Target position confidence estimation method and device, medium and product

Abstract

The invention relates to the technical field of information, and provides a target position confidence estimation method and device, a medium and a product. The target position confidence estimation method comprises the following steps: acquiring a sensing target positioning deviation; inputting the sensing target positioning deviation into a preset positioning error model to obtain a first target position confidence coefficient; obtaining a target position confidence degree estimated by Kalman filtering; fusing the target position confidence estimated by the Kalman filtering and the first target position confidence according to a first weight proportion to obtain a final target position confidence; wherein the first weight ratio is changed according to the change of the frame positioning data output by the sensing target. By means of the configuration mode, the accuracy of sensing the confidence degree of the target position can be improved on the premise that a small amount of data is used.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to a method, device, medium and product for estimating the confidence level of a target position. Background Art

[0002] The "vehicle-road-cloud integration" solution for intelligent connected vehicles is a Chinese-style autonomous driving technology route. The "road" side of the vehicle-road-cloud integration solution includes roadside sensing equipment, which can sense targets far away on the road and achieve beyond-visual-range sensing functions that cannot be achieved by a single vehicle. Roadside sensing equipment usually includes split equipment and integrated equipment. Split equipment refers to roadside sensing equipment with cameras, millimeter-wave radars, laser radars, and computing units installed separately. Integrated equipment is usually a roadside sensing equipment with cameras, millimeter-wave radars, and computing units integrated into one. The most common integrated equipment is the radar-vision integrated machine, which integrates cameras, millimeter-wave radars, and computing units. It is widely used for easy installation and deployment.

[0003] When roadside sensing devices report perceived target data, it usually includes information such as the location and speed of the perceived target. There is a certain deviation in the location and speed of the target perceived by the actual device. The device that obtains the target information (such as the cloud control platform) cannot determine the credibility of the data. Therefore, it is necessary to evaluate the confidence of the target information reported by the roadside sensing device.

[0004] However, the inventors found that there are at least the following technical problems in the related art:

[0005] Currently, researchers in the industry mainly focus on evaluating the confidence of target recognition types, but there is less research on the confidence of perceived target positioning results. Summary of the invention

[0006] One object of the present application is to provide a method for estimating the confidence level of a target position, at least to solve the problem of how to improve the accuracy of the confidence level of the perceived target position while using a small amount of data.

[0007] To achieve the above objectives, some embodiments of the present application provide the following aspects:

[0008] In a first aspect, some embodiments of the present application further provide a method for estimating a target position confidence, the method comprising:

[0009] Get the perceived target positioning distance;

[0010] Inputting the perceived target positioning distance into a preset positioning error model to obtain a first target position confidence;

[0011] Get the confidence of the target position estimated by Kalman filter;

[0012] The target position confidence estimated by the Kalman filter and the first target position confidence are merged according to a first weight ratio to obtain a final target position confidence;

[0013] The first weight ratio changes according to changes in frame positioning data output by the perception target.

[0014] In a second aspect, some embodiments of the present application further provide an electronic device, comprising: one or more processors; and a memory storing computer program instructions, wherein the computer program instructions, when executed, cause the processor to perform the steps of the method described above.

[0015] In a third aspect, some embodiments of the present application further provide a computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method as described above.

[0016] In a fourth aspect, some embodiments of the present application further provide a computer program product, comprising a computer program / instruction, which implements the steps of the method described above when executed by a processor.

