CN111367968A - Driving data processing method, device, equipment and storage medium - Google Patents
Driving data processing method, device, equipment and storage medium Download PDFInfo
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Abstract
The application provides a driving data processing method, a driving data processing device, driving data processing equipment and a storage medium. The driving data processing method includes: the driving operation data under the first driving scene are collected, the degree grade of the driving operation data reflecting the driving behavior violence degree is determined according to a preset first mapping table, the driving operation data and the degree grade are processed, and the driving behavior mode is determined. Compared with the mode that the conventional processing method directly performs statistical analysis, the degree grade and the driving operation data are analyzed and processed, and the processing result more accurately reflects the driving operation characteristics of the user.
Description
Technical Field
The present application relates to the field of intelligent driving technologies, and in particular, to a driving data processing method, apparatus, device, and storage medium.
Background
With the development of the field of intelligent driving, research on driving data of users to determine driving behavior patterns of users also receives wide attention.
The driving behavior pattern refers to driving operation that a user should perform on a certain road condition under certain driving data. The existing driving data processing method generally comprises the following steps: the method comprises the steps of collecting multiple groups of driving data of a user, carrying out statistical analysis on the multiple groups of driving data, determining the distribution range of the driving data, and determining the driving behavior mode of the user according to the distribution range of the driving data. In general, the severity of user operation cannot be accurately determined and reflected directly through statistical analysis, so that the generated driving behavior pattern cannot accurately reflect the user operation characteristics.
The existing driving data processing method cannot reflect the user operation intensity when processing data, so that the generated driving behavior mode cannot accurately reflect the user operation characteristics.
Disclosure of Invention
The application provides a driving data processing method, a driving data processing device, driving data processing equipment and a storage medium. The method solves the technical problem that the generated driving behavior mode can not accurately reflect the user operation characteristics because the severity of the user operation can not be reflected when the existing method is used for data processing.
In a first aspect, the present application provides a driving data processing method, including:
acquiring at least one group of driving operation data of a first user in a first driving scene;
determining the degree grade of each driving operation data according to a first mapping table, wherein the first mapping table is used for representing the mapping relation between the driving operation data and the driving behavior severity degree under the first driving scene;
and processing the degree grade of each group of driving operation data and each group of driving operation data by using a learning model to generate a first driving behavior mode of the first user in a first driving scene.
Optionally, before acquiring at least one set of driving operation data of the user in the first driving scene, the method further includes:
acquiring first road condition data at the k +1 th moment and first driving data of a vehicle at the k th moment, wherein k is a positive integer;
and determining a first driving scene at the k +1 moment according to a second mapping table, the first road condition data at the k +1 moment and the first driving data at the k moment, wherein the second mapping table represents the mapping relation among the road condition data, the driving data and the driving scene.
Optionally, the driving operation data includes: brake operation data, throttle operation data, steering wheel operation data, gear operation data, light operation data and whistling operation data.
Optionally, the method further comprises:
acquiring a second driving scene where a second user is located currently;
predicting a second driving behavior mode of the second user in a second driving scene according to the first mode database;
determining whether the second driving behavior pattern matches a standard behavior pattern in a second driving scenario;
and if not, generating prompt information for prompting the driving behavior of the second user.
Optionally, the method further comprises:
determining a driving scene set of the first road according to the road condition data of the first road and the second mapping table, wherein the driving scene set comprises at least one third driving scene;
calculating third users with abnormal modes in each third driving scene from the second mode database, wherein the third users with abnormal modes are used for representing users with driving behavior modes not matched with the standard behavior modes;
and if the number of the third users in each third driving scene meets the preset condition, determining that the first road is a dangerous road.
In a second aspect, the present application provides a driving data processing apparatus comprising:
the acquisition module is used for acquiring at least one group of driving operation data of a first user in a first driving scene;
the determining module is used for determining the degree grade of each driving operation data according to a first mapping table, wherein the first mapping table is used for representing the mapping relation between the driving operation data and the driving behavior severity degree in a first driving scene;
and the processing module is used for processing the degree grade of each group of driving operation data and each group of driving operation data by using a learning model and generating a first driving behavior mode of the first user in a first driving scene.
Optionally, the apparatus further comprises: the device comprises a prediction module and a prompt module;
the acquisition module is further used for acquiring a second driving scene where a second user is currently located;
the prediction module is used for predicting a second driving behavior mode of a second user in a second driving scene according to the first mode database;
the determining module is used for determining whether the second driving behavior mode is matched with the standard behavior mode in the second driving scene;
and the prompting module is also used for generating prompting information for prompting the driving behavior of the second user if the driving behavior of the second user is not matched with the driving behavior of the first user.
