CN117284302A - User-specific driving mode generation method, system, vehicle, electronic equipment and storage medium - Google Patents

User-specific driving mode generation method, system, vehicle, electronic equipment and storage medium Download PDF

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Publication number
CN117284302A
CN117284302A CN202210678310.3A CN202210678310A CN117284302A CN 117284302 A CN117284302 A CN 117284302A CN 202210678310 A CN202210678310 A CN 202210678310A CN 117284302 A CN117284302 A CN 117284302A
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China
Prior art keywords
user
driving
driving mode
data
vehicle
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CN202210678310.3A
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Chinese (zh)
Inventor
陈仪
翟钧
贺小栩
彭政瑜
李晓弘
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Chongqing Changan New Energy Automobile Technology Co Ltd
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Chongqing Changan New Energy Automobile Technology Co Ltd
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Priority to CN202210678310.3A priority Critical patent/CN117284302A/en
Publication of CN117284302A publication Critical patent/CN117284302A/en
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W40/09—Driving style or behaviour
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/10—Accelerator pedal position
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/12—Brake pedal position
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/18—Steering angle
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/30—Driving style

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Mathematical Physics (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention provides a method, a system, a vehicle, electronic equipment and a storage medium for generating a user-specific driving mode, wherein the method comprises the steps of burying user driving data; user driving data analysis: analyzing user driving behavior data according to the data embedded point content at the cloud end to generate a user portrait under the corresponding content; dedicated driving mode parameter setting: and according to the analysis of the driving behaviors of the user, different driving styles are corresponding to different driving mode parameters, the special driving mode parameter value of the user is set, and the special driving mode suitable for the user is generated. Pushing the exclusive driving mode. The method and the device can generate the special driving mode suitable for the user and recommend the special driving mode suitable for the driving habit of the user to the user, so that the driving experience of the user is improved, and the customization level of the vehicle is improved.

Description

User-specific driving mode generation method, system, vehicle, electronic equipment and storage medium
Technical Field
The application belongs to the technical field of automobile control, and particularly relates to a customization technology of a vehicle driving mode.
Background
With the rapid development of automobile technology, attention is paid to the driving experience of users, and the requirement for customization is also a direction of future efforts.
The driving mode of the current vehicle is basically limited to a fixed mode set at the factory of the host factory, such as a normal mode (normal mode, also default mode), a SPORT mode (SPORT mode), and an eco mode (economy mode), and in addition, the current mainstream has a vehicle-end custom driving mode function added. The selection of the driving mode is completed depending on the user's selection. According to data analysis, the frequency of the driving mode selection of the user is found to be low, and the user can not select and set the driving mode after leaving the factory, so that the default driving mode is always used. Although the default driving mode has universality, the default driving mode cannot adapt to personalized driving requirements of different users in different driving scenes, and because driving behavior habits of different users are different, the conventional fixed driving modes are difficult to meet driving preferences of different users, so that the users feel that the vehicle cannot drive according to own expectations, and the driving experience of the users in different driving environments is influenced.
Therefore, how to select a driving mode suitable for the user through the driving behavior analysis of the user, so as to improve the driving experience of the user is a problem to be solved at present.
Disclosure of Invention
The present application aims to solve the above-mentioned problems existing in the prior art, and in a first aspect, a method for generating a user-specific driving mode is provided, which generates a specific driving mode suitable for a user based on analysis of driving behavior of the user, so as to select and recommend the driving mode suitable for the user in different scenes, and improve driving experience of the user.
In another aspect, the present application further provides a vehicle, a system, an electronic device, and a storage medium for executing the above method.
The application is realized by the following technical scheme:
in a first aspect, the present application provides a method for generating a user-specific driving pattern, including:
step 1, a user driving data buries points, and a cloud big data platform collects user driving behavior data of each automobile.
Step 2, user driving data analysis: analyzing user driving behavior data according to the data embedded point content at the cloud end to generate a user portrait under the corresponding content; the user representation at least comprises acceleration style, braking style, steering style and skid energy recovery meeting conditions.
Step 3, dedicated driving mode parameter setting: and according to the analysis of the driving behaviors of the user, different driving styles are corresponding to different driving mode parameters, the special driving mode parameter value of the user is set, and the special driving mode suitable for the user is generated.