[0017] Compared with the related art, in the solution provided in the embodiment of the present application, the perceived target positioning distance is obtained; the perceived target positioning distance is input into a preset positioning error model to obtain a first target position confidence; the target position confidence estimated by the Kalman filter is obtained; the target position confidence estimated by the Kalman filter and the first target position confidence are fused according to a first weight ratio to obtain the final target position confidence; wherein the first weight ratio changes according to the change of the frame positioning data output by the perceived target. Through the above configuration, the present application can improve the accuracy of the perceived target position confidence under the premise of using a small amount of data. Since a single use of the preset positioning error model requires a large number of perceived target positioning distances, the present application adopts the target position confidence estimated by the Kalman filter and the first target position confidence to be merged according to the first weight ratio, thereby obtaining the final target position confidence, which does not require a large number of perceived target positioning distances. The present application adopts the target position confidence estimated by the Kalman filter and the first target position confidence to be merged to obtain the final target position confidence, which can avoid loopholes in the calculation of a single method resulting in large errors. Therefore, the present application can improve the accuracy of the perceived target position confidence, and because the first weight ratio changes according to the change of the frame positioning data output by the perceived target, that is, the first weight ratio of the present application changes according to the change of the actual situation, the present application can adjust the first weight ratio according to different situations, thereby further improving the accuracy of the perceived target position confidence. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0019] Figure 1 It is a schematic flow chart of the main steps of a method for estimating the confidence level of a target position according to an embodiment of the present application;

[0020] Figure 2 It is a schematic diagram of an exemplary structure of an electronic device according to the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0022] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart of the main steps of a method for estimating the confidence level of a target position according to an embodiment of the present application. Figure 1 As shown, the method for estimating the target position confidence in the embodiment of the present application mainly includes the following steps S101 to S104.

[0023] Step S101: Acquire the perceived target positioning distance.

[0024] In this embodiment, first, a rectangular coordinate system is constructed with the position of the radar-vision integrated device as the origin, and RTK obtains the coordinate information of the perceived target (such as a smart car) in the rectangular coordinate system. The coordinate information can be used to determine the distance from the perceived target to the x-axis or y-axis of the rectangular coordinate system. The distance from the perceived target to the x-axis or y-axis of the rectangular coordinate system can be used as the positioning distance of the perceived target.

[0025] Step S102: inputting the perceived target positioning distance into a preset positioning error model to obtain a first target position confidence level.

[0026] In this embodiment, the perceived target positioning distance is evaluated through a preset positioning error model to obtain a first target position confidence.

[0027] In one implementation, the preset positioning error model is constructed according to the following steps S1021 to S1024:

[0028] Step S1021: Acquire multiple perception target positioning data collected by RTK and multiple perception target positioning data collected by the corresponding radar-vision integrated device.

[0029] In one implementation, step S1021 may further include steps S10211 to S10212:

[0030] Step S10211: construct a rectangular coordinate system with the position of the integrated radar and vision device as the origin, and collect data through the integrated radar and vision device to obtain multiple sensing target positioning data collected by the corresponding integrated radar and vision device

[0031] In this embodiment, a rectangular coordinate system can be established with the position of the integrated radar and vision device installation pole as the origin, the X axis facing due east, the Y axis facing due north, and "meter" as the distance unit. The multiple sensing target positioning data (coordinates in rectangular coordinates) collected by the integrated radar and vision device are represented by (px, py).

[0032] Step S10212: Acquire the perception target positioning data in multiple geodetic coordinate systems collected by RTK, and transform the perception target positioning data in the multiple geodetic coordinate systems collected by RTK into the rectangular coordinate system to obtain the multiple perception target positioning data collected by RTK.

[0033] In this embodiment, the perception target positioning data (coordinates in multiple geodetic coordinate systems) in multiple geodetic coordinate systems collected by RTK are converted into the rectangular coordinate system to obtain the multiple perception target positioning data (coordinates in rectangular coordinates) collected by RTK. rtk ,py rtk )express.

[0034] It should also be noted that RTK equipment is a device that can measure precise positions, and the measurement position error can reach the centimeter level. The positioning accuracy of high-precision RTK equipment can reach the millimeter level. The true value vehicle is a vehicle with an RTK device installed at the top center of the vehicle. When the vehicle is driving, the RTK device outputs the vehicle motion data. The true value vehicle mainly collects the true value motion data of the target perceived by the integrated radar and vision machine as a motion reference benchmark to calculate the positioning error of the sensor sensing the target. RTK can output information such as vehicle position and speed. The vehicle drives within the sensing range of the integrated radar and vision machine, and records RTK data and the target data perceived by the integrated radar and vision machine at the same time.

