CN114461671A - Data processing method, vehicle, and computer-readable storage medium - Google Patents
Data processing method, vehicle, and computer-readable storage medium Download PDFInfo
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- CN114461671A CN114461671A CN202111615850.9A CN202111615850A CN114461671A CN 114461671 A CN114461671 A CN 114461671A CN 202111615850 A CN202111615850 A CN 202111615850A CN 114461671 A CN114461671 A CN 114461671A
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
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Abstract
The invention discloses a data processing method, a vehicle and a computer readable storage medium. The data processing method comprises the following steps: acquiring driving data of a vehicle; determining a scene value degree of the driving data; dividing the driving data into invalid driving data and valid driving data according to the scene value degree; and filtering the invalid driving data. According to the data processing method, the driving data can be divided into invalid driving data and valid driving data according to the scene value degree, and the invalid driving data can be filtered, so that automatic screening is realized, and the time and cost required for screening the data can be reduced.
Description
Technical Field
The present invention relates to data processing technologies, and in particular, to a data processing method, a vehicle, and a computer-readable storage medium.
Background
In the running process of the vehicle, the running data of the vehicle can be collected and stored in the database. However, since a large amount of invalid data may be generated during the acquisition process, in the related art, the valid data is usually extracted manually, which is time-consuming and costly.
Disclosure of Invention
The invention provides a data processing method, a vehicle and a computer readable storage medium.
The data processing method provided by the embodiment of the invention comprises the following steps:
acquiring driving data of a vehicle;
determining a scene value degree of the driving data;
dividing the driving data into invalid driving data and valid driving data according to the scene value degree;
and filtering the invalid driving data.
According to the data processing method, the driving data can be divided into invalid driving data and valid driving data according to the scene value degree, and the invalid driving data can be filtered, so that automatic screening is realized, and the time and cost required for screening the data can be reduced.
In some embodiments, the determining the scene rating of the travel data comprises:
determining the complexity of the self vehicle, the complexity of the target vehicle, the complexity of the road and the complexity of the traffic identification according to the driving data;
and determining the scene value degree according to the complexity of the own vehicle, the complexity of the target vehicle, the complexity of the road and the complexity of the traffic sign.
In some embodiments, the travel data includes an acceleration and an angular velocity of the vehicle, and the determining the complexity of the vehicle from the travel data includes:
and determining the complexity of the vehicle according to the acceleration, the acceleration weight, the angular velocity and the angular velocity weight of the vehicle.
In some embodiments, the determining the complexity of the target vehicle according to the driving data includes:
and determining the complexity of the target vehicle according to the distance between the target vehicle and the self vehicle, the speed of the target vehicle and the weight of the target vehicle.
In some embodiments, the driving data includes a drivable area within a second preset range of the own vehicle, and the determining the road complexity according to the driving data includes:
and determining the road complexity according to the travelable area and the road weight.
In some embodiments, the driving data includes traffic signs within a third preset range of the own vehicle, and the determining the complexity of the traffic signs according to the driving data includes:
and determining the complexity of the traffic sign according to the traffic sign and the weight of the traffic sign.
In some embodiments, the dividing the driving data into invalid driving data and valid driving data according to the scene worth degree comprises:
dividing the driving data of which the scene value degree is lower than a scene value degree threshold value into invalid driving data;
dividing the driving data of which the scene value degree is higher than the scene value degree threshold value into the effective driving data.
In some embodiments, the valid driving data includes a scene continuous time, and the data processing method further includes:
dividing the effective driving data of which the scene continuous time is less than a scene time threshold into disuse driving data;
dividing the effective driving data of which the scene continuous time is greater than a scene time threshold value into standby driving data.
In some embodiments, the travel data includes a distance traveled by the vehicle, and the data processing method further includes:
and segmenting the effective driving data of which the driving distance of the self-vehicle exceeds a distance threshold value.
In some embodiments, the travel data includes a travel time of the own vehicle, and the data processing method further includes:
and segmenting the effective driving data of which the driving time of the self vehicle exceeds a time threshold.
In some embodiments, the data processing method further comprises:
determining a scene tag of the valid driving data;
and saving the effective driving data and the scene label.