In an embodiment of the present application, the above method further includes:
step 4, pushing a special driving mode: when the driving range of the user reaches the set mileage value, the exclusive driving mode parameters analyzed in the mileage value range are pushed to the vehicle for the user to select.
In an embodiment of the present application, the driving behavior data in step 1 includes behavior data of operating an accelerator pedal, a brake pedal, and a steering wheel by a user, trip mileage, and vehicle speed data.
In an embodiment of the present application, the accelerator pedal driving behavior data includes: accelerator pedal opening and trigger time. The brake pedal driving behavior data includes: brake pedal development and trigger time. The steering wheel steering behavior data includes: steering wheel angle degree, steering wheel angle rate, and trigger time. The trip mileage includes: total mileage of vehicle.
In one embodiment of the present application, the user driving behavior data analysis includes: and calculating the times of the accelerator pedal opening being more than A% and the average speed of the user for stepping on the accelerator pedal under the times according to the accelerator pedal opening data of each Xkm, and classifying the acceleration styles of the user. Acceleration styles are categorized into at least two categories: mild and aggressive classes.
In one embodiment of the present application, the user driving behavior data analysis includes: according to the steering wheel steering degree and steering wheel steering rate of each Xkm, calculating the average steering rate and the number of times of turning the steering wheel under different vehicle speeds, and classifying the steering style. User steering styles are categorized into at least three categories: mild class, standard class, aggressive class.
In one embodiment of the present application, the user driving behavior data analysis includes: and calculating the number of times that the opening of the brake pedal is larger than B% according to the opening data of the brake pedal of each Xkm, and the average speed of stepping on the brake pedal of a user under the number of times. User braking styles are categorized into at least two categories: mild and aggressive classes.
In one embodiment of the present application, the user driving behavior data analysis includes: screening out the number of times of stepping on the brake pedal and stepping on the accelerator pedal in the user 1s when the accelerator pedal is switched from more than 0 to 0 according to the brake pedal opening data and the accelerator pedal opening data of each Xkm, and defining the recovery requirement meeting the sliding capacity according to the number of times of stepping on the brake pedal by the user.
In an embodiment of the present application, the defined sliding capacity recovery requirement satisfies the following specific situations: defining that under the condition that an accelerator pedal is switched from more than 0 to 0, the number of times that a user presses a brake pedal is more than C% of the number of times that the user presses the brake pedal in Xkm, the sliding capacity recovery requirement of the user is not met, and the rest conditions are met; the number of times that the user presses the accelerator pedal is larger than C of times that the user presses the accelerator pedal in Xkm, the sliding energy recovery intensity of the user is over-winning, and the rest conditions are satisfied.
In a second aspect, the present application further provides a user-specific driving pattern generation system, which at least includes the following unit modules:
and the user driving data embedded point module is used for acquiring user driving behavior data of each automobile through a cloud big data platform.
The user driving data analysis module: the method comprises the steps of analyzing driving behavior data of a user according to data embedded point content, and generating a user portrait under corresponding content;
dedicated driving mode parameter setting module: and the system is used for corresponding different driving styles to different driving mode parameters according to the driving behavior analysis of the user, setting the special driving mode parameter value of the user and generating a special driving mode suitable for the user.
In an embodiment of the present application, the above system further includes the following unit modules:
and a dedicated driving mode recommendation module: when the driving range of the user reaches the set mileage value, the exclusive driving mode parameters analyzed in the mileage value range are pushed to the vehicle for the user to select.
In a third aspect, the present application further provides an electronic device, including: one or more processors; and storage means for storing one or more programs which, when executed by the one or more processors, cause the electronic device to implement the method described above.
In a fourth aspect, the present application also provides a vehicle comprising the electronic device described above, preferably a new energy vehicle.
In a fifth aspect, the present application also provides a computer-readable storage medium having stored thereon computer-readable instructions which, when executed by a processor of a computer, cause the computer to perform the method described above.
According to the technical scheme, based on the operation of the brake pedal, the accelerator pedal and the steering wheel by the user, the driving style of the corresponding user is generated by utilizing big data analysis, different driving styles correspond to different driving mode parameters, the special driving mode suitable for the user is generated and recommended to the user, the special driving mode suitable for the driving habit of the automobile user can be selected, the driving experience of the user is improved, and the customization level of the vehicle is improved.
