle, as well as the corresponding third observation matrix and third observation noise covariance. Then, based on the third observation matrix and the target vehicle's last updated historical speed information, determine the target vehicle's predicted speed information. Next, based on the historical driving state covariance, the third observation matrix, and the third observation noise covariance, determine the third gain matrix. Finally, based on the third gain matrix, the target speed information, and the predicted speed information, update the first driving state to obtain the third driving state.

[0141] Specifically, the speed sensor can be the vehicle's CAN bus, and the target speed information can be wheel speed data, such as the weighted average of four wheel speeds or the wheel speed of a single wheel. The target speed information can be represented as K = [v], where, v x v represents the velocity in the due east direction in the map coordinate system. y This represents the velocity in the due north direction in the map coordinate system. The third observation matrix and the third observation noise covariance are related to the performance of the velocity sensor.

[0142] Based on the historical driving state covariance, the third observation matrix, and the third observation noise covariance, the third gain matrix is ​​determined as shown in the following formula (7):

[0143] K3 = P1 k *C′3 / (C3*P1 k *C′3+R3)……………….(7)

[0144] Where K3 represents the third gain matrix, P1 k C3 represents the historical driving state covariance of the target vehicle in the last update, C′3 represents the third observation matrix, C′3 represents the transpose of the third observation matrix, and R3 represents the third observation noise covariance.

[0145] Based on the third gain matrix, target speed information, and predicted speed information, the first driving state is updated to obtain the third driving state, as shown in the following formula (8):

[0146] X3′ k =X1′ k +K3*(Y3 k -Y3)………………….(8)

[0147] Where K3 represents the third gain matrix, X1′ k Indicates the first driving state, X3′ k Y3 represents the third driving state of the target vehicle. k Y1 represents the target velocity information, and Y2 represents the predicted velocity information.

[0148] Optionally, the first driving state is updated based on the second gain matrix, the target positioning coordinates, and the predicted positioning coordinates. After obtaining the second driving state, the historical driving state covariance is updated based on the third gain matrix, the third observation matrix, and the third observation noise covariance, as shown in the following formula (9):

[0149] P3 k =(I-K3*C3)P1 k *(I-K3*C3)′+K3*R3*K′3………….(9)

[0150] Among them, P3 k K represents the updated historical driving state covariance, K3 represents the third gain matrix, and K′3 represents the transpose of the third gain matrix.

[0151] Implementation Method 3: Obtain the acceleration information collected by the onboard inertial navigation system (INS) on the target vehicle, as well as the corresponding fourth observation noise covariance. Then, based on the acceleration information, the previously updated historical state transition matrix, and the first driving state, predict the fourth driving state of the target vehicle. Next, update the historical driving state covariance based on the historical state transition matrix, the previously updated historical state transition covariance, and the fourth observation noise covariance.

[0152] Specifically, the vehicle-mounted inertial navigation equipment can be an inertial measurement unit (IMU) used to detect the acceleration information of the target vehicle. The acceleration information can be represented as J = (accx, accy, omega), where accx represents the acceleration in the east direction, accy represents the acceleration in the north direction, and omega represents the angular acceleration. Fourth, the observation noise covariance is related to the performance of the vehicle-mounted inertial navigation equipment. The state transition matrix can be a state transition Jacobian matrix.

[0153] Based on acceleration information, the previously updated historical state transition matrix, and the first driving state, the fourth driving state of the target vehicle is predicted, as shown in the following formula (10):

[0154] X4′ k =A k-1 X1′ k +B k-1 *J………………….(10)

[0155] Among them, X4′ k This represents the third driving state of the target vehicle, X1′. k Indicates the first driving state, A k-1 B represents the historical state transition matrix of the last update. k-1 This represents the input Jacobian matrix, and J represents the acceleration information.

[0156] Based on the historical state transition matrix, the previously updated historical state transition covariance, and the fourth observation noise covariance, the historical driving state covariance is updated as shown in the following formula (11):

[0157] P4 k =A k-1 P1 k *(A k-1 )′+Bk-1 *N*B′ k-1 +Q……….(11)

[0158] Among them, P4 k Let N represent the updated historical driving state covariance, N represent the fourth observation noise covariance, and Q represent the state transition process noise covariance.

[0159] In this embodiment, positioning data is collected by multiple vehicle-mounted sensors, and different vehicle driving status update schemes are formulated for the data collected by each type of vehicle-mounted sensor, thereby improving the timeliness and accuracy of vehicle driving status updates.

