CN113386777B - Vehicle adaptive control method, system, vehicle and computer storage medium - Google Patents
Vehicle adaptive control method, system, vehicle and computer storage medium Download PDFInfo
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- CN113386777B CN113386777B CN202110706510.0A CN202110706510A CN113386777B CN 113386777 B CN113386777 B CN 113386777B CN 202110706510 A CN202110706510 A CN 202110706510A CN 113386777 B CN113386777 B CN 113386777B
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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
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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
- B60W2540/00—Input parameters relating to occupants
- B60W2540/043—Identity of occupants
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
- Y02T10/84—Data processing systems or methods, management, administration
Abstract
The invention discloses a vehicle self-adaptive control method, a system, a vehicle and a computer storage medium, wherein the vehicle self-adaptive control method comprises the steps of obtaining identity identification information of a current driver; under the condition that the identification information of the current driver is matched with the stored portrait file, setting the vehicle according to the driving characteristics in the portrait file; various driving parameters of the current driver in the use process of the current vehicle are obtained, a model is generated based on the trained portrait, and the driving characteristics in the portrait file are updated according to the driving parameters. According to the embodiment of the invention, the vehicle is set according to the driving characteristics in the image file, so that the vehicle using requirement of a driver can be more comprehensively adapted; and can constantly optimize the archives of drawing with driver assorted in driving cycle process at every turn, realize vehicle adaptive control and optimization, let the vehicle know user more, reduce driver's operation, promote the competitiveness of vehicle.
Description
Technical Field
The invention relates to the technical field of vehicle control, in particular to a vehicle self-adaptive control method, a vehicle self-adaptive control system, a vehicle and a computer storage medium.
Background
At present, a part of automobiles are preset with a driver identification function, when different drivers use the same automobile, the automobile calls a driving file corresponding to the current driver, various functions in the automobile are automatically set, for example, static settings such as setting air conditioner temperature, setting seat angle, setting rearview mirror orientation and the like are set, and a self-adaptive vehicle adjusting mode is provided for the driver.
The settings of the driving files are recorded by a main control system of the vehicle after being manually adjusted, then the user actively inputs own information to match the current driving files, and the user also needs to call the driving files when the driving files are updated subsequently, and then the driving files are matched again after the user finishes the adjustment, so that the operation is very complicated; on the other hand, at present, the driving files are recorded only aiming at static settings, and dynamic settings such as driving behaviors of drivers cannot be recorded, so that the driving experience is influenced. In a word, although the current vehicle can switch different driving files according to the driver, a plurality of places influencing the user experience still exist, and the self-adaptive driving requirement cannot be really realized.
Disclosure of Invention
The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.
The embodiment of the invention provides a vehicle self-adaptive control method, a vehicle self-adaptive control system, a vehicle and a computer storage medium, which can reduce the operation of a driver and improve the competitiveness of the vehicle.
In a first aspect, an embodiment of the present invention provides a vehicle adaptive control method, including:
acquiring identity identification information of a current driver;
under the condition that the identification information of the current driver is matched with the stored portrait file, setting the vehicle according to the driving characteristics in the portrait file, wherein the driving characteristics are used for expressing the vehicle use habit of the driver;
the method comprises the steps of obtaining driver state information, driver action information, vehicle state information and environment information of a current driver in the use process of the current vehicle, generating a model based on a trained portrait, and updating driving characteristics in a portrait file according to the driver state information, the driver action information, the vehicle state information and the environment information.
In a second aspect, an embodiment of the present invention provides a vehicle adaptive control system, including at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the vehicle adaptive control method according to the first aspect.
In a third aspect, the embodiment of the invention further provides a vehicle, which includes the vehicle adaptive control system according to the second aspect.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions for causing a computer to execute the vehicle adaptive control method according to the first aspect.