The vehicle provided by the embodiment of the invention comprises a memory, a processor and a computer executable program stored in the memory, wherein the processor is used for executing the computer executable program to realize the steps of the data processing method of the embodiment.
In the vehicle, the driving data can be divided into invalid driving data and valid driving data according to the scene value degree, and the invalid driving data is filtered, so that automatic screening is realized, and the time and cost required by data screening can be reduced.
The embodiment of the invention provides a computer readable storage medium, on which a computer program is stored, wherein the computer program realizes the data processing method of the above embodiment when being executed by a processor.
In the computer-readable storage medium, the driving data can be divided into invalid driving data and valid driving data according to the scene value degree, and the invalid driving data can be filtered, so that automatic screening is realized, and the time and cost required for screening the data can be reduced.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a data processing method of an embodiment of the present invention;
FIG. 2 is a schematic illustration of a vehicle according to an embodiment of the present invention;
fig. 3 to 12 are flowcharts of a data processing method according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
The following disclosure provides many different embodiments or examples for implementing different application scenarios of the present invention. To simplify the disclosure of the present invention, specific example components and regions are described below. Of course, they are merely examples and are not intended to limit the present invention. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples, such repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed.
Autopilot is a vehicle unmanned operation that is accomplished by an automated control system. In recent years, vehicle intelligent technology is rapidly developed, and an auxiliary driving technology and a partial automatic driving technology enter an industrialization stage; the conditional automated driving and highly automated driving techniques enter a test validation phase.
During automatic driving development, a vehicle can collect a large amount of driving data and store the driving data in a database. However, since a large amount of invalid data may be generated during the acquisition process, in the related art, the valid data is usually extracted manually, which is time-consuming and costly.
Referring to fig. 1 and fig. 2, a data processing method according to an embodiment of the present invention includes:
012: acquiring driving data of the vehicle 10;
014: determining scene value degree of the driving data;
016: dividing the driving data into invalid driving data and valid driving data according to the scene value degree;
018: invalid driving data is filtered out.
The data processing method of the embodiment of the invention can be realized by the vehicle 10 of the embodiment of the invention. Specifically, referring to FIG. 2, the vehicle 10 includes a memory 11 and a processor 12. The memory 11 stores therein a computer-executable program. The processor 12 is configured to: acquiring driving data of a vehicle; determining scene value degree of the driving data; dividing the driving data into invalid driving data and valid driving data according to the scene value degree; invalid driving data is filtered out.
In the data processing method and the vehicle 10, the driving data can be divided into invalid driving data and valid driving data according to the scene value degree, and the invalid driving data can be filtered, so that automatic screening is realized, the time and cost required for screening the data can be reduced, in addition, the invalid driving data is filtered, and the invalid driving data can be effectively prevented from occupying storage resources.
Specifically, at least a portion of the travel data of the vehicle 10 may be detected by a detection sensor in the vehicle 10, the detection sensor including, for example, a camera, a lidar, a millimeter wave radar, or the like, and the travel data includes detection information of the detection sensor, a vehicle state, a vehicle position, vehicle driving data, a sensing result, a decision plan (e.g., whether to accelerate or decelerate, whether to turn, or not), and the like.
By analyzing the driving data, the scene value degree corresponding to each time point of the driving data can be obtained, the higher the scene value degree is, the more valuable the corresponding driving data is, the lower the scene value degree is, the less valuable the corresponding driving data is, therefore, the driving data can be divided into the useless driving data and the valuable effective driving data according to the scene value degree, the driving data are arranged according to the time (such as data acquisition time) sequence, the useless driving data can be filtered, and the valuable effective driving data can be stored for subsequent use, such as a deep learning related model for training automatic driving.
Referring to fig. 3, in some embodiments, step 014 (determining scene price of travel data) includes:
0142: determining the complexity of the self vehicle, the complexity of the target vehicle, the complexity of the road and the complexity of the traffic identification according to the driving data;
0144: and determining the scene value degree according to the complexity of the own vehicle, the complexity of the target vehicle, the complexity of the road and the complexity of the traffic identification.