Optionally, the apparatus, comprising: a statistical module;
the obtaining module is further used for determining a driving scene set of the first road according to the road condition data of the first road and the second mapping table, wherein the driving scene set comprises at least one third driving scene;
the statistical module is used for counting third users with abnormal modes in each third driving scene from the second mode database, wherein the third users with abnormal modes are used for representing users with driving behavior modes not matched with the standard behavior modes;
the determining module is further used for determining the first road as a dangerous road if the number of the third users in each third driving scene meets a preset condition.
In a third aspect, the present application provides a processing apparatus comprising:
a memory for storing a program;
a processor for executing the program stored in the memory, the processor being configured to perform the driving data processing method according to the first aspect and the alternative when the program is executed.
In a fourth aspect, the present application provides a computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the driving data method according to the first aspect and the alternative.
The application provides a driving data processing method, a device, equipment and a storage medium, wherein the method comprises the following steps: the driving operation data of the user are collected, the degree grade corresponding to each driving operation data is determined to determine the severity of each driving operation data, the driving operation data and the corresponding degree grade are analyzed, and the generated driving behavior mode accurately reflects the user operation characteristics.
Drawings
Fig. 1 is a schematic view of an application scenario based on a driving data processing method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a driving data processing method according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a driving data processing method according to a second embodiment of the present application;
fig. 4 is a schematic flow chart of a driving data processing method according to a third embodiment of the present application;
fig. 5 is a schematic structural diagram of a driving data processing device according to a fourth embodiment of the present application;
fig. 6 is a schematic structural diagram of a processing apparatus according to the fifth embodiment of the present application.
Detailed Description
To make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be clearly and completely described below with reference to the drawings in the present application, and it is obvious that the described embodiments are some, but not all embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The existing driving data processing method generally collects a large amount of driving data, then carries out statistical analysis on the driving data to obtain data characteristics of the driving data, and then determines the driving behavior mode of a user by using the data characteristics. However, the driving data characteristics are widely distributed, and obvious data characteristics cannot be found generally, so that the driving behavior pattern of the user cannot be accurately determined.
The application provides a driving data processing method, which aims to solve the technical problems and has the following inventive concept: the driving operation data of the user are collected, the driving operation data are classified according to degree grades, the degree grade of each driving operation data is determined, the degree grade can reflect the intensity degree of the user operation, the degree grade of each driving operation data and each driving operation data are analyzed, and the generated driving behavior mode accurately reflects the user operation characteristics.
Fig. 1 is a schematic view of an application scenario on which a driving data processing method according to an embodiment of the present application is based. As shown in fig. 1, a sensor located inside a vehicle collects driving operation data of a user when driving the vehicle and driving data of the vehicle, the sensor sends the driving operation data and the driving data to a vehicle terminal 101, and the vehicle terminal 101 further determines road condition data of the vehicle according to a positioning system of the vehicle and high-precision map data. After acquiring the data, the vehicle end 101 sends the data to the processing device 102, and the processing device 102 processes the data to generate a driving behavior pattern of the user in a certain driving scene.
Different pattern databases may be constructed according to the type of the collected historical driving operation data. For example: if only one user driving operation data, and corresponding road condition data and driving data are collected, a mode database reflecting driving behavior modes of the user in different driving scenes can be generated. If the driving data of various users, and corresponding road condition data and driving data are collected, a mode database reflecting the driving behavior mode of each user in different driving scenes can be generated. If the driving operation data of the standard user, the corresponding road condition data and the corresponding driving data are collected, a mode database for reflecting the standard behavior modes in different driving scenes can be generated.
Fig. 2 is a schematic flow chart of a driving data processing method according to a first embodiment of the present application, and as shown in fig. 2, the driving data processing method according to the present embodiment is applied to a processing device, where the processing device is a computer, a server, or other devices. The driving data processing method comprises the following steps:
s201, at least one group of driving operation data of the first user in the first driving scene is obtained.
