CN112829758A - Automobile driving style self-learning method, device, equipment and storage medium - Google Patents
Automobile driving style self-learning method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN112829758A CN112829758A CN202110024370.9A CN202110024370A CN112829758A CN 112829758 A CN112829758 A CN 112829758A CN 202110024370 A CN202110024370 A CN 202110024370A CN 112829758 A CN112829758 A CN 112829758A
- Authority
- CN
- China
- Prior art keywords
- driving
- vehicle
- training
- driving style
- characteristic parameters
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 61
- 238000012549 training Methods 0.000 claims description 87
- 230000008569 process Effects 0.000 claims description 36
- 230000001133 acceleration Effects 0.000 claims description 26
- 238000004590 computer program Methods 0.000 claims description 10
- 230000005540 biological transmission Effects 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 9
- 230000008859 change Effects 0.000 description 6
- 230000006870 function Effects 0.000 description 3
- 230000006641 stabilisation Effects 0.000 description 3
- 238000011105 stabilization Methods 0.000 description 3
- 239000000428 dust Substances 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000011664 signaling Effects 0.000 description 1
Images
Classifications
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Mathematical Physics (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
The invention discloses a method, a device, equipment and a storage medium for self-learning of automobile driving style in automobile control, wherein the method comprises the following steps of switching a vehicle driving state into a general mode; acquiring driver operation information and vehicle running information; extracting operation characteristic parameters and driving characteristic parameters from the driver operation information and the vehicle driving information respectively; searching and classifying in a driving style classifier according to the operation characteristic parameters and the driving characteristic parameters to obtain a classification result; identifying and matching the driving style of the driver according to the classification result to obtain a driving style matching result, and storing the driving style matching result; according to the driving style matching result, the driving state of the vehicle is changed, and the method and the device can enable a driver to select the driving style of the driver by self so as to adapt to different driver styles, improve driving comfort, improve vehicle using experience of users and increase the technological sense of the vehicle.
Description
Technical Field
The invention relates to the field of automobile control, in particular to a self-learning method, a device, equipment and a storage medium for automobile driving style.
Background
At present, automobiles gradually become vehicles which can not be separated by people, most people need to be on the vehicles, the driving styles (power output characteristic curves) of the automobiles at present basically have three modes, namely a normal mode, a sport mode and an eco mode, after the automobiles leave factories, automobile manufacturers generally only provide the three driving modes, but the three modes certainly cannot meet the driving preferences of thousands of automobile consumers, the driving style of each consumer is definitely different and cannot meet drivers with different driving styles, the automobile manufacturers carry out driving detection before mass production of automobile models, and only carry out subjective evaluation by drivers with rich driving experience, the subjective feeling of each person is not completely consistent, the final evaluation of the driving performance is carried out by scoring, and after the score meets the requirements of the automobile manufacturers, the vehicles can be produced in quantity, so that the driving style is locked after leaving the factory and cannot be modified, the user cannot set the driving style by himself, the user has no active option or has small option, diversified user requirements and complex road conditions cannot be met, independent learning can not be carried out according to the driving habits of different drivers, and customized services and enjoyment are provided for the user.
Disclosure of Invention
The invention aims to provide a self-learning method, a device, equipment and a storage medium which are more comfortable to drive and meet different user requirements.
In order to achieve the above purposes, the technical scheme adopted by the invention is as follows:
the invention provides a self-learning method for the driving style of an automobile, which comprises the following steps:
switching the vehicle running state to a general mode;
acquiring driver operation information and vehicle running information;
extracting operation characteristic parameters and driving characteristic parameters from the driver operation information and the vehicle driving information respectively according to the driver operation information and the vehicle driving information;
searching and classifying in a driving style classifier according to the operation characteristic parameters and the driving characteristic parameters to obtain a classification result;
identifying and matching the driving style of the driver according to the classification result to obtain a driving style matching result, and storing the driving style matching result;
and changing the vehicle running state according to the driving style matching result.
Optionally, the driver operation information is acquired through a vehicle CAN bus, and the vehicle running information is acquired through a sensor mounted on the vehicle;
the operating characteristic parameters include, but are not limited to, a turn signal status, a steering wheel angle, a steering wheel angular acceleration, an accelerator pedal travel, a brake pedal travel, a clutch pedal travel, and a transmission gear, and the driving characteristic parameters include, but are not limited to, a vehicle speed, a position, an acceleration, a yaw rate, a master cylinder pressure, and a speed, a distance, and an acceleration of the vehicle relative to a surrounding vehicle.
Optionally, the following conditions are simultaneously satisfied when the driver operation information and the vehicle running information are acquired:
the vehicle running for not less than the preset time condition (1)
Maximum vehicle speed not less than 100km/h condition (2)
Accelerator pedal travel cover all position conditions 0-100% (3)
The following accelerator pedal travel within the preset time:
the travel of an accelerator pedal is more than or equal to 0 percent and less than 30 percent
The travel of an accelerator pedal is more than or equal to 30 percent and less than 65 percent
Condition (4) that the travel of the accelerator pedal is more than or equal to 65% and less than or equal to 100%
And (5) vehicle starting process, braking process and steering process conditions within preset times.
Optionally, the driving style classifier is generated by:
switching the vehicle running state to a general mode;
acquiring training operation information and training driving information of different trainers within preset time;
extracting training operation characteristic parameters and training driving characteristic parameters from the training operation information and the training driving information respectively according to the training operation information and the training driving information;
marking the training operation characteristic parameters and the training driving characteristic parameters of different trainers to mark the driving style of the corresponding trainer;
and learning and training the training operation characteristic parameters and the training driving characteristic parameters of different trainers to generate a driving style classifier.