It should be understood that the description of this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.
Drawings
FIG. 1 is a diagram of a proprietary driving pattern demand framework in one embodiment of the present application;
FIG. 2 is a flow chart of the number of times corresponding to the accelerator pedal opening interval in each acceleration mode according to an embodiment of the present disclosure;
FIG. 3 is a flowchart of pedal average rate calculation in one embodiment of the present application;
FIG. 4 is a flow chart of calculating the number of steering wheel turns corresponding to each vehicle speed interval in an embodiment of the present application;
FIG. 5 is a flowchart of calculating the average rotation angle change rate according to an embodiment of the present application;
FIG. 6 is a flow chart of a calculation of the number of times the brake pedal opening is greater than 18% in an embodiment of the present application;
FIG. 7 is a flow chart of a brake pedal depression average rate calculation in an embodiment of the present application;
FIG. 8 is a front end interface display diagram in accordance with one embodiment of the present application;
the drawings described above are for better understanding of the present invention, and are not to be construed as limiting the present invention.
Detailed Description
Other advantages and effects of the present application will be readily apparent to those skilled in the art from the present disclosure, as illustrated by the following detailed description of the embodiments. The present application may be embodied or carried out in other specific embodiments, and the details of the present application may be modified or changed from various points of view and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that, the illustrations provided in the following embodiments merely illustrate the basic concepts of the application by way of illustration, and only the components related to the application are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complex.
The current driving mode of the vehicle is mainly set by a host factory, and the content of the self-defined driving mode is added for the user to select. According to data analysis, the frequency of the driving mode selection of the user is found to be low, and the driving mode is not set after the user leaves the factory, so that the factory leaving mode of the host machine is maintained. The current driving pattern scheme cannot specify the specific driving pattern requirements of the user and what driving pattern is appropriate for the driving style of the user.
Based on the problems existing in the prior art, the application provides a special driving mode based on user driving behavior data analysis to solve the problems.
An exclusive driving mode demand framework in an embodiment of the present application is shown in fig. 1, and takes a new energy automobile as an example, and includes data source, data calculation, pushing and front end interface display.
Data source: the system is user driving behavior data of each new energy automobile collected by a cloud big data platform.
Data calculation and pushing: and at the cloud, performing off-line calculation and analysis on the collected driving behavior data of the user to generate driving styles corresponding to the user, wherein different driving styles correspond to different driving mode parameters, generate a special driving mode suitable for the user, push the special driving mode to the vehicle and recommend the special driving mode to the user.
Front end interface display: when the vehicle reaches a running distance of 5000km, pushing a user-specific driving mode pushing report at the vehicle machine end, wherein the mode is shown in fig. 8, generating a specific driving mode setting item, and simultaneously recording the driving mode.
An embodiment is provided to illustrate a method for generating a dedicated driving mode based on user driving behavior data analysis, where a vehicle applied by the method may have a conventional basic driving mode, and the basic driving mode is typically an economy mode, a comfort mode, a SPORT mode, and a custom mode.
In one embodiment, the custom mode parameters are as follows:
acceleration mode: standard/express
Steering assist mode: comfort/standard/exercise
Brake pedal assist mode: comfort/exercise
Grade of coasting energy recovery: class 1-100 (precision 1).
In an embodiment, the method comprises the steps of:
step 1, a user driving data buries points, a cloud big data platform collects user driving behavior data of each automobile, and the automobile can record data of accelerator pedal driving behavior, brake pedal driving behavior, steering wheel steering behavior, travel mileage, speed and current exclusive driving mode parameters of the user.
And 2, analyzing the driving behaviors of the user, and analyzing the driving behaviors of the user according to the content of the data embedded points at the cloud end to generate user figures under the corresponding content, wherein other conditions comprise acceleration style, braking style, steering style and sliding energy recovery meeting conditions. The acquired driving behavior data of the user is subjected to offline calculation and analysis, and the driving style of the corresponding user is generated.