The vehicle self-adaptive control method provided by the embodiment of the invention at least has the following beneficial effects: the driving characteristics of the vehicle used by the driver are formed by collecting the state information, the action information, the state information and the environment information of the driver, so that an image file matched with the current driver is obtained, the vehicle is set according to the driving characteristics in the image file, and the vehicle using requirements of the driver can be more comprehensively adapted; and the driver uses the vehicle at every turn and collects driver state information, driver action information, vehicle state information and environmental information to constantly optimize the archives of drawing with driver matched with in driving cycle process at every turn, realize vehicle adaptive control and optimization, let the vehicle know more the user, reduce driver's operation, promote the competitiveness of vehicle.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is an overall method flow diagram of a vehicle adaptive control method provided by one embodiment of the present invention;
FIG. 2 is a flowchart of creating an image file according to an embodiment of the present invention;
FIG. 3 is a flow chart of refining a newly created image file according to one embodiment of the present invention;
FIG. 4 is a flow chart for obtaining driving tendencies of a driver according to one embodiment of the invention;
FIG. 5 is a flow chart of updating driving characteristics provided by one embodiment of the present invention;
FIG. 6 is a schematic diagram of a process for deriving driving characteristics from input parameters according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a vehicle adaptive control system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, an embodiment of the present invention provides a vehicle adaptive control method, including, but not limited to, the following steps S100, S200, S300, and S400.
S100, acquiring identity identification information of a current driver;
step S200, under the condition that the identification information of the current driver is matched with the stored portrait file, setting the vehicle according to the driving characteristics in the portrait file, wherein the driving characteristics are used for expressing the vehicle use habit of the driver;
and step S300, acquiring the driver state information, the driver action information, the vehicle state information and the environment information of the current driver in the vehicle using process, generating a model based on the trained portrait, and updating the driving characteristics in the portrait file according to the driver state information, the driver action information, the vehicle state information and the environment information.
At present, part of fuel-powered automobiles or electric automobiles have the functions of identifying drivers and automatically setting the vehicles, but the functions are more basic, usually some static settings of the vehicles, such as rearview mirror angles, temperatures of vehicle-mounted air conditioners, air outlet quantity, air outlet directions and the like, are collected, the settings need to be manually set by users and recorded by the vehicles, the settings cannot be automatically changed in the subsequent vehicle use process, the drivers need to set the settings once again and then the settings are recorded by the vehicles when the settings need to be changed, and the operation is troublesome; in addition, some vehicles provide different driving modes (e.g., comfort mode, sport mode), but these driving modes are preset by manufacturers and cannot be carefully adapted to the driving habits of individual drivers. The problems mentioned above all result in that the vehicle cannot conform to the use habit of a single driver, the operation of the driver in the vehicle using process is increased, and the user experience needs to be improved.
Based on the method, the vehicle self-adaptive control method provides a multi-dimensional adjusting function, and intelligently constructs the image file of the driver based on the collected multi-dimensional data, so that the requirements of the driver are met.
The vehicle carries out identification to the current driver through different modes to obtain identification information after or in the process of getting on the bus by the current driver, wherein, the identification mode can include following several:
acquiring vehicle positioning information and position information of an intelligent terminal carried by a driver under the condition that the driver does not get on the vehicle, and when the driver opens a main driving door, determining the identity identification information of a person entering the main driving position through Bluetooth distance measurement; the T-BOX is used as a driver identity information acquisition device related to mobile phone Bluetooth/APP, can acquire vehicle positioning information and position information sent by an intelligent terminal carried by a driver, and judges whether the driver approaches the vehicle or not; when a driver opens a main driving door, the T-BOX confirms that the user is the driver through Bluetooth ranging, and the process can be matched with the following second identification mode to realize accurate identification.
Receiving a key interaction instruction of an intelligent terminal carried by a driver, and determining identity identification information of the driver according to a key interaction result; the driver sends an instruction to unlock or control the vehicle through the intelligent terminal, so that the corresponding portrait archive can be called, and the intelligent terminal often provides a remote unlocking function or a control function on the premise of user login, so that the driver can be identified through interaction between the intelligent terminal (such as operation in an APP) and the vehicle.
Acquiring a driver image of a main driving position, and identifying the driver image according to an image identification technology to determine identity identification information of the driver; when a driver opens a main driving door, the camera identifies the face characteristics, and identity identification information is determined through the face characteristics, so that the identity of the user and whether the driver is the driver are determined.
Detecting the seat pressure of the main driving position, and determining the identity identification information of the driver according to the detected pressure and/or pressure distribution; when the driver sits to the driving position, the identity of the driver is confirmed according to the seat pressure magnitude and pressure distribution (fed back by a position sensor in the seat) of the main driving position.
