CN111367968B - Driving data processing method, device, equipment and storage medium - Google Patents
Driving data processing method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN111367968B CN111367968B CN202010182345.9A CN202010182345A CN111367968B CN 111367968 B CN111367968 B CN 111367968B CN 202010182345 A CN202010182345 A CN 202010182345A CN 111367968 B CN111367968 B CN 111367968B
- Authority
- CN
- China
- Prior art keywords
- driving
- operation data
- data
- user
- scene
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
- Y02T10/84—Data processing systems or methods, management, administration
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 comprises the following steps: and collecting driving operation data in a first driving scene, determining the degree level of the driving operation data reflecting the intensity of driving behaviors according to a preset first mapping table, and processing the driving operation data and the degree level to determine a driving behavior mode. Compared with the existing processing method which directly carries out statistical analysis, the method and the device carry out analysis processing on the degree grade and the driving operation data, and the processing result reflects the driving operation characteristics of the user more accurately.
Description
Technical Field
The present application relates to the field of intelligent driving technologies, and in particular, to a driving data processing method, device, equipment, and storage medium.
Background
With the development of the intelligent driving field, the research on the driving data of the user is also receiving extensive attention to determine the driving behavior pattern of the user.
The driving behavior mode refers to a driving operation that a user should make on a certain road condition under certain driving data. The existing driving data processing method generally comprises the following steps: and acquiring multiple groups of driving data of the 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 operation severity of the user cannot be accurately determined and reflected directly through statistical analysis, so that the generated driving behavior mode cannot accurately reflect the operation characteristics of the user.
The conventional driving data processing method cannot reflect the operation severity of a user when the data processing is performed, so that the generated driving behavior mode cannot accurately reflect the operation characteristics of the user.
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 cannot accurately reflect the operation characteristics of the user due to the fact that the operation intensity of the user cannot be reflected when the data is processed by the existing method.
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 level of each driving operation data according to a first mapping table, wherein the first mapping table is used for representing a mapping relation between the driving operation data and the intensity level of driving behaviors in a first driving scene;
and processing the degree level 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 the 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+1th moment and first driving data of the vehicle at the k moment, wherein k is a positive integer;
and determining a first driving scene at the k+1 moment according to the 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, accelerator operation data, steering wheel operation data, gear operation data, light operation data and whistle operation data.
Optionally, the method further comprises:
acquiring a second driving scene where a second user is currently located;
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 the second driving scene;
if the first user driving behavior is not matched with the second user driving behavior, generating prompt information for prompting the second user driving behavior.
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;
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 the standard behavior modes;
and if the number of the third users in each third driving scene meets the preset condition, determining the first road as 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 the first user in the first driving scene;
the determining module is used for determining the degree level 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 severity of driving behaviors 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 the learning model to generate a first driving behavior mode of the first user in the first driving scene.
Optionally, the apparatus further comprises: a prediction module and a prompt module;
the acquisition module is also 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 the 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;
the prompting module is also used for generating prompting information for prompting the driving behavior of the second user if the prompting information is not matched with the driving behavior of the second user.
Optionally, the apparatus comprises: a statistics module;
the acquisition 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 statistics 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 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.
In a third aspect, the present application provides a processing apparatus comprising:
a memory for storing a program;
a processor for executing a program stored in the memory, the processor being configured to execute the driving data processing method according to the first aspect and the optional aspects when the program is executed.
In a fourth aspect, the present application provides a computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform the driving data method according to the first aspect and alternatives.
The application provides a driving data processing method, a device, equipment and a storage medium, wherein the method comprises the following steps: and collecting driving operation data of the user, determining the corresponding degree level of each driving operation data to determine the intensity of each driving operation data, analyzing and processing the driving operation data and the corresponding degree level, and accurately reflecting the operation characteristics of the user by the generated driving behavior mode.
