Multi-sensor fusion vehicle speed estimation method and related equipment

Abstract

The invention discloses a multi-sensor fusion vehicle speed estimation method and related equipment, relates to the field of vehicle speed estimation, and mainly aims to solve the problem of low accuracy of vehicle speed data acquired by a single channel at present. The method comprises the steps that an initial sensing data set of a target vehicle is determined, the sensing data set comprises sensing vehicle speed data and a corresponding sensor noise standard deviation, and the sensing vehicle speed data is obtained based on a self sensor or a network-connected sensor; removing abnormal sensor data in the initial sensing data set based on an unsupervised clustering algorithm to determine a target sensing data set; and based on the target sensing data set, determining fusion vehicle speed data of the target vehicle. The method is used for the multi-sensor fusion vehicle speed estimation process.

Description

Technical Field

[0001] The present invention relates to the field of vehicle speed estimation, and in particular to a multi-sensor fusion vehicle speed estimation method and related equipment. Background Art

[0002] The speed estimation algorithm is the basis of many vehicle control algorithms such as stability control. The actual speed of the vehicle is obtained through speed estimation and passed to other algorithm modules as a known quantity. Therefore, the accuracy of the speed estimation algorithm directly affects other vehicle control algorithms and is a key part of vehicle control.

[0003] There are many technical routes for vehicle speed estimation algorithms at present. Some are based on the integration of the acceleration data of the vehicle inertial navigation, but there is a problem of cumulative integration error; some use wheel speed to convert into vehicle speed, but there are inaccuracies in slip and turning conditions. The above existing technologies for obtaining vehicle speed data through a single channel all have certain errors and low accuracy. Summary of the invention

[0004] In view of the above problems, the present invention provides a multi-sensor fusion vehicle speed estimation method and related equipment, the main purpose of which is to solve the problem of low accuracy of vehicle speed data currently obtained through a single channel.

[0005] In order to solve at least one of the above technical problems, in a first aspect, the present invention provides a multi-sensor fusion vehicle speed estimation method, the method comprising:

[0006] Determine an initial sensor data set of the target vehicle, wherein the sensor data set includes sensor vehicle speed data and corresponding sensor noise standard deviation, and the sensor vehicle speed data is obtained based on its own sensor or networked sensor;

[0007] Eliminating abnormal sensor data in the initial sensor data set based on an unsupervised clustering algorithm to determine a target sensor data set;

[0008] The fused vehicle speed data of the target vehicle is determined based on the target sensor data set.

[0009] Optionally, the removing abnormal sensor data in the initial sensor data set based on an unsupervised clustering algorithm to determine a target sensor data set includes:

[0010] Circularly calculating the aggregation degree of the initial sensor data set to obtain an aggregation degree set, wherein the aggregation degree set is sorted by size;

[0011] Determining a maximum degree of aggregation in the set of degrees of aggregation;

[0012] When the maximum concentration is greater than a preset concentration, a sensor data set corresponding to the maximum concentration is acquired as the target sensor data set.

[0013] Optionally, the cyclically calculating the aggregation degree of the initial sensor data set to obtain an aggregation degree set includes:

[0014] Positioning error sensor;

[0015] Eliminating the sensor vehicle speed data corresponding to the error sensor to obtain a second sensor data set;

[0016] The standard deviation of the second sensor data set is obtained to determine the degree of aggregation.

[0017] Optionally, obtaining a standard deviation of the second sensor data set to determine the degree of aggregation includes:

[0018] The degree of aggregation was calculated based on the following formula:

[0019]

[0020] Where L is the degree of aggregation, σ v is the standard deviation of the second sensor data set, τ σ and τ N is an adjustable weight coefficient, and N is the total amount of data in the second sensor data set.

[0021] Optionally, the positioning error sensor comprises:

[0022] Determining an average value of the sensor vehicle speed data in the initial sensor data set;

[0023] Constructing a vehicle speed difference set based on the vehicle speed difference between each sensor vehicle speed data in the initial sensor data set and the average value;

[0024] determining a maximum vehicle speed difference in the vehicle speed difference set;

[0025] The sensor corresponding to the maximum vehicle speed difference is determined to be an error sensor.