Further, the invention also provides a self-learning device for the driving style of the automobile, which comprises:
the learning initialization module is used for switching the vehicle running state into a general mode;
the driving information acquisition module is used for acquiring the operation information of a driver and the vehicle running information;
the learning parameter extraction module is used for extracting operation characteristic parameters and driving characteristic parameters from the driver operation information and the vehicle driving information respectively according to the driver operation information and the vehicle driving information;
the style classification module is used for searching and classifying in the driving style classifier according to the operation characteristic parameters and the driving characteristic parameters to obtain a classification result;
the style matching module is used for identifying and matching the driving style of the driver according to the classification result to obtain a driving style matching result, and storing the driving style matching result;
and the driving state changing module is used for changing the driving state of the vehicle according to the driving style matching result.
Optionally, the driver operation information is acquired through a vehicle CAN bus, and the vehicle driving information is acquired through a sensor mounted on the vehicle.
Optionally, the information acquisition module needs to satisfy the following conditions at the same time:
the vehicle is driven for not less than the preset time condition (6)
The highest vehicle speed is not less than 100km/h (7)
Accelerator pedal travel covers all position conditions 0-100% (8)
The following accelerator pedal travel within the preset time:
the travel of an accelerator pedal is more than or equal to 0 percent and less than 30 percent
The travel of an accelerator pedal is more than or equal to 30 percent and less than 65 percent
Condition (9) that the travel of the accelerator pedal is more than or equal to 65% and less than or equal to 100%
And (10) vehicle starting process, braking process and steering process conditions within preset times.
Optionally, the style classification module includes:
the training initialization unit is used for switching the vehicle running state into a general mode;
the training information acquisition unit is used for acquiring training operation information and training driving information of different trainers within preset time;
a training parameter extracting unit, configured to extract a training operation characteristic parameter and a training driving characteristic parameter from training operation information and training driving information, respectively, according to the training operation information and the training driving information;
the marking unit is used for marking the training operation characteristic parameters and the training driving characteristic parameters of different trainers so as to mark the driving style of the corresponding trainer;
and the generating unit is used for learning and training the training operation characteristic parameters and the training driving characteristic parameters of different trainers to generate the driving style classifier.
Further, the invention also provides a self-learning device for the driving style of the automobile, which comprises:
a processor;
a memory for storing a computer program;
the processor is used for executing the computer program to enable the automobile driving style self-learning device to execute the automobile driving style self-learning method according to any one of the above items.
Further, the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, controls an apparatus in which the storage medium is located to perform any of the above-mentioned self-learning methods for driving style of automobiles.
Compared with the prior art, the invention has the beneficial effects that:
the automobile driving style self-learning method, the device, the equipment and the storage medium can enable a driver to select own driving style by himself, so that the driver can teach own love vehicle by himself and can select the 'power style' of the own love vehicle, so that the vehicle meets the driving style requirements of consumers, the driving style self-learning capability of the driving style of the vehicle is realized, the driving style self-learning method is suitable for different driver styles, the driving comfort is improved, the vehicle using experience of users is improved, and the technological sense of the vehicle is also improved.
Drawings
FIG. 1 is a schematic flow chart of a self-learning method for driving style of a vehicle according to the present invention;
FIG. 2 is a schematic flow chart illustrating the generation of a driving style classifier in the self-learning method for driving styles of automobiles according to the present invention;
FIG. 3 is a block diagram of the self-learning device for driving style of vehicle according to the present invention;
FIG. 4 is a block diagram of the style classification module of the self-learning apparatus for driving style of vehicle according to the present invention;
FIG. 5 is a block diagram of the self-learning device for driving style of vehicle according to the present invention;
FIG. 6 is a block diagram of a computer system in the present invention;
FIG. 7 is a schematic illustration of the self-learning of the present invention;
fig. 8 is a graph of driving style characteristics stored by the driving style classifier/style classification module of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. The following description is presented to disclose the invention so as to enable any person skilled in the art to practice the invention. The preferred embodiments in the following description are given by way of example only, and other obvious variations will occur to those skilled in the art.
In fig. 7, the VCU vehicle controller collects signals related to drivability determination to recommend a driving style of a driver, such as stroke information of an accelerator pedal and a brake pedal, and may establish signal communication with the MCU motor controller, the ESC electronic stability system controller, the BCM vehicle body controller, the EHU vehicle-mounted large screen controller, and the like through the CAN bus; the MCU motor controller is used for receiving a VCU vehicle controller control torque demand signal and controlling the motor to respond to a vehicle torque demand; the ESC electronic stabilization system controller is used for transmitting signals such as vehicle speed, steering wheel turning angle and the like to the VCU vehicle control unit; the BCM vehicle body controller transmits a turning-on and turning-off signal of a steering lamp of a driver to the VCU vehicle control unit; the EHU vehicle-mounted large screen controller is mainly used for confirming the driving requirements of a driver, can be used for starting a program for intelligently learning the driving style of a vehicle, can ensure that a user confirms whether the driving style meets the driving requirements of the current driver, and can store a plurality of self-defined driving style setting signals and the like.
Referring to fig. 1 and 7, the self-learning method includes the following steps:
s100) switching the driving state of the vehicle to a general mode, after the driver selects the driving style self-learning, before the vehicle enters the driving style self-learning, first, forcibly switching the current driving mode of the vehicle to a normal mode (general mode), and then, learning the driving style of the driver. For example. After the driver starts the console, the driver selects to enter a user-defined driving mode, and then the EHU vehicle-mounted large-screen controller sends a user-defined driving mode starting signal to the VCU vehicle controller.