And 3, setting the exclusive driving mode parameter, namely setting the exclusive driving mode parameter value of the user according to the conclusion of the driving behavior analysis of the user, namely, different driving styles correspond to different driving mode parameters.
The following describes in detail the embodiments of the steps:
step 1, in one embodiment, the user driving data embedding point of step 1 is specifically as follows:
for example, the following table is a vehicle-end data embedded point signal
Sequence number Signal name (English) Signal name (Chinese) Remarks
1 vin Vehicle unique number
2 VcuAccrPedlPosnGb Accelerator pedal travel
3 EpsSasSteerAg Steering angle of steering wheel
4 EpsSteerAgRate Steering wheel angle rate
5 IBBrkPedlTrvlAct Actual brake pedal travel
6 VcuEnyRecyclMod Energy recovery mode
The vehicle records data of the accelerator pedal driving behavior, the brake pedal driving behavior, the steering wheel steering behavior, the trip mileage and the vehicle speed of the user and the current exclusive driving mode parameters of the user.
The accelerator pedal driving behavior includes: throttle pedal opening and trigger time
The brake pedal driving behavior includes: brake pedal development and trigger time
Steering behavior of the steering wheel: steering wheel angle, steering wheel angle rate and trigger time
Trip mileage includes: total mileage of vehicle.
In the step 2, in an embodiment, the user driving behavior analysis in the step 2 mainly analyzes the user driving behavior according to the content of the data embedded points to generate the user portrait under the corresponding content, and other conditions including acceleration style, braking style, steering style and skid energy recovery are satisfied.
The driving behavior of the user is analyzed according to the content of the data embedded points, and the specific data analysis process is as follows:
user acceleration style:
according to the accelerator opening data of each Xkm (which is a set value, the aim is to accurately and efficiently obtain the driving habit data of the user, for example, the accelerator opening data can be preferably set to 5000 km), the times of the accelerator opening being more than A% and the average speed of the user when the accelerator is stepped on under the times are calculated. Classifying the acceleration styles of users into two types by using a big data clustering algorithm: mild and aggressive classes.
Referring specifically to fig. 2, the number calculation rule corresponding to the accelerator pedal opening interval in each acceleration mode in this step is that is, the specific algorithm of the number of acceleration modes:
firstly, judging whether the type of the acceleration mode set by a user is standard or quick, selecting driving behaviors of which the opening of an accelerator pedal of the user is more than 80% when the type of the acceleration mode is standard, and counting the times of the driving behaviors. And when the acceleration mode is fast, selecting driving behaviors of which the opening of the accelerator pedal of the user is more than 60%, and counting the times of the driving behaviors.
Referring specifically to fig. 3, the pedal average rate calculation method in this step is:
firstly judging whether the opening degree of an accelerator pedal is continuously increased from small to large, selecting a user driving behavior that the change value of the opening degree of the accelerator pedal (the maximum opening degree of the accelerator pedal in the continuous change process-the minimum opening degree of the accelerator pedal) is more than 10%, and calculating the average speed number of times of calculating the average speed value of the accelerator pedal by the sum of the average speed value of the accelerator pedal and the average speed value of the accelerator pedal through the change value of the opening degree of the accelerator pedal and the process time = the average speed value of the accelerator pedal.
It can be seen from the above calculation that the pedal opening with the accelerator pedal opening varying by more than 10% is incorporated into the calculation of the average rate, and the selection principle is to ensure that the pedal depression of the user is the driving requirement and not the erroneous depression.
(2) User turns to style:
and calculating the average steering speed and the times of turning the steering wheel under different speeds of the user according to the steering degree and the steering speed of the steering wheel of the user per 5000 km. Classifying the user steering styles into three types by using a big data clustering algorithm: mild class, standard class, aggressive class.
