CN113859247B

CN113859247B - User identification method and device for vehicle, vehicle machine and storage medium

Info

Publication number: CN113859247B
Application number: CN202010614448.8A
Authority: CN
Inventors: 曹欣; 杨冬生; 刘柯; 王欢
Original assignee: BYD Co Ltd
Current assignee: BYD Co Ltd
Priority date: 2020-06-30
Filing date: 2020-06-30
Publication date: 2023-07-11
Anticipated expiration: 2040-06-30
Also published as: CN113859247A

Abstract

The embodiment of the invention provides a vehicle user identification method, a device, a vehicle machine and a storage medium, wherein the vehicle user identification method comprises the following steps: when a brake pedal is detected to act in the running process of a vehicle, first action information of the pedal action and power information of the vehicle corresponding to the first action information are collected; according to the first action information and the power information of the vehicle, first characteristic information is obtained, and the first characteristic information is used for identifying the identity of a user; and identifying the user identity information of the vehicle according to the first characteristic information. According to the technical scheme, the identity recognition of the driving user is realized only by depending on the running information such as pedal action information, power information and the like under the condition that the user privacy information is not related, the risk of revealing the user privacy information can be reduced, and higher recognition accuracy can be obtained.

Description

User identification method and device for vehicle, vehicle machine and storage medium

Technical Field

The present invention relates to the technical field of motor vehicles, and in particular, to a vehicle user identification method, a vehicle user identification device, a vehicle machine, and a computer-readable storage medium.

Background

With the development of automobile electronic technology, the aim of providing personalized service for a driving user, or identifying whether the driving user has driving qualification or monitoring the driving behavior of the driving user is achieved by adding a user identity identification function to an automobile. In the related art, identification of a user identity needs to be realized by collecting information such as face information, fingerprint information, voiceprint information and the like of the user.

As can be seen from the above, in the existing user identification scheme, because biometric information of the user needs to be collected, there is a risk of disclosure of private data of the user in the application process.

Disclosure of Invention

The technical problem to be solved by the embodiment of the invention is to provide a vehicle user identification method for realizing the identity identification of a driving user only by depending on running information such as pedal action information, power information and the like under the condition of not involving user privacy information.

Correspondingly, the embodiment of the invention also provides a vehicle user identification device, a vehicle machine and a computer readable storage medium, which are used for ensuring the realization and the application of the method.

To solve the above-mentioned problem, an embodiment of a first aspect of the present invention provides a vehicle user identification method, including:

When a brake pedal is detected to act in the running process of a vehicle, first action information of the pedal action and power information of the vehicle corresponding to the first action information are collected;

according to the first action information and the power information of the vehicle, first characteristic information is obtained, and the first characteristic information is used for identifying the identity of a user;

and identifying the user identity information of the vehicle according to the first characteristic information.

Optionally, the step of acquiring the first motion information of the pedal motion when the brake pedal motion is detected and the step of acquiring the first motion information of the pedal motion and the step of acquiring the power information of the vehicle corresponding to the first motion information when the brake pedal motion is detected during the running of the vehicle includes:

if the brake pedal is detected to act, recording the stroke of the brake pedal;

under the condition that the stroke of the brake pedal is detected to be increased to be greater than or equal to a preset stroke threshold value, determining that the brake pedal meets a first preset condition, and acquiring first action information of the brake pedal and power information of the vehicle;

And stopping collecting the first action information of the brake pedal and the power information of the vehicle under the condition that the stroke of the brake pedal is detected to be smaller than or equal to a preset stroke threshold value.

Optionally, the obtaining the first feature information according to the first motion information and the power information of the vehicle includes:

under the condition that the action generated by the brake pedal is detected and accords with the first preset condition, second action information of the accelerator pedal is acquired;

respectively acquiring a first set of time-space domain characteristic information and a first set of frequency domain characteristic information in the first action information, and a second set of time-space domain characteristic information and a second set of frequency domain characteristic information in the second action information;

configuring a feature matrix according to the first set of time-space domain feature information, the first set of frequency domain feature information, the second set of time-space domain feature information, the second set of frequency domain feature information and the power information, so as to take the feature matrix as the first feature information,

wherein the power information includes at least one of a lateral acceleration, a longitudinal acceleration, a vehicle speed, a motor speed, and a vehicle torque of the vehicle.

Optionally, the identifying the user identity information of the vehicle according to the first feature information includes:

recording the action quantity of the brake pedal;

obtaining a plurality of groups of first characteristic information matched with the action quantity under the condition that the action quantity of the brake pedal is larger than or equal to a preset identification quantity;

and identifying user identity information of the vehicle according to a plurality of sets of the first characteristic information.