Meanwhile, after the driver selects to enter the 'user-defined driving mode', the VCU vehicle controller sends a 'user-defined driving mode' word to be displayed on the central control console, so that the driver can be reminded conveniently; of course, the driver can also consciously participate in the intelligent learning process of the VCU vehicle controller, for example, the driver wants to start faster and stronger power output, and can greatly step on the accelerator when the vehicle starts, and at the moment, the VCU vehicle controller can record the driving habit and the characteristics of the driver and take the driving habit and the characteristics as the basis for subsequently pushing the driving style.
S200) acquiring the driver operation information and the vehicle running information, specifically, in this embodiment, acquiring the driver operation information through a vehicle CAN bus, and acquiring the vehicle running information through a sensor mounted on the vehicle.
S300) extracting operation characteristic parameters and running characteristic parameters from the driver operation information and the vehicle running information, respectively, according to the driver operation information and the vehicle running information, wherein in the embodiment, the operation characteristic parameters include, but are not limited to, a turn signal state, a steering wheel angle, a steering wheel angular acceleration, an accelerator pedal stroke, a brake pedal stroke, a clutch pedal stroke, a transmission gear position and the like, and the running characteristic parameters include, but are not limited to, a vehicle speed, a position, an acceleration, a yaw angular velocity, a master cylinder pressure, and a speed, a distance and an acceleration of the vehicle relative to a surrounding vehicle and the like.
In the single automobile driving style self-learning process, the VCU vehicle control unit can record the driving habits of the driver in real time, so that the driving style requirements of the driver are inferred, and the driving characteristic data recorded by the VCU vehicle control unit is as follows:
travel of an accelerator pedal: recording the number of times of stepping on the accelerator and counting the interval of the change rate of the accelerator, wherein the interval is mainly used for reflecting the speed of the driver stepping on the accelerator;
vehicle speed, position, acceleration, yaw rate, speed, distance and acceleration of the vehicle relative to the surrounding vehicle: the recording duration of the vehicle speed section can be recorded, and the vehicle speed change and the change rate interval in the driving process of a driver are mainly shown;
the state of the steering lamp: recording which turn lights are turned on and turn-on times of the turn lights, wherein the turn lights are mainly used for assisting in recording and representing the habit of overtaking of a driver;
steering wheel angle, steering wheel angular acceleration: recording the times that the turning angle of the steering wheel is not 0 or the times that the turning angle is larger than a certain value (preset value) and the angular acceleration when the steering wheel is operated, wherein the times and the angular acceleration are mainly used for assisting in recording and representing the habit of overtaking of a driver;
the braking times are as follows: the driving style representation is mainly used for representing whether a driver brakes frequently and whether the braking is rapid or the rapid acceleration and the rapid deceleration are rapid;
when braking, the master cylinder pressure is sent to the VCU vehicle control unit by the ESC electronic stabilization system controller, and the VCU vehicle control unit sequentially judges whether the driver has frequent braking and sudden braking or sudden acceleration and sudden deceleration driving style representation.
Meanwhile, in the present embodiment, acquiring the driver operation information and the vehicle travel information requires that the following conditions be satisfied simultaneously:
the vehicle running for not less than the preset time condition (1)
Maximum vehicle speed not less than 100km/h condition (2)
All positions with the accelerator pedal travel (i.e. accelerator opening) covering 0-100%
Condition (3)
The following accelerator pedal strokes within a preset time, for example, at least 20 minutes are acquired for each of the following accelerator pedal strokes:
the travel of an accelerator pedal is more than or equal to 0 percent and less than 30 percent (namely the opening degree of a small accelerator)
The travel of an accelerator pedal is more than or equal to 30 percent and less than 65 percent (namely the opening degree of the throttle of people)
The travel of the accelerator pedal is more than or equal to 65 percent and less than or equal to 100 percent (namely the throttle opening is large)
Condition (4)
And (5) at least acquiring conditions (5) of the vehicle starting process, the braking process and the steering process within preset times, such as the vehicle starting process, the braking process and the steering process.
The purpose of the condition (1) is to enable a vehicle controller VCU to acquire rich driving habits of a driver and more accurately push a driving style torque characteristic curve, the purpose of the condition (2) is to basically ensure that all vehicle speed sections are learned, and the purpose of the condition (3) is to acquire driving actions of the driver from no stepping on the accelerator to the bottom in the driving process.
S400) searching and classifying in the driving style classifier according to the operation characteristic parameters and the driving characteristic parameters to obtain a classification result.
As shown in fig. 8, the driving style characteristic curves include a linear driving style and a linear power output, for example, a driving characteristic curve a, i.e., the normal mode (general mode), in which the torque and the accelerator opening degree are in a linear relationship; some of the power generated by slightly stepping on the accelerator can be output vigorously, and the starting is very quick, such as a driving characteristic curve A; some accelerator output power at the front half section is very slow, is used for urban traffic jam road conditions, can well control the speed of the vehicle, and is suitable for many urban women. Such as curve G; some of the power is output more strongly under the opening degree of the front half section of the accelerator, so that certain 'riding dust' power requirements are met, the power output is more slowly interrupted by the accelerator, and the power output of the rear half section of the accelerator is more rapidly, so that the power-saving device is suitable for overtaking, such as a driving characteristic curve E.