Referring to fig. 4, in this step, a method for calculating the number of steering wheel turns corresponding to each vehicle speed section is shown;
according to the uploaded vehicle speed signal and steering wheel angle degree, selecting the times of different angles of the steering wheel under different vehicle speeds of a user, and defining the times as the times of intense steering, wherein the times are specifically as follows:
counting the times of 0<V which is less than or equal to 20km/h and the steering wheel rotation angle which is more than 360 degrees in the driving process of a user, and marking as N1;
counting the times that 20km/h < V < 30km/h, steering wheel rotation angle is more than 180 degrees in the driving process of a user, and marking as N2;
counting the times that 30km/h < V < 60km/h, steering wheel rotation angle is more than 90 degrees in the driving process of a user, and marking as N3;
counting the times that 60km/h < V < 80km/h, steering wheel rotation angle is more than 60 degrees in the driving process of a user, and recording as N4;
counting the times that 80km/h < V and the steering wheel angle is more than 30 degrees in the driving process of a user, and marking as N5;
finally, the number of times of intense steering N=N1+N2+N3+N4+N5 in the driving process is counted.
Referring to fig. 5, the average rotation angle change rate calculation flow in this step is shown;
firstly judging whether the steering wheel opening continuously changes in one direction, selecting the driving behavior of a user with the steering wheel angle opening change value (the maximum steering angle-the minimum steering angle in the continuous change process) of more than 30% in the process, and calculating the sum of all calculated average angle change rate values of each time by the steering wheel angle opening change value/process time = the average angle change rate of the user.
(3) User braking style: based on the brake pedal opening data for each 5000km of the user, the number of times the brake pedal opening is greater than B% (e.g., 18%) and the average rate at which the user's brake pedal is depressed are calculated. Classifying the braking styles of users into two types by using a big data clustering algorithm: mild and aggressive classes.
Referring to fig. 6, a flowchart of calculation of the number of times the brake pedal opening is greater than 18% (this ratio is the initial value obtained by checking the present data). In particular, the number of times of user driving behaviors that the vehicle speed is more than 5km/h and the travel of a user brake pedal is more than 18% is counted.
Referring to fig. 7, a brake pedal depression average rate calculation flowchart is shown:
firstly judging whether the vehicle speed is greater than 5km/h, judging whether the opening degree of a brake pedal is continuously greater than 5km/h from a small side, selecting the driving behavior of a user with the variation value of the opening degree of the brake pedal (the maximum opening degree of the brake pedal in the continuous variation process-the minimum opening degree of the brake pedal) greater than 3%, and calculating the average speed number of times of the stepping of the brake pedal=the average speed of the stepping of the brake pedal of the user by the sum of the average speed of the stepping of the brake pedal which is calculated by the variation value of the opening degree of the brake pedal which is calculated by the process time=the average speed of the stepping of the brake pedal.
(4) Coasting energy recovery requirements:
and screening the number of times of pressing the brake pedal and the accelerator pedal in 1s when the accelerator pedal is switched from more than 0 to 0 according to the brake pedal opening data and the accelerator pedal opening data of each 5000km of the user. A user who has a number of times of pressing the brake pedal higher than 20% in this case is defined, and the user's slip ability recovery requirement is not satisfied, and the remaining cases are satisfied. When the glide capacity recovery is not satisfied, the capacity recovery level is adjusted upward by 10 levels. The current coasting energy recovery value is maintained when satisfied.
Step 3, in an embodiment of the present application, the specific driving mode parameter setting in step 3 is to set the specific driving mode parameter value according to the conclusion of the user driving behavior analysis, in the following manner
The user acceleration style is aggressive, and the acceleration mode parameter in the exclusive driving mode is set to be fast.
The acceleration style of the user is mild, and the acceleration mode parameter in the exclusive driving mode is set as a standard.
The steering style of the user is mild, and the steering power assisting mode in the exclusive driving mode is set to be comfortable.
The steering style of the user is standard, and the steering power assisting mode in the exclusive driving mode is set as standard.
The steering style of the user is aggressive, and the steering power assisting mode in the exclusive driving mode is set to be movement.
The user braking style is mild, and the brake pedal boosting mode in the exclusive driving mode is set to be comfortable.
The user braking style is aggressive, and the brake pedal boosting mode in the exclusive driving mode is set to be movement.
And when the recovery level of the sliding capacity of the user is not satisfied, setting the sliding energy of the user in the exclusive driving mode. The harvest level is adjusted up a certain number of steps, for example 10 steps (highest adjustment level 100 steps), above the user's original set level.
When the user sliding capacity recovery level is satisfied, the user sliding capacity recovery level in the exclusive driving mode is set to be consistent with the original user setting level.