[0026] Optionally, determining the fused vehicle speed data of the target vehicle based on the target sensor data set includes:

[0027] Acquire a probability density function value of the target sensor data set, wherein the target sensor data set is assumed to obey a normal distribution;

[0028] Taking the sensor vehicle speed data in the target sensor data set as single observation data, obtaining the probability of occurrence of the target sensor data set, wherein the probability is expressed as a function of the expected vehicle speed;

[0029] Determine the actual vehicle speed corresponding to the case with the maximum probability based on the maximum likelihood estimation method;

[0030] The fused vehicle speed data is determined based on the actual vehicle speed corresponding to the case with the maximum probability.

[0031] Optionally, determining the fused vehicle speed data based on the actual vehicle speed corresponding to the case with the maximum probability includes:

[0032] The fused vehicle speed data is determined based on the following formula:

[0033]

[0034] Among them, V x is the fused vehicle speed data, M is the total amount of target sensor data set, V xi is the sensor vehicle speed data in the target sensor data set, σ i is the sensor noise standard deviation corresponding to the sensor speed data.

[0035] In a second aspect, an embodiment of the present invention further provides a multi-sensor fusion vehicle speed estimation device, comprising:

[0036] A first determination unit is used to determine an initial sensor data set of a target vehicle, wherein the sensor data set includes sensor vehicle speed data and a corresponding sensor noise standard deviation, and the sensor vehicle speed data is obtained based on a self-sensor or a networked sensor;

[0037] A second determining unit, configured to eliminate abnormal sensor data in the initial sensor data set based on an unsupervised clustering algorithm to determine a target sensor data set;

[0038] The third determination unit is used to determine the fused vehicle speed data of the target vehicle based on the target sensor data set.

[0039] In order to achieve the above-mentioned purpose, according to the third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the above-mentioned program is executed by a processor, the steps of the above-mentioned multi-sensor fusion vehicle speed estimation method are implemented.

[0040] In order to achieve the above-mentioned purpose, according to the fourth aspect of the present invention, an electronic device is provided, comprising at least one processor and at least one memory connected to the processor; wherein the above-mentioned processor is used to call the program instructions in the above-mentioned memory to execute the steps of the above-mentioned multi-sensor fusion vehicle speed estimation method.

[0041] Through the above technical scheme, the multi-sensor fusion vehicle speed estimation method and related equipment provided by the present invention solve the problem of low accuracy of vehicle speed data currently obtained through a single channel. The present invention determines the initial sensor data set of the target vehicle, wherein the sensor data set includes sensor speed data and corresponding sensor noise standard deviation, and the sensor speed data is obtained based on its own sensor or networked sensor; removes abnormal sensor data in the initial sensor data set based on an unsupervised clustering algorithm to determine the target sensor data set; and determines the fused vehicle speed data of the target vehicle based on the target sensor data set. In the above scheme, considering that with the advent of the era of intelligent networking, each vehicle can obtain multiple vehicle speed estimation information from different sources, the vehicle speed estimation information obtained from multiple channels is fused using an unsupervised clustering algorithm and a multi-sensor fusion algorithm to obtain its own vehicle speed estimation information, which has the advantage of higher accuracy compared to the vehicle speed data obtained through a single channel.

[0042] Correspondingly, the multi-sensor fusion vehicle speed estimation device, equipment and computer-readable storage medium provided in the embodiments of the present invention also have the above-mentioned technical effects.