Determining a voice command from a main driving position through two microphones positioned at different positions in the vehicle, and identifying the voice command according to the voiceprint so as to determine the identity identification information of the driver; when a driver opens a vehicle door or sits at a driving position, the driver is prompted to perform voice recording (in some cases, 2 microphones at different positions are needed to confirm that voice is emitted from the side of the driver seat), and voiceprint recognition is performed according to the recorded voice so as to confirm the identity of the user and whether the identity of the driver is the same.
If the identified identity identification information of the current driver cannot be matched with the portrait file, the current driver is considered to be a new user of the vehicle, and the portrait file can be established for the new user in an active configuration mode or a passive configuration mode. Referring to fig. 2, the details are as follows:
step S510, creating an image file associated with the identification information;
and step S520, generating a portrait file according to the trained portrait generation model and the driver state information, the driver action information, the vehicle state information and the environment information of the current driver in the vehicle using process.
In the aspect of actively configuring a newly created portrait archive, a driver can be reminded of entering user information through a central control screen of a vehicle, and the entered information can comprise one or more of the following combinations: 1. prompting a user to download a special app for authentication and acquiring an interactive key; 2. prompting the user to enter facial images facing the camera in the vehicle at different angles (the central control screen can synchronously display the entered picture and prompt the user to enter actions); 3. prompting a user to perform voice input to perform voiceprint recognition; 4. the user is prompted to adjust the seat, adjust the rearview mirror, and have the user sit in the seat in different positions (to capture the seat pressure profiles for the different positions).
After the user information is recorded, filling a newly-built portrait file according to the recorded user information, starting to record the driver state information, the driver action information, the vehicle state information and the environment information of the driving process based on the portrait file, and perfecting the newly-built portrait file based on a trained portrait generation model.
In the aspect of passively configuring newly-built portrait archives, users can be distinguished according to the seat adjusting position and the rearview mirror position of a driver in the driving process, the face information of the users is input on the premise that the users do not need to operate, the face information is collected through a camera when the users open a vehicle door at each time, and the user archives are gradually and accurately matched.
The difference between the active configuration and the passive configuration is that if the automatic adjustment of the seat/rearview mirror in the above functions is to be realized, the passive configuration necessarily requires a camera to additionally collect face information, and the time for realizing the automatic adjustment of the seat/rearview mirror by the passive configuration is longer, because the passive version needs to learn and match the identity of the user, and has no influence on other functions.
Referring to FIG. 3, therefore, the process of actively configuring and passively configuring a new portrait file can be achieved by the following steps:
step S511, acquiring personal information input by a current driver on a control screen of the vehicle, generating identity recognition information based on the personal information, and creating an image file associated with the identity recognition information;
alternatively, the first and second electrodes may be,
and S512, acquiring the driver seat state and the rearview mirror position of the current driver in the vehicle using process, determining the identification information according to the driver seat state and the rearview mirror position, and creating an image file associated with the identification information.
Referring to fig. 4, the manner of creating the portrait file may also be implemented by obtaining driving habits of the user through questionnaires, specifically by the following steps:
step S530, providing a questionnaire for the current driver through a control screen in the vehicle, wherein the questionnaire comprises a plurality of questions related to the use habits of the vehicle;
step S540, receiving the filled-in answer of the current driver;
and step S550, determining the image file of the current driver according to the questions in the questionnaire, the weights corresponding to the questions and the filled answers.
In the above steps, a questionnaire is provided to the user through the control screen in the vehicle, and assuming that the questionnaire has n questions, each question has different answers and different scores of various features, which can be expressed by the following formula:
fn(x)＝A1a1(x)+A2a2(x)+…+Anan(x)
wherein A isiWeight representing the feature corresponding to the ith question, ai(x) The characteristic function of the ith topic, i ═ 1,2,3, …, n. The driving habits of the current user can be preliminarily judged according to the scores of the questions in the questionnaire. It will be appreciated that in addition to providing a questionnaire to a user using the central control screen of the vehicle, the questionnaire may be transmitted to the user's smart terminal, where the user completes the questionnaire and transmits the questionnaire answers to the vehicle.
All of the above are the process of creating image file. Under the condition that the portrait file associated with the current driver is stored in the vehicle, the corresponding portrait file can be called to set the vehicle by identifying the identity identification information of the current driver.