When the user's glide energy recovery level is over, the user's glide ability recovery level in the exclusive driving mode is set to be adjusted downward by a certain number of steps, for example, 10 steps (lowest adjustment level 0 step) above the user's original setting level.
In a further embodiment of the present application, step 4, dedicated driving mode pushing may be further included.
In this step, when the driving range of the user reaches 5000km, the specific driving mode parameters analyzed in the 5000km range can be pushed to the vehicle for the user to select.
In an embodiment of the application, a system for generating a user-specific driving pattern is provided, the system at least comprising the following unit modules:
the user driving data embedded point module: and collecting the driving behavior data of the users of the new energy automobiles by a cloud big data platform. The vehicle records data of the current exclusive driving mode parameters of the user, such as accelerator pedal driving behavior, brake pedal driving behavior, steering behavior of a steering wheel, travel mileage and speed, and uploads the data to a cloud big data platform.
The user driving data analysis module: and at the cloud, performing off-line calculation and analysis on the collected user driving behavior data to generate a driving style of a corresponding user, namely analyzing the user driving behavior data according to the data embedded point content to generate a user portrait under the corresponding content.
Dedicated driving mode parameter setting module: and the system is used for corresponding different driving styles to different driving mode parameters according to the driving behavior analysis of the user, setting the special driving mode parameter value of the user and generating a special driving mode suitable for the user.
And a dedicated driving mode recommendation module: when the driving range of the user reaches the set mileage value, the exclusive driving mode parameters analyzed in the mileage value range are pushed to the vehicle for the user to select.
In an embodiment of the application, there is also provided an electronic device including: one or more processors; and storage means for storing one or more programs which, when executed by the one or more processors, cause the electronic device to implement the method described above.
In an embodiment of the application, there is also provided a vehicle comprising the above-described electronic device, the vehicle preferably being a new energy automobile.
In an embodiment of the application, there is also provided a computer readable storage medium having stored thereon computer readable instructions which, when executed by a processor of a computer, cause the computer to perform the method described above.
The system of the above embodiments may be in the form of software and/or hardware. The system may be provided in a server.
The electronic device provided in the above embodiment may be a vehicle machine or other vehicle-mounted device in a vehicle. The electronic device may also be a server.
The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the application described and/or claimed in this application.
In some embodiments, part or all of the computer program may be loaded and/or installed onto the device via the ROM and/or the communication unit. The method or one or more steps described above may be performed when the computer program is loaded into RAM and executed by a computing unit.
It will be apparent to those skilled in the art that the various forms of flow shown above may be used to reorder, add or delete steps. For example, the steps described in the present application may be executed in parallel, sequentially, or in a different order, so long as the desired results of the technical solutions disclosed in the present application can be achieved, and the present application is not limited herein.

Claims (17)

1. A method of generating a user-specific driving pattern, comprising:
step 1, a user driving data burial point, and a cloud big data platform collects user driving behavior data of each automobile;
step 2, data analysis: analyzing user driving behavior data according to the data embedded point content at the cloud end to generate user portraits under the corresponding content, wherein the user portraits comprise acceleration style, braking style, steering style and skid energy recovery meeting conditions;
step 3, dedicated driving mode parameter setting: and according to the analysis of the driving behaviors of the user, different driving styles are corresponding to different driving mode parameters, the special driving mode parameter value of the user is set, and the special driving mode suitable for the user is generated.
2. The method according to claim 1, wherein in step 1, the driving behavior data includes behavior data of operating an accelerator pedal, a brake pedal, and a steering wheel, trip mileage, and vehicle speed data.
3. The method for generating a user-specific driving pattern according to claim 2, wherein,
the accelerator pedal driving behavior data includes: throttle pedal opening and triggering time;
the brake pedal driving behavior data includes: development and triggering time of a brake pedal;
the steering wheel steering behavior data includes: steering wheel angle degree, steering wheel angle rate and triggering time;
the trip mileage includes: total mileage of vehicle.
4. A method of generating a user-specific driving pattern according to claim 2 or 3, wherein the user driving behavior data analysis of step 2 comprises: and calculating the times of the accelerator pedal opening being more than A% and the average speed of the user for stepping on the accelerator pedal under the times according to the accelerator pedal opening data of each Xkm, and classifying the acceleration styles of the user.