[0043] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0045] Figure 1 A schematic flow chart of a multi-sensor fusion vehicle speed estimation method provided by an embodiment of the present invention is shown;

[0046] Figure 2 A schematic block diagram showing the composition of a multi-sensor fusion vehicle speed estimation device provided by an embodiment of the present invention is shown;

[0047] Figure 3 A schematic block diagram of the composition of a multi-sensor fusion vehicle speed estimation electronic device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0048] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0049] In order to solve the problem of low accuracy of vehicle speed data currently obtained through a single channel, an embodiment of the present invention provides a multi-sensor fusion vehicle speed estimation method, such as Figure 1 As shown, the method includes:

[0050] S101, determining an initial sensor data set of a target vehicle, wherein the sensor data set includes sensor vehicle speed data and a corresponding sensor noise standard deviation, and the sensor vehicle speed data is obtained based on a self-sensor or a networked sensor;

[0051] It should be noted that the networked vehicle speed sensors that collect the sensor speed data are classified according to their purpose rather than the type of information collected. For example, a camera collects video information, but is combined with a vehicle speed estimation algorithm module to estimate the vehicle speed through the video, then the camera and the vehicle speed estimation algorithm module can be defined together as a vehicle speed estimation sensor.

[0052] At the same time, according to the source, sensors can be divided into two categories, one is the vehicle-mounted speed sensor, including but not limited to the vehicle-mounted inertial navigation IMU, wheel speed counter and speed estimation algorithm module composed of the speed sensor or the vehicle-mounted camera and the video-based real-time speed estimation algorithm module composed of the speed sensor, etc. The other is the networked speed sensor, including but not limited to the smart camera on the smart networked road, the speed estimation algorithm module based on the camera video information and the speed sensor composed of the module for real-time communication with the vehicle or other types of networked speed sensors, etc. Each networked speed sensor collects the speed information of multiple target vehicles and sends the speed information to the target vehicles in real time.

[0053] Furthermore, considering that sensor information generally carries noise, the present embodiment assumes that each sensor can obtain the standard deviation of its inherent noise in advance, store it, and perform maintenance and update. In order to ensure the effect of data fusion, the speed sensor will simultaneously send the standard deviation of the inherent noise while sending the speed information in real time.

[0054] Specifically, this application assumes that the target vehicle receives a total of N sensors {S i Each sensor sends the target vehicle's speed information and its inherent noise standard deviation {(V xi ,σi )}i=1,2...N.

[0055] S102, eliminating abnormal sensor data in the initial sensor data set based on an unsupervised clustering algorithm to determine a target sensor data set;

[0056] Based on the above scheme, this application takes into account that the data of some sensors may have errors in certain situations. For example, estimating the vehicle speed by wheel speed may be inaccurate in the case of tire slippage, and real-time estimation of the vehicle speed by video data collected by the camera may also be inaccurate in extreme weather conditions. Therefore, multiple sensors are required to complement each other to ensure the accuracy of vehicle speed estimation in various situations. Although common vehicle speed estimation algorithms will classify inaccurate sensor situations and use prior knowledge to correct the sensors. However, this algorithm requires manual prediction of possible abnormal situations of the sensor, and then proposes specific response methods for different situations.

[0057] The above step S102 also includes S1021, S1022, and S1023:

[0058] S1021, cyclically calculating the aggregation degree of the initial sensor data set to obtain an aggregation degree set, wherein the aggregation degree set is sorted by size;

[0059] The above step S1021 also includes S10211, S10212 and S10213:

[0060] S10211. Positioning error sensor; specifically, comprising:

[0061] Determine an average value of the sensor speed data in the initial sensor data set; construct a speed difference set based on the speed difference between each sensor speed data in the initial sensor data set and the average value; determine the maximum speed difference in the speed difference set; and determine that the sensor corresponding to the maximum speed difference is an error sensor.

[0062] S10212, eliminating the sensor vehicle speed data corresponding to the error sensor to obtain a second sensor data set;

[0063] S10213: Obtain the standard deviation of the second sensor data set to determine the degree of aggregation. Specifically, the method includes:

[0064]

[0065] Where L is the degree of aggregation, σ v is the standard deviation of the second sensor data set, τ σ and τ N is an adjustable weight coefficient, and N is the total amount of data in the second sensor data set.

[0066] S1022, determining the maximum degree of aggregation in the degree of aggregation set;

[0067] S1023. When the maximum concentration is greater than a preset concentration, obtain a sensor data set corresponding to the maximum concentration as the target sensor data set.

[0068] The above S1021, S1022, and S1023 specifically include the following steps 1-9:

[0069] Step 1: Assume that there are N vehicle speed data in the initial sensor data set, and find the average value of the N sensor speed data.