Drawings
Fig. 1 is a schematic view of an application scenario on which a driving data processing method according to a first embodiment of the present application is based;
fig. 2 is a flow chart of a driving data processing method according to an embodiment of the application;
fig. 3 is a flow chart of a driving data processing method according to a second embodiment of the present application;
fig. 4 is a 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 a fifth embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The existing driving data processing method generally collects a large amount of driving data, performs statistical analysis on the driving data to obtain data characteristics of the driving data, and determines a driving behavior mode of a user by using the data characteristics. However, driving data features are widely distributed, and obvious data features 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 the application is characterized in that: and collecting driving operation data of the user, classifying the driving operation data according to the degree grade, determining the degree grade of each driving operation data, wherein the degree grade can reflect the intensity of the user operation, analyzing and processing the degree grade of each driving operation data and each driving operation data, and the generated driving behavior mode accurately reflects the operation characteristics of the user.
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 and running data of the vehicle when a user drives the vehicle, the driving operation data and the running data are transmitted to a vehicle machine side 101 by the sensor, and the vehicle machine side 101 also determines road condition data of the running of the vehicle according to a positioning system of the vehicle and high-precision map data. After acquiring the data, the vehicle-mounted terminal 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.
Depending on the type of historical driving operation data collected, different pattern databases may be constructed. For example: if only one user driving operation data, and corresponding road condition data and driving data are collected, a mode database reflecting the driving behavior modes of the user in different driving scenes can be generated. If the driving data of various users, the corresponding road condition data and the corresponding driving data are collected, a mode database reflecting the driving behavior modes of the various users in different driving scenes can be generated. If the driving operation data of the standard user, the corresponding road condition data and driving data are collected, a mode database for reflecting the standard behavior modes under different driving scenes can be generated.
Fig. 2 is a flow chart of a driving data processing method according to an embodiment of the present application, as shown in fig. 2, the driving data processing method 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 a first user in a first driving scene is acquired.
And in the running process of the vehicle, determining the road condition data of the vehicle at the next moment through a positioning system of the vehicle and map data in a high-precision map. Wherein, road conditions data includes: any one or more of intersection data, road congestion data, road grade, road turning curvature, lane attributes, roadside equipment data, and the like. And collecting running data of the vehicle at the current moment, wherein the running data of the vehicle comprises the following steps: any one or more of the combination of the running speed, the front-rear vehicle distance and the like. For example: the vehicle running speed is determined by the vehicle-mounted sensor, and the front-rear vehicle distance is determined by the 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 out a corresponding driving scene in the first mapping table according to the collected road condition data at the next moment and the driving data at the current moment. For example: after the parking is started, the vehicle passes through a straight road junction, passes through a right-turn road junction at a low speed, passes through a left-turn road junction at a high speed, enters a congestion road section, runs on the congestion road section, and leaves the congestion road section.
And collecting driving operation data of the user after obtaining the current driving scene. User operation data is to be collected continuously at a higher frequency, for example: not lower than 10Hz. Wherein the driving operation data includes: brake operation data, accelerator operation data, steering wheel operation data, gear operation data, light operation data and whistle operation data. The brake operation data comprise acceleration, acceleration change rate, vehicle speed and brake times. The accelerator operation data includes acceleration, acceleration change rate, vehicle speed, and the number of accelerator times. The steering wheel operation data includes steering angle, callback position, and number of operations. The number of gear operations includes a gear-shifting position and a number of operations. The number of light operations includes the number of operations and switching the light type. The blast operation data includes the number of blasts.
S202, determining the degree level 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 a mapping relation between driving operation data and the intensity of driving behaviors in the first driving scene. The degree level of the driving operation data in the first driving scene is determined by: the line determines the degree level of each operation data in each set of driving operation data, and the degree level of each operation data in each set of driving operation data is accumulated to obtain the degree level of each set of driving operation data.