S500) identifying and matching the driving style of the driver according to the classification result to obtain a driving style matching result, and storing the driving style matching result.
The VCU vehicle control unit records driver operation information and vehicle running information in real time, and after a single automobile driving style self-learning process is finished, the VCU recommends a driving curve according to the driver operation information and the vehicle running information collected in the automobile driving style self-learning process.
The VCU vehicle control unit sends a driving characteristic curve learning recommended value of this time, such as driving style 1, to the EHU vehicle-mounted large screen controller, and after a driver clicks 'save', learning is successful;
when the driver drives the vehicle next time, the driver can select the driving style 1 by clicking the driving style selection key, and the driver can experience the effect of the previous intelligent driving style learning of the vehicle;
in addition, a plurality of intelligently learned driving styles, such as driving style 2, driving style 3 and the like, can be stored in the VCU vehicle control unit, and a user can store and delete the driving style modes learned by the vehicle.
After finding the driving style which is favored by the driver, the driver can save the favorite driving style; therefore, the driving characteristics of the vehicle can be learned successfully, and the unique driving characteristic requirements of the public are met.
S600) changing the vehicle driving state according to the driving style matching result.
Specifically, referring to fig. 2, S400) the driving style classifier is generated by the following steps: s401) switches the vehicle running state to the normal mode.
S402) acquiring training operation information and training driving information of different trainers within preset time;
s403) extracting training operation characteristic parameters and training driving characteristic parameters from the training operation information and the training driving information respectively according to the training operation information and the training driving information;
s404) marking training operation characteristic parameters and training driving characteristic parameters of different trainers to mark corresponding driving styles of the trainers;
s405) learning and training the training operation characteristic parameters and the training driving characteristic parameters of different trainees according to a built-in algorithm to generate a driving style classifier.
Referring to fig. 3 and 7, the self-learning apparatus includes:
the learning initialization module is used for switching the driving state of the vehicle into a general mode, after the driver selects the driving style self-learning, before the vehicle enters the driving style self-learning, the current driving mode of the vehicle is first forcibly converted into a normal mode (the general mode), and the driving style learning of the driver is carried out on the basis. For example. After the driver starts the console, the driver selects to enter a user-defined driving mode, and then the EHU vehicle-mounted large-screen controller sends a user-defined driving mode starting signal to the VCU vehicle controller.
Meanwhile, after the driver selects to enter the 'user-defined driving mode', the VCU vehicle controller sends a 'user-defined driving mode' word to be displayed on the central control console, so that the driver can be reminded conveniently; of course, the driver can also consciously participate in the intelligent learning process of the VCU vehicle controller, for example, the driver wants to start faster and stronger power output, and can greatly step on the accelerator when the vehicle starts, and at the moment, the VCU vehicle controller can record the driving habit and the characteristics of the driver and take the driving habit and the characteristics as the basis for subsequently pushing the driving style.
The learning parameter extraction module is configured to obtain driver operation information and vehicle driving information, and specifically, in this embodiment, the driver operation information is obtained through a vehicle CAN bus, and the vehicle driving information is obtained through a sensor mounted on an automobile.
The learning parameter extraction module is used for extracting operation characteristic parameters and running characteristic parameters from the driver operation information and the vehicle running information respectively according to the driver operation information and the vehicle running information, wherein in the embodiment, the operation characteristic parameters comprise but are not limited to a steering lamp state, a steering wheel angle, steering wheel angular acceleration, accelerator pedal travel, brake pedal travel, clutch pedal travel, transmission gear position and the like, and the running characteristic parameters comprise but are not limited to vehicle speed, position, acceleration, yaw angular velocity, brake master cylinder pressure, speed, distance and acceleration of the vehicle relative to surrounding vehicles and the like.
In the single automobile driving style self-learning process, the VCU vehicle control unit can record the driving habits of the driver in real time, so that the driving style requirements of the driver are inferred, and the driving characteristic data recorded by the VCU vehicle control unit is as follows:
travel of an accelerator pedal: recording the number of times of stepping on the accelerator and counting the interval of the change rate of the accelerator, wherein the interval is mainly used for reflecting the speed of the driver stepping on the accelerator;
vehicle speed, position, acceleration, yaw rate, speed, distance and acceleration of the vehicle relative to the surrounding vehicle: the recording duration of the vehicle speed section can be recorded, and the vehicle speed change and the change rate interval in the driving process of a driver are mainly shown;
the state of the steering lamp: recording which turn lights are turned on and turn-on times of the turn lights, wherein the turn lights are mainly used for assisting in recording and representing the habit of overtaking of a driver;
steering wheel angle, steering wheel angular acceleration: recording the times that the turning angle of the steering wheel is not 0 or the times that the turning angle is larger than a certain value (preset value) and the angular acceleration when the steering wheel is operated, wherein the times and the angular acceleration are mainly used for assisting in recording and representing the habit of overtaking of a driver;
the braking times are as follows: the driving style representation is mainly used for representing whether a driver brakes frequently and whether the braking is rapid or the rapid acceleration and the rapid deceleration are rapid;
when braking, the master cylinder pressure is sent to the VCU vehicle control unit by the ESC electronic stabilization system controller, and the VCU vehicle control unit sequentially judges whether the driver has frequent braking and sudden braking or sudden acceleration and sudden deceleration driving style representation.