[0035] Step S1022: Based on the multiple sensing target positioning data collected by the RTK and the multiple sensing target positioning data collected by the corresponding radar-vision integrated device, position difference data of multiple axes are obtained and a matrix of the position difference data of the axes is constructed.

[0036] In one implementation, step S1022 may further include steps S10221 to S10222:

[0037] Step S10221: align the multiple perception target positioning data collected by the RTK and the multiple perception target positioning data collected by the corresponding radar-vision integrated device in time sequence to obtain multiple groups of data.

[0038] In this embodiment, each set of data includes a perception target positioning data collected by the RTK and a corresponding perception target positioning data collected by the radar-vision integrated device. The perception target positioning data collected by the RTK and the corresponding perception target positioning data collected by the radar-vision integrated device after time alignment can establish a corresponding relationship more accurately to improve the accuracy of the valuation results.

[0039] Step S10222: subtract each set of data to obtain position difference data of multiple axes and construct a matrix of the position difference data of the axes.

[0040] In this embodiment, each set of data is subtracted, and Ex=pxrtk-px and

[0041] Ey=pyrtk-py, to obtain the position difference data of multiple axes and construct a matrix of the position difference data of the axes, wherein the position difference data of the axes include X-axis error and Y-axis error, Ex is the X-axis error, Ey is the Y-axis error, and the matrix of the position difference data of the axes can be represented by E.

[0042] Step S1023: Based on the positioning data of multiple perception targets collected by the RTK, the distances from the multiple perception targets to the integrated radar and vision device are obtained, and a matrix of the distances from the perception targets to the integrated radar and vision device is constructed.

[0043] In this embodiment, based on the positioning data of multiple perception targets collected by the RTK, the distances from multiple perception targets to the integrated radar and vision device are obtained and a matrix of the distances from the perception targets to the integrated radar and vision device is constructed. The matrix of the distances from the perception targets to the integrated radar and vision device is represented by D.

[0044] Step S1024: Based on the matrix of the position difference data of the axis and the matrix of the distance from the perceived target to the radar and vision integrated device, the coefficients of the preset positioning error model are obtained.

[0045] In this implementation, the coefficients of the preset positioning error model are obtained based on the matrix of the position difference data of the axis and the matrix of the distance from the perceived target to the radar and vision integrated device, which is represented by A.

[0046] That is, D·A=E

[0047] Expand to

[0048] Then, A=(D T ·D) -1 D T E, and then A can be solved, where E and d must correspond to each other, that is, E is the X-axis error matrix, and D is the matrix of the position difference data of the x-axis.

[0049] Step S1025: constructing the preset positioning error model based on the coefficients of the preset positioning error model.

[0050] In this implementation, A is substituted into the preset positioning error model to construct the preset positioning error model, and the positioning deviation dx_err (first confidence level) of the target in the X-axis direction is calculated as follows:

[0051] dx_err=a0+a1x+a2(x) 2 +a3(x) 3

[0052] Among them, x is the distance of the current perceived target from the x-axis. The x in the formula can also be replaced by the Kalman filter to estimate the target position px.

[0053] In one embodiment, the preset positioning error model is a third-order polynomial. When the model order is lower than the third order, the fitting ability is poor. When the model order is higher than the third order, the calculation is slightly improved, but the calculation formula becomes complicated. The present application adopts a third-order polynomial to achieve both accuracy and calculation efficiency.

[0054] Step S103: Obtaining the target position confidence estimated by Kalman filtering;

[0055] In this embodiment, the positioning result of the integrated radar and vision device is usually fused and calculated using a filtering algorithm. The commonly used filtering algorithm is the Kalman filtering algorithm. The CV model is usually used to establish the Kalman filtering equation. The filtered estimated data includes the target position and speed, and can output the estimated positioning and speed error, that is, the covariance matrix of the filtering state, the covariance matrix P.