The degree level of each set of driving operation data is obtained by first determining the degree level of each operation data in each set of driving operation data. Wherein the driving operation data includes: steering wheel operation. The driving scene is that the vehicle passes through the right turn intersection at a low speed. Two sets of driving operation data are collected: the first group, steering wheel turned 8 ° and then recalled to 0 °, the number of operations was 1. And the second group, the steering wheel turns to 25 degrees and then is recalled to 2 degrees, the steering wheel turns to 10 degrees and then is recalled 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 the functions corresponding to the first mapping table for calculation, and determining the grades 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 the degree of each group of driving operation data can be obtained by weighting and superposing according to the degree of each data reflecting the driving intensity degree in each group of driving operation data. For example: compared with the lamplight switching and whistling times, the three data of the accelerator force, the brake force and the gear shifting times can reflect the driving intensity, and can be given high weight.
S203, the degree level of each group of driving operation data and each group of driving operation data are processed by using a learning model, and a first driving behavior mode of the first user in a first driving scene is generated.
Wherein the learning model may be, but is not limited to: a neural network-based learning model, a machine learning-based learning model, a deep learning-based learning model, a data mining-based learning model. And processing the driving operation data and the degree level of the driving operation data by using the learning model to generate a driving behavior mode 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 intensity degree is taken as the generated driving behavior mode, and the generated driving behavior mode accurately represents the operation characteristics of the user.
In the prior art, in order to normalize the driving behavior of a user, the following scheme is generally adopted: and collecting driving operation data of the user at the current moment, judging that the driving operation data reach the preset operation data, and prompting the user to standardize the operation if the judgment result is yes. However, in the prior art, after the user makes an overdriving driving operation, the user is prompted, and the driving behavior of the user cannot be prompted in advance. The second embodiment of the application provides a driving data processing method, which aims to solve the problems.
Fig. 3 is a flowchart 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-mounted device, a mobile phone, or a server. When the processing device is a vehicle terminal or a mobile phone terminal, the first mode database and the standard mode database need to be updated in time. As shown in fig. 3, the 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 currently located.
The step of acquiring the second driving scenario where the second user is currently located is already described in detail in the first embodiment S101, and the repeated parts are not repeated. Also to be described is: when the prompting device is initialized, the first mode database and the standard mode database need to be loaded.
The first mode data stores driving behavior modes of the second user in different driving scenes, and the first mode database is constructed in the following manner: historical driving operation data of the second user in different driving scenes is collected, and the driving data processing method shown in the first embodiment is utilized to process the historical driving operation data, so that driving behavior patterns of the second user in different driving scenes are obtained.
The standard mode database stores standard behavior modes under different driving scenes, and the standard mode database is constructed in the following manner: 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 standard behavior patterns under different driving scenes.
The standard user is a user which has less long-term driving, fewer violations and accidents, and fewer sudden stop, sudden deceleration, sudden start and sudden turn probabilities and meets the conditions. The above conditions can be adjusted according to the actual situation. Specifically, whether the user is standard or not is determined according to the ratio of acceleration/deceleration to time and the ratio of steering angle to time compared with a preset threshold.
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, determining that the driving behavior pattern of the second user does not match the standard behavior pattern means that: it is determined whether the driving behavior pattern of the second user is consistent with the standard behavior pattern.
S304, generating prompt information for prompting the driving behavior of the second user.
And generating prompt information according to the difference between the standard behavior mode and the driving behavior mode of the user after determining that the driving behavior mode of the second user is not matched with the standard behavior mode. The user is reminded to carry out safe driving in a voice or picture mode. The prompt types can be 14 types as follows: 1. slow speed reduction, 2, fast speed reduction, 3, slow speed increase, 4, fast speed increase, 5, slow turning, 6, fast turning, 7, delayed turning, 8, advanced turning, 9, upshift, 10, downshift, 11, turn on steering lamp, 12, turn off steering lamp, 13, turn on high beam, 14, turn off high beam.