Meanwhile, in the present embodiment, acquiring the driver operation information and the vehicle travel information requires that the following conditions be satisfied simultaneously:
the vehicle is driven for not less than the preset time condition (6)
The highest vehicle speed is not less than 100km/h (7)
All positions with the accelerator pedal travel (i.e. accelerator opening) covering 0-100%
Condition (8)
The following accelerator pedal strokes within a preset time, for example, at least 20 minutes are acquired for each of the following accelerator pedal strokes:
the travel of an accelerator pedal is more than or equal to 0 percent and less than 30 percent (namely the opening degree of a small accelerator)
The travel of an accelerator pedal is more than or equal to 30 percent and less than 65 percent (namely the opening degree of the throttle of people)
The travel of the accelerator pedal is more than or equal to 65 percent and less than or equal to 100 percent (namely the throttle opening is large)
Condition (9)
And (3) collecting conditions (10) of at least 10 times of vehicle starting process, braking process and steering process within preset times, such as the vehicle starting process, the braking process and the steering process.
The condition (6) aims to enable a vehicle controller VCU to acquire rich driving habits of a driver and more accurately push a driving style torque characteristic curve, the condition (7) aims to basically ensure that all vehicle speed sections are learned, and the condition (8) aims to acquire driving actions of the driver from no stepping on the accelerator to the bottom in the driving process.
And the style classification module is used for searching and classifying in the driving style classifier according to the operation characteristic parameters and the driving characteristic parameters to obtain a classification result.
As shown in fig. 8, the driving style characteristic curves include a linear driving style and a linear power output, for example, a driving characteristic curve a, i.e., the normal mode (general mode), in which the torque and the accelerator opening degree are in a linear relationship; some of the power generated by slightly stepping on the accelerator can be output vigorously, and the starting is very quick, such as a driving characteristic curve A; some accelerator output power at the front half section is very slow, is used for urban traffic jam road conditions, can well control the speed of the vehicle, and is suitable for many urban women. Such as curve G; some of the power is output more strongly under the opening degree of the front half section of the accelerator, so that certain 'riding dust' power requirements are met, the power output is more slowly interrupted by the accelerator, and the power output of the rear half section of the accelerator is more rapidly, so that the power-saving device is suitable for overtaking, such as a driving characteristic curve E.
And the style matching module is used for identifying and matching the driving style of the driver according to the classification result to obtain a driving style matching result and storing the driving style matching result.
The VCU vehicle control unit records driver operation information and vehicle running information in real time, and after a single automobile driving style self-learning process is finished, the VCU recommends a driving curve according to the driver operation information and the vehicle running information collected in the automobile driving style self-learning process.
The VCU vehicle control unit sends a driving characteristic curve learning recommended value of this time, such as driving style 1, to the EHU vehicle-mounted large screen controller, and after a driver clicks 'save', learning is successful;
when the driver drives the vehicle next time, the driver can select the driving style 1 by clicking the driving style selection key, and the driver can experience the effect of the previous intelligent driving style learning of the vehicle;
in addition, a plurality of intelligently learned driving styles, such as driving style 2, driving style 3 and the like, can be stored in the VCU vehicle control unit, and a user can store and delete the driving style modes learned by the vehicle.
After finding the driving style which is favored by the driver, the driver can save the favorite driving style; therefore, the driving characteristics of the vehicle can be learned successfully, and the unique driving characteristic requirements of the public are met.
And the driving state changing module is used for changing the driving state of the vehicle according to the driving style matching result.
Specifically, referring to fig. 4, the style classification module includes:
and the training initialization unit is used for switching the vehicle running state into a general mode.
The training information acquisition unit is used for acquiring training operation information and training driving information of different trainers within preset time;
the training parameter extracting unit is used for extracting training operation characteristic parameters and training driving characteristic parameters from the training operation information and the training driving information respectively according to the training operation information and the training driving information;
the marking unit is used for marking the training operation characteristic parameters and the training driving characteristic parameters of different trainers so as to mark the driving style of the corresponding trainer;
and the generating unit is used for learning and training the training operation characteristic parameters and the training driving characteristic parameters of different trainers according to a built-in algorithm to generate the driving style classifier.
Referring to fig. 5, the driving style self-learning device 50 of the vehicle comprises a processor 51, and a memory 52 for storing a computer program, wherein the processor 51 is used for executing the computer program to make the driving style self-learning device 50 of the vehicle perform the driving style self-learning method of the vehicle in any of the embodiments of the present application.
Referring to FIG. 6, a computer system 60 may be embodied in the form of a general purpose computing device. Computer system 60 includes a memory 62, a processor 63, and a bus 61 that connects the various system components.
The memory 62 may include, for example, system memory, non-volatile storage media, and the like. The system memory stores, for example, an operating system, an application program, a Boot Loader (Boot Loader), and other programs. The system memory may include volatile storage media such as random access memory ((RAM) and/or cache memory, non-volatile storage media such as storing instructions for corresponding embodiments of at least one of the processing methods to perform the distributed transaction, non-volatile storage media including, but not limited to, disk storage, optical storage, flash memory, and the like.
The processor 63 may be implemented as discrete hardware components, such as a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gates or transistors, or the like. Accordingly, each of the modules, such as the judging module and the determining module, may be implemented by a Central Processing Unit (CPU) executing instructions in a memory for performing the corresponding step, or may be implemented by a dedicated circuit for performing the corresponding step.
The bus 61 may employ any of a variety of bus architectures. For example, bus structures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, and Peripheral Component Interconnect (PCI) bus.
The computer system 60 may also include an input-output interface 64, a network interface 65, a storage interface 66, and the like. These output interface 64, network interface 65, storage interface 66, and memory 62 may be connected to processor 61 via bus 60. The input/output interface 64 may provide a connection interface for input/output devices such as a display, a mouse, a keyboard, and the like. The network interface 65 provides a connection interface for various networking devices. The storage interface 66 provides a connection interface for external storage devices such as a floppy disk, a U-disk, and an SD card.
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable apparatus to produce a machine, such that the execution of the instructions by the processor results in an apparatus that implements the functions specified in the flowchart and/or block diagram block or blocks.
These computer-readable program instructions may also be stored in a computer-readable memory that can direct a computer to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and/or block diagram block or blocks.
The automobile driving style self-learning method, the device, the equipment and the storage medium can enable a driver to select own driving style by himself, so that the driver can teach own love vehicle by himself and can select the 'power style' of the own love vehicle, so that the vehicle meets the driving style requirements of consumers, the driving style self-learning capability of the driving style of the vehicle is realized, the driving style self-learning method is suitable for different driver styles, the driving comfort is improved, the vehicle using experience of users is improved, and the technological sense of the vehicle is also improved.
Claims (10)
1. A self-learning method for automobile driving style is characterized by comprising the following steps:
switching the vehicle running state to a general mode;
acquiring driver operation information and vehicle running information;
extracting operation characteristic parameters and driving characteristic parameters from the driver operation information and the vehicle driving information respectively according to the driver operation information and the vehicle driving information;
searching and classifying in a driving style classifier according to the operation characteristic parameters and the driving characteristic parameters to obtain a classification result;
identifying and matching the driving style of the driver according to the classification result to obtain a driving style matching result, and storing the driving style matching result;
and changing the vehicle running state according to the driving style matching result.
2. The self-learning method for automobile driving style according to claim 1, wherein the driver operation information is obtained through a vehicle CAN bus, and the vehicle driving information is obtained through a sensor installed on an automobile;
the operating characteristic parameters include, but are not limited to, a turn signal status, a steering wheel angle, a steering wheel angular acceleration, an accelerator pedal travel, a brake pedal travel, a clutch pedal travel, and a transmission gear, and the driving characteristic parameters include, but are not limited to, a vehicle speed, a position, an acceleration, a yaw rate, a master cylinder pressure, and a speed, a distance, and an acceleration of the vehicle relative to a surrounding vehicle.
3. The self-learning method for automobile driving style according to claim 2, characterized in that the following conditions are satisfied simultaneously when the driver operation information and the vehicle driving information are acquired:
the vehicle running for not less than the preset time condition (1)
Maximum vehicle speed not less than 100km/h condition (2)
Accelerator pedal travel cover all position conditions 0-100% (3)
The following accelerator pedal travel within the preset time:
the travel of an accelerator pedal is more than or equal to 0 percent and less than 30 percent
The travel of an accelerator pedal is more than or equal to 30 percent and less than 65 percent
Condition (4) that the travel of the accelerator pedal is more than or equal to 65% and less than or equal to 100%
And (5) vehicle starting process, braking process and steering process conditions within preset times.
4. The self-learning method for automobile driving style according to claim 3, wherein the driving style classifier is generated by the following steps:
switching the vehicle running state to a general mode;
acquiring training operation information and training driving information of different trainers within preset time;
extracting training operation characteristic parameters and training driving characteristic parameters from the training operation information and the training driving information respectively according to the training operation information and the training driving information;
marking the training operation characteristic parameters and the training driving characteristic parameters of different trainers to mark the driving style of the corresponding trainer;
and learning and training the training operation characteristic parameters and the training driving characteristic parameters of different trainers to generate a driving style classifier.
5. An automobile driving style self-learning device is characterized by comprising:
the learning initialization module is used for switching the vehicle running state into a general mode;
the driving information acquisition module is used for acquiring the operation information of a driver and the vehicle running information;
the learning parameter extraction module is used for extracting operation characteristic parameters and driving characteristic parameters from the driver operation information and the vehicle driving information respectively according to the driver operation information and the vehicle driving information;
the style classification module is used for searching and classifying in the driving style classifier according to the operation characteristic parameters and the driving characteristic parameters to obtain a classification result;
the style matching module is used for identifying and matching the driving style of the driver according to the classification result to obtain a driving style matching result, and storing the driving style matching result;
and the driving state changing module is used for changing the driving state of the vehicle according to the driving style matching result.
6. The self-learning device for automobile driving style according to claim 5, wherein the driver operation information is obtained through a vehicle CAN bus, and the vehicle driving information is obtained through a sensor installed on an automobile.
7. The self-learning device for automobile driving style according to claim 6, wherein the information acquisition module simultaneously satisfies the following conditions:
the vehicle is driven for not less than the preset time condition (6)
The highest vehicle speed is not less than 100km/h (7)
Accelerator pedal travel covers all position conditions 0-100% (8)
The following accelerator pedal travel within the preset time:
the travel of an accelerator pedal is more than or equal to 0 percent and less than 30 percent
The travel of an accelerator pedal is more than or equal to 30 percent and less than 65 percent
Condition (9) that the travel of the accelerator pedal is more than or equal to 65% and less than or equal to 100%
And (10) vehicle starting process, braking process and steering process conditions within preset times.
8. The self-learning device for automobile driving style according to claim 7, wherein the style classification module comprises:
the training initialization unit is used for switching the vehicle running state into a general mode;
the training information acquisition unit is used for acquiring training operation information and training driving information of different trainers within preset time;
a training parameter extracting unit, configured to extract a training operation characteristic parameter and a training driving characteristic parameter from training operation information and training driving information, respectively, according to the training operation information and the training driving information;
the marking unit is used for marking the training operation characteristic parameters and the training driving characteristic parameters of different trainers so as to mark the driving style of the corresponding trainer;
and the generating unit is used for learning and training the training operation characteristic parameters and the training driving characteristic parameters of different trainers to generate the driving style classifier.