And determining road condition data of the vehicle at the next moment by a positioning system of the vehicle and map data in the high-precision map during the driving process of the vehicle. Wherein, road conditions data includes: intersection data, road congestion data, road grade, road turning curvature, lane attributes, wayside facility data and the like. And collecting the running data of the vehicle at the current moment, wherein the running data of the vehicle comprises: any one or more combinations of the running speed, the distance between the front and the rear vehicles and the like. For example: the vehicle running speed is determined through a vehicle-mounted sensor, and the front and rear vehicle distances are determined through a vehicle-mounted radar.
And determining the driving scene of the vehicle according to a second mapping table stored locally, wherein the second mapping table represents the mapping relation among the road condition data, the driving data and the driving scene. And finding a corresponding driving scene in the first mapping table according to the collected road condition data at the next moment and the collected driving data at the current moment. For example: after the vehicle is stopped and started, the vehicle passes through a straight road junction, a right-turn road junction at a low speed, a left-turn road junction at a high speed, a congested road section for running, a congested road section and the like.
And then collecting the driving operation data of the user after the current driving scene is obtained. User operational data is to be collected continuously at a higher frequency, for example: not lower than 10 Hz. Wherein the driving operation data includes: brake operation data, throttle operation data, steering wheel operation data, gear operation data, light operation data and whistling operation data. The brake operation data includes acceleration, acceleration rate, vehicle speed, and the number of times of braking. The accelerator operation data includes acceleration, acceleration rate, vehicle speed, and accelerator times. The steering wheel operation data includes a steering angle, a return position, and the number of operations. The number of shift-stage operations includes shifting a shift stage and the number of operations. The number of light operations includes the number of operations and the switching light type. The blast operation data includes blast times.
S202, determining the degree grade of each group of driving operation data according to the first mapping table.
When the processing equipment is initialized, a first mapping table is loaded to the local, and the first mapping table is used for representing the mapping relation between the driving operation data and the driving behavior severity degree in the first driving scene. Determining a degree rank of the driving operation data in the first driving scene by: and determining the degree grade of each operation data in each group of driving operation data by a line, and accumulating the degree grade of each operation data in each group of driving operation data to obtain the degree grade of each group of driving operation data.
Next, the degree level of each set of driving operation data is obtained by way of example, and the degree level of each operation data in each set of driving operation data is determined. Wherein the driving operation data includes: and (4) operating a steering wheel. The driving scene is that the vehicle passes through the right-turn intersection at low speed. Two sets of driving performance data were collected: in the first group, the steering wheel rotates to 8 degrees and then returns to 0 degree, and the operation times are 1. In the second group, the steering wheel rotates to 25 degrees and then returns to 2 degrees, the steering wheel rotates to 10 degrees and then returns to 0 degree, and the operation times are 2. And substituting the first group of driving operation data and the second driving operation data into a function corresponding to the first mapping table for calculation, and determining the grade corresponding to the first group of driving operation data and the second driving operation data.
And accumulating the degree grades of the data in each group of driving operation data to obtain the degree grade of each group of driving operation data. And carrying out weighted superposition according to the degree of the driving severity degree reflected by each data in each group of driving operation data to obtain the degree grade of each group of driving operation data. For example: the three data of the accelerator force, the brake force and the gear shifting times can reflect the driving severity degree better than the light switching and whistling times, and high weights can be given to the three data.
And S203, processing the degree grade of each group of driving operation data and each group of driving operation data by using a learning model, and generating a first driving behavior mode of the first user in a first driving scene.
Among them, the learning model may be, but is not limited to: neural network based learning models, machine learning based learning models, deep learning based learning models, data mining based learning models. And processing the driving operation data and the degree grade of the driving operation data by using the learning model to generate a driving behavior pattern of the user in a certain driving scene.
In the driving data processing method provided by the embodiment of the application, after the driving operation data under different driving scenes are collected, the degree grade of each driving operation data is determined, the degree grade reflecting the driving severity is taken as the generated driving behavior mode, and the generated driving behavior mode accurately reflects the user operation characteristics.
In the prior art, in order to standardize the driving behavior of the user, the following scheme is generally adopted: and collecting the driving operation data of the user at the current moment, judging that the driving operation data reaches the preset operation data, and prompting the user to carry out standard operation if the judgment result is yes. However, in the prior art, the user is prompted after the user performs an overstimulated driving operation, and the driving behavior of the user cannot be prompted in advance. The second embodiment of the present application provides a driving data processing method, which aims to solve the above problems.