The data processing method of the embodiment of the invention can be realized by the vehicle 10 of the embodiment of the invention. Specifically, referring to fig. 2, the processor 12 is configured to: determining the complexity of the self vehicle, the complexity of the target vehicle, the complexity of the road and the complexity of the traffic identification according to the driving data; and determining the scene value degree according to the complexity of the own vehicle, the complexity of the target vehicle, the complexity of the road and the complexity of the traffic identification.
In this way, the scene value degree of the travel data can be acquired.
Specifically, if the driving scene is complex, it is indicated that the content of the corresponding driving scene to be analyzed is more, and the factors to be considered when the vehicle 10 is driving are more, so the scene value degree is higher; when the driving scene is simple, it is described that the corresponding driving scene has a low content to be analyzed, and the vehicle 10 has a low scene value because there are few factors to be considered when driving. The scene value can be determined according to the own vehicle complexity, the target vehicle complexity, the road complexity and the traffic sign complexity, and specifically, the scene value can be the sum of the own vehicle complexity, the target vehicle complexity, the road complexity and the traffic sign complexity.
Referring to fig. 4, in some embodiments, the driving data includes an acceleration and an angular velocity of the vehicle, and the determining the complexity of the vehicle according to the driving data in step 0142 includes:
01422: and determining the complexity of the vehicle according to the acceleration, the acceleration weight, the angular velocity and the angular velocity weight of the vehicle.
The data processing method of the embodiment of the invention can be realized by the vehicle 10 of the embodiment of the invention. Specifically, referring to fig. 2, the processor 12 is configured to: and determining the complexity of the vehicle according to the acceleration, the acceleration weight, the angular velocity and the angular velocity weight of the vehicle.
In this way, the complexity of the vehicle can be determined by the acceleration and the angular velocity of the vehicle.
Specifically, the host vehicle may refer to the current vehicle 10, the greater the acceleration of the host vehicle, the greater the speed of the host vehicle, and the greater the angular velocity of the host vehicle, the greater the rotation amplitude of the host vehicle, and therefore, the complexity of the host vehicle may be determined by the acceleration of the host vehicle, the acceleration weight, the angular velocity of the host vehicle, and the angular velocity weight, and specifically, the complexity of the host vehicle may be a sum of a first product and a second product, where the first product is a product of the acceleration of the host vehicle and the acceleration weight, and the second product is a product of the acceleration of the host vehicle and the angular velocity weight. The acceleration weight and the angular velocity weight may be preset weight values, and are not limited herein.
Referring to fig. 5, in some embodiments, the driving data includes a distance between the target vehicle and the host vehicle within a first preset range of the host vehicle, and a speed of the target vehicle, and the determining the complexity of the target vehicle according to the driving data in step 0142 includes:
01424: and determining the complexity of the target vehicle according to the distance between the target vehicle and the own vehicle, the speed of the target vehicle and the weight of the target vehicle.
The data processing method of the embodiment of the invention can be realized by the vehicle 10 of the embodiment of the invention. Specifically, referring to fig. 2, the processor 12 is configured to: and determining the complexity of the target vehicle according to the distance between the target vehicle and the own vehicle, the speed of the target vehicle and the weight of the target vehicle.
In this way, the complexity of the target vehicle can be determined by the distance between the target vehicle and the own vehicle and the speed of the target vehicle.
Specifically, the host vehicle may refer to the current vehicle 10, and the target vehicle may refer to another vehicle within a first preset range of the host vehicle, where the first preset range may be a preset range, such as 30 m. The distance between the target vehicle and the self vehicle and the speed of the target vehicle have an influence on the driving of the self vehicle, and therefore, the complexity of the target vehicle can be determined according to the distance between the target vehicle and the self vehicle, the speed of the target vehicle and the weight of the target vehicle, and specifically, the complexity of the target vehicle can be the product of the distance between the target vehicle and the self vehicle, the speed of the target vehicle and the weight of the target vehicle. The target vehicle weight may be a preset weight value, and is not specifically limited herein.
In some embodiments, if there are multiple target vehicles within the first preset range of the own vehicle, the target vehicle complexity of each target vehicle may be calculated, and the target vehicle complexities of the target vehicles are summed to obtain a target vehicle complexity and value, and the scene value is determined according to the own vehicle complexity, the target vehicle complexity, the road complexity, and the traffic sign complexity, specifically, the scene value is determined according to the own vehicle complexity, the target vehicle complexity and value, the road complexity, and the traffic sign complexity.