Under the above conditions, when the current driver drives the vehicle again, the vehicle records the behavior habit and the driving condition of the current driver again, wherein the behavior habit and the driving condition comprise driver state information, driver action information, vehicle state information and environment information, and specifically, the driver state information comprises at least one of a driver body temperature value and a driver heart rate; the driver action information comprises at least one of the depth of an accelerator pedal, the depth of a brake pedal, the gear position of a gearbox, the angle of a steering wheel, the setting of a vehicle-mounted air conditioner, the operation mode of a headlamp, the operation mode of a skylight and the operation mode of a windscreen wiper; the vehicle state information comprises at least one of vehicle speed, temperature value in the cabin, driver seat state, rearview mirror position, vehicle motor torque and air quality in the vehicle; the environmental information includes at least one of a vehicle-to-vehicle distance, vehicle positioning information, a traffic sign, a lane attribute, and an external natural environment parameter.
The collected parameters have mutual influence or interaction relation to determine different driving characteristics in the portrait file, and the driving parameters collected at this time can generate a model based on the trained portrait and update the driving characteristics, so that the portrait file of the user is continuously maintained in each driving process. For example, referring to fig. 5, updating the driving characteristics may be achieved by:
step S310, a model is generated based on the trained portrait, and feature extraction is carried out according to the state information of the driver, the action information of the driver, the state information of the vehicle and the environment information to obtain a plurality of feature values;
step S320, obtaining target driving characteristics according to each characteristic value and the weight corresponding to each characteristic value;
step S330, updating the driving characteristics in the stored portrait file according to the target driving characteristics.
And performing feature extraction on the acquired parameters, for example, quantifying the air outlet adjustment operation of the vehicle-mounted air conditioner into the angle of a grille of the air conditioner, and extracting the traffic sign into corresponding traffic indication information through image identification. Different driving characteristics are then calculated based on the preset weights corresponding to the characteristic values. For example, the driving characteristics include a driver temperature tolerance characteristic, a driving behavior tendency, a visual field requirement characteristic, and a passenger concentricity characteristic;
the temperature tolerance characteristic is determined according to the setting of the vehicle-mounted air conditioner in the vehicle state information and the external natural environment parameters in the environment information;
the driving behavior tendency is determined according to the action information of the driver;
the vision field requirement characteristic is determined according to a traffic sign and lane attributes in the environment information and a headlamp operation mode, a skylight operation mode and a wiper operation mode in the vehicle state information;
the passenger homothetic characteristic is determined based on a passenger compartment state of the vehicle.
Mathematically, this can be represented by the following formula:
where p denotes a certain driving characteristic, BkRepresents the weight, beta, corresponding to the k-th parameterkThe characteristic value of the kth parameter, i ═ 1,2,3, …, m.
For driving behavior tendencies, adaptation between the power system, the chassis system and the body system is concerned. The functions with respect to the respective systems are as follows:
a power system: the control system comprises a vehicle energy supply (HV BAT), a vehicle energy supply (CHG), a vehicle driving force output (MOT) and a vehicle low-voltage (12V) power supply (DC-DC), wherein a VCU is responsible for controlling the functions (for example, acquiring a driver gear (forward/backward/neutral) and an accelerator pedal signal, considering HV BAT output capacity, calculating an instruction torque which can meet the driver demand and cannot exceed the HV BAT output capacity and sending the instruction torque to an MCU (microprogrammed control unit), and the MCU controls the MOT to output corresponding driving force);
a chassis system: steering of the whole vehicle (EPS), braking of the whole vehicle (ESP, EPB, ESB), stability control of the vehicle body (ESP). In general, the EPS performs steering assist according to a driver's steering wheel operation; the ESB collects a brake pedal signal of a driver, and performs braking control on the whole vehicle (meanwhile, a braking recovery instruction is sent to the VCU, and the VCU performs energy recovery control through the MOT); and the EPB acquires a hand brake operation signal of a driver to perform parking braking. When the ADAS function is started, the ADAS acquires the information of the surrounding environment of the vehicle through sensors such as a millimeter wave radar and a front camera, acquires the current position (through T-BOX) of the vehicle, simulates and calculates operation signals such as a gear, an accelerator pedal, a brake pedal and a steering target angle of the driver according to the setting (target speed and destination) of the driver, and sends the signals to ECUs such as a VCU, an EPS and an ESB so that the vehicle can run according to the intention of the driver.