[0070]

[0071] Step 2: Calculate the speed difference set {D i}i=1,2...N.

[0072]

[0073] Step 3: sort the vehicle speed differences, find the maximum vehicle speed difference, and determine that the sensor corresponding to the maximum vehicle speed difference is the error sensor.

[0074] Step 4: Assume that the sensor speed corresponding to the maximum speed difference is indexed as j, and remove the sensor speed data V of the error sensor with the maximum speed difference. xj , and obtain the above second sensor data set, namely {V xi}i=1,2...N,i≠j.

[0075] Step 5: Calculate the standard deviation σ of the second sensor data set v :

[0076]

[0077] Step 6: Define the aggregation degree of the second sensor data set as L, and calculate the aggregation degree of the remaining vehicle speed sets:

[0078]

[0079] Where L is the degree of aggregation, σ v is the standard deviation of the second sensor data set, τ σ and τ N is an adjustable weight coefficient, and N is the total amount of data in the second sensor data set.

[0080] Record the current set of elements and clustering index, and return to step 1.

[0081] Step 7, loop termination condition: when there is only one sensor data left in the set, end the loop.

[0082] Step 8: Assume that the aggregation degree set obtained through multiple cycles is {L i}i=1,2...N; sort the elements in the clustering set.

[0083] Step 9: Screening result judgment: Take the maximum aggregation degree L in the aggregation degree set max If the maximum aggregation degree L max Less than the given preset aggregation L s , it is believed that the data does not have a clustering effect, the sensor data of the current vehicles are different, and it is impossible to determine which sensors are abnormal.

[0084] L≥L s (5)

[0085] For example, if the maximum aggregation L max Greater than the above preset concentration L s , then take the maximum aggregation L max The sensor data set included in the corresponding loop is taken as a normal sensor data set, that is, the target sensor data set, and it is assumed that a data set containing M (M>1) vehicle speed data and {V xi}i=1,2...M. Filter out the standard deviation of the inherent noise of the sensor to which the data belongs, and obtain the target sensor data set {(V xi ,σ i )}i=1,2…M.

[0086] Based on the above scheme, this application assumes that most sensors are operating normally under normal circumstances, and only a small number of sensors occasionally have inaccurate predictions. In response to this phenomenon, the present invention uses an unsupervised clustering algorithm to automatically identify a small number of abnormal sensor data without the need to predict possible sensor abnormal scenarios.

[0087] S103: Determine fused vehicle speed data of the target vehicle based on the target sensor data set.

[0088] The above step S103 further includes S1031:

[0089] Acquire a probability density function value of the target sensor data set, wherein the target sensor data set is assumed to obey a normal distribution;

[0090] Taking the sensor vehicle speed data in the target sensor data set as single observation data, obtaining the probability of occurrence of the target sensor data set, wherein the probability is expressed as a function of the expected vehicle speed;

[0091] Determine the actual vehicle speed corresponding to the case with the maximum probability based on the maximum likelihood estimation method;

[0092] The fused vehicle speed data is determined based on the actual vehicle speed corresponding to the case with the maximum probability. Specifically, it includes:

[0093] The fused vehicle speed data is determined based on the following formula:

[0094]

[0095] Among them, V x is the fused vehicle speed data, M is the total amount of target sensor data set, V xi is the sensor vehicle speed data in the target sensor data set, σ i is the sensor noise standard deviation corresponding to the sensor speed data.

[0096] For example, it is assumed that the data of each sensor follows a normal distribution and the expectation is that the actual vehicle speed V x , then the probability density function P(V xi ) is as follows:

[0097]

[0098] In the target sensor data set {(V xi ,σ i )}i=1,2...M, the transmitted sensor speed data is used as the single observation data. Assuming that the observer data distributions are independent of each other, the probability P of the observation combination can be expressed as:

[0099]

[0100] The above probability takes the expected speed as the unknown quantity. The probability of this observation combination can be expressed as a function of the expected speed f(V x ).