[0056] In one implementation, the covariance matrix P is in the following form:

[0057]

[0058] The non-diagonal elements of the covariance matrix P are irrelevant to this solution and are not listed for the time being. The square root of the diagonal elements in the filter state estimation covariance matrix is ​​σ px Indicates the estimated target position deviation on the X-axis, σ py Represents the estimated target position deviation on the Y axis. The above two can be used as reference data for calculating the position confidence of the perceived target data; σ vx Indicates the estimated target speed deviation on the X axis, σ vy Indicates the estimated target speed deviation on the Y axis. The above two can be used as reference data for calculating the confidence of the perceived target data speed.

[0059] It should also be noted that the confidence v_conf of the target constant speed data perceived by the integrated radar is v_conf = γ2σ v

[0060] Among them, σ v represents the speed error of the perceived target on the X-axis or Y-axis estimated by the Kalman filter, 2σ v According to statistical calculations, there is a 95% probability that the positioning deviation can be included. γ is the amplification factor, and its values ​​are as follows: when the perceived target outputs the first 10 frames of constant speed data, γ = 10, which means that the Kalman filter estimates the target speed has not converged, the speed calculation result is unreliable, and the speed error of the assessment is amplified; after the perceived target outputs 10 frames of positioning data, γ = 1, which means that the Kalman filter estimates the target speed to converge, the estimated target speed is more accurate, and the estimated target constant speed deviation is also more accurate.

[0061] Step S104: Fusing the target position confidence estimated by the Kalman filter and the first target position confidence according to a first weight ratio to obtain a final target position confidence, wherein the first weight ratio changes according to changes in frame positioning data output by the perceived target.

[0062] In this embodiment, since the target position confidence estimated by the Kalman filter has low accuracy when the Kalman filter has not converged, the present application fuses the target position confidence estimated by the Kalman filter and the first target position confidence according to a first weight ratio to obtain the final target position confidence, so as to improve the accuracy of obtaining the final target position confidence, wherein the first weight ratio changes according to the change of the frame positioning data output by the perceived target, and the frame positioning data can determine whether the Kalman filter converges.

[0063] In one embodiment, the fusion model may be p_conf=α·d_err+β·2σ p ; Among them, p_conf represents the final target position confidence, α:β is the first weight ratio, σ pIt is the positioning deviation (confidence) of the perceived target on the X-axis or Y-axis estimated by the Kalman filter. d_err can be dx_err or dy_err but must be consistent with 2σ p Corresponding.

[0064] In one embodiment, when the frame positioning data output by the sensing target is less than a first preset threshold, the first weight ratio is one to zero. The first preset threshold may be 10 frames. When it is less than 10 frames, the Kalman filter has not converged and its confidence is unreliable.

[0065] In one embodiment, when the frame positioning data output by the sensing target is greater than or equal to a first preset threshold, the first weight ratio is 0.5 to 0.5. The first preset threshold may be 10 frames. When it is greater than 10 frames, the Kalman filter converges and its confidence is credible.

[0066] In one embodiment, when the coefficients of the preset positioning error model are not configured and the frame positioning data output by the perception target is less than the first preset threshold, since the Kalman filter has not converged, the confidence determined by the Kalman filter and the confidence determined by the preset positioning error model are both unreliable, and the first weight ratio can be set to zero to five; when the coefficients of the preset positioning error model are not configured and the frame positioning data output by the perception target is greater than or equal to the first preset threshold, the final confidence can only be determined by the Kalman filter, and the first weight ratio is zero to one. Among them, the first preset threshold can be 10 frames.