A vehicle body system: the driver can send related instructions to each ECU through a DA screen (e.g. Tesla large screen), or each ECU can respectively collect operation signals of the driver.
The three systems mentioned above mainly have the following aspects related to the parameter adjustment of the functions:
the time tau of function actuation; (e.g. air-conditioner/wiper opening in advance without driver's operation, etc.)
II, the strength s of functional actuation; (see the following description)
And thirdly, the response speed r of the function actuation. (see the following description)
Assume that the VCU sends a basic function of commanded torque to the MCU, based on driver and vehicle speed, as:
TqCmd＝F(AP，Speed)，
the adaptive control ECU will send the parameter value s of the target torque to the VCU based on the user profileTqCmdAnd rTqCmdThe torque intensity adjustment and the torque corresponding adjustment are respectively shown, the parameter value of the adaptive ECU is obtained based on the correlation function of the image characteristic and each function, and the power type score of the user is assumed to be higher, so that under the condition that the stepping amount of an accelerator pedal of a driver is not changed, the instruction torque output of the VCU is larger, the change rate is faster, and the vehicle power performance is better after the parameter adjustment. For example:
1. the VCU is responsible for collecting signals of an accelerator pedal of a driver, calculating and sending corresponding instruction torque to the MCU, and the MCU controls the MOT to output the torque and the vehicle to run. The VCU simultaneously sends information such as an accelerator pedal signal of a driver, the vehicle speed, the motor torque and the like to the self-adaptive control ECU.
2. The adaptive control ECU calculates the adjustment coefficient of the driver for the vehicle dynamic demand according to the information such as the change rate and the amplitude of the accelerator signal stepped by the driver, the vehicle speed and the like, and the VCU adjusts the command torque after receiving the coefficient.
If the driver likes the acceleration starting of the big foot accelerator, and then the driver looses the accelerator when the vehicle reaches a middle-high speed (more than 80 km/h), the adaptive control ECU judges that the driver is of a type with high dynamic requirement, a dynamic coefficient 1.3 (assumed value) is sent to the VCU, after the VCU receives the coefficient, when the driver again gets the big foot accelerator, the command torque sent to the MCU by the VCU is 1.3 times of the original torque, the vehicle power is enhanced, and the acceleration sense is improved.
It can be understood that the adaptive control ECU may be an existing control chip of the vehicle itself, or may be a control chip additionally provided based on the vehicle adaptive control scheme of the present invention, and the adaptive control ECU acquires vehicle running parameters and controls the vehicle running parameters by connecting a power system, a chassis system and a body system in the vehicle.
In order to clarify the specific implementation manner of the adaptive control method for a vehicle according to the embodiment of the present invention, referring to fig. 6, the following description is made by using several simple examples:
example 1:
when a driver A enters a passenger cabin, the self-adaptive control ECU identifies the user A, and then sends a seat/rearview mirror adjusting instruction, and the seat ECU/rearview mirror ECU receives the instruction and then performs adjusting control.
Example 2:
in spring and summer, after a driver A enters a passenger cabin, the self-adaptive control ECU detects the body temperature of a user A, judges whether to start the air conditioner or not according to the heat resistance degree of the user A, calculates and sends an air conditioner starting instruction and a temperature, air volume and air direction adjusting instruction, and controls the compressor and the air blower to work after the air conditioner ECU receives the instruction.
Example 3:
in spring and summer, the temperature of the passenger cabin is 30 ℃, the driver A enters the passenger cabin, and other passengers are in the vehicle. The driver A is very heat-resistant (the air conditioner is turned on at the temperature of more than 33 ℃ in normal times), but the homologism degree of the driver A is high. The self-adaptive control ECU calculates and sends an air conditioner starting instruction and a temperature, air volume and air direction adjusting instruction after detecting that other passengers exist on the vehicle, and the air conditioner ECU controls the compressor and the air blower to work after receiving the instruction.
Example 4:
in summer, the self-adaptive control ECU identifies that the vehicle is in a company according to the traveling habit of the driver and deduces that the driver is going to work, firstly calculates and sends out the window opening and air exchange instructions according to the difference between the outside air temperature and the temperature of the passenger compartment, and then sends an operation inquiry of 'whether the air conditioner is opened' to the mobile phone of the driver through the T-BOX, if the driver clicks yes, the self-adaptive ECU calculates and sends out the air conditioner opening instruction and the temperature and air volume adjusting instruction to reduce the temperature of the passenger compartment.