[0160] To better explain the embodiments of this application, the following describes the process of a method for updating the driving status of a vehicle provided by the embodiments of this application in conjunction with specific implementation scenarios.

[0161] First, let's introduce the system architecture, such as... Figure 7 As shown, the system includes a positioning data acquisition device 701, a positioning module 702, a map retrieval module 703, and a positioning result display module 704. The positioning data acquisition device 701 is located in the target vehicle. The positioning module 702, the map retrieval module 703, and the positioning result display module 704 can be located in the same device or in different devices. Furthermore, the positioning module 702, the map retrieval module 703, and the positioning result display module 704 can all be located in the terminal device, all in the server, or some in the terminal device and some in the server. This application does not make any specific limitations on these aspects.

[0162] Furthermore, such as Figure 8 As shown, the positioning data acquisition device 701 includes an in-vehicle inertial navigation system, a global navigation satellite system, an in-vehicle camera, and a vehicle CAN bus.

[0163] The vehicle-mounted inertial navigation system is an inertial sensor installed in the trunk of the vehicle to sense the vehicle's acceleration information in real time for use by the positioning module 702.

[0164] A sensor mounted on the roof of the vehicle is part of the Global Navigation Satellite System (GNSS) that receives satellite signals in real time and outputs positioning coordinates for use by the positioning module 702.

[0165] The vehicle-mounted camera is a visual sensor installed on the windshield of a vehicle. It is used to identify traffic elements such as lane lines on the road surface in real time and output the images to the positioning module 702.

[0166] The vehicle body CAN bus is a CAN bus receiving device installed on the vehicle body bus, used to read information such as the current wheel speed and steering wheel angle of the vehicle in real time for use by the positioning module 702.

[0167] The positioning module 702 uses positioning data collected by the vehicle inertial navigation equipment, global navigation satellite system, vehicle camera, and vehicle CAN bus, as well as map data in the map calling module 703, to update the vehicle's driving status, and then outputs the positioning results (including the vehicle's coordinate position and speed attitude relative to the map) to the positioning display module 704 for display.

[0168] The map retrieval module 703 is used to load high-precision map data issued by the electronic horizon system, query and return map data of a small surrounding area based on the input coordinates, and provide it to the positioning module 702.

[0169] The positioning display module 704 is used to display the map and real-time vehicle positioning trajectory. This module receives the real-time positioning coordinates output by the positioning module 702, overlays them onto the map for real-time rendering, thereby demonstrating and verifying the positioning effect.

[0170] The following details the process by which the positioning module 702 updates the vehicle's driving status using positioning data collected from the vehicle's inertial navigation system, global navigation satellite system, vehicle camera, and vehicle CAN bus, as well as map data from the map calling module 703. Figure 9 As shown, it includes the following steps:

[0171] Step S901: Initialize system state.

[0172] Specifically, the vehicle's driving state can be represented as X = (x, y, v_x, v_y, theta), where x represents the position in the east direction in the map coordinate system, y represents the position in the north direction in the map coordinate system, v_x represents the velocity in the east direction in the map coordinate system, v_y represents the velocity in the north direction in the map coordinate system, and theta represents the heading angle in the map coordinate system. Acceleration information can be represented as J = (accx, accy, omega), where accx represents the acceleration in the east direction, accy represents the acceleration in the north direction, and omega represents the angular acceleration.

[0173] Let the initial driving state of the vehicle be X = X0, and the initial driving state covariance be P = P0. The initial state transition Jacobian matrix is ​​A0 = [1, 0, 1, 0, 0; 0, 1, 0, 1, 0; 0, 0, 1, 0, 0; 0, 0, 0, 1, 0; 0, 0, 0, 1]; the initial input Jacobian matrix is ​​B0 = [0, 0, 0; 0, 0, 0; 1, 0, 0; 0, 1, 0; 0, 0, 1]; the initial observation noise covariance matrix is ​​N = [0.1, 0, 0; 0, 0.1, 0; 0, 0, 0.1]; and the initial state transition noise covariance matrix is ​​Q = [0.1, 0, 0, 0, 0; 0, 0.1, 0, 0; 0, 0, 0, 0.1, 0; 0, 0, 0, 0, 0.1].

[0174] Step S902: Read the positioning data collected by the positioning data acquisition device 701.