[0067] Based on the above steps S101-S104, the perceived target positioning distance is obtained; the perceived target positioning distance is input into a preset positioning error model to obtain a first target position confidence; the target position confidence estimated by the Kalman filter is obtained; the target position confidence estimated by the Kalman filter and the first target position confidence are merged according to a first weight ratio to obtain a final target position confidence; wherein the first weight ratio changes according to changes in frame positioning data output by the perceived target. Through the above configuration, the present application can improve the accuracy of the perceived target position confidence under the premise of using a small amount of data. Since a single use of the preset positioning error model requires a large number of perceived target positioning distances, the present application adopts the target position confidence estimated by the Kalman filter and the first target position confidence to be merged according to the first weight ratio, thereby obtaining the final target position confidence, which does not require a large number of perceived target positioning distances. The present application adopts the target position confidence estimated by the Kalman filter and the first target position confidence to be merged to obtain the final target position confidence, which can avoid loopholes in the calculation of a single method resulting in large errors. Therefore, the present application can improve the accuracy of the perceived target position confidence, and because the first weight ratio changes according to the change of the frame positioning data output by the perceived target, that is, the first weight ratio of the present application changes according to the change of the actual situation, the present application can adjust the first weight ratio according to different situations, thereby further improving the accuracy of the perceived target position confidence.

[0068] The step division of the above methods is only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0069] It is worth mentioning that all modules involved in this embodiment are logic modules. In practical applications, a logic unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed by this application, but this does not mean that there are no other units in this embodiment.

[0070] In addition, some embodiments of the present application also provide an electronic device. The electronic device may be a digital computer in various forms, such as a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, etc. The electronic device may also be a mobile device in various forms, such as a personal digital processing, a cellular phone, a smart phone, a wearable device, and other similar computing devices.

[0071] The electronic device includes: one or more processors; and a memory storing computer program instructions, wherein when the computer program instructions are executed, the processor executes the steps of the method provided in any one or more of the above embodiments. Figure 2 An exemplary structural diagram of the electronic device is disclosed. Figure 2 As shown, the electronic device includes: one or more processors 1101, memory 1102, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Among them, the components shown in this article, their connections and relationships, and their functions are only used as examples, and are not intended to limit the implementation of the present application described and / or required herein.

[0072] The electronic device may further include: an input device 1103 and an output device 1104. The processor 1101, the memory 1102, the input device 1103 and the output device 1104 may be connected via a bus or other means. Figure 2 The example of connecting through bus is taken in the following.

[0073] The input device 1103 can receive input digital or character information, and generate key signal input related to the user settings and function control of the electronic device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick and other input devices. The output device 1104 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display and a plasma display. In some embodiments, the display device may be a touch screen.

[0074] To provide interaction with a user, the electronic device may be a computer. The computer has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0075] In the embodiments of the present application, a computer program / instruction is stored on a computer-readable medium, and when the computer program / instruction is executed by a processor, the steps of the method provided by any one or more of the above embodiments are implemented. The computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more computer-readable instructions.

[0076] The memory 1102 can be used as a non-transient computer-readable storage medium, which can be used to store non-transient software programs, non-transient computer executable programs and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 1102, so as to implement the program instructions / modules corresponding to the method provided by any one or more embodiments in the embodiments of the present application.

[0077] The memory 1102 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 1102 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 1102 may optionally include a memory remotely arranged relative to the processor 1101, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0078] It should be noted that the computer-readable medium described in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0079] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0080] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet).

[0081] In the above-described embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. For example, it can be implemented by using an application specific integrated circuit (ASIC), a general-purpose computer or any other similar hardware device. In certain embodiments, the software program of the present application can be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including relevant data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and similar devices. In addition, some steps or functions of the present application can be implemented by hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0082] The computer program product provided in the embodiment of the present application includes one or more computer programs / instructions, and when the computer program / instructions are executed by the processor, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive so lid state di sk (SSD)), etc.

[0083] The flow chart or block diagram in the accompanying drawings shows the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0084] The scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim may also be implemented by one unit or device through software or hardware. The words "first", "second", etc. are only used to distinguish the description, and do not indicate any particular order, nor can they be understood as indicating or implying relative importance.