9. An automobile driving style self-learning device, comprising:
a processor;
a memory for storing a computer program;
the processor is configured to execute the computer program to cause the automotive driving style self-learning device to perform the automotive driving style self-learning method according to any one of claims 1-4.
10. A computer-storable medium on which a computer program is stored, wherein the computer program, when being executed by a processor, controls an apparatus in which the storage medium is located to perform the method for self-learning of driving style of a vehicle according to any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110024370.9A CN112829758A (en) | 2021-01-08 | 2021-01-08 | Automobile driving style self-learning method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110024370.9A CN112829758A (en) | 2021-01-08 | 2021-01-08 | Automobile driving style self-learning method, device, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112829758A true CN112829758A (en) | 2021-05-25 |
Family
ID=75928874
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110024370.9A Pending CN112829758A (en) | 2021-01-08 | 2021-01-08 | Automobile driving style self-learning method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112829758A (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113335286A (en) * | 2021-07-15 | 2021-09-03 | 上海洛轲智能科技有限公司 | Torque map generation method and device for vehicle, electronic device and storage medium |
| CN113386777A (en) * | 2021-06-24 | 2021-09-14 | 广汽本田汽车有限公司 | Vehicle adaptive control method, system, vehicle and computer storage medium |
| CN113859243A (en) * | 2021-09-02 | 2021-12-31 | 潍柴动力股份有限公司 | Hydraulic construction machinery auxiliary driving method, device, electronic device and storage medium |
| CN113954849A (en) * | 2021-10-13 | 2022-01-21 | 华人运通(江苏)技术有限公司 | Electric automobile control method and device, storage medium and vehicle |
| CN114132333A (en) * | 2021-12-14 | 2022-03-04 | 阿维塔科技(重庆)有限公司 | Intelligent driving system optimization method and device and computer readable storage medium |
| CN114228722A (en) * | 2021-12-06 | 2022-03-25 | 上海前晨汽车科技有限公司 | Driving style dividing method, device, equipment, storage medium and program product |
| CN114684196A (en) * | 2022-03-30 | 2022-07-01 | 智道网联科技(北京)有限公司 | Unmanned vehicle driving style setting method and cloud server |
| CN114906163A (en) * | 2022-06-08 | 2022-08-16 | 重庆长安新能源汽车科技有限公司 | A method and system for recommending an exclusive driving mode for a smart car |
| CN115230725A (en) * | 2021-08-20 | 2022-10-25 | 广州汽车集团股份有限公司 | A driving assistance system control method and device |
| CN115489512A (en) * | 2022-11-17 | 2022-12-20 | 苏州魔视智能科技有限公司 | Vehicle driving control method, device, equipment and medium |
| CN115556590A (en) * | 2022-11-08 | 2023-01-03 | 星河智联汽车科技有限公司 | Automobile single-pedal energy recovery adaptive control method and system |
| WO2023193736A1 (en) * | 2022-04-06 | 2023-10-12 | 长城汽车股份有限公司 | Vehicle control method and apparatus |
| CN118665498A (en) * | 2024-06-06 | 2024-09-20 | 中国第一汽车股份有限公司 | Vehicle control method, device, vehicle, storage medium, and program product |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104590274A (en) * | 2014-11-26 | 2015-05-06 | 浙江吉利汽车研究院有限公司 | Driving behavior self-adaptation system and method |
| CN108995654A (en) * | 2018-07-06 | 2018-12-14 | 北京理工大学 | A kind of driver status recognition methods and system |
| CN108995653A (en) * | 2018-07-06 | 2018-12-14 | 北京理工大学 | A kind of driver's driving style recognition methods and system |
| FR3074123A1 (en) * | 2018-05-29 | 2019-05-31 | Continental Automotive France | EVALUATING A DRIVING STYLE OF A DRIVER OF A ROAD VEHICLE IN MOTION BY AUTOMATIC LEARNING |
| CN111038485A (en) * | 2019-12-30 | 2020-04-21 | 山东大学 | Hybrid electric vehicle control method and system based on driving style recognition |
| CN111376911A (en) * | 2018-12-29 | 2020-07-07 | 北京宝沃汽车有限公司 | Vehicle and driving style self-learning method and device thereof |
| CN112861910A (en) * | 2021-01-07 | 2021-05-28 | 南昌大学 | Network simulation machine self-learning method and device |
-
2021
- 2021-01-08 CN CN202110024370.9A patent/CN112829758A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104590274A (en) * | 2014-11-26 | 2015-05-06 | 浙江吉利汽车研究院有限公司 | Driving behavior self-adaptation system and method |
| FR3074123A1 (en) * | 2018-05-29 | 2019-05-31 | Continental Automotive France | EVALUATING A DRIVING STYLE OF A DRIVER OF A ROAD VEHICLE IN MOTION BY AUTOMATIC LEARNING |
| CN108995654A (en) * | 2018-07-06 | 2018-12-14 | 北京理工大学 | A kind of driver status recognition methods and system |
| CN108995653A (en) * | 2018-07-06 | 2018-12-14 | 北京理工大学 | A kind of driver's driving style recognition methods and system |
| CN111376911A (en) * | 2018-12-29 | 2020-07-07 | 北京宝沃汽车有限公司 | Vehicle and driving style self-learning method and device thereof |
| CN111038485A (en) * | 2019-12-30 | 2020-04-21 | 山东大学 | Hybrid electric vehicle control method and system based on driving style recognition |