[0175] Step S903: Determine whether the positioning data acquisition device 701 is a vehicle-mounted inertial navigation device. If yes, proceed to step S904; otherwise, proceed to step S906.

[0176] Step S904: Predict the current driving state of the vehicle based on the acceleration information collected by the vehicle-mounted inertial navigation equipment.

[0177] Specifically, the acceleration information J = (accx, accy, omega) collected by the onboard inertial navigation system (INS) of the target vehicle, along with the corresponding observation noise covariance, is obtained. Then, based on the acceleration information, the previously updated historical state transition matrix, the previously updated historical driving state, and the input Jacobian matrix, the current driving state of the target vehicle is predicted. Finally, the historical driving state covariance is updated based on the historical state transition matrix, the previously updated historical state transition covariance, and the observation noise covariance.

[0178] In step S905, the current driving status of the vehicle is output to the positioning display module 704 for display.

[0179] After executing S905, return to execute step S902.

[0180] Step S906: Determine whether the positioning data acquisition device 701 is a global navigation satellite system. If yes, proceed to step S907; otherwise, proceed to step S909.

[0181] Step S907: Update the vehicle's driving status based on the vehicle's positioning coordinates collected by the Global Navigation Satellite System.

[0182] Specifically, the system acquires the target positioning coordinates collected by the vehicle's Global Navigation Satellite System, along with the observation Jacobian matrix and observation noise covariance corresponding to the onboard positioning equipment. Then, based on the observation Jacobian matrix and the target vehicle's last updated historical positioning coordinates, the predicted positioning coordinates of the target vehicle are determined. Next, based on the historical driving state covariance, the observation Jacobian matrix, and the observation noise covariance, the gain matrix is ​​determined. Subsequently, based on the gain matrix, the target positioning coordinates, and the predicted positioning coordinates, the vehicle's driving state is updated to obtain the vehicle's current driving state. Finally, based on the gain matrix, the observation matrix, and the observation noise covariance, the historical driving state covariance is updated.

[0183] The target location coordinates can be represented as W = (x, y), where x represents the position in the due east direction in the map coordinate system, and y represents the position in the due north direction in the map coordinate system. The observation Jacobian matrix can be C = [1, 0, 0, 0, 0; 0, 1, 0, 0, 0], and the observation noise covariance can be R = [std_lon]. 2 std_lat 2 ], where std_lon represents the longitude covariance and std_lat represents the latitude covariance.

[0184] In step S908, the current driving status of the vehicle is output to the positioning display module 704 for display.

[0185] After executing S908, return to execute step S902.

[0186] Step S909: Determine whether the positioning data acquisition device 701 is a vehicle CAN bus. If yes, proceed to step S910; otherwise, proceed to step S912.

[0187] Step S910: Update the vehicle's driving status based on the target speed information collected by the vehicle's CAN bus.

[0188] Specifically, the system acquires the target speed information collected by the vehicle's CAN bus, along with the observation Jacobian matrix and observation noise covariance corresponding to the speed sensor. Then, based on the observation Jacobian matrix and the target vehicle's previously updated historical speed information, the predicted speed information of the target vehicle is determined. Next, based on the historical driving state covariance, the observation Jacobian matrix, and the observation noise covariance, the gain matrix is ​​determined. Finally, based on the gain matrix, the target speed information, and the predicted speed information, the vehicle's driving state is updated to obtain the vehicle's current driving state. Finally, based on the gain matrix, the observation matrix, and the observation noise covariance, the historical driving state covariance is updated.

[0189] The target velocity information can be represented as K = [v], where, vx v represents the velocity in the due east direction in the map coordinate system. y This represents the velocity in the due north direction in the map coordinate system. The observation Jacobian matrix can be C = [0, 0, v]. x / rho,v y / rho,0],the observation noise covariance can be R=[0.1].

[0190] Step S911: Output the vehicle's current driving status to the positioning display module 704 for display.

[0191] After executing S911, return to execute step S902.

[0192] Step S912: Determine whether the positioning data acquisition device 701 is a vehicle-mounted camera. If so, proceed to step S913; otherwise, proceed to step S902.

[0193] Step S913: Update the vehicle's driving status based on the N frames of visual perception images captured by the vehicle-mounted camera.

[0194] Specifically, a first reference position point on the reference lane line is obtained from each frame of the visually perceived image. The obtained N first reference position points are curve-fitted to obtain the target lane line curve. Then, the target lane line curve is resampled to obtain M resampled first reference position points. Each obtained first reference position point is matched with map data to obtain its corresponding second reference position point in the map data, as well as the reference position information of each second reference position point in the map coordinate system. Then, based on the obtained reference position information and the positional relationship between each first reference position point and the target vehicle, the target vehicle's target position information in the map coordinate system is determined. The target position information can be represented as X = (x, y, theta), where x represents the position in the east direction in the map coordinate system, y represents the position in the north direction in the map coordinate system, and theta represents the heading angle in the map coordinate system, where the heading angle represents the clockwise angle from the north direction.

[0195] The observation Jacobian matrix and observation noise covariance corresponding to the vehicle-mounted camera are obtained. Then, based on the observation Jacobian matrix and the vehicle's last updated historical position information, the predicted position information of the vehicle is determined. Next, based on the vehicle's last updated historical driving state covariance, the observation Jacobian matrix, and the observation noise covariance, the gain matrix is ​​determined. Based on the gain matrix, target position information, and predicted position information, the vehicle's driving state is updated to obtain the vehicle's current driving state. Finally, based on the gain matrix, the observation Jacobian matrix, and the observation noise covariance, the historical driving state covariance is updated.

[0196] Among them, the observed Jacobian matrix can be C = [1, 0, 0, 0, 0; 0, 1, 0, 0, 0; 0, 0, 0, 1], and the observed noise covariance can be R = [0.2, 0, 0; 0, 0.2, 0; 0, 0, 0.2].

[0197] In step S914, the current driving status of the vehicle is output to the positioning display module 704 for display.

[0198] After executing S914, return to execute step S902.

[0199] Optionally, after the positioning display module 704 displays the current driving status of the vehicle, the driver or the autonomous driving module in the terminal device can adjust the vehicle's position, attitude, or speed based on the current driving status of the vehicle.

[0200] For example, the location display module 704 is configured to display the vehicle's current location as follows: Figure 10a As shown, by Figure 10a It is known that the vehicle's current position is very close to the left lane line, and no lane-changing operation has been performed. Therefore, the driver or the autonomous driving module can control the vehicle to move away from the left lane line while controlling the vehicle to move forward, until the vehicle's position is between the two lane lines, as shown in the following example. Figure 10b As shown.

[0201] It should be noted that the application scenarios of the method for updating the vehicle driving status in this application embodiment are not limited to autonomous driving or assisted driving scenarios, but can also be applied to map navigation, vehicle networking, vehicle-road cooperation, intelligent transportation and other scenarios. This application does not make specific limitations in this regard.

[0202] In this embodiment, multiple frames of visual perception images containing the same reference lane line are acquired. Then, a first reference position point on the reference lane line is obtained from each frame of visual perception image. The positional relationship between each first reference position point and the target vehicle satisfies preset conditions. Compared with a single frame of visual perception image acquired by a camera, the multiple first reference position points are not limited by the effective sensing distance of the camera, ensuring the accuracy of the positional information of the multiple first reference position points. Therefore, when determining the target vehicle's target position information in the map coordinate system based on the obtained first reference position points, and updating the target vehicle's driving status based on the target position information, the accuracy of the updated target vehicle's driving status can be effectively improved. By acquiring positioning data through multiple vehicle-mounted sensors and developing different vehicle driving status update schemes for the data acquired by each type of vehicle-mounted sensor, the timeliness and accuracy of vehicle driving status updates are improved.

[0203] Based on the same technical concept, embodiments of this application provide a device for updating the driving status of a vehicle, such as... Figure 11 As shown, the device 1100 includes:

[0204] The acquisition module 1101 is used to acquire N frames of visually perceived images acquired by the image acquisition device, wherein the N frames of visually perceived images contain the same reference lane line, and N is a positive integer greater than 1;

[0205] Extraction module 1102 is used to obtain a first reference position point on the reference lane line from each frame of visual perception image, wherein the positional relationship between each first reference position point and the target vehicle satisfies a preset condition;

[0206] The positioning module 1103 is used to determine the target position information of the target vehicle in the map coordinate system based on the obtained first reference position points, the positional relationship between the first reference position points and the target vehicle, and map data.

[0207] The update module 1104 is used to update the driving status of the target vehicle based on the target location information to obtain a first driving status.

[0208] Optionally, the positioning module 1103 is specifically used for:

[0209] The obtained first reference position points are matched with map data to obtain the second reference position points corresponding to each of the first reference position points in the map data, as well as the reference position information of each second reference position point in the map coordinate system.

[0210] Based on the obtained reference location information and the positional relationship between each first reference location point and the target vehicle, the target location information of the target vehicle in the map coordinate system is determined.

[0211] Optionally, the positioning module 1103 is further configured to:

[0212] Before determining the target vehicle's target position information in the map coordinate system based on the obtained first reference position points, the positional relationship between the first reference position points and the target vehicle, and map data, curve fitting is performed on the obtained N first reference position points to obtain the target lane line curve.

[0213] The target lane curve is resampled to obtain M resampled first reference position points, where M is a positive integer greater than 1 and less than or equal to N.

[0214] Optionally, the update module 1104 is specifically used for:

[0215] Obtain the first observation matrix and the first observation noise covariance corresponding to the image acquisition device;

[0216] Based on the first observation matrix and the target vehicle's last updated historical location information, the predicted location information of the target vehicle is determined;

[0217] Based on the historical driving state covariance of the target vehicle in the last update, the first observation matrix, and the first observation noise covariance, the first gain matrix is ​​determined.

[0218] The driving state of the target vehicle is updated based on the first gain matrix, the target position information, and the predicted position information to obtain a first driving state.

[0219] Optionally, the update module 1104 is further configured to:

[0220] The historical driving state covariance is updated based on the first gain matrix, the first observation matrix, and the first observation noise covariance.

[0221] Optionally, the update module 1104 is further configured to:

[0222] Based on the first gain matrix, the first observation matrix, and the first observation noise covariance, after updating the historical driving state covariance, the target positioning coordinates collected by the vehicle-mounted positioning device on the target vehicle, as well as the second observation matrix and the second observation noise covariance corresponding to the vehicle-mounted positioning device are obtained.

[0223] Based on the second observation matrix and the target vehicle's last updated historical positioning coordinates, the predicted positioning coordinates of the target vehicle are determined.

[0224] The second gain matrix is ​​determined based on the historical driving state covariance, the second observation matrix, and the second observation noise covariance.

[0225] The first driving state is updated based on the second gain matrix, the target positioning coordinates, and the predicted positioning coordinates to obtain the second driving state.

[0226] Optionally, the update module 1104 is further configured to:

[0227] The historical driving state covariance is updated based on the second gain matrix, the second observation matrix, and the second observation noise covariance.

[0228] Optionally, the update module 1104 is further configured to:

[0229] Based on the first gain matrix, the first observation matrix, and the first observation noise covariance, after updating the historical driving state covariance, the target speed information collected by the speed sensor on the target vehicle, as well as the third observation matrix and the third observation noise covariance corresponding to the speed sensor are obtained.

[0230] Based on the third observation matrix and the target vehicle's last updated historical speed information, the predicted speed information of the target vehicle is determined;

[0231] The third gain matrix is ​​determined based on the historical driving state covariance, the third observation matrix, and the third observation noise covariance.

[0232] The first driving state is updated based on the third gain matrix, the target speed information, and the predicted speed information to obtain the third driving state.

[0233] Optionally, the update module 1104 is further configured to:

[0234] The historical driving state covariance is updated based on the third gain matrix, the third observation matrix, and the third observation noise covariance.

[0235] Optionally, the update module 1104 is further configured to:

[0236] Based on the first gain matrix, the first observation matrix, and the first observation noise covariance, after updating the historical driving state covariance, the acceleration information collected by the vehicle-mounted inertial navigation system on the target vehicle and the fourth observation noise covariance corresponding to the vehicle-mounted inertial navigation system are obtained.

[0237] Based on the acceleration information, the previously updated historical state transition matrix, and the first driving state, the fourth driving state of the target vehicle is predicted.

[0238] The historical driving state covariance is updated based on the historical state transition matrix, the previously updated historical state transition covariance, and the fourth observation noise covariance.

[0239] Based on the same technical concept, embodiments of this application provide a computer device, such as... Figure 12 As shown, it includes at least one processor 1201 and a memory 1202 connected to at least one processor. In this embodiment, the specific connection medium between the processor 1201 and the memory 1202 is not limited. Figure 12 Taking the connection between processor 1201 and memory 1202 via a bus as an example, the bus can be divided into address bus, data bus, control bus, etc.