Example 5:
the driver A drives the vehicle for relatively risking (every time the driver A drives the vehicle for 100km/h, the distance between the driver A and the front vehicle is kept within 60m, the driver A starts to step on the brake when the distance between the driver A and the front vehicle is reduced to 40m, the driver A brakes and steps on the front vehicle when the distance between the driver A and the front vehicle is reduced to be less than 20m, and the driver A decelerates when passing a traffic light and approaches), the adaptive control ECU calculates a higher brake sensitivity coefficient value and sends the higher brake sensitivity coefficient value to the chassis system, so that when the driver steps on the brake, the vehicle response is faster and the brake effect is better; when a driver starts an ACC (adaptive cruise control) function of the ADAS, the adaptive control ECU calculates and sends a vehicle distance coefficient (range: 0.5-1.5), the ADAS can control the vehicle and the front vehicle to keep the vehicle distance to be properly reduced after receiving the vehicle distance coefficient (for example, the vehicle distance is generally kept at 150m per km/h by default, a 0.5 coefficient is received, and the ADAS considers safety factors such as road surface environment, vehicle braking capacity and the like, so that the vehicle distance coefficient is kept at 80m), and the driver can feel that the ADAS function is very suitable for the driver.
The embodiment of the invention also provides a vehicle adaptive control system, which comprises at least one processor and a memory, wherein the memory is used for being in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the aforementioned vehicle adaptive control method.
The embodiment of the invention also provides a vehicle which comprises the vehicle self-adaptive control system. The vehicle self-adaptive control system forms the driving characteristics of the vehicle used by the driver by collecting the state information of the driver, the action information of the driver, the state information of the vehicle and the environment information to obtain an image file matched with the current driver, and sets the vehicle according to the driving characteristics in the image file, so that the vehicle self-adaptive control system can more comprehensively adapt to the vehicle using requirements of the driver; and the driver uses the vehicle at every turn and collects driver state information, driver action information, vehicle state information and environmental information to constantly optimize the archives of drawing with driver matched with in driving cycle process at every turn, realize vehicle adaptive control and optimization, let the vehicle know more the user, reduce driver's operation, promote the competitiveness of vehicle.
Referring to fig. 7, it is exemplified that the control processor 1001 and the memory 1002 in the vehicle adaptive control system 1000 may be connected by a bus. The memory 1002, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer-executable programs. Further, the memory 1002 may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk memory, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 1002 may optionally include memory located remotely from the control processor 1001, which may be connected to the vehicle adaptive control system 1000 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Those skilled in the art will appreciate that the configuration of the apparatus shown in FIG. 7 does not constitute a limitation of the vehicle adaptive control system 1000, and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
Also provided is a computer-readable storage medium storing computer-executable instructions, which are executed by one or more control processors, for example, by one control processor 1001 in fig. 7, and which can cause the one or more control processors to execute the vehicle adaptive control method in the above-described method embodiment, for example, to execute the above-described method steps S100 to S300 in fig. 1, method steps S510 to S520 in fig. 2, method steps S511 to S512 in fig. 3, method steps S530 to S550 in fig. 4, and method steps S310 to S330 in fig. 5.
The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
It will be understood by those of ordinary skill in the art that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, or suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
While the preferred embodiments of the present invention have been described, the present invention is not limited to the above embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and such equivalent modifications or substitutions are included in the scope of the present invention defined by the claims.