Fig. 3 is a schematic flow chart of a driving data processing method according to a second embodiment of the present application. As shown in fig. 3, the driving data processing method provided in the second embodiment of the present application is applied to a processing device, where the processing device is a vehicle terminal, a mobile phone terminal, or a server terminal. When the processing device is a vehicle end or a mobile phone end, the first mode database and the standard mode database need to be updated in time. As shown in fig. 3, a driving data processing method provided in the second embodiment of the present application includes the following steps:
s301, acquiring a second driving scene where a second user is located currently.
The obtaining of the second driving scenario where the second user is currently located is already described in detail in embodiment one S101, and repeated descriptions are omitted. It should also be noted that: when the prompting device is initialized, the first mode database and the standard mode database are required to be loaded.
The first mode data store driving behavior modes of the second user in different driving scenes, and the first mode database is constructed in the following mode: and collecting historical driving operation data of the second user in different driving scenes, and processing the historical driving operation data by using the driving data processing method shown in the first embodiment to obtain driving behavior patterns of the second user in different driving scenes.
The standard mode database stores standard behavior modes under different driving scenes, and the standard mode database is constructed in the following mode: historical operation data of a plurality of standard users under different driving scenes are collected, and the historical driving operation data are processed by using the driving data processing method shown in the first embodiment to obtain the standard behavior patterns under different driving scenes.
The standard users refer to users who have long-term driving, few violations and accident records, and few sudden stop, sudden deceleration, sudden start and sudden turn probabilities, and the users who meet the conditions are used as the standard users. The above conditions can be adjusted according to actual conditions. Specifically, whether the user is a standard user is judged according to the ratio of acceleration/deceleration to time and the ratio of steering angle to time compared with a preset threshold.
And S302, predicting a second driving behavior mode of the second user in a second driving scene according to the first mode database.
And determining a second driving behavior mode under the second driving scene from the first mode database according to the second driving scene where the second user is currently located.
S303, determining whether the second driving behavior pattern is matched with the standard behavior pattern in the second driving scene. If not, the process proceeds to S304, otherwise, the process proceeds to S305.
Wherein, when determining that the driving behavior pattern of the second user does not match the standard behavior pattern, it means: it is determined whether the driving behavior pattern of the second user is consistent with the standard behavior pattern.
And S304, generating prompt information for prompting the driving behavior of the second user.
And when the driving behavior mode of the second user is determined not to be matched with the standard behavior mode, generating prompt information according to the difference between the standard behavior mode and the driving behavior mode of the user. And reminding the user of safe driving in a sound or picture mode. The prompting types can be 14 types as follows: 1. slow down, 2, slow down as soon as possible, 3, slow up, 4, fast up, 5, slow turn, 6, fast turn, 7, delay turn, 8, advance turn, 9, upshift, 10, downshift, 11, turn on turn signal, 12, turn off turn signal, 13, turn on high beam, 14, turn off high beam.
S305 determines whether or not to terminate driving, if so, ends the flow, and if not, proceeds to S301.
According to the driving data processing method provided by the embodiment of the application, the driving behavior mode of the second user in the second driving scene at present is predicted through the first mode database, the driving behavior mode is matched with the standard behavior mode, the driving behavior of the user is prompted under the condition that the driving behavior mode is not matched, the driving behavior can be prompted in advance, and the situation that the user carries out violent driving operation is avoided.
In the prior art, in order to determine the reasonableness of road design, the following method is generally adopted: and collecting accident data of the road and mining the accident data. However, the above method needs to acquire reliable data after a certain number of accidents occur, and the reasonableness of road design cannot be evaluated in advance before the accidents occur. The third embodiment of the application provides a driving data processing method, and aims to solve the problems.
Fig. 4 is a schematic flow chart of a driving data processing method according to a third embodiment of the present application. As shown in fig. 4, the driving data processing method provided by the present embodiment is applied to a processing device, which is a computer, a server, or the like. The treatment method comprises the following steps:
s401, determining a driving scene set of the first road according to the road condition data of the first road and the second mapping table.
And when the processing equipment is initialized, the second mapping table, the second mode database and the standard mode database are loaded locally. The second mapping table represents the mapping relation among the road condition data, the driving data and the driving scenes, and the second mode database is used for storing the driving behavior modes of different users in different driving scenes. The second pattern database is constructed in the following way: historical driving operation data of different users in different driving scenes are collected, and the driving behavior patterns of the different users in the different driving scenes are obtained by processing the historical driving operation data by using the driving data processing method shown in the first embodiment. The method for constructing the standard pattern database has already been described in detail in S301 in the second embodiment, and repeated descriptions are omitted.