[0085] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily mention changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims, and the above embodiments should be regarded as exemplary and non-restrictive.

Claims

1. A method for estimating the confidence level of a target position, characterized in that: The method comprises: Get the perceived target positioning distance; Inputting the perceived target positioning distance into a preset positioning error model to obtain a first target position confidence; Get the confidence of the target position estimated by Kalman filter; The target position confidence estimated by the Kalman filter and the first target position confidence are merged according to a first weight ratio to obtain a final target position confidence; The first weight ratio changes according to changes in frame positioning data output by the perception target.

2. The method for estimating target position confidence according to claim 1, characterized in that: The method also includes constructing the preset positioning error model according to the following steps: Obtain multiple sensing target positioning data collected by RTK and multiple sensing target positioning data collected by the corresponding radar vision integrated device; Based on the multiple sensing target positioning data collected by the RTK and the multiple sensing target positioning data collected by the corresponding radar vision integrated device, position difference data of multiple axes are obtained and a matrix of the position difference data of the axes is constructed; Based on the positioning data of multiple perception targets collected by the RTK, the distances from the multiple perception targets to the integrated radar and vision device are obtained, and a matrix of the distances from the perception targets to the integrated radar and vision device is constructed; Based on the matrix of the position difference data of the axis and the matrix of the distance from the perceived target to the radar-visual integrated device, the coefficients of the preset positioning error model are obtained; The preset positioning error model is constructed based on coefficients of the preset positioning error model.

3. The method for estimating target position confidence according to claim 2, characterized in that: The acquiring of the plurality of sensing target positioning data collected by the RTK and the corresponding plurality of sensing target positioning data collected by the integrated radar and vision device also includes: A rectangular coordinate system is constructed with the position of the integrated radar and vision device as the origin, and data is collected through the integrated radar and vision device to obtain multiple sensing target positioning data collected by the corresponding integrated radar and vision device; Acquire the perception target positioning data in multiple geodetic coordinate systems collected by RTK, and transform the perception target positioning data in multiple geodetic coordinate systems collected by RTK into the rectangular coordinate system to acquire the multiple perception target positioning data collected by RTK.

4. The method for estimating target position confidence according to claim 2, characterized in that: The method of obtaining position difference data of multiple axes and constructing a matrix of the position difference data of the axes based on the multiple perception target positioning data collected by the RTK and the corresponding multiple perception target positioning data collected by the integrated radar vision device comprises: After aligning the multiple perception target positioning data collected by the RTK and the multiple perception target positioning data collected by the corresponding integrated radar and vision device in time sequence, multiple groups of data are obtained; wherein each group of data includes one perception target positioning data collected by the RTK and one perception target positioning data collected by the corresponding integrated radar and vision device; Each set of data is subtracted to obtain position difference data of multiple axes and construct a matrix of the position difference data of the axes.

5. The method for estimating target position confidence according to claim 1, characterized in that: Also includes: The preset positioning error model is a third-order polynomial.

6. The method for estimating target position confidence according to claim 1, characterized in that: The following steps are also included to obtain the first weight ratio: When the frame positioning data output by the sensing target is less than a first preset threshold, the first weight ratio is one to zero; When the frame positioning data output by the perception target is greater than or equal to a first preset threshold, the first weight ratio is 0.5 to 0.

5.

7. The method for estimating target position confidence according to claim 1, characterized in that: The following steps are also included to obtain the first weight ratio: When the coefficients of the preset positioning error model are not configured and the frame positioning data output by the perception target is less than a first preset threshold, the first weight ratio is zero to five; When the coefficients of the preset positioning error model are not configured and the frame positioning data output by the perception target is greater than or equal to a first preset threshold, the first weight ratio is zero to one.

8. An electronic device, characterized in that: The electronic device comprises: one or more processors; and A memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method as claimed in any one of claims 1 to 7.

9. A computer readable medium having a computer program / instructions stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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