[0101]

[0102] Use the maximum likelihood estimation method to obtain the actual vehicle speed V that maximizes the probability under this observation combination x :

[0103] V x = argmax f(V x ) (8)

[0104] The solution is: the real vehicle speed V x Multi-sensor fusion estimation of:

[0105]

[0106] Among them, V x is the fused vehicle speed data, M is the total amount of target sensor data set, V xi is the sensor vehicle speed data in the target sensor data set, σ i is the sensor noise standard deviation corresponding to the sensor speed data.

[0107] Finally, the estimated vehicle speed is sent to other control modules to complete vehicle control.

[0108] It should be noted that the present application also includes the confidence calculation of the vehicle speed estimation value. The above confidence characterizes the accuracy of the data estimated after the fusion of multiple sensors. The present invention uses the sensor aggregation L obtained in the second step max The indicator is used as the confidence of the fused data.

[0109] Through the above technical scheme, the multi-sensor fusion vehicle speed estimation method provided by the present invention solves the problem of low accuracy of vehicle speed data currently obtained through a single channel. The present invention determines the initial sensor data set of the target vehicle, wherein the sensor data set includes sensor speed data and corresponding sensor noise standard deviation, and the sensor speed data is obtained based on its own sensor or networked sensor; removes abnormal sensor data in the initial sensor data set based on an unsupervised clustering algorithm to determine the target sensor data set; and determines the fused vehicle speed data of the target vehicle based on the target sensor data set. In the above scheme, considering that with the advent of the era of intelligent networking, each vehicle can obtain multiple vehicle speed estimation information from different sources, the vehicle speed estimation information obtained from multiple channels is fused using an unsupervised clustering algorithm and a multi-sensor fusion algorithm to obtain its own vehicle speed estimation information, which has the advantage of higher accuracy compared to the vehicle speed data obtained through a single channel.

[0110] Furthermore, as a response to the above Figure 1 In order to realize the method shown in the figure, the embodiment of the present invention also provides a multi-sensor fusion vehicle speed estimation device for Figure 1 The device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this device embodiment will not repeat the details of the aforementioned method embodiment one by one, but it should be clear that the device in this embodiment can correspond to all the contents of the aforementioned method embodiment. Figure 2 As shown, the device includes: a first determining unit 21, a second determining unit 22 and a third determining unit 23, wherein

[0111] A first determination unit 21 is used to determine an initial sensor data set of a target vehicle, wherein the sensor data set includes sensor vehicle speed data and a corresponding sensor noise standard deviation, and the sensor vehicle speed data is obtained based on a self-sensor or a networked sensor;

[0112] A second determining unit 22, configured to eliminate abnormal sensor data in the initial sensor data set based on an unsupervised clustering algorithm to determine a target sensor data set;

[0113] The third determination unit 23 is used to determine the fused vehicle speed data of the target vehicle based on the target sensor data set.

[0114] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and a multi-sensor fusion vehicle speed estimation method is implemented by adjusting kernel parameters, which can solve the problem of low accuracy of vehicle speed data currently obtained through a single channel.

[0115] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program. When the program is executed by a processor, the multi-sensor fusion vehicle speed estimation method is implemented.

[0116] An embodiment of the present invention provides a processor, which is used to run a program, wherein the multi-sensor fusion vehicle speed estimation method is executed when the program is running.

[0117] An embodiment of the present invention provides an electronic device, the electronic device comprising at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the multi-sensor fusion vehicle speed estimation method as described above

[0118] An embodiment of the present invention provides an electronic device 30, such as Figure 3 As shown, the electronic device includes at least one processor 301, and at least one memory 302 and a bus 303 connected to the processor; wherein the processor 301 and the memory 302 communicate with each other through the bus 303; the processor 301 is used to call the program instructions in the memory to execute the above-mentioned multi-sensor fusion vehicle speed estimation method.

[0119] The intelligent electronic devices in this article can be PCs, PADs, mobile phones, etc.

[0120] The present application also provides a computer program product, which, when executed on a process management electronic device, is suitable for executing a program that initializes the steps of the above-mentioned multi-sensor fusion vehicle speed estimation method.

[0121] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0122] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0123] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0124] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0125] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0126] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 This corresponds to the flow of control of the memory in the embodiment.