After the processing device is initialized, the second mapping table is searched by using the road condition data of the first road, and the driving scene set of the first road is determined. Wherein the set of driving scenarios includes at least one third driving scenario.
S402, third users with abnormal modes in each third driving scene are counted from the second mode database.
And randomly selecting a first preset number of mode data from the second mode database aiming at each third driving scene, wherein the first preset number can be selected according to the compromise of statistical calculation amount and accuracy. And determining whether the driving behavior mode of each third user is matched with the standard behavior mode in the third driving scene, and if not, determining that the third user is the user with the abnormal mode. The number of third users with abnormal patterns can be obtained for each third driving scenario.
And S403, judging whether the number of the third users in each third driving scene meets a preset condition, if so, entering S404, and otherwise, entering an ending process.
The preset condition is that the total number of the third users reaches a preset value, or the total number of the third users in several driving scenes reaches a preset value. The selection of the driving scene and the setting of the preset value can be determined according to specific requirements.
S404, if the number of the third users in each third driving scene meets a preset condition, determining that the first road is a dangerous road.
And if the number of the third users in each third driving scene meets the preset condition, determining that the first road is a dangerous road. After predicting the dangerous road, safety measures are taken in advance, such as: traffic control, speed limit, current limit, etc., and accident handling resources and emergency resources may also be allocated in advance. Or by investigating the road, a road adjustment strategy is determined, for example: expanding lanes, adjusting the camber and the gradient, adding reminding facilities and the like.
In the driving data processing method provided by the embodiment of the application, the third driving scenes corresponding to the first road are determined according to the road condition data of the road, the third users with abnormal modes in each third driving scene are counted, and whether the first road is a dangerous road is judged according to the third users. Compared with the existing method, the method can predict the dangerous road in advance, does not need to determine according to a large amount of accident data, and reduces life and property loss.
Fig. 5 is a schematic flowchart of a driving data processing apparatus according to a fourth embodiment of the present application, and as shown in fig. 5, the present application provides a driving data processing apparatus 500, including:
an obtaining module 501, configured to obtain at least one set of driving operation data of a first user in a first driving scene;
a determining module 502, configured to determine a degree level of each driving operation data according to a first mapping table, where the first mapping table is used to represent a mapping relationship between the driving operation data and a driving behavior severity in a first driving scene;
the processing module 503 is configured to process the degree level of each set of driving operation data and each set of driving operation data by using a learning model, and generate a first driving behavior pattern of the first user in the first driving scene.
Optionally, the obtaining module 501 is further configured to obtain first road condition data at a k +1 th time and first driving data of the vehicle at the k th time, where k is a positive integer;
the determining module 502 is further configured to determine the first driving scenario at the k +1 th time according to a second mapping table, the first road condition data at the k +1 th time, and the first driving data at the k +1 th time, where the second mapping table represents a mapping relationship between the road condition data, the driving data, and the driving scenario.
Optionally, the driving operation data includes: brake operation data, throttle operation data, steering wheel operation data, gear operation data, light operation data and whistling operation data.
Optionally, the apparatus further comprises: a prediction module 504 and a prompt module 505;
the obtaining module 501 is further configured to obtain a second driving scene where a second user is currently located;
the prediction module 504 is configured to predict a second driving behavior pattern of the second user in a second driving scenario according to the first pattern database;
the determining module 502 is further configured to determine whether the second driving behavior pattern matches a standard behavior pattern in a second driving scenario;
and the prompt module 505 is used for generating prompt information for prompting the driving behavior of the second user if the driving behavior of the second user is not matched with the driving behavior of the first user.
Optionally, the apparatus further comprises: a statistics module 506;
the obtaining module 501 is further configured to determine a driving scene set of the first road according to the road condition data of the first road, where the driving scene set includes at least one third driving scene;
the statistic module 506 is configured to calculate a third user with abnormal pattern in each third driving scenario from the second pattern database, where the third user with abnormal pattern is used to indicate a user whose driving behavior pattern does not match the standard behavior pattern;
the determining module 502 is further configured to determine that the first road is a dangerous road if the number of third users in each third driving scenario meets a preset condition.
Fig. 6 is a schematic structural diagram of a processing apparatus according to the fifth embodiment of the present application. As shown in fig. 6, the present embodiment provides a processing apparatus 600 including: a transmitter 601, a receiver 602, a memory 603, and a processor 602.