| CN112861910A (en) * | 2021-01-07 | 2021-05-28 | 南昌大学 | Network simulation machine self-learning method and device |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113386777A (en) * | 2021-06-24 | 2021-09-14 | 广汽本田汽车有限公司 | Vehicle adaptive control method, system, vehicle and computer storage medium |
| CN113335286A (en) * | 2021-07-15 | 2021-09-03 | 上海洛轲智能科技有限公司 | Torque map generation method and device for vehicle, electronic device and storage medium |
| CN113335286B (en) * | 2021-07-15 | 2022-07-26 | 上海洛轲智能科技有限公司 | Torque map generation method and device for vehicle, electronic device and storage medium |
| CN115230725B (en) * | 2021-08-20 | 2023-05-12 | 广州汽车集团股份有限公司 | Driving assistance system control method and device |
| CN115230725A (en) * | 2021-08-20 | 2022-10-25 | 广州汽车集团股份有限公司 | A driving assistance system control method and device |
| CN113859243A (en) * | 2021-09-02 | 2021-12-31 | 潍柴动力股份有限公司 | Hydraulic construction machinery auxiliary driving method, device, electronic device and storage medium |
| CN113954849B (en) * | 2021-10-13 | 2023-05-02 | 华人运通(江苏)技术有限公司 | Electric automobile control method and device, storage medium and vehicle |
| CN113954849A (en) * | 2021-10-13 | 2022-01-21 | 华人运通(江苏)技术有限公司 | Electric automobile control method and device, storage medium and vehicle |
| CN114228722A (en) * | 2021-12-06 | 2022-03-25 | 上海前晨汽车科技有限公司 | Driving style dividing method, device, equipment, storage medium and program product |
| CN114228722B (en) * | 2021-12-06 | 2023-10-24 | 上海前晨汽车科技有限公司 | Driving style dividing method, apparatus, device, storage medium, and program product |
| CN114132333A (en) * | 2021-12-14 | 2022-03-04 | 阿维塔科技(重庆)有限公司 | Intelligent driving system optimization method and device and computer readable storage medium |
| CN114684196A (en) * | 2022-03-30 | 2022-07-01 | 智道网联科技(北京)有限公司 | Unmanned vehicle driving style setting method and cloud server |
| WO2023193736A1 (en) * | 2022-04-06 | 2023-10-12 | 长城汽车股份有限公司 | Vehicle control method and apparatus |
| CN114906163A (en) * | 2022-06-08 | 2022-08-16 | 重庆长安新能源汽车科技有限公司 | A method and system for recommending an exclusive driving mode for a smart car |
| CN115556590A (en) * | 2022-11-08 | 2023-01-03 | 星河智联汽车科技有限公司 | Automobile single-pedal energy recovery adaptive control method and system |
| CN115489512A (en) * | 2022-11-17 | 2022-12-20 | 苏州魔视智能科技有限公司 | Vehicle driving control method, device, equipment and medium |
| CN118665498A (en) * | 2024-06-06 | 2024-09-20 | 中国第一汽车股份有限公司 | Vehicle control method, device, vehicle, storage medium, and program product |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112829758A (en) | Automobile driving style self-learning method, device, equipment and storage medium | |
| CN108995653B (en) | Method and system for identifying driving style of driver | |
| EP2857276B1 (en) | Driver assistance system | |
| JP6970117B2 (en) | Control data creation method for driver rule-based support | |
| CN113002545B (en) | Vehicle control method and device and vehicle | |
| CN111409648B (en) | A driving behavior analysis method and device | |
| CN111516693A (en) | A method and vehicle-mounted terminal for an adaptive driving mode | |
| CN112874519B (en) | Control method and system for adaptive cruise, storage medium and electronic device | |
| CN104590274A (en) | Driving behavior self-adaptation system and method | |
| CN109808706A (en) | Learning type assistant driving control method, device, system and vehicle | |
| CN113954855B (en) | Self-adaptive matching method for automobile driving mode | |
| CN112519788A (en) | Method and device for determining driving style and automobile | |
| CN105761329A (en) | Method of identifying driver based on driving habits | |
| US20250065890A1 (en) | Methods and systems for driver monitoring using in-cabin contextual awareness | |
| DE202013007367U1 (en) | Audio system for a vehicle | |
| CN112861910A (en) | Network simulation machine self-learning method and device | |
| CN113606329B (en) | Vehicle and driving mode determining method, determining system and TCU thereof | |
| CN112109715B (en) | Method, device, medium and system for generating vehicle power output strategy | |
| WO2023125849A1 (en) | Display interaction method and system for acc vehicle-following distance adjustment, braking distance calculation method and apparatus, and vehicle and medium | |
| CN113401135B (en) | Driving function intelligent configuration pushing method, device, equipment and storage medium | |
| CN116061851A (en) | Method, system, device and storage medium for setting vehicle pedal feel | |
| CN111798717B (en) | Electric vehicle control system and method supporting VR driving training | |
| CN117284302A (en) | User-specific driving mode generation method, system, vehicle, electronic equipment and storage medium | |
| CN118457637B (en) | Driving assistance method, device, vehicle, storage medium and product | |
| CN115179958B (en) | A cruise control method, system and electronic device based on driving behavior |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20210525 |
|
| WD01 | Invention patent application deemed withdrawn after publication |