S305, judging whether to terminate driving, if so, ending the flow, and if not, turning 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 where the second user is currently located is predicted through the first mode database, the driving behavior mode is matched with the standard behavior mode, and under the condition of no match, the driving behavior of the user is prompted, so that the driving behavior can be prompted in advance, and the user is prevented from performing excessively intense driving operation.
In the prior art, in order to determine the rationality of road design, the following method is generally adopted: accident data of the road are collected, and the accident data are mined. However, the above method needs to acquire reliable data after a certain number of accidents, and cannot evaluate the rationality of road design in advance before the accidents happen. The third embodiment of the application provides a driving data processing method, which aims to solve the problems.
Fig. 4 is a 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 in the present embodiment is applied to a processing device, which is a computer, a server, or the like. The processing 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 pattern database is used for storing driving behavior patterns of different users in different driving scenes. The construction mode of the second mode database is as follows: historical driving operation data of different users in different driving scenes are collected, and the driving data processing method shown in the first embodiment is utilized to process the historical driving operation data, so that driving behavior patterns of different users in different driving scenes are obtained. The construction method of the standard schema database is already described in detail in S301 in the second embodiment, and the repeated parts will not be described again.
After the processing equipment is initialized, the second mapping table is searched for 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 comprises at least one third driving scenario.
S402, third users with abnormal modes in each third driving scene are counted from the second mode database.
The first preset number of mode data is randomly selected from the second mode database for each third driving scene, wherein the first preset number can be selected according to the balance of the statistical calculation amount and the accuracy. And determining whether the driving behavior pattern of each third user is matched with the standard behavior pattern in the third driving scene, and if not, judging that the third user is a user with abnormal pattern. For each third driving scenario, the number of third users with abnormal patterns may be obtained.
S403, judging whether the number of the third users in each third driving scene meets the preset condition, if so, entering S404, otherwise, entering an ending flow.
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 driving scenes and the setting of preset values can be determined according to specific requirements.
S404, if the number of third users in each third driving scene meets the preset condition, determining the first road as a dangerous road.
And if the number of the third users in each third driving scene meets the preset condition, determining the first road as a dangerous road. After predicting a dangerous road, safety measures are taken in advance, such as: limiting, speed limiting, current limiting and the like, and accident handling resources and emergency resources can be allocated in advance. Or by examining the road, determining a road adjustment strategy, such as: expanding lanes, adjusting the camber and 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, and the third users with abnormal modes in each third driving scene are counted, so that whether the first road is a dangerous road is judged according to the third driving scenes. Compared with the existing method, the method can predict dangerous roads in advance, does not need to be determined according to a large amount of accident data, and reduces loss of life and property.
Fig. 5 is a flow chart 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 scenario;
a determining module 502, configured to determine a 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 driving operation data and a severity of driving behavior in a first driving scenario;
the processing module 503 is configured to process the 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 mode of the first user in the first driving scenario.
Optionally, the acquiring module 501 is further configured to acquire first road condition data at a kth+1 time and first driving data of the vehicle at the kth time, where k is a positive integer;
the determining module 502 is further configured to determine a first driving scenario at the k+1 time according to a second mapping table, the first road condition data at the k+1 time, and the first driving data at the k time, where the second mapping table represents a mapping relationship among the road condition data, the driving data, and the driving scenario.
Optionally, the driving operation data includes: brake operation data, accelerator operation data, steering wheel operation data, gear operation data, light operation data and whistle operation data.
Optionally, the apparatus further comprises: a prediction module 504 and a hint module 505;
the obtaining module 501 is further configured to obtain a second driving scenario in which 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 the second driving scenario;
the prompting module 505 is configured to generate prompting information for prompting the driving behavior of the second user if the first user is not matched with the second 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 statistics module 506 is configured to count third users with abnormal patterns in each third driving scenario from the second pattern database, where the third users with abnormal patterns are used to represent users whose driving behavior patterns do not match the standard behavior patterns;
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 a fifth embodiment of the present application. As shown in fig. 6, the processing apparatus 600 provided in this embodiment includes: 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 a memory to perform the steps performed by the driving data processing method in the above-described embodiment. Reference may be made in particular to the description of the embodiments of the driving data processing method described above.