5. The user-specific driving pattern generation method according to claim 4, wherein the acceleration styles are categorized into at least two categories: mild and aggressive classes.
6. A method of generating a user-specific driving pattern according to claim 2 or 3, wherein the user driving behavior data analysis of step 2 comprises: according to the steering wheel steering degree and steering wheel steering rate of each Xkm, calculating the average steering rate and the number of times of turning the steering wheel under different vehicle speeds, and classifying the steering style.
7. A user-specific driving pattern generation method according to claim 2 or 3, characterized in that the user steering styles are categorized into at least three categories: mild class, standard class, aggressive class.
8. A method of generating a user-specific driving pattern according to claim 2 or 3, wherein the user driving behavior data analysis of step 2 comprises: and calculating the number of times that the opening of the brake pedal is larger than B% according to the opening data of the brake pedal of each Xkm, and the average speed of stepping on the brake pedal of a user under the number of times.
9. The user-specific driving pattern generation method according to claim 9, wherein the user braking styles are categorized into at least two categories: mild and aggressive classes.
10. A method of generating a user-specific driving pattern according to claim 2 or 3, wherein the user driving behavior data analysis of step 2 comprises: screening out the number of times of stepping on the brake pedal and stepping on the accelerator pedal in the user 1s when the accelerator pedal is switched from more than 0 to 0 according to the brake pedal opening data and the accelerator pedal opening data of each Xkm, and defining the recovery requirement meeting the sliding capacity according to the number of times of stepping on the brake pedal by the user.
11. The user-specific driving pattern generation method according to claim 8, wherein the defined glide capability recovery requirement satisfies the condition, specifically:
defining that under the condition that an accelerator pedal is switched from more than 0 to 0, the number of times that a user presses a brake pedal is more than C% of the number of times that the user presses the brake pedal in Xkm, the sliding capacity recovery requirement of the user is not met, and the rest conditions are met; the number of times that the user presses the accelerator pedal is larger than C of times that the user presses the accelerator pedal in Xkm, the sliding energy recovery intensity of the user is over-winning, and the rest conditions are satisfied.
12. A method of generating a user-specific driving pattern according to claim 2 or 3, wherein in step 3, the specific driving pattern is a custom pattern of the vehicle, and the pattern parameters include:
acceleration mode: standard/fast;
steering assist mode: comfort/standard/exercise;
brake pedal assist mode: comfort/exercise;
grade of coasting energy recovery: 1-100 grades.
13. A method of generating a user-specific driving pattern according to any one of claims 1-3, further comprising step 4, specific driving pattern pushing: when the driving range of the user reaches the set mileage value, the exclusive driving mode parameters analyzed in the mileage value range are pushed to the vehicle for the user to select.
14. A user-specific driving pattern generation system, comprising:
the user driving data embedded point module: collecting user driving behavior data of each automobile by a cloud big data platform;
the user driving data analysis module: the method comprises the steps of analyzing driving behavior data of a user according to data embedded point content, and generating a user portrait under corresponding content; the user figures comprise acceleration style, braking style, steering style and the condition of recovering the sliding energy;
dedicated driving mode parameter setting module: and the system is used for corresponding different driving styles to different driving mode parameters according to the driving behavior analysis of the user, setting the special driving mode parameter value of the user and generating a special driving mode suitable for the user.
15. An electronic device, comprising:
one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the electronic device to implement the method of any of claims 1-13.
16. A vehicle comprising the electronic device according to claim 15, wherein the vehicle is a new energy vehicle having an autopold function or an electronic hand brake function and an electric brake function.
17. A computer readable storage medium having stored thereon computer readable instructions which, when executed by a processor of a computer, cause the computer to perform the method of any of claims 1 to 13.
CN202210678310.3A 2022-06-16 2022-06-16 User-specific driving mode generation method, system, vehicle, electronic equipment and storage medium Pending CN117284302A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117708438A (en) * 2024-02-06 2024-03-15 浙江大学高端装备研究院 Motorcycle driving mode recommendation method and system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117708438A (en) * 2024-02-06 2024-03-15 浙江大学高端装备研究院 Motorcycle driving mode recommendation method and system

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