Referring to fig. 6, in some embodiments, the driving data includes a drivable area within a second predetermined range of the host vehicle, and the determining the road complexity according to the driving data in step 0142 includes:
01426: and determining the road complexity according to the drivable area and the road weight.
The data processing method of the embodiment of the invention can be realized by the vehicle 10 of the embodiment of the invention. Specifically, referring to fig. 2, the processor 12 is configured to: and determining the road complexity according to the drivable area and the road weight.
In this way, the road complexity can be determined by the travelable area.
Specifically, the second preset range may be a preset range, and the second preset range may be the same as or different from the first preset range. The travelable region area has an influence on driving of the own vehicle, and therefore, the road complexity may be determined from the travelable region area and the road weight, and specifically, the road complexity may be a product of the travelable region area and the road weight. The road weight may be a preset weight value, and is not specifically limited herein.
Referring to fig. 7, in some embodiments, the driving data includes traffic signs within a third preset range of the host vehicle, and the determining the complexity of the traffic signs according to the driving data in step 0142 includes:
01428: and determining the complexity of the traffic sign according to the traffic sign and the weight of the traffic sign.
The data processing method of the embodiment of the invention can be realized by the vehicle 10 of the embodiment of the invention. Specifically, referring to fig. 2, the processor 12 is configured to: and determining the complexity of the traffic sign according to the traffic sign and the weight of the traffic sign.
In this manner, traffic sign complexity may be determined by the traffic sign.
Specifically, the third preset range may be a preset range, and the third preset range may be the same as or different from the first preset range and the second preset range. The traffic signs may include deceleration passing signs, attention pedestrian signs, straight and right turn signs, vehicle slow running signs, right road running signs, right sharp turn signs, straight running signs, and the like. The traffic sign has an influence on driving of the vehicle, and therefore, the traffic sign complexity can be determined according to the traffic sign and the traffic sign weight, specifically, the traffic sign complexity can be the traffic sign weight, wherein different traffic signs can have different traffic sign weights, for example, a scene of a sign turning sharply right is relatively complex, and therefore, the corresponding traffic sign weight is relatively large, and a scene of a straight sign is relatively simple, and therefore, the corresponding traffic sign weight is relatively small. The traffic sign weight may be a preset weight value, and is not specifically limited herein.
In some embodiments, if a plurality of traffic signs exist in the third preset range of the self-vehicle, the traffic sign complexity of each traffic sign may be calculated, and the traffic sign complexities of the traffic signs are summed to obtain the traffic sign complexity and value, and the scene value is determined according to the self-vehicle complexity, the target vehicle complexity, the road complexity, and the traffic sign complexity, specifically, the scene value is determined according to the self-vehicle complexity, the target vehicle complexity, the road complexity, and the traffic sign complexity and value.
Referring to fig. 8, in some embodiments, the step 016 (dividing the driving data into invalid driving data and valid driving data according to the scene value degree) includes:
0162: dividing the driving data with the scene value degree lower than the scene value degree threshold value into invalid driving data;
0164: and dividing the driving data with the scene value degree higher than the scene value degree threshold value into effective driving data.
The data processing method of the embodiment of the invention can be realized by the vehicle 10 of the embodiment of the invention. Specifically, referring to fig. 2, the processor 12 is configured to: dividing the driving data with the scene value degree lower than the scene value degree threshold value into invalid driving data; and dividing the driving data with the scene value degree higher than the scene value degree threshold value into effective driving data.
Referring to fig. 9, in some embodiments, the effective driving data includes a scene continuous time, and the data processing method further includes:
022: dividing effective driving data with scene continuous time smaller than a scene time threshold value into disuse driving data;
024: and dividing the effective driving data with the scene continuous time larger than the scene time threshold value into standby driving data.
The data processing method of the embodiment of the invention can be realized by the vehicle 10 of the embodiment of the invention. Specifically, referring to fig. 2, the processor 12 is configured to: dividing effective driving data with scene continuous time smaller than a scene time threshold value into disuse driving data; and dividing the effective driving data with the scene continuous time larger than the scene time threshold value into standby driving data.