Alternatively, the memory 603 may be separate or integrated with the processor 604.
When the memory 603 is provided separately, the processing device further comprises a bus for connecting the memory 603 and the processor 604.
The embodiment of the application also provides a computer readable storage medium, wherein computer execution instructions are stored in the computer readable storage medium, and when the processor executes the computer execution instructions, the driving data processing method executed by the processing device is realized.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application.
Claims (8)
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 level of each group of driving operation data according to a first mapping table, wherein the first mapping table is used for representing a mapping relation between the driving operation data and the severity of driving behaviors in the first driving scene;
processing the degree level 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 the first driving scene;
the determining the degree level of each set of the driving operation data according to the first mapping table includes:
determining the degree grade of each operation data in each group of driving operation data, 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;
the acquiring at least one set of driving operation data of the first user in the first driving scene comprises:
determining a current driving scene of the vehicle according to a second mapping table stored locally, wherein the second mapping table represents the mapping relation among road condition data, driving data and driving scenes; collecting driving operation data of the first user after obtaining the current driving scene;
the method further comprises the steps of:
acquiring a second driving scene where a second user is currently located;
predicting a second driving behavior mode of the second user in the second driving scene according to the first mode database;
determining whether the second driving behavior pattern matches a standard behavior pattern in the second driving scenario;
and if the driving behaviors of the second user are not matched, generating prompt information for prompting the driving behaviors of the second user.
2. The method of claim 1, further comprising, prior to said obtaining at least one set of driving maneuver data for the user in the first driving scenario:
acquiring first road condition data at the k+1th moment and first driving data of the vehicle at the k 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 combination of brake operation data, accelerator operation data, steering wheel operation data, gear operation data, lamplight operation data and whistle operation data.
4. A 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;
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 the standard behavior modes;
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.
5. A driving data processing apparatus, characterized by comprising:
the acquisition module is used for acquiring at least one group of driving operation data of the first user in the first driving scene;
the determining module is used for determining the degree level of each driving operation data according to a first mapping table, wherein the first mapping table is used for representing a mapping relation between the driving operation data and the intensity of driving behaviors in the first driving scene;
the processing module is used for processing the degree level 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 the first driving scene;
the determining module is specifically configured to: determining the degree grade of each operation data in each group of driving operation data, 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;
the acquisition module is specifically configured to: determining a current driving scene of the vehicle according to a second mapping table stored locally, wherein the second mapping table represents the mapping relation among road condition data, driving data and driving scenes; collecting driving operation data of the first user after obtaining the current driving scene;
the apparatus further comprises: a prediction module and a prompt module;
the acquisition module is also 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 the second user in the second driving scene according to the first mode database;
the determining 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 prompting information is not matched with the driving behavior of the second user.
6. The apparatus of claim 5, wherein the apparatus further comprises: a statistics module;
the acquisition 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 statistics 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 the 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 scenario meets a preset condition.
7. 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 execute the driving data processing method according to any one of claims 1 to 4 when the program is executed.
