A method and device for real-time vehicle road recognition

Abstract

The present invention discloses a method and device for real-time vehicle road identification, relating to the field of intelligent vehicle driving technology. The method comprises improving a one-third octave band analysis method based on a preset method to obtain a pan-one-third octave band analysis method; obtaining a pan-one-third octave band analysis result of a test vehicle's acceleration based on the pan-one-third octave band analysis method; training a road detection model based on the pan-one-third octave band analysis result of the test vehicle and the corresponding vehicle state information and road surface type; and identifying the current road surface type using the trained road detection model based on the pan-one-third octave band analysis result of the current vehicle's acceleration and vehicle state information. The present invention can display the road surface type on which the vehicle is traveling in real time, thereby improving the safety and comfort of vehicle driving.

Description

Technical Field

[0001] The present invention relates to the field of vehicle intelligent driving technology, and in particular to a method and device for real-time vehicle road recognition. Background Art

[0002] When a driver is driving a vehicle, it is important to obtain information about the type of road surface (stone road, cobblestone road, smooth asphalt road, rough asphalt road, cement road, etc.) in a timely manner to ensure safe driving of the vehicle and improve driving comfort.

[0003] Currently, the technical solution for identifying the road ahead of a vehicle is to collect data on the road ahead in real time through a camera, and then determine whether there is any obstructing vehicle on the road ahead. If there is no obstructing vehicle, then based on the road ahead image information, determine whether there are potholes on the road ahead. If there is an obstructing vehicle, then based on the shaking amplitude of the obstructing vehicle, determine whether there are potholes on the road ahead.

[0004] It can be seen that the current recognition of vehicle driving roads is mainly based on visual recognition technology. For this method, if the visibility is low in weather conditions such as rain and fog, there are problems such as low recognition rate and poor reliability, which makes it impossible to provide effective reference for vehicle driving. Summary of the Invention

[0005] In view of the defects existing in the prior art, the purpose of the present invention is to provide a real-time vehicle driving road identification method and device, which can display the type of road surface the vehicle is driving on in real time and improve the safety and comfort of vehicle driving.

[0006] To achieve the above objectives, the present invention provides a method for real-time vehicle road recognition, which specifically includes the following steps:

[0007] Based on the preset method, the one-third octave band analysis method is improved to obtain the pan-one-third octave band analysis method;

[0008] Obtaining pan-one-third octave band analysis results of the test vehicle's acceleration based on the pan-one-third octave band analysis method;

[0009] The road detection model is trained using the pan-one-third octave analysis results of the test vehicle and the corresponding vehicle status information and road surface type;

[0010] Based on the pan-one-third octave analysis results of the current vehicle's acceleration and vehicle status information, the current road surface type is identified through the trained road detection model.

[0011] On the basis of the above technical solutions,

[0012] The acceleration is measured by an accelerometer mounted on a wheel hub bearing support of the left rear wheel or the right rear wheel of the vehicle;

[0013] The accelerometer is a unidirectional accelerometer with a vertical measurement direction, and is used to measure the vertical acceleration applied to the wheel, and obtain an acceleration spectrum through analysis.

[0014] On the basis of the above technical solution, the one-third octave band analysis method is improved based on the preset method to obtain the pan-one-third octave band analysis method. The specific improvement method is:

[0015] For the acceleration spectrum measured by the accelerometer, based on the road spectrum characteristics of various road surfaces, the values of the center frequencies and the corresponding upper and lower limit frequencies in the original one-third octave band range of 20-100 Hz are retained as the values of the 9th to 18th positions in the pan-one-third octave band;

[0016] According to the multiple relationship between two adjacent center frequencies, based on the center frequency of the 9th position in the pan-one-third octave band, the center frequency of the previous position is calculated in sequence to obtain the center frequencies of the 1st to 8th positions in the pan-one-third octave band;

[0017] According to the relationship between the center frequency in the one-third octave band and its upper and lower limit frequencies, the upper and lower limit frequencies of the first to eighth positions in the pan-one-third octave band are obtained based on the center frequencies of the first to eighth positions in the pan-one-third octave band;

[0018] According to the obtained values of the 9th to 18th digits of the pan-one-third octave band, as well as the center frequencies of the 1st to 8th digits and the corresponding upper and lower limit frequencies, the values of the center frequencies of the 1st to 18th digits of the pan-one-third octave band and the corresponding upper and lower limit frequencies are finally obtained;

[0019] For the values of the 1st to 18th center frequencies and the corresponding upper and lower limit frequencies in the pan-one-third octave band, the one-third octave band analysis method is used to obtain the vibration energy of the acceleration spectrum in each center frequency segment, and 18 values of the acceleration spectrum are obtained, that is, the pan-one-third octave band analysis results.

[0020] Based on the above technical solution, the road detection model is trained using the pan-one-third octave analysis results of the test vehicle and the corresponding vehicle state information and road surface type. Specifically, the pan-one-third octave analysis results of the test vehicle are:

[0021] Based on the accelerometer and the pan-one-third octave analysis method, the pan-one-third octave analysis results of the acceleration measured by various vehicles on different types of roads and at different speeds are obtained.

[0022] Based on the above technical solution, the road detection model is trained using the pan-one-third octave analysis results of the test vehicle and the corresponding vehicle state information and road surface type. The specific steps are as follows:

[0023] The pan-one-third octave band analysis results obtained during each test of the test vehicle and the corresponding vehicle status information are used as input data;

[0024] The road surface type corresponding to the pan-one-third octave band analysis result of each test of the test vehicle is used as output data;

[0025] The training samples are assembled based on the input data and output data, and the constructed road detection model is trained using the assembled training samples.

[0026] On the basis of the above technical solutions,

[0027] The vehicle status information includes tire parameters, tire pressure and vehicle speed;

[0028] The tire parameters include tire width, tire aspect ratio, and applicable rim size of the tire;

[0029] The road surface types include stone roads, cobblestone roads, smooth asphalt roads, rough asphalt roads and cement roads.

[0030] Based on the above technical solution, the method of identifying the current road surface type using the trained road detection model based on the pan-one-third octave analysis results of the current vehicle acceleration and vehicle status information includes the following specific steps:

[0031] The accelerometer installed in the current vehicle measures the acceleration data of the current vehicle and transmits it to the vehicle ECU;

[0032] The vehicle ECU transmits acceleration data and tire pressure and speed in vehicle status information to the vehicle computer;

[0033] The vehicle's road recognition app analyzes acceleration data using the pan-one-third octave band analysis method to obtain pan-one-third octave band analysis results.

[0034] The road recognition app inputs vehicle status information and the pan-one-third octave analysis results into the trained road detection model to obtain and display the current road surface type.

[0035] Based on the above technical solution, the current road surface type is obtained and displayed as follows:

[0036] Based on the obtained road surface type, the corresponding road name, road picture and animated image information are displayed on the vehicle screen.

[0037] On the basis of the above technical solution, the construction of output data also includes:

[0038] Different road surface types are numbered based on a numbering method, and the road surface type number is used as the output data of the training sample. When the trained road detection model is used to identify the road surface type, the road surface type number is directly output.

[0039] The present invention provides a real-time vehicle road recognition device, comprising:

[0040] An improvement module, configured to improve the one-third octave band analysis method based on a preset method to obtain a pan-one-third octave band analysis method;

[0041] an acquisition module, configured to acquire a pan-one-third octave band analysis result of the acceleration of the test vehicle based on a pan-one-third octave band analysis method;

[0042] A training module for training a road detection model using pan-one-third octave band analysis results of a test vehicle and corresponding vehicle state information and road surface type;

[0043] The recognition module is used to identify the current road surface type through the trained road detection model based on the pan-one-third octave analysis results of the current vehicle acceleration and vehicle status information.

[0044] Compared with the existing technology, the advantages of the present invention are: characterizing the road spectrum through the response spectrum of the accelerometer installed on the wheel; determining the relevant parameters of the road detection model by analyzing the influencing factors of the acceleration response; forming a pan-one-third octave analysis method by modifying the one-third octave band, simplifying the expression of the acceleration spectrum characteristics; obtaining a trained road detection model by constructing a large number of training samples; after real-time input data is input into the road detection model, the type of road the vehicle is traveling on in real time can be identified, and its related information (road name, road picture, animated image, etc.) can be displayed on the vehicle screen; the present invention has a simple principle, a simple structure, and simple calculation, can display the type of road surface the vehicle is traveling on in real time, and can improve the safety and comfort of vehicle driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0046] Figure 1This is a flow chart of a method for real-time road identification of a vehicle according to an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of the structure of the hardware system that the real-time driving road recognition method of the present invention relies on. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0049] See also Figure 1 As shown, an embodiment of the present invention provides a method for real-time vehicle road recognition, which specifically includes the following steps:

[0050] S1: Based on the preset method, the one-third octave band analysis method is improved to obtain the pan-one-third octave band analysis method;

[0051] First of all, it should be noted that the acceleration is measured by an accelerometer installed on the wheel hub bearing support of the left or right rear wheel of the vehicle; the accelerometer is a unidirectional accelerometer with a vertical measurement direction. It is used to measure the vertical acceleration on the wheel and obtain the acceleration spectrum through analysis.

[0052] When a vehicle travels on different types of road surfaces (smooth, rough, bumpy), the differences between these types of road surfaces are reflected in their road spectra. In the present invention, the road spectrum is characterized by the vertical response spectrum of an accelerometer mounted on the left or right rear wheel hub bearing support. The data measured by the accelerometer is sent to the vehicle ECU (electronic control unit) in real time. The accelerometer is installed on the rear wheel because the force on the rear wheel is simpler than that on the front wheel, which performs the steering function.

[0053] In the present invention, the one-third octave band analysis method is improved based on a preset method to obtain a pan-one-third octave band analysis method. The specific improvement method is:

[0054] S101: For the acceleration spectrum measured by the accelerometer, based on the road spectrum characteristics of various road surfaces, retain the values of each center frequency and the corresponding upper and lower limit frequencies in the original one-third octave band range of 20-100 Hz as the values of the 9th to 18th bits of the pan-one-third octave band;

[0055] S102: Based on the multiple relationship between two adjacent center frequencies and the center frequency of the ninth position in the pan-one-third octave band, the center frequencies of the previous positions are calculated in sequence to obtain the center frequencies of the first to eighth positions in the pan-one-third octave band;

[0056] S103: according to the relationship between the center frequency in the one-third octave band and its upper and lower limit frequencies, based on the center frequencies of the first to eighth positions in the pan-one-third octave band, obtaining the upper and lower limit frequencies of the first to eighth positions in the pan-one-third octave band;

[0057] S104: finally obtaining the values of the center frequencies and the corresponding upper and lower frequency limits of the 1st to 18th positions of the pan-one-third octave band based on the obtained values of the 9th to 18th positions of the pan-one-third octave band, and the center frequencies and the corresponding upper and lower frequency limits of the 1st to 8th positions;

[0058] S105: For each center frequency from the 1st to the 18th in the pan-one-third octave band and the corresponding upper and lower frequency limits, a one-third octave band analysis method is used to obtain the vibration energy of the acceleration spectrum in each center frequency segment. 18 values of the acceleration spectrum are obtained, i.e., the pan-one-third octave band analysis results are obtained.

[0059] The response measured by an accelerometer is characterized by its spectrum. Generally speaking, the road spectra of various road surfaces are low-frequency. Therefore, in the present invention, the frequency range of the acceleration spectrum is 0-100 Hz. Since the lowest center frequency of the one-third octave band analysis is 12.5 Hz (the lower frequency limit is 11.2 Hz), which does not meet the lower frequency limit requirement of the present invention, it needs to be improved. The improved analysis method can be called pan-one-third octave band analysis. The following is a detailed description of the improved method of the present invention in conjunction with Table 1.

[0060] Table 1 Pan-one-third octave band center frequency and its upper and lower limit frequencies

[0061] NO. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Center frequency (Hz) 2 2.5 3.15 4 5 6.3 8 10 12.5 16 20 25 31.5 40.0 50.0 63.0 80.0 100.0 Frequency lower limit (Hz) 1.78 2.24 2.8 3.55 4.5 5.6 7.1 9 11.2 14.1 17.8 22.4 28 35.5 45 56 71 90 Upper frequency limit (Hz) 2.24 2.8 3.55 4.5 5.6 7.1 9 11.2 14.1 17.8 22.4 28 35.5 45 56 71 90 112

[0062] Table 1 shows the center frequencies and their upper and lower limits of the pan-one-third octave band. The 9th to 18th center frequencies and their upper and lower limits are the values within the original 20-100Hz range of the one-third octave band and are not changed here. As can be seen from the one-third octave band characteristics, the ratio of adjacent center frequencies is fixed, that is, the ratio of the center frequency behind divided by the center frequency ahead is 2. 1 / 3 , so the lowest center frequency of the one-third octave band, 12.5Hz, is divided by 2 1 / 3 , that is, the previous center frequency is 10Hz; divide 12.5Hz by 2 1 / 3 , which means the previous center frequency is 8Hz.... Thus, we get the 1st to 8th center frequencies in Table 1. Then, based on the relationship between the one-third octave band center frequency and its upper and lower frequency limits, we can calculate the upper and lower frequency limits corresponding to the 1st to 8th center frequencies, as shown in Table 1.

[0063] As shown in Table 1, the pan-one-third octave band has a total of 18 center frequencies. Compared to the center frequencies within the 20-100 Hz range of the one-third octave band, the pan-one-third octave band adds eight more center frequencies, from the first to the eighth. After determining the pan-one-third octave band center frequencies and their upper and lower limits, a similar method to the one-third octave band analysis is used to determine the vibration energy of the acceleration spectrum at each center frequency. Therefore, after pan-one-third octave band analysis, 18 values of the acceleration spectrum can be obtained.

[0064] S2: Obtain the pan-one-third octave band analysis results of the test vehicle's acceleration based on the pan-one-third octave band analysis method;

[0065] In the present invention, the road detection model is trained using the pan-one-third octave analysis results of the test vehicle and the corresponding vehicle state information and road surface type. Specifically, the pan-one-third octave analysis results of the test vehicle are:

[0066] Using accelerometers and a pan-one-third octave band analysis method, we obtain pan-one-third octave band analysis results of the acceleration measured for various vehicles traveling on different types of roads and at different speeds. Specifically, we obtain pan-one-third octave band analysis results for the acceleration measured for various vehicles (using different tires and tire pressures) traveling on different types of roads and at different speeds, representing the 18 values of the acceleration spectrum.

[0067] In the present invention, the road detection model is trained using the pan-one-third octave analysis results of the test vehicle and the corresponding vehicle state information and road surface type. The specific steps are as follows:

[0068] S201: using the pan-one-third octave band analysis results obtained during each test of the test vehicle and the corresponding vehicle status information as input data;

[0069] S202: Outputting the road surface type corresponding to the pan-one-third octave band analysis result of each test of the test vehicle as output data;

[0070] S203: Complete the formation of training samples based on the input data and the output data, and use the formed training samples to train the constructed road detection model.

[0071] Vehicle status information includes tire parameters, tire pressure, and speed. Tire parameters include tire width, tire aspect ratio, and the tire's compatible rim size. For example, in a 225 / 60 / R17 tire, 225 indicates a 225mm section width; 60 represents the aspect ratio, meaning the ratio of the tire's section height to its section width is 60%; R indicates it's a radial tire; and 17 indicates it's compatible with a 17-inch rim. In the present invention, the accelerometer's response to the tire is related to these three parameters: section width, aspect ratio, and compatible rim size.

[0072] Pavement types include stone roads, cobblestone roads, smooth asphalt roads, rough asphalt roads, cement roads, etc.

[0073] The training samples obtained must be comprehensive and representative. Comprehensiveness includes a wide range of tire parameters, tire pressures, road surface types, and vehicle speeds. Representativeness means that the training samples must represent the configuration (tire parameters, tire pressures) and driving conditions of a wide range of actual users' vehicles. For example, a training sample traveling at 100 km / h on a cobblestone road would not be representative.

[0074] S3: Train the road detection model using the pan-one-third octave analysis results of the test vehicle and the corresponding vehicle state information and road surface type;

[0075] Once the training samples are obtained, the road detection model is trained. For each training sample, 18 acceleration spectrum values, three tire parameters, one tire pressure, and one vehicle speed—a total of 23 parameters—are used as input data for the road detection model. The road surface type serves as the model's output data. All training sample input data is fed into the road detection model, and the model's internal parameters are automatically adjusted based on the output data corresponding to each training sample until the target requirements are met, resulting in a fully trained road detection model.

[0076] S4: Based on the pan-one-third octave analysis results of the current vehicle acceleration and the vehicle status information, the current road surface type is identified through the trained road detection model.

[0077] In the present invention, based on the pan-one-third octave analysis results of the current vehicle acceleration and vehicle status information, the current road surface type is identified by a trained road detection model. The specific steps include:

[0078] S401: An accelerometer installed in the current vehicle measures acceleration data of the current vehicle and transmits it to the vehicle ECU;

[0079] S402: The vehicle ECU transmits the acceleration data and the tire pressure and speed in the vehicle status information to the vehicle computer. The tire parameters can be directly set in the vehicle computer.

[0080] S403: The vehicle's road recognition app (application) analyzes the acceleration data based on a pan-one-third octave band analysis method to obtain a pan-one-third octave band analysis result.

[0081] S404: The road recognition app inputs the vehicle status information and the pan-one-third octave analysis results obtained by analysis into the trained road detection model to obtain and display the current road surface type.

[0082] The hardware system structure involved in the real-time road recognition method of the present invention is as follows: Figure 2 As shown, the entire system is built into the vehicle and includes an accelerometer, the vehicle ECU, and the onboard computer, which includes a road recognition app. All of this hardware is powered by the vehicle's battery (either a storage battery or a power battery).

[0083] The role of the vehicle ECU is to act as a bridge for transmitting data between the vehicle computer and the accelerometer. The acceleration data is transmitted to the vehicle computer through the vehicle ECU. At the same time, two-way communication is also carried out between the vehicle computer and the vehicle ECU to obtain vehicle-related information, such as tire pressure, vehicle speed, etc.

[0084] The vehicle's computer receives acceleration data transmitted by the vehicle's ECU and communicates with it to obtain relevant vehicle information. The road identification app in the vehicle performs spectrum analysis on the acceleration data. It inputs the acceleration spectrum data, along with the vehicle's tire model, tire pressure, and speed data, into a pre-trained road detection model to identify the type of road the vehicle is currently traveling on. The corresponding image and road information pre-loaded into the computer are then displayed on the screen.

[0085] In the present invention, the current road surface type is obtained and displayed, specifically: based on the obtained road surface type, the corresponding road name, road picture and animated image information are displayed on the vehicle screen.

[0086] In the present invention, the construction of output data also includes: numbering different road surface types based on a numbering method (for example, numbering stone roads as 1, cobblestone roads as 2...), using the road surface type number as the output data of the training sample, and directly outputting the road surface type number when using the trained road detection model to identify the road surface type.

[0087] For the identification of the current road surface type, the specific usage process is: set the tire parameters in the road identification APP of the vehicle computer. When the vehicle is driving, the road identification APP obtains tire pressure and speed data through the vehicle ECU, obtains accelerometer test data through the vehicle ECU, and performs a pan-one-third octave analysis on it to obtain 18 values of the acceleration spectrum data. Then, the above data (23 in total) are input into the trained road detection model to output the road surface type number in real time. The road identification APP will display the road name, road picture, animated image and other information corresponding to the road surface type number pre-set on the vehicle computer on the vehicle computer screen for the driver and passengers to view.

[0088] The real-time vehicle road identification method of the embodiment of the present invention characterizes the road spectrum through the response spectrum of the accelerometer installed on the wheel; determines the relevant parameters of the road detection model by analyzing the influencing factors of the acceleration response; forms a pan-one-third octave analysis method by modifying the one-third octave band, simplifying the expression of the acceleration spectrum characteristics; obtains a trained road detection model by constructing a large number of training samples; after the real-time input data is input into the road detection model, the type of the vehicle's real-time road can be identified, and its related information (road name, road picture, animation, etc.) can be displayed on the vehicle screen; the present invention has a simple principle, a simple structure, and simple calculation, can display the type of road surface the vehicle is traveling in real time, and can improve the safety and comfort of vehicle driving.

[0089] In one possible implementation, an embodiment of the present invention further provides a readable storage medium, which is located in a PLC (Programmable Logic Controller) controller. The readable storage medium stores a computer program, which, when executed by a processor, implements the following steps of the method for real-time vehicle road identification:

[0090] Based on the preset method, the one-third octave band analysis method is improved to obtain the pan-one-third octave band analysis method;

[0091] Obtaining pan-one-third octave band analysis results of the test vehicle's acceleration based on the pan-one-third octave band analysis method;

[0092] The road detection model is trained using the pan-one-third octave analysis results of the test vehicle and the corresponding vehicle status information and road surface type;

[0093] Based on the pan-one-third octave analysis results of the current vehicle's acceleration and vehicle status information, the current road surface type is identified through the trained road detection model.

[0094] The storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: 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 this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or device.

[0095] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination thereof.

[0096] Computer program code for performing the operations of the present invention can 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" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0097] An embodiment of the present invention provides a real-time vehicle road recognition device, which includes an improvement module, an acquisition module, a training module and a recognition module.

[0098] The improvement module is used to improve the one-third octave band analysis method based on a preset method to obtain a pan-one-third octave band analysis method; the acquisition module is used to obtain the pan-one-third octave band analysis results of the test vehicle's acceleration based on the pan-one-third octave band analysis method; the training module is used to train the road detection model through the pan-one-third octave band analysis results of the test vehicle and the corresponding vehicle status information and road surface type; the identification module is used to identify the current road surface type through the trained road detection model based on the pan-one-third octave band analysis results of the current vehicle's acceleration and the vehicle status information.

[0099] The foregoing is merely a list of specific embodiments of the present application, intended to enable those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the broadest scope consistent with the principles and novel features of the present application.

[0100] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor or other programmable data processing device to produce 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 flowcharts and / or block diagrams. 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.

Claims

1. A method for real-time vehicle road recognition, characterized in that: The specific steps include: Based on the preset method, the one-third octave band analysis method is improved to obtain the pan-one-third octave band analysis method; Obtaining pan-one-third octave band analysis results of the test vehicle's acceleration based on the pan-one-third octave band analysis method; The road detection model is trained using the pan-one-third octave analysis results of the test vehicle and the corresponding vehicle status information and road surface type; Based on the pan-one-third octave analysis results of the current vehicle's acceleration and vehicle status information, the current road surface type is identified through the trained road detection model; The acceleration is measured by an accelerometer installed on the left or right rear wheel hub bearing support of the vehicle; the accelerometer is a unidirectional accelerometer with a vertical measurement direction, which is used to measure the vertical acceleration on the wheel and obtain the acceleration spectrum through analysis; The one-third octave band analysis method is improved based on the preset method to obtain the pan-one-third octave band analysis method. The specific improvement method is: For the acceleration spectrum measured by the accelerometer, based on the road spectrum characteristics of various road surfaces, the values of the center frequencies and the corresponding upper and lower limit frequencies in the original one-third octave band range of 20-100 Hz are retained as the values of the 9th to 18th positions in the pan-one-third octave band; According to the multiple relationship between two adjacent center frequencies, based on the center frequency of the 9th position in the pan-one-third octave band, the center frequency of the previous position is calculated in sequence to obtain the center frequencies of the 1st to 8th positions in the pan-one-third octave band; According to the relationship between the center frequency in the one-third octave band and its upper and lower limit frequencies, the upper and lower limit frequencies of the first to eighth positions in the pan-one-third octave band are obtained based on the center frequencies of the first to eighth positions in the pan-one-third octave band; According to the obtained values of the 9th to 18th digits of the pan-one-third octave band, as well as the center frequencies of the 1st to 8th digits and the corresponding upper and lower limit frequencies, the values of the center frequencies of the 1st to 18th digits of the pan-one-third octave band and the corresponding upper and lower limit frequencies are finally obtained; For the values of the 1st to 18th center frequencies and the corresponding upper and lower limit frequencies in the pan-one-third octave band, the one-third octave band analysis method is used to obtain the vibration energy of the acceleration spectrum in each center frequency segment, and 18 values of the acceleration spectrum are obtained, that is, the pan-one-third octave band analysis results.

2. The method for real-time vehicle road recognition according to claim 1, wherein: The road detection model is trained using the pan-one-third octave analysis results of the test vehicle and the corresponding vehicle state information and road surface type, wherein the pan-one-third octave analysis results of the test vehicle are specifically: Based on the accelerometer and the pan-one-third octave analysis method, the pan-one-third octave analysis results of the acceleration measured by various vehicles on different types of roads and at different speeds are obtained.

3. The method for real-time vehicle road recognition according to claim 2, wherein: The road detection model is trained using the pan-one-third octave analysis results of the test vehicle and the corresponding vehicle state information and road surface type. The specific steps are as follows: The pan-one-third octave band analysis results obtained during each test of the test vehicle and the corresponding vehicle status information are used as input data; The road surface type corresponding to the pan-one-third octave band analysis result of each test of the test vehicle is used as output data; The training samples are assembled based on the input data and output data, and the constructed road detection model is trained using the assembled training samples.

4. The method for real-time vehicle road recognition according to claim 3, wherein: The vehicle status information includes tire parameters, tire pressure and vehicle speed; The tire parameters include tire width, tire aspect ratio, and applicable rim size of the tire; The road surface types include stone roads, cobblestone roads, smooth asphalt roads, rough asphalt roads and cement roads.

5. The method for real-time vehicle road recognition according to claim 3, wherein: The method of identifying the current road surface type using a trained road detection model based on the pan-one-third octave analysis results of the current vehicle acceleration and the vehicle status information includes the following specific steps: The accelerometer installed in the current vehicle measures the acceleration data of the current vehicle and transmits it to the vehicle ECU; The vehicle ECU transmits acceleration data and tire pressure and speed in vehicle status information to the vehicle computer; The vehicle's road recognition app analyzes acceleration data using the pan-one-third octave band analysis method to obtain pan-one-third octave band analysis results. The road recognition app inputs vehicle status information and the pan-one-third octave analysis results into the trained road detection model to obtain and display the current road surface type.

6. The method for real-time vehicle road recognition according to claim 5, wherein: The current road surface type is obtained and displayed as follows: Based on the obtained road surface type, the corresponding road name, road picture and animated image information are displayed on the vehicle screen.

7. The method for real-time vehicle road recognition according to claim 3, wherein: For the construction of output data, it also includes: Different road surface types are numbered based on a numbering method, and the road surface type number is used as the output data of the training sample. When the trained road detection model is used to identify the road surface type, the road surface type number is directly output.

8. A real-time vehicle road recognition device, characterized in that: include: An improvement module, configured to improve the one-third octave band analysis method based on a preset method to obtain a pan-one-third octave band analysis method; an acquisition module, configured to acquire a pan-one-third octave band analysis result of the acceleration of the test vehicle based on a pan-one-third octave band analysis method; A training module for training a road detection model using pan-one-third octave band analysis results of a test vehicle and corresponding vehicle state information and road surface type; An identification module is used to identify the current road surface type using a trained road detection model based on the pan-one-third octave analysis results of the current vehicle acceleration and vehicle status information; The acceleration is measured by an accelerometer installed on the left or right rear wheel hub bearing support of the vehicle; the accelerometer is a unidirectional accelerometer with a vertical measurement direction, which is used to measure the vertical acceleration on the wheel and obtain the acceleration spectrum through analysis; The one-third octave band analysis method is improved based on the preset method to obtain the pan-one-third octave band analysis method. The specific improvement method is: For the acceleration spectrum measured by the accelerometer, based on the road spectrum characteristics of various road surfaces, the values of the center frequencies and the corresponding upper and lower limit frequencies in the original one-third octave band range of 20-100 Hz are retained as the values of the 9th to 18th positions in the pan-one-third octave band; According to the multiple relationship between two adjacent center frequencies, based on the center frequency of the 9th position in the pan-one-third octave band, the center frequency of the previous position is calculated in sequence to obtain the center frequencies of the 1st to 8th positions in the pan-one-third octave band; According to the relationship between the center frequency in the one-third octave band and its upper and lower limit frequencies, the upper and lower limit frequencies of the first to eighth positions in the pan-one-third octave band are obtained based on the center frequencies of the first to eighth positions in the pan-one-third octave band; According to the obtained values of the 9th to 18th digits of the pan-one-third octave band, as well as the center frequencies of the 1st to 8th digits and the corresponding upper and lower limit frequencies, the values of the center frequencies of the 1st to 18th digits of the pan-one-third octave band and the corresponding upper and lower limit frequencies are finally obtained; For the values of the 1st to 18th center frequencies and the corresponding upper and lower limit frequencies in the pan-one-third octave band, the one-third octave band analysis method is used to obtain the vibration energy of the acceleration spectrum in each center frequency segment, and 18 values of the acceleration spectrum are obtained, that is, the pan-one-third octave band analysis results.

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