In this way, the disuse travel data and the standby travel data can be divided according to the scene continuous time.
Specifically, after the driving data are arranged according to the time sequence, useless driving data without value can be filtered out, and valuable effective driving data can be stored, wherein the useless driving data are filtered out, so that originally continuous driving data can be divided into a plurality of sections of effective driving data, the duration of each section of effective driving data is obtained to be used as scene continuous time, if the scene continuous time is less than a scene time threshold value, the section of effective driving data is too short, the section of effective driving data can be reserved due to scene value degree calculation error, and therefore, the effective driving data with the scene continuous time less than the scene time threshold value can be divided into disuse driving data, and the disuse driving data can be filtered out; if the scene continuous time is greater than the scene time threshold, the effective driving data of the section is normal, therefore, the effective driving data of which the scene continuous time is less than the scene time threshold can be divided into the standby driving data, and the standby driving data can be stored.
Referring to fig. 10, in some embodiments, the driving data includes a driving distance of the vehicle, and the data processing method further includes:
026: and segmenting effective driving data of which the driving distance of the self-vehicle exceeds a distance threshold.
The data processing method of the embodiment of the invention can be realized by the vehicle 10 of the embodiment of the invention. Specifically, referring to fig. 2, the processor 12 is configured to: and segmenting effective driving data of which the driving distance of the self vehicle exceeds a distance threshold value.
Thus, the effective driving data can be segmented according to the driving distance of the vehicle.
Specifically, the valid driving data of the vehicle driving distance exceeding the distance threshold may be specifically segmented, and specifically, the standby driving data of the vehicle driving distance exceeding the distance threshold may be segmented. If the driving distance of the self-vehicle exceeds the distance threshold, the standby driving data of the section is large, the subsequent scene recognition is not facilitated, and the storage is not facilitated, so that the standby driving data of which the driving distance of the self-vehicle exceeds the distance threshold can be segmented to form two or more sections of standby driving data.
Referring to fig. 11, in some embodiments, the driving data includes a driving time of the vehicle, and the data processing method further includes:
028: and segmenting effective driving data of which the driving time of the vehicle exceeds a time threshold.
The data processing method of the embodiment of the invention can be realized by the vehicle 10 of the embodiment of the invention. Specifically, referring to fig. 2, the processor 12 is configured to: and segmenting effective driving data of which the driving time of the vehicle exceeds a time threshold.
Thus, the effective driving data can be segmented according to the driving time of the vehicle.
Specifically, the valid driving data of the vehicle driving time exceeding the time threshold may be specifically segmented, and specifically, the standby driving data of the vehicle driving time exceeding the time threshold may be segmented. If the running time of the self-vehicle exceeds the time threshold, the standby running data of the section is large, which is not beneficial to subsequent scene recognition and storage, therefore, the standby running data of which the running time of the self-vehicle exceeds the time threshold can be segmented to form two or more sections of standby running data.
Referring to fig. 12, in some embodiments, the data processing method further includes:
032: determining a scene tag of valid driving data;
034: and saving the effective driving data and the scene label.
The data processing method of the embodiment of the invention can be realized by the vehicle 10 of the embodiment of the invention. Specifically, referring to fig. 2, the processor 12 is configured to: determining a scene tag of valid driving data; and saving the effective driving data and the scene label.
In this way, the scene tag of the valid travel data can be obtained and the valid travel data and the scene tag can be stored.
Specifically, determining a scene label of valid driving data may refer to determining a scene label of the driving data to be used or the segmented driving data to be used. The scene of the to-be-used travel data may be determined by means of scene recognition to determine the scene tag, for example, the to-be-used travel data includes image information, weather in the image information may be recognized by means of image recognition, and the weather information is taken as the scene tag. The scene tag includes weather, location, time, driving behavior scene, etc., and is not limited in any way. The standby driving data and the segmented standby driving data can be stored in a database one by one, and the scene label and the standby driving data can be stored together.
The invention provides a computer readable storage medium, on which a computer program is stored, wherein the computer program realizes the data processing method of any one of the above embodiments when being executed by a processor.