[0240] In this embodiment of the application, the memory 1202 stores instructions that can be executed by at least one processor 1201. By executing the instructions stored in the memory 1202, at least one processor 1201 can perform the steps of the above-described method for updating the vehicle driving state.

[0241] The processor 1201 serves as the control center of the computer device, connecting to various parts of the device via various interfaces and lines. It updates the vehicle's driving status by running or executing instructions stored in the memory 1202 and accessing data stored in the memory 1202. Optionally, the processor 1201 may include one or more processing units. The processor 1201 may integrate an application processor and a modem processor. The application processor primarily handles the operating system, user interface, and applications, while the modem processor primarily handles wireless communication. It is understood that the modem processor may not be integrated into the processor 1201. In some embodiments, the processor 1201 and the memory 1202 may be implemented on the same chip; in other embodiments, they may be implemented on separate chips.

[0242] Processor 1201 can be a general-purpose processor, such as a central processing unit (CPU), digital signal processor, application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0243] Memory 1202, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory 1202 may include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic memory, magnetic disk, optical disk, etc. Memory 1202 can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. In the embodiments of this application, memory 1202 can also be a circuit or any other device capable of implementing storage functions for storing program instructions and / or data.

[0244] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing a computer program executable by a computer device, which, when run on the computer device, causes the computer device to perform the steps of the method for updating the vehicle's driving state described above.

[0245] The system involved in the embodiments of this application can be a distributed system formed by connecting clients and multiple nodes (any form of computer device in the network, such as the server and terminal device in the embodiments of this application) through network communication.

[0246] Taking a distributed system as an example, see blockchain system. Figure 13 , Figure 13 This is an optional structural diagram of the distributed system 1300 provided in this embodiment of the invention applied to a blockchain system. It consists of multiple nodes 1301 (any form of computer device connected to the network, such as servers and terminal devices in this embodiment) and clients 1302. The nodes form a peer-to-peer (P2P) network. The P2P protocol is an application layer protocol running on top of the Transmission Control Protocol (TCP). In the distributed system, any machine, such as a server or terminal device, can join and become a node. A node includes a hardware layer, a middleware layer, an operating system layer, and an application layer.

[0247] See Figure 13 The functions of each node in the blockchain system shown include:

[0248] 1) Routing: A basic function of nodes used to support communication between nodes.

[0249] In addition to routing capabilities, nodes can also have the following functions:

[0250] 2) Application: Deployed in a blockchain, this application implements specific business functions based on actual business needs. It records data related to these functions, forming record data. The record data carries a digital signature to indicate the source of the task data. The record data is sent to other nodes in the blockchain system, whereby, upon successful verification of the record data's source and integrity, it is added to a temporary block. A specific business function could be updating the vehicle's driving status, as described in this application embodiment.

[0251] 3) A blockchain consists of a series of blocks that are sequentially generated. Once a new block is added to the blockchain, it will not be removed. The blocks contain the data submitted by the nodes in the blockchain system.

[0252] See Figure 14 , Figure 14 This is an optional schematic diagram of the block structure provided in this embodiment of the invention. Each block includes the hash value of the transaction records stored in this block (the hash value of this block) and the hash value of the previous block. The blocks are connected through their hash values ​​to form a blockchain. Additionally, the block may include information such as a timestamp when it was generated. A blockchain is essentially a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains relevant information used to verify the validity of the information (anti-counterfeiting) and to generate the next block.

[0253] Those skilled in the art will understand that embodiments of the present invention can be provided as methods or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0254] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0255] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0256] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0257] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0258] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for updating the driving status of a vehicle, characterized in that, include: Acquire N frames of visually perceived images captured by an image acquisition device, wherein the N frames of visually perceived images contain the same reference lane line, and N is a positive integer greater than 1; From each frame of visually perceived image, a first reference position point on the reference lane line is obtained, wherein the positional relationship between each first reference position point and the target vehicle satisfies a preset condition; The obtained N first reference position points are subjected to curve fitting to obtain the target lane line curve; The target lane curve is resampled to obtain M resampled first reference position points, where M is a positive integer greater than 1 and less than or equal to N; The M resampled first reference position points are matched with the map data to obtain the second reference position points corresponding to each of the M resampled first reference position points in the map data, as well as the reference position information of each second reference position point in the map coordinate system. Based on the obtained reference position information and the positional relationship between the M resampled first reference position points and the target vehicle, the target position information of the target vehicle in the map coordinate system is determined; The driving status of the target vehicle is updated based on the target location information to obtain a first driving status.

2. The method as described in claim 1, characterized in that, The step of updating the driving status of the target vehicle based on the target location information to obtain a first driving status includes: Obtain the first observation matrix and the first observation noise covariance corresponding to the image acquisition device; Based on the first observation matrix and the target vehicle's last updated historical location information, the predicted location information of the target vehicle is determined; Based on the historical driving state covariance of the target vehicle in the last update, the first observation matrix, and the first observation noise covariance, the first gain matrix is ​​determined. The driving state of the target vehicle is updated based on the first gain matrix, the target position information, and the predicted position information to obtain a first driving state.

3. The method as described in claim 2, characterized in that, Also includes: The historical driving state covariance is updated based on the first gain matrix, the first observation matrix, and the first observation noise covariance.

4. The method as described in claim 3, characterized in that, After updating the historical driving state covariance based on the first gain matrix, the first observation matrix, and the first observation noise covariance, the method further includes: Obtain the target positioning coordinates collected by the vehicle-mounted positioning device on the target vehicle, as well as the second observation matrix and the second observation noise covariance corresponding to the vehicle-mounted positioning device; Based on the second observation matrix and the target vehicle's last updated historical positioning coordinates, the predicted positioning coordinates of the target vehicle are determined. The second gain matrix is ​​determined based on the historical driving state covariance, the second observation matrix, and the second observation noise covariance. The first driving state is updated based on the second gain matrix, the target positioning coordinates, and the predicted positioning coordinates to obtain the second driving state.

5. The method as described in claim 4, characterized in that, Also includes: The historical driving state covariance is updated based on the second gain matrix, the second observation matrix, and the second observation noise covariance.

6. The method as described in claim 3, characterized in that, After updating the historical driving state covariance based on the first gain matrix, the first observation matrix, and the first observation noise covariance, the method further includes: Obtain the target speed information collected by the speed sensor on the target vehicle, as well as the third observation matrix and third observation noise covariance corresponding to the speed sensor; Based on the third observation matrix and the target vehicle's last updated historical speed information, the predicted speed information of the target vehicle is determined; The third gain matrix is ​​determined based on the historical driving state covariance, the third observation matrix, and the third observation noise covariance. The first driving state is updated based on the third gain matrix, the target speed information, and the predicted speed information to obtain the third driving state.

7. The method as described in claim 6, characterized in that, Also includes: The historical driving state covariance is updated based on the third gain matrix, the third observation matrix, and the third observation noise covariance.

8. The method as described in claim 3, characterized in that, After updating the historical driving state covariance based on the first gain matrix, the first observation matrix, and the first observation noise covariance, the method further includes: Acquire the acceleration information collected by the vehicle-mounted inertial navigation system on the target vehicle, and the fourth observation noise covariance corresponding to the vehicle-mounted inertial navigation system; Based on the acceleration information, the previously updated historical state transition matrix, and the first driving state, the fourth driving state of the target vehicle is predicted. The historical driving state covariance is updated based on the historical state transition matrix, the previously updated historical state transition covariance, and the fourth observation noise covariance.

9. A device for updating the driving status of a vehicle, characterized in that, include: The acquisition module is used to acquire N frames of visually perceived images acquired by the image acquisition device, wherein the N frames of visually perceived images contain the same reference lane line, and N is a positive integer greater than 1; The extraction module is used to obtain a first reference position point on the reference lane line from each frame of visual perception image, wherein the positional relationship between each first reference position point and the target vehicle satisfies a preset condition; The positioning module is used to perform curve fitting on the obtained N first reference position points to obtain a target lane line curve; resample the target lane line curve to obtain M resampled first reference position points, where M is a positive integer greater than 1 and less than or equal to N; match the M resampled first reference position points with map data to obtain the corresponding second reference position points of each of the M resampled first reference position points in the map data, and the reference position information of each second reference position point in the map coordinate system; based on the obtained reference position information and the positional relationship between the M resampled first reference position points and the target vehicle, determine the target position information of the target vehicle in the map coordinate system. The update module is used to update the driving status of the target vehicle based on the target location information to obtain a first driving status.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, It stores a computer program executable by a computer device, which, when run on the computer device, causes the computer device to perform the steps of the method according to any one of claims 1 to 8.

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