A transmitter 601 for transmitting instructions and data;
a receiver 602 for receiving instructions and data;
a memory 603 for storing computer-executable instructions;
a processor 604 for executing computer-executable instructions stored in the memory to implement the steps performed by the driving data processing method in the above-described embodiments. Reference may be made in particular to the relevant description in the foregoing driving data processing method embodiments.
Alternatively, the memory 603 may be separate or integrated with the processor 604.
When the memory 603 is separately provided, the processing device further includes a bus for connecting the memory 603 and the processor 604.
The embodiment of the application also provides a computer-readable storage medium, wherein a computer executing instruction is stored in the computer-readable storage medium, and when the processor executes the computer executing instruction, the driving data processing method executed by the processing device is realized.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A driving data processing method characterized by comprising:
acquiring at least one group of driving operation data of a first user in a first driving scene;
determining the degree grade of each group of the driving operation data according to a first mapping table, wherein the first mapping table is used for representing the mapping relation between the driving operation data and the driving behavior severity degree under the first driving scene;
and processing the degree grade of each group of the driving operation data and each group of the driving operation data by using a learning model to generate a first driving behavior pattern of the first user in the first driving scene.
2. The method of claim 1, wherein prior to said obtaining at least one set of driving maneuver data of the user in the first driving scenario, further comprising:
acquiring first road condition data at the k +1 th moment and first driving data of a vehicle at the k th moment, wherein k is a positive integer;
and determining a first driving scene at the k +1 moment according to a second mapping table, the first road condition data at the k +1 moment and the first driving data at the k moment, wherein the second mapping table represents the mapping relation among the road condition data, the driving data and the driving scene.
3. The method of claim 1, wherein the driving operation data comprises: any one or more of brake operation data, accelerator operation data, steering wheel operation data, gear operation data, light operation data and whistling operation data.
4. The method according to any one of claims 1 to 3, further comprising:
acquiring a second driving scene where a second user is located currently;
predicting a second driving behavior pattern of the second user in the second driving scene according to a first pattern database;
determining whether the second driving behavior pattern matches a standard behavior pattern in the second driving scenario;
and if not, generating prompt information for prompting the driving behavior of the second user.
5. The method according to any one of claims 1 to 3, further comprising:
determining a driving scene set of a first road according to road condition data of the first road and a second mapping table, wherein the driving scene set comprises at least one third driving scene;
calculating a third user with abnormal mode in each third driving scene from a second mode database, wherein the third user with abnormal mode is used for representing the user with the driving behavior mode not matched with the standard behavior mode;
and if the number of the third users in each third driving scene meets a preset condition, determining that the first road is a dangerous road.
6. A driving data processing apparatus characterized by comprising:
the acquisition module is used for acquiring at least one group of driving operation data of a first user in a first driving scene;
the determining module is used for determining the degree grade of each driving operation data according to a first mapping table, wherein the first mapping table is used for representing the mapping relation between the driving operation data and the driving behavior intensity in the first driving scene;
and the processing module is used for processing the degree grade of each group of the driving operation data and each group of the driving operation data by using a learning model and generating a first driving behavior mode of the first user in the first driving scene.
7. The apparatus of claim 6, further comprising: the device comprises a prediction module and a prompt module;
the acquisition module is further used for acquiring a second driving scene where a second user is located currently;
the prediction module is used for predicting a second driving behavior mode of the second user in the second driving scene according to a first mode database;
the determination module is further configured to determine whether the second driving behavior pattern matches a standard behavior pattern in the second driving scenario;
and the prompting module is used for generating prompting information for prompting the driving behavior of the second user if the driving behavior of the second user is not matched with the driving behavior of the second user.
8. The apparatus of claim 6, further comprising: a statistical module;
the obtaining module is further configured to determine a driving scene set of the first road according to road condition data of the first road and a second mapping table, where the driving scene set includes at least one third driving scene;
the statistical module is used for counting third users with abnormal modes in each third driving scene from a second mode database, wherein the third users with abnormal modes are used for representing users with driving behavior modes not matched with standard behavior modes;
the determining module is further configured to determine that the first road is a dangerous road if the number of third users in each third driving scene meets a preset condition.
9. A processing device, comprising:
a memory for storing a program;
a processor for executing the program stored in the memory, the processor being configured to execute the driving data processing method according to any one of claims 1 to 5 when the program is executed.
10. A computer-readable storage medium comprising instructions that, when executed on a computer, cause the computer to perform the driving data method of any of claims 1 to 5.
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