For example, in the case of a computer program being executed, the following steps may be implemented:
012: acquiring driving data of a vehicle;
014: determining scene value degree of the driving data;
016: dividing the driving data into invalid driving data and valid driving data according to the scene value degree;
018: invalid driving data is filtered out.
The computer-readable storage medium may be provided in the vehicle 10 or in another terminal, and the vehicle 10 can communicate with the other terminal to obtain the corresponding program.
It is understood that the computer-readable storage medium may include: any entity or device capable of carrying a computer program, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), software distribution medium, and the like. The computer program includes computer program code. The computer program code may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), and software distribution medium.
In some embodiments of the present invention, the processor 12 may be a single chip integrated with a processor, a memory, a communication module, and the like. The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processing module-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the description of the present specification, reference to the description of the terms "one embodiment", "some embodiments", "an illustrative embodiment", "an example", "a specific example", or "some examples", etc., means that a particular feature or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
In the description of the present invention, it is to be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are only for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (13)
1. A data processing method, characterized in that the data processing method comprises:
acquiring driving data of a vehicle;
determining a scene value degree of the driving data;
dividing the driving data into invalid driving data and valid driving data according to the scene value degree;
and filtering the invalid driving data.
2. The data processing method of claim 1, wherein the determining the scene-value-degree of the travel data comprises:
determining the complexity of the self vehicle, the complexity of the target vehicle, the complexity of the road and the complexity of the traffic identification according to the driving data;
and determining the scene value degree according to the complexity of the own vehicle, the complexity of the target vehicle, the complexity of the road and the complexity of the traffic sign.
3. The data processing method according to claim 2, wherein the travel data includes a vehicle acceleration and a vehicle angular velocity, and the determining the vehicle complexity from the travel data includes:
and determining the complexity of the vehicle according to the acceleration, the acceleration weight, the angular velocity and the angular velocity weight of the vehicle.
4. The data processing method according to claim 2, wherein the driving data includes a distance between a target vehicle and the host vehicle within a first preset range of the host vehicle, and a speed of the target vehicle, and the determining a complexity of the target vehicle according to the driving data includes:
and determining the complexity of the target vehicle according to the distance between the target vehicle and the self vehicle, the speed of the target vehicle and the weight of the target vehicle.
5. The data processing method according to claim 2, wherein the travel data includes a travelable area within a second preset range of the own vehicle, and the determining the road complexity according to the travel data includes:
and determining the road complexity according to the travelable area and the road weight.
6. The data processing method of claim 2, wherein the driving data comprises traffic signs within a third preset range of the vehicle, and the determining the complexity of the traffic signs according to the driving data comprises:
and determining the complexity of the traffic sign according to the traffic sign and the weight of the traffic sign.
7. The data processing method according to claim 1, wherein the dividing of the travel data into invalid travel data and valid travel data according to the scene worth degree includes:
dividing the driving data of which the scene value degree is lower than a scene value degree threshold value into invalid driving data;
dividing the driving data of which the scene value degree is higher than the scene value degree threshold value into the effective driving data.
8. The data processing method of claim 1, wherein the valid driving data comprises a scene continuous time, the data processing method further comprising:
dividing the effective driving data of which the scene continuous time is less than a scene time threshold into disuse driving data;
dividing the effective driving data of which the scene continuous time is greater than a scene time threshold value into standby driving data.
9. The data processing method according to claim 1, wherein the travel data includes a self-vehicle travel distance, the data processing method further comprising:
and segmenting the effective driving data of which the driving distance of the self vehicle exceeds a distance threshold.
10. The data processing method according to claim 1, wherein the travel data includes a travel time of the own vehicle, the data processing method further comprising:
and segmenting the effective driving data of which the driving time of the self vehicle exceeds a time threshold.
11. The data processing method of claim 1, further comprising:
determining a scene tag of the valid driving data;
and saving the effective driving data and the scene label.
12. A vehicle, characterized in that the vehicle comprises a memory, a processor and a computer executable program stored in the memory, the processor being adapted to execute the computer executable program to implement the steps of the data processing method of any of claims 1-11.
13. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the data processing method of any one of claims 1 to 11.
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