[0127] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can 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 can 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 (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 can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can 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 (SSD)), etc.

[0128] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0129] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0130] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0131] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0132] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0133] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-sensor fusion vehicle speed estimation method, characterized in that: include: Determine an initial sensor data set of the target vehicle, wherein the sensor data set includes sensor vehicle speed data and corresponding sensor noise standard deviation, and the sensor vehicle speed data is obtained based on its own sensor or networked sensor; Eliminating abnormal sensor data in the initial sensor data set based on an unsupervised clustering algorithm to determine a target sensor data set; The fused vehicle speed data of the target vehicle is determined based on the target sensor data set.

2. The method according to claim 1, characterized in that The method of eliminating abnormal sensor data in the initial sensor data set based on an unsupervised clustering algorithm to determine a target sensor data set includes: Circularly calculating the aggregation degree of the initial sensor data set to obtain an aggregation degree set, wherein the aggregation degree set is sorted by size; Determining a maximum degree of aggregation in the set of degrees of aggregation; When the maximum concentration is greater than a preset concentration, a sensor data set corresponding to the maximum concentration is acquired as the target sensor data set.

3. The method according to claim 2, characterized in that The looping of calculating the aggregation of the initial sensor data set to obtain an aggregation set includes: Positioning error sensor; Eliminating the sensor vehicle speed data corresponding to the error sensor to obtain a second sensor data set; The standard deviation of the second sensor data set is obtained to determine the degree of aggregation.

4. The method according to claim 3, characterized in that The obtaining a standard deviation of the second sensor data set to determine the degree of aggregation comprises: The degree of aggregation was calculated based on the following formula: Where L is the degree of aggregation, σ v is the standard deviation of the second sensor data set, τ σ and τ N is an adjustable weight coefficient, and N is the total amount of data in the second sensor data set.

5. The method according to claim 3, characterized in that: The positioning error sensor comprises: Determining an average value of the sensor vehicle speed data in the initial sensor data set; Constructing a vehicle speed difference set based on the vehicle speed difference between each sensor vehicle speed data in the initial sensor data set and the average value; determining a maximum vehicle speed difference in the vehicle speed difference set; The sensor corresponding to the maximum vehicle speed difference is determined to be an error sensor.

6. The method according to claim 1, characterized in that The determining the fused vehicle speed data of the target vehicle based on the target sensor data set includes: Acquire a probability density function value of the target sensor data set, wherein the target sensor data set is assumed to obey a normal distribution; Taking the sensor vehicle speed data in the target sensor data set as single observation data, obtaining the probability of occurrence of the target sensor data set, wherein the probability is expressed as a function of the expected vehicle speed; Determine the actual vehicle speed corresponding to the case with the maximum probability based on the maximum likelihood estimation method; The fused vehicle speed data is determined based on the actual vehicle speed corresponding to the case with the maximum probability.

7. The method according to claim 6, characterized in that The determining the fused vehicle speed data based on the actual vehicle speed corresponding to the case with the maximum probability includes: The fused vehicle speed data is determined based on the following formula: Among them, V x is the fused vehicle speed data, M is the total amount of target sensor data set, V xi is the sensor vehicle speed data in the target sensor data set, σ i is the sensor noise standard deviation corresponding to the sensor speed data.

8. A multi-sensor fusion vehicle speed estimation device, characterized in that: Also includes: A first determination unit is used to determine an initial sensor data set of a target vehicle, wherein the sensor data set includes sensor vehicle speed data and a corresponding sensor noise standard deviation, and the sensor vehicle speed data is obtained based on a self-sensor or a networked sensor; A second determining unit, configured to eliminate abnormal sensor data in the initial sensor data set based on an unsupervised clustering algorithm to determine a target sensor data set; The third determination unit is used to determine the fused vehicle speed data of the target vehicle based on the target sensor data set.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed by a processor, the steps of the multi-sensor fusion vehicle speed estimation method according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: The electronic device includes at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the steps of the multi-sensor fusion vehicle speed estimation method as described in any one of claims 1 to claim 7.

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