8. A computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform the driving data method according to any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010182345.9A CN111367968B (en) | 2020-03-16 | 2020-03-16 | Driving data processing method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010182345.9A CN111367968B (en) | 2020-03-16 | 2020-03-16 | Driving data processing method, device, equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111367968A CN111367968A (en) | 2020-07-03 |
| CN111367968B true CN111367968B (en) | 2023-09-05 |
Family
ID=71208835
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010182345.9A Active CN111367968B (en) | 2020-03-16 | 2020-03-16 | Driving data processing method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111367968B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112329657B (en) * | 2020-11-10 | 2022-07-01 | 易显智能科技有限责任公司 | Method and related device for sensing upper body movement of driver |
| CN112269827B (en) * | 2020-11-17 | 2022-09-20 | 苏州智加科技有限公司 | Data processing method and device, computer equipment and computer readable storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004185504A (en) * | 2002-12-05 | 2004-07-02 | Denso Corp | Drive assist system |
| WO2019047651A1 (en) * | 2017-09-05 | 2019-03-14 | 百度在线网络技术（北京）有限公司 | Driving behavior prediction method and device, and unmanned vehicle |
| CN110264825A (en) * | 2019-07-31 | 2019-09-20 | 交通运输部公路科学研究所 | A kind of drive simulation safe evaluation method, apparatus and system |
-
2020
- 2020-03-16 CN CN202010182345.9A patent/CN111367968B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004185504A (en) * | 2002-12-05 | 2004-07-02 | Denso Corp | Drive assist system |
| WO2019047651A1 (en) * | 2017-09-05 | 2019-03-14 | 百度在线网络技术（北京）有限公司 | Driving behavior prediction method and device, and unmanned vehicle |
| CN110264825A (en) * | 2019-07-31 | 2019-09-20 | 交通运输部公路科学研究所 | A kind of drive simulation safe evaluation method, apparatus and system |
Non-Patent Citations (1)
| Title |
|---|
| 钮建伟 ; 张雪梅 ; 孙一品 ; 秦华 ; .险情中驾驶人接管自动驾驶车辆的驾驶行为研究.中国公路学报.2018,(06),全文. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111367968A (en) | 2020-07-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108022450B (en) | Auxiliary driving method based on cellular network and traffic control unit | |
| US9052997B2 (en) | Vehicle behavior prediction apparatus and vehicle behavior prediction method | |
| CN110562258B (en) | Method for vehicle automatic lane change decision, vehicle-mounted equipment and storage medium | |
| CN102390320B (en) | Vehicle anti-collision early warning system based on vehicle-mounted sensing network | |
| US11161497B2 (en) | Hybrid vehicle and method of controlling mode transition | |
| CN111367968B (en) | Driving data processing method, device, equipment and storage medium | |
| US10755565B2 (en) | Prioritized vehicle messaging | |
| CN111785023A (en) | Vehicle collision risk early warning method and system | |
| CN109910880B (en) | Vehicle behavior planning method and device, storage medium and terminal equipment | |
| JP4867479B2 (en) | Vehicle deceleration determination system, signal control device, in-vehicle device, signal control method, vehicle deceleration determination method, and computer program | |
| CN112734242A (en) | Method and device for analyzing availability of vehicle running track data, storage medium and terminal | |
| CN110733507A (en) | Lane changing and road isolating method, device, equipment and storage medium | |
| CN116434523A (en) | Vehicle active safety control method and device based on constraint degree in information perception scene | |
| CN113442935A (en) | Method and system for judging poor driving behavior of commercial vehicle | |
| CN113460087A (en) | Automatic driving graded takeover interaction method and device and readable storage medium | |
| CN115731695A (en) | Scene security level determination method, device, equipment and storage medium | |
| CN113868875B (en) | Method, device and equipment for automatically generating test scene and storage medium | |
| CN112285717A (en) | Method for controlling vehicle-mounted radar signal and electronic equipment | |
| EP3905220A1 (en) | Accident index calculation device, information providing device, content selection device, insurance premium setting device, accident index calculation method, and program | |
| CN111524389A (en) | Vehicle driving method and device | |
| CN113903169B (en) | Traffic optimization method and device, electronic equipment and storage medium | |
| CN115966100B (en) | Driving safety control method and system | |
| CN111443621B (en) | Model generation method, model generation device and electronic equipment | |
| CN216002550U (en) | Automatic driving graded takeover interaction system | |
| CN113836790B (en) | Method and device for evaluating intelligent grade of electric bicycle and computer equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |