CN115179954A - Vehicle data processing method, apparatus, storage medium, and program product - Google Patents
Vehicle data processing method, apparatus, storage medium, and program product Download PDFInfo
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Abstract
The application provides a vehicle data processing method, a device, a storage medium and a program product. The method comprises the following steps: acquiring corresponding road surface material information of the first vehicle at a target acquisition moment, road surface friction audio information generated by friction and vibration between tires of the first vehicle and the road surface, whole vehicle information and a target positioning position in the running process of the first vehicle at a target road section; inputting the road surface friction audio information, the road surface material information and the whole vehicle information corresponding to the target acquisition moment into a prediction model to obtain the road surface friction coefficient of the target road section at the target positioning position; the prediction model is used for representing the mapping relation between audio information generated by friction and vibration of tires and the road surface friction coefficient when different vehicles run on the road surfaces made of different materials at different speeds. The method can obtain the road surface friction coefficient required by intelligent driving of the vehicle.
Description
Technical Field
The present application relates to vehicle data processing technologies, and in particular, to a vehicle data processing method, device, storage medium, and program product.
Background
The Society of Automotive Engineers (SAE) standard classifies the smart driving capabilities of a vehicle into 6 levels, L0-L5. Among them, vehicles at a level of L4 or less need to be driven intelligently according to a set Operation Design Domain (ODD). ODD has certain requirements on the road surface friction coefficient. Therefore, how to obtain the road friction coefficient becomes the key for realizing intelligent driving of the vehicle.
Disclosure of Invention
The application provides a vehicle data processing method, a device, a storage medium and a program product, which are used for solving the problem of how to obtain the road surface friction coefficient.
In a first aspect, the present application provides a vehicle data processing method, the method being applied to a first vehicle, the method comprising:
acquiring corresponding road surface material information of the first vehicle at a target acquisition moment, road surface friction audio information generated by friction and vibration between tires of the first vehicle and the road surface, whole vehicle information and a target positioning position in the running process of the first vehicle at a target road section;
inputting the road surface friction audio information, the road surface material information and the whole vehicle information corresponding to the target acquisition moment into a prediction model to obtain the road surface friction coefficient of the target road section at the target positioning position; the prediction model is used for representing the mapping relation between audio information generated by friction and vibration of tires and the road surface friction coefficient when different vehicles run on the road surfaces made of different materials at different speeds.
Optionally, acquiring road surface friction audio information generated by road surface friction vibration and a tire of the first vehicle corresponding to a target acquisition time in a driving process of the first vehicle on a target road section, includes:
acquiring road surface friction audio data generated by friction and vibration of tires and the road surface, which are acquired at the target acquisition moment, by using a sound acquisition sensor arranged at least one wheel of the first vehicle;
and preprocessing the road friction audio data to obtain road friction audio information corresponding to the target acquisition time.
Optionally, the road friction audio information comprises: frequency spectrum of road friction audio data;
the preprocessing the road friction audio data to obtain the road friction audio information corresponding to the target acquisition time comprises the following steps:
performing first preprocessing on the road surface friction audio data to remove noise in the road surface friction audio data;
and carrying out second preprocessing on the road surface friction audio data after the noise is removed to obtain the frequency spectrum of the road surface friction audio data.
Optionally, the obtaining of the road material information corresponding to the first vehicle at the target acquisition time in the driving process of the first vehicle at the target road segment includes:
acquiring road surface image data acquired by a sensing module of the first vehicle at the target acquisition moment;
and acquiring the road surface material information corresponding to the target acquisition time according to the road surface image data.
Optionally, the method further comprises:
sending at least one positioning position corresponding to the target road section at least one acquisition moment and a road surface friction coefficient of the at least one positioning position to a cloud platform, so that the cloud platform adds the road surface friction coefficient of the target road section at the at least one positioning position to high-precision map data of the target road section; the at least one acquisition time comprises the target acquisition time.
In a second aspect, the present application provides a vehicle data processing method, which is applied to a cloud platform, and includes:
receiving at least one positioning position of a target road section reported by a first vehicle and a corresponding road surface friction coefficient; the road surface friction coefficient corresponding to the at least one positioning position is obtained based on at least one piece of road surface material information corresponding to the moment, road surface friction audio information generated by friction and vibration between tires of the first vehicle and the road surface and whole vehicle information when the first vehicle runs on a target road section;
and adding the road surface friction coefficient of the target road section corresponding to at least one positioning position to the high-precision map data of the target road section to obtain the updated high-precision map data of the target road section.
Optionally, the method further comprises:
and sending the updated high-precision map data of the target road section to at least one second vehicle running on the target road section, so that the second vehicle can carry out intelligent driving control according to the road surface friction coefficient in the updated high-precision map data.
In a third aspect, the present application provides a vehicle data processing apparatus, the apparatus being applied to a first vehicle, the apparatus comprising:
the acquisition module is used for acquiring road surface material information corresponding to a target acquisition moment, road surface friction audio information generated by friction and vibration between tires of the first vehicle and the road surface, whole vehicle information and a target positioning position when the first vehicle runs on a target road section;
the processing module is used for inputting the road surface friction audio information, the road surface material information and the whole vehicle information corresponding to the target acquisition moment into a prediction model to obtain the road surface friction coefficient of the target road section at the target positioning position; the prediction model is used for representing the mapping relation between audio information generated by friction and vibration of tires and the road surface friction coefficient when different vehicles run on the road surfaces made of different materials at different speeds.
In a fourth aspect, the present application provides a vehicle data processing apparatus, where the apparatus is applied to a cloud platform, and the apparatus includes:
the receiving module is used for receiving at least one positioning position of a target road section reported by a first vehicle and a corresponding road surface friction coefficient; the road surface friction coefficient corresponding to the at least one positioning position is obtained based on at least one piece of road surface material information corresponding to the moment of collection, road surface friction audio information generated by friction and vibration between tires of the first vehicle and the road surface and whole vehicle information when the first vehicle runs in a target road section;
and the processing module is used for adding the road surface friction coefficient of the target road section corresponding to at least one positioning position into the high-precision map data of the target road section to obtain the updated high-precision map data of the target road section.
In a fifth aspect, the present application provides a vehicle for carrying out the method of any one of the first aspect.
In a sixth aspect, the present application provides a cloud platform for implementing the method according to any one of the second aspect.
In a seventh aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively coupled to the processor; the memory stores computer execution instructions; the processor executes computer-executable instructions stored by the memory to implement the method of any one of the first or second aspects.
In an eighth aspect, the present application provides a computer-readable storage medium having stored thereon computer-executable instructions for implementing the method of any one of the first or second aspects when executed by a processor.
In a ninth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, performs the method of any one of the first or second aspects.
The vehicle data processing method, the vehicle data processing device, the storage medium and the program product provided by the application provide a new road surface friction coefficient mode, and the road surface friction coefficient of the target road section at the target positioning position can be obtained based on the collected road surface material information, the road surface friction audio information and the whole vehicle information generated by the friction and vibration between the tire of the first vehicle and the road surface and the target positioning position in the driving process of the vehicle at the target road section.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic structural diagram of a vehicle according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a vehicle data processing method according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart of another vehicle data processing method provided by the embodiment of the present application;
fig. 4 is a schematic structural diagram of a vehicle data processing device according to an embodiment of the present application;
FIG. 5 is a schematic structural diagram of another vehicle data processing device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. The drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the concepts of the application by those skilled in the art with reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
An ODD is an external condition where an intelligent driving vehicle can run normally and safely, such as road type, driving area, speed, environment (weather, day/night, etc.), etc. And the vehicles with the grades of L4 and below need to be intelligently driven according to the set ODD. The ODD has certain requirements on the road surface friction coefficient. For example, when the friction coefficient of the road surface is less than a certain value (e.g., 0.5), the smart driving function of the vehicle must not be turned on. Therefore, how to obtain the road friction coefficient becomes the key for realizing intelligent driving of the vehicle.
At present, a visual or vehicle dynamics model is mainly used for predicting and calculating the friction coefficient of the road surface. For example, an Electronic Stability Controller (ESC) system of a vehicle indirectly calculates a friction coefficient of a road surface currently located under wheels of the vehicle through traction force of the vehicle (i.e., power driving the vehicle to run) by means of a mathematical model of dynamics.
The inventor finds out through research that when different vehicles run on different road surfaces with different speeds, the audio frequency generated by the friction vibration between the tire and the road surface is different under the influence of the friction coefficient of the road surface. Different vehicles as referred to herein may refer to different types of vehicles (e.g., a large truck, a passenger car, a sedan, a tricycle, etc.), or vehicles of the same type but different overall vehicle mass (e.g., both trucks but different weights), etc. It should be noted that the definitions for different vehicles may be further subdivided according to actual needs. For example, different models of vehicles of the same type are also considered different vehicles and the like.
In view of this, the present application provides a new way of obtaining a road surface friction coefficient, that is, the road surface friction coefficient is accurately obtained by using various factors, such as audio frequency, vehicle information, and road surface material information, generated by friction and vibration between a tire and a road surface, so as to improve the accuracy of the road surface friction coefficient.
The following describes how to acquire the road surface friction coefficient using the audio frequency generated by the frictional shock between the tire and the road surface, taking the first vehicle (i.e., the vehicle for acquiring the road surface friction coefficient) as an example.
To facilitate understanding of the method of the present embodiment, the following description will first discuss the architecture of a first vehicle according to the present application. Fig. 1 is a schematic structural diagram of a vehicle according to an embodiment of the present disclosure. As shown in fig. 1, the first vehicle may include: the intelligent driving sensing module and the intelligent driving control processing module. The intelligent driving perception module may include: the system comprises a sound acquisition sensor, a perception submodule, a positioning submodule, a sensor capable of acquiring information of the whole vehicle and the like. The vehicle information may include, for example, at least one of the following: vehicle speed, acceleration, tire pressure, vehicle mass, etc.
The number of sound collection sensors is not limited in the present application. For example, a sound collection sensor is disposed at least one wheel of the first vehicle, for example, one sound collection sensor may be disposed at each wheel, one sound collection sensor may be disposed at one of the wheels, or a part of the wheels may be disposed with the sound collection sensor. Fig. 1 is a schematic view of an example of a sound collection sensor.
The sound collection sensor is used for collecting road surface friction audio data generated by the friction vibration between the tire of the first vehicle and the road surface when the first vehicle runs. In a specific implementation, the sound collection sensor may be, for example, a microphone, or a microphone array.
And the perception submodule is used for acquiring road surface image data of the first vehicle in the driving direction. In a specific implementation, the sensing module may include at least one of the following: laser radar, millimeter wave radar, cameras, ultrasonic radar, and the like.
And the positioning submodule is used for acquiring the positioning position of the first vehicle. In a specific implementation, the Positioning sub-module may be any device having a Positioning function, such as a Global Positioning System (GPS).
The intelligent driving control processing module (hereinafter referred to as a control module) may be a vehicle-mounted terminal or a part of the vehicle-mounted terminal. In the embodiment of the application, the control module can acquire road friction audio data acquired by the sound acquisition sensor, acquire road image data acquired by the sensing module and acquire a positioning position acquired by the positioning sub-module. In addition, the control module CAN acquire the whole vehicle information of the first vehicle from other sensors or modules through a Controller Area Network (CAN) Network, so that the obtained information CAN be utilized to obtain the road surface friction coefficient corresponding to the positioning position.
The technical solution of the embodiment of the present application is described in detail with reference to fig. 1. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 2 is a schematic flow chart of a vehicle data processing method according to an embodiment of the present application. The execution subject of the method may be, for example, a control module of the first vehicle. As shown in fig. 1, the method may include:
s101, obtaining road surface material information corresponding to the target collection time, road surface friction audio information generated by friction and vibration between tires of the first vehicle and the road surface, whole vehicle information and a target positioning position of the first vehicle in the running process of the first vehicle at a target road section.
The target road section may be any road section, or any road section for which the road surface friction coefficient needs to be acquired. The target acquisition time may be any acquisition time.
In this embodiment, during the running of the first vehicle on the target road segment, the sound collection sensor of the first vehicle may collect road surface friction audio data generated by the friction and vibration between the tire of the first vehicle and the road surface at the target collection time; the perception submodule can synchronously acquire road surface image data of a first vehicle in the driving direction, other sensors on the vehicle can synchronously acquire data of whole vehicle information forming the first vehicle, and in addition, the positioning submodule on the vehicle can synchronously acquire a target positioning position of the first vehicle. Therefore, the control module can acquire road surface material information corresponding to the target acquisition time, road surface friction audio information generated by friction and vibration between tires of the first vehicle and the road surface, whole vehicle information and a target positioning position.
According to a possible implementation mode, the control module can preprocess the road friction audio data acquired by the sound acquisition sensor to obtain the road friction audio information. Or, the first vehicle may be provided with a preprocessing module for preprocessing road friction audio data acquired by the sound acquisition sensor, and transmitting the road friction audio information to the control module after obtaining the road friction audio information.
The preprocessing can be to remove noise in the road friction audio data, and the road friction audio information is the road friction audio data after the noise is removed; or, the preprocessing may be to extract a frequency spectrum of the road friction audio data, where the road friction audio information is the frequency spectrum of the road friction audio data; alternatively, the preprocessing may be to remove noise in the road friction audio data and extract a frequency spectrum of the road friction audio data. Firstly, performing first preprocessing on road surface friction audio data to remove noise in the road surface friction audio data; and then, carrying out second preprocessing on the road friction audio data after the noise is removed to obtain a frequency spectrum of the road friction audio data. At this time, the road friction audio information is a frequency spectrum of the road friction audio data.
The preprocessing specifically includes actions related to the desired representation of the road friction audio information. It should be noted that, for how to perform noise removal on the audio data and how to extract the frequency spectrum, reference may be made to the implementation manner in the prior art, and details are not repeated.
It should be noted that, when the first vehicle is provided with a plurality of sound collection sensors, one piece of road friction audio data may be selected from the plurality of sound collection sensors to be preprocessed to obtain road friction audio information, the road friction audio data collected by the plurality of sound collection sensors may be merged and then the preprocessing operation may be performed, or the preprocessing operation may be performed on the road friction audio data collected by each sound collection sensor. In this implementation, the control module acquires a plurality of road friction audio information.
According to a possible implementation mode, the control module can preprocess the road surface image data collected by the perception submodule to obtain the road surface material information. Or, the first vehicle can be provided with a preprocessing module for preprocessing the road surface image data collected by the sensing sub-module, and after the road surface material information is obtained, the road surface material information is transmitted to the control module. Taking the example that the control module preprocesses the road image data acquired by the sensing submodule, the control module may, for example, input the road image data (e.g., image features or point cloud data) acquired by the sensing submodule into a pre-trained convolutional neural network to obtain the output road material information.
The road surface material information mentioned here is used to represent the material of the road surface, and may be, for example: such as any of epoxy pavement, cement pavement, concrete, asphalt, gravel, etc. In a specific implementation, the road surface material information may be, for example, a road surface material, or a mark corresponding to the road surface material.
In a possible implementation manner, the control module may directly use the positioning position acquired by the positioning sub-module as the target positioning position, where the target positioning position may be a longitude and a latitude. The control module may also obtain a specific position (for example, a GPS positioning position + Real-Time Kinematic (RTK) positioning position) of the first vehicle on the target road segment at the target Time by using the longitude and latitude acquired by the positioning sub-module in combination with the map data (for example, high-precision map data), and use the position as the target positioning position.
S102, inputting road surface friction audio information, road surface material information and whole vehicle information corresponding to the target acquisition time into a prediction model to obtain a road surface friction coefficient of a target road section at a target positioning position; the prediction model is used for representing the mapping relation between audio information generated by friction and vibration of tires and the road surface and the friction coefficient of the road surface when different vehicles run on the road surface made of different materials at different speeds.
The control module is preset with a prediction model, and the prediction model is obtained by training collected audio information, whole vehicle information, road surface material information and corresponding actual road surface friction coefficients when vehicles of different types (such as a large truck, a passenger car, a tricycle and the like) pass through the test in advance and respectively drive on the road surfaces of different materials at different speeds (such as 5, 10, 15, 8230; 100 kilometers/hour and the like), so that the prediction model can obtain a curve map reflecting the mapping relation between the audio information and the friction coefficients when the different vehicles drive on different road surfaces at different speeds through discrete data. Namely, the prediction model can be used for representing the mapping relation between the audio information generated by the friction and vibration between the tire and the road surface and the friction coefficient of the road surface when different vehicles run on the road surface with different materials at different speeds.
Therefore, in this embodiment, after obtaining the road surface material information corresponding to the target collection time, the road surface friction audio information generated by the friction and vibration between the tire of the first vehicle and the road surface, the vehicle information, and the target location position, the control module may input the road surface friction audio information, the road surface material information, and the vehicle information corresponding to the target collection time into the prediction model, so as to use the mapping relationship obtained by the prediction model to accurately obtain the road surface friction coefficient of the target road segment at the target location position.
For example, taking the example that the prediction model is trained by using the frequency spectrums of the road friction audio data of 4 tires, in this example, the first vehicle may be provided with a sound collection sensor at each tire of the 4 tires, and the control module may perform preprocessing on the road friction audio data collected by the 4 sound collection sensors to obtain the frequency spectrums of the 4 road friction audio data. Meanwhile, the control module can preprocess the road surface image data collected by the perception submodule to obtain the road surface material information. Then, the control module can input the frequency of 4 pieces of road surface friction audio data, road surface material information and whole vehicle information into a prediction model to obtain a road surface friction coefficient.
Specifically, the expression form of each piece of information input to the prediction model is related to the expression form of the piece of information used in the model training.
With continued reference to fig. 1, in this example, the control module may include, for example: the device comprises a receiver, a preprocessing chip and an AI chip.
The receiver is used for receiving data collected by all parts in the intelligent driving perception module. In a specific implementation, the receiver may be, for example, a communication interface.
And the preprocessing chip is used for preprocessing the received data so as to meet the requirement of the prediction model on the input data. For example, the road friction audio data is preprocessed to obtain the road friction audio information. And/or preprocessing the road image data to obtain road material information and the like.
The Artificial Intelligence (AI) chip is preset with a prediction model and used for receiving road surface friction audio information, road surface material information and vehicle information of a target positioning position corresponding to a target acquisition time sent by the preprocessing chip and calculating to obtain a road surface friction coefficient of a target road section at the target positioning position.
It should be understood that fig. 1 is only an exemplary illustration of one possible configuration of the first vehicle, and specific configurations of the intelligent driving control processing module of the first vehicle, and in addition, the naming of each module in fig. 1 is also only an illustration, and the present application is not limited thereto.
The vehicle data processing method provided in this embodiment provides a new road friction coefficient mode, and may obtain the road friction coefficient of the target road segment at the target location position based on the road material information acquired during the driving process of the vehicle at the target road segment, the road friction audio information generated by the friction and vibration between the tire of the first vehicle and the road, the vehicle information, and the target location position. Because different vehicles run on the road surfaces made of different materials at different speeds, the audio frequency generated by the friction and vibration between the tires and the road surfaces is different under the influence of the friction coefficient of the road surfaces. Therefore, the road surface friction coefficient can be accurately obtained by using various factors such as audio, vehicle information and road surface material information generated by friction and vibration of the tire and the road surface, and the accuracy of the road surface friction coefficient is improved.
After the control module of the first vehicle obtains the road surface friction coefficient of the target road section at the target positioning position, the control module can use the road surface friction coefficient to judge whether the current road surface meets the ODD of intelligent driving, so that a vehicle dynamic model is accurately optimized, and intelligent driving judgment is carried out. With continued reference to fig. 1, in this implementation manner, the control module may further include a Micro Controller Unit (MCU), for example, and the first vehicle may further include an entire vehicle control execution module. The MCU is used for adjusting an intelligent driving strategy of the whole vehicle according to the road surface friction coefficient acquired by the AI chip, generating a control instruction and sending the control instruction to the whole vehicle execution control module, and the control instruction is used for controlling the whole vehicle.
Optionally, the control module of the first vehicle may also execute the steps shown in fig. 2 in a circulating manner to obtain at least one positioning location corresponding to the target road segment traveled by the first vehicle at the at least one acquisition time and the road friction coefficient of the at least one positioning location, and package and upload the road friction coefficient to the cloud platform to update the high-precision map.
Fig. 3 is a schematic flow chart of another vehicle data processing method according to an embodiment of the present application. As shown in fig. 3, the method may include:
s201, the first vehicle sends at least one positioning position corresponding to the target road section at least one acquisition moment and a road surface friction coefficient of the at least one positioning position to the cloud platform.
Correspondingly, the cloud platform receives at least one positioning position corresponding to the target road section at least one acquisition moment and the road surface friction coefficient of the at least one positioning position.
Illustratively, the first vehicle may also include a communication module, such as a Telematics BOX (T-BOX), for example. The control module of the first vehicle can package and send the at least one positioning position corresponding to the target road section acquired by the control module at the at least one acquisition moment and the road surface friction coefficient of the at least one positioning position to the cloud platform through the communication module.
S202, the cloud platform adds the road surface friction coefficient of the target road section corresponding to at least one positioning position to the high-precision map data of the target road section to obtain the updated high-precision map data of the target road section.
It should be noted that when a plurality of first vehicles simultaneously perform the above-mentioned road surface friction coefficient acquisition and report to the cloud platform, the road surface friction coefficient information of each road section in the high-precision map data can be quickly and efficiently created in a crowdsourcing manner.
Therefore, when other follow-up vehicles run to the target road section, the road surface friction coefficient of the running direction can be directly obtained through the high-precision map, and therefore intelligent driving control can be carried out. For example, a vehicle dynamics model is quickly and accurately optimized. By the mode, the road surface friction coefficient is not required to be predicted by the vehicle, and the intelligent driving efficiency and speed are improved.
Optionally, the updated high-precision map data of the target road segment may be actively downloaded and acquired from a cloud platform by other vehicles, or may be automatically distributed to a vehicle traveling on the target road segment after the cloud platform obtains the updated high-precision map data of the target road segment.
Illustratively, with continuing reference to fig. 3, after the step S202, the method may further include, for example:
and S203, the cloud platform sends the updated high-precision map data of the target road section to the second vehicle.
The second vehicle may be a vehicle traveling on the target road segment, may include the first vehicle, or may not include the first vehicle, and is specifically related to the current traveling position of the first vehicle.
Illustratively, the second vehicle may also include a communication module, such as a T-BOX, for example. The cloud platform can send the high-precision map to a control module of the second vehicle through a communication module of the second vehicle.
And S204, the second vehicle performs intelligent driving control according to the road surface friction coefficient in the updated high-precision map data.
Therefore, the second vehicle can directly know the road surface friction coefficient of the driving direction through the high-precision map, and intelligent driving control can be performed. For example, a vehicle dynamics model is quickly and accurately optimized. By the mode, the road surface friction coefficient is not required to be predicted by the vehicle, and the intelligent driving efficiency and speed are improved.
At present, in the prior art, the vehicle is generally used for predicting the road friction coefficient, and the predicted road friction coefficient is used for performing intelligent driving control of the vehicle, so that the intelligent driving control efficiency is low. In the embodiment, the road surface friction coefficient acquired by the first vehicle is reported by the first vehicle in a crowdsourcing mode, and the cloud platform updates the road surface friction coefficient to the high-precision map data, so that other subsequent vehicles can directly use the road surface friction coefficient to perform intelligent driving control, the self vehicle does not need to predict the road surface friction coefficient, and the intelligent driving efficiency and speed are improved.
Fig. 4 is a schematic structural diagram of a vehicle data processing device according to an embodiment of the present application. As shown in fig. 4, the apparatus is applied to a first vehicle, and may include: an acquisition module 301 and a processing module 302. Optionally, the apparatus may further include a sending module 303.
The acquisition module 301 is configured to acquire road surface material information corresponding to a target acquisition time, road surface friction audio information generated by friction and vibration between tires of the first vehicle and a road surface, vehicle information, and a target positioning position of the first vehicle during driving of the first vehicle on a target road section;
the processing module 302 is configured to input the road friction audio information, the road material information, and the vehicle information corresponding to the target acquisition time into a prediction model, so as to obtain a road friction coefficient of the target road segment at the target positioning position; the prediction model is used for representing the mapping relation between audio information generated by friction and vibration of tires and the road surface friction coefficient when different vehicles run on the road surfaces made of different materials at different speeds.
Optionally, the obtaining module 301 is specifically configured to obtain a sound collecting sensor installed at least one wheel of the first vehicle, obtain road friction audio data generated by friction and vibration between a tire and a road collected at the target collecting time, and preprocess the road friction audio data to obtain road friction audio information corresponding to the target collecting time.
Illustratively, the road friction audio information includes: frequency spectrum of road friction audio data; an obtaining module 301, specifically configured to perform first preprocessing on the road friction audio data, and remove noise in the road friction audio data; and carrying out second preprocessing on the road surface friction audio data after the noise is removed to obtain a frequency spectrum of the road surface friction audio data.
Optionally, the obtaining module 301 is specifically configured to obtain road surface image data that is collected by the sensing module of the first vehicle at the target collection time; and acquiring the road surface material information corresponding to the target acquisition time according to the road surface image data.
Optionally, the sending module 303 is configured to send, to a cloud platform, at least one positioning position corresponding to the target road segment at least one acquisition time and a road surface friction coefficient of the at least one positioning position, so that the cloud platform adds the road surface friction coefficient of the target road segment at the at least one positioning position to the high-precision map data of the target road segment; the at least one acquisition instant comprises the target acquisition instant.
The vehicle data processing apparatus provided in this embodiment may execute the actions of the control module of the first vehicle in the foregoing method embodiments, and the implementation principle and technical effects are similar, which are not described herein again.
Fig. 5 is a schematic structural diagram of another vehicle data processing device according to an embodiment of the present application. As shown in fig. 5, the apparatus is applied to a cloud platform, and may include: a receiving module 401 and a processing module 402. Optionally, the apparatus may further include a sending module 403.
A receiving module 401, configured to receive at least one positioning position of a target road segment reported by a first vehicle and a corresponding road surface friction coefficient; the road surface friction coefficient corresponding to the at least one positioning position is obtained based on at least one piece of road surface material information corresponding to the moment, road surface friction audio information generated by friction and vibration between tires of the first vehicle and the road surface and whole vehicle information when the first vehicle runs on a target road section;
a processing module 402, configured to add a road friction coefficient corresponding to the target road segment at the at least one positioning position to the high-precision map data of the target road segment, so as to obtain updated high-precision map data of the target road segment.
Optionally, the sending module 403 is configured to send the updated high-precision map data of the target road segment to at least one second vehicle traveling on the target road segment, so that the second vehicle performs intelligent driving control according to the road friction coefficient in the updated high-precision map data.
The vehicle data processing device provided in this embodiment may execute the actions of the cloud platform in the foregoing method embodiments, and the implementation principle and technical effects are similar, which are not described herein again.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 6, the electronic device 600 may include: at least one processor 601, a memory 602.
The memory 602 is used for storing programs. In particular, the program may include program code comprising computer operating instructions.
The memory 602 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor 601 is configured to execute computer executable instructions stored in the memory 602 to implement the methods described in the foregoing method embodiments. The processor 601 may be a CPU, or an Application Specific Integrated Circuit (ASIC), or one or more modules configured to implement any of the embodiments of the present Application.
The electronic device 600 may also include a communication interface 603, through which the processor 601 may communicatively interact with other systems or modules or sensors on the vehicle.
In a specific implementation, if the communication interface 603, the memory 602 and the processor 601 are implemented independently, the communication interface 603, the memory 602 and the processor 601 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. Buses may be classified as address buses, data buses, control buses, etc., but do not represent only one bus or type of bus.
Optionally, in a specific implementation, if the communication interface 603, the memory 602, and the processor 601 are integrated into a chip, the communication interface 603, the memory 602, and the processor 601 may complete communication through an internal interface.
The present application also provides a computer-readable storage medium, which may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and in particular, the computer-readable storage medium stores program instructions, and the program instructions are used in the method in the foregoing embodiments.
The present application also provides a program product comprising execution instructions stored in a readable storage medium. The at least one processor of the electronic device may read the execution instructions from the readable storage medium, and the execution of the execution instructions by the at least one processor causes the electronic device to implement the methods provided by the various embodiments described above.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A vehicle data processing method, applied to a first vehicle, the method comprising:
acquiring corresponding road surface material information of the first vehicle at a target acquisition moment, road surface friction audio information generated by friction and vibration between tires of the first vehicle and the road surface, whole vehicle information and a target positioning position in the running process of the first vehicle at a target road section;
inputting the road surface friction audio information, the road surface material information and the whole vehicle information corresponding to the target acquisition moment into a prediction model to obtain the road surface friction coefficient of the target road section at the target positioning position; the prediction model is used for representing the mapping relation between audio information generated by friction and vibration of tires and the road surface and the friction coefficient of the road surface when different vehicles run on the road surface made of different materials at different speeds.
2. The method according to claim 1, wherein acquiring road surface friction audio information generated by road surface friction vibration of a tire of the first vehicle corresponding to a target acquisition time during the first vehicle driving in a target road section comprises:
acquiring road surface friction audio data generated by friction and vibration between a tire and a road surface and collected at the target collection moment by a sound collection sensor arranged at least one wheel of the first vehicle;
and preprocessing the road friction audio data to obtain road friction audio information corresponding to the target acquisition time.
3. The method of claim 2, wherein the road friction audio information comprises: frequency spectrum of road friction audio data;
preprocessing the road friction audio data to obtain road friction audio information corresponding to the target acquisition time, wherein the method comprises the following steps:
performing first preprocessing on the road surface friction audio data to remove noise in the road surface friction audio data;
and carrying out second preprocessing on the road surface friction audio data after the noise is removed to obtain the frequency spectrum of the road surface friction audio data.
4. The method according to claim 2, wherein the obtaining of the road surface material information corresponding to the target collection time of the first vehicle during the driving process in the target road segment comprises:
acquiring road surface image data acquired by a sensing module of the first vehicle at the target acquisition moment;
and acquiring the road surface material information corresponding to the target acquisition time according to the road surface image data.
5. The method according to any one of claims 1-4, further comprising:
sending at least one positioning position corresponding to the target road section at least one acquisition moment and a road surface friction coefficient of the at least one positioning position to a cloud platform, so that the cloud platform adds the road surface friction coefficient of the target road section at the at least one positioning position to high-precision map data of the target road section; the at least one acquisition time comprises the target acquisition time.
6. A vehicle data processing method is applied to a cloud platform, and comprises the following steps:
receiving at least one positioning position of a target road section reported by a first vehicle and a corresponding road surface friction coefficient; the road surface friction coefficient corresponding to the at least one positioning position is obtained based on at least one piece of road surface material information corresponding to the moment, road surface friction audio information generated by friction and vibration between tires of the first vehicle and the road surface and whole vehicle information when the first vehicle runs on a target road section;
and adding the road surface friction coefficient of the target road section corresponding to at least one positioning position to the high-precision map data of the target road section to obtain the updated high-precision map data of the target road section.
7. The method of claim 6, further comprising:
and sending the updated high-precision map data of the target road section to at least one second vehicle running on the target road section, so that the second vehicle can carry out intelligent driving control according to the road surface friction coefficient in the updated high-precision map data.
8. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer execution instructions;
the processor executes computer-executable instructions stored by the memory to implement the method of any of claims 1-7.
9. A computer-readable storage medium, having stored therein computer-executable instructions for implementing the vehicle data processing method of any one of claims 1 to 7 when executed by a processor.
10. A computer program product, characterized in that it comprises a computer program which, when being executed by a processor, carries out the method of any one of claims 1-7.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210938002.XA CN115179954A (en) | 2022-08-05 | 2022-08-05 | Vehicle data processing method, apparatus, storage medium, and program product |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202210938002.XA CN115179954A (en) | 2022-08-05 | 2022-08-05 | Vehicle data processing method, apparatus, storage medium, and program product |
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| CN202210938002.XA Pending CN115179954A (en) | 2022-08-05 | 2022-08-05 | Vehicle data processing method, apparatus, storage medium, and program product |
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