Claims (8)
1. A vehicle adaptive control method comprising:
acquiring identity identification information of a current driver;
under the condition that the identification information of the current driver is matched with the stored portrait file, setting the vehicle according to the driving characteristics in the portrait file, wherein the driving characteristics are used for expressing the vehicle use habit of the driver;
acquiring driver state information, driver action information, vehicle state information and environment information of a current driver in the use process of the current vehicle, generating a model based on a trained portrait, and updating driving characteristics in a portrait file according to the driver state information, the driver action information, the vehicle state information and the environment information, wherein the driver state information comprises driver temperature tolerance characteristics, driving behavior tendency, visual field requirement characteristics and passenger homocentric characteristics;
under the condition that the identification information of the current driver is not matched with the stored portrait archive, the method further comprises the following steps:
creating an image file associated with the identification information;
generating a portrait file according to driver state information, driver action information, vehicle state information and environment information of a current driver in the use process of the current vehicle on the basis of a trained portrait generation model, wherein the vehicle state information comprises an in-cabin temperature value, and the environment information comprises an external natural environment parameter;
the updating the driving characteristics in the pictorial archive according to the driver state information, the driver action information, the vehicle state information, and the environment information includes:
based on a trained portrait generation model, extracting features according to the driver state information, the driver action information, the vehicle state information and the environment information to obtain a plurality of feature values;
obtaining target driving characteristics according to the characteristic values and the weights corresponding to the characteristic values;
the driving characteristics in the stored portrait file are updated according to the target driving characteristics.
2. The vehicle adaptive control method according to claim 1, wherein the driver state information includes at least one of a driver body temperature value and a driver heart rate; the driver action information comprises at least one of an accelerator pedal depth, a brake pedal depth, a gear position of a gearbox, a steering wheel angle, a vehicle-mounted air conditioner setting, a headlamp operation mode, a skylight operation mode and a windscreen wiper operation mode; the vehicle state information comprises at least one of vehicle speed, driver seat state, rearview mirror position, vehicle motor torque and air quality in the vehicle; the environmental information includes at least one of a vehicle-to-vehicle distance, vehicle positioning information, a traffic sign, and lane attributes.
3. The vehicle adaptive control method according to claim 1, wherein the acquiring of the identification information of the current driver includes at least one of:
when a driver does not get on the bus, acquiring vehicle positioning information and position information of an intelligent terminal carried by the driver, and when the driver opens a main driving door, confirming identity identification information of a person entering the main driving position through Bluetooth ranging;
receiving a key interaction instruction of an intelligent terminal carried by a driver, and determining identity identification information of the driver according to a key interaction result;
acquiring a driver image of a main driving position, and identifying the driver image according to an image identification technology to determine identity identification information of a driver;
detecting the seat pressure of a main driving position, and determining the identity identification information of a driver according to the detected pressure and/or pressure distribution;
the voice command from the main driving position is determined through two microphones which are positioned at different positions in the vehicle, and the voice command is recognized according to the voiceprint so as to determine the identity recognition information of the driver.
4. The adaptive vehicle control method of claim 1, wherein said creating a representation file associated with said identification information comprises:
acquiring personal information input by a current driver on a vehicle central control screen, generating identity identification information based on the personal information, and creating an image file associated with the identity identification information;
alternatively, the first and second electrodes may be,
the method comprises the steps of obtaining the driver seat state and the rearview mirror position of a current driver in the vehicle using process, determining identification information according to the driver seat state and the rearview mirror position, and creating a picture file associated with the identification information.
5. The adaptive vehicle control method of claim 1, wherein the obtaining of personal information currently input by a driver at a control screen of a vehicle, generating identification information based on the personal information, and creating a representation file associated with the identification information comprises:
providing a questionnaire to a current driver through a control screen in the vehicle, wherein the questionnaire comprises a plurality of questions related to vehicle use habits;
receiving a filling answer of a current driver;
and determining the image file of the current driver according to the questions in the questionnaire, the weights corresponding to the questions and the filled answers.
6. The vehicle adaptive control method according to claim 1,
the temperature tolerance characteristic is determined according to the setting of a vehicle-mounted air conditioner in the vehicle state information and the external natural environment parameter in the environment information;
the driving behavior tendency is determined according to the driver action information;
the vision field requirement characteristic is determined according to a traffic sign and lane attributes in the environment information and a headlamp operation mode, a skylight operation mode and a wiper operation mode in the vehicle state information;
the passenger homothetic characteristic is determined based on a passenger compartment state of the vehicle.
7. A vehicle adaptive control system comprising at least one processor and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the vehicle adaptive control method of any one of claims 1 to 6.
8. A vehicle comprising the vehicle adaptive control system of claim 7.
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| CN114407907A (en) * | 2022-01-18 | 2022-04-29 | 上汽通用五菱汽车股份有限公司 | Intelligent driving system parameter self-adaptive adjusting method, equipment and storage medium |
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