Optionally, the identifying the user identity information of the vehicle according to the multiple sets of the first feature information includes:

determining the feature matrix meeting a second preset condition as the first feature information, and respectively inputting a plurality of groups of first feature information into a trained classification recognition model to correspondingly output a plurality of recognition identifications;

dividing the same identification in the plurality of identification identifications into a group to obtain at least one group of identification;

and calculating the duty ratio of each group of the identification marks in the plurality of identification marks, and determining the identification mark with the largest duty ratio as the identification mark of the vehicle user.

Optionally, before determining the feature matrix meeting the second preset condition as the first feature information and inputting multiple groups of the first feature information into the trained classification recognition model respectively so as to correspondingly output multiple recognition identifiers, the method further includes:

Detecting whether the feature matrix is associated with a label or not;

and under the condition that the feature matrix is not associated with the label, determining the feature matrix as the first feature information meeting the second preset condition.

Optionally, the method further comprises:

under the condition that the feature matrix is associated with the label, determining that the feature matrix does not meet the second preset condition, and marking the feature matrix which does not meet the second preset condition as second feature information, wherein the second feature information is used for training the classification recognition model,

and inputting the second characteristic information and the label into a training model of the classification recognition model, and updating the classification recognition model according to a training result of the training model.

Optionally, in the case of detecting a brake pedal actuation, before acquiring first actuation information of the pedal actuation and power information of the vehicle corresponding to the first actuation information, the method further includes:

receiving feedback information input by a user according to the identity prompt information;

determining whether the user is an identifiable user according to the feedback information;

generating the label according to input operation of a user under the condition that the user is not the identifiable user, so as to establish an association relation between the label and the feature matrix under the condition that the feature matrix is used as the second feature information;

In the case that the user is the identifiable user, whether the brake pedal is actuated is directly detected, so that the feature matrix is used as the first feature information.

Optionally, the method further comprises:

and inputting the first characteristic information and the identified identity into a training model, wherein the training model is used for training the classification recognition model.

An embodiment of a second aspect of the present invention provides a vehicle user identification apparatus, including:

the acquisition module is used for acquiring first action information of the pedal action and power information of the vehicle corresponding to the first action information under the condition that the action of a brake pedal is detected in the running process of the vehicle;

the acquisition module is used for acquiring first characteristic information according to the first action information and the power information of the vehicle, wherein the first characteristic information is used for identifying the identity of a user;

and the identification module is used for identifying the user identity information of the vehicle according to the first characteristic information.

An embodiment of a third aspect of the present invention provides a vehicle apparatus, including: a user identification device of a vehicle according to an embodiment of the second aspect of the present invention.

An embodiment of a fourth aspect of the invention provides a computer readable storage medium comprising a method of user identification of a vehicle according to any of the embodiments of the first aspect of the invention.

According to the embodiment of the invention, the first action information of the pedal action and the power information of the vehicle in the execution process of the pedal action are collected when the brake pedal action is detected, and different driving habits of different users on the vehicle can be quantitatively reflected through the collected first action information and the collected power information of the vehicle, so that the first characteristic information for identifying the identity can be generated according to the first action information and the power information.

The identity of the driving user is identified according to the first characteristic information, on one hand, the identity identification of the driving user is realized only by depending on running information such as pedal action information, power information and the like under the condition of not involving the user privacy information, so that the risk of leakage of the user privacy information can be reduced, on the other hand, the user identity identification is carried out by adopting the first characteristic information extracted based on the running information, and higher identification precision can be ensured.

Drawings

FIG. 1 is a flow chart of steps of an embodiment of a method for user identification of a vehicle of the present invention;

FIG. 2 is a flow chart of steps of another embodiment of a method of identifying a user of a vehicle of the present invention;

FIG. 3 is a flow chart of steps of yet another embodiment of a method for user identification of a vehicle of the present invention;

FIG. 4 is a flowchart of the steps of yet another embodiment of a method for user identification of a vehicle of the present invention;

FIG. 5 is a block diagram of an embodiment of a user identification device of a vehicle of the present invention;

FIG. 6 is a block diagram of another embodiment of a user identification device of the vehicle of the present invention;

fig. 7 is a block diagram of a vehicle machine embodiment of the present invention.

Detailed Description

In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present invention and features in the embodiments may be combined with each other.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.

Referring to fig. 1, a flowchart illustrating steps of an embodiment of a vehicle user identification method according to the present invention may specifically include the following steps:

step S102, when a brake pedal is detected to be operated during running of the vehicle, first operation information of the pedal operation and power information of the vehicle corresponding to the first operation information are collected.

In this embodiment, the execution process corresponds to a brake pedal actuation interval, and the first actuation information of the brake pedal is collected in the brake pedal actuation interval, and the specific collection manner may be implemented by detecting a travel signal of the brake pedal.

In addition, in order to further improve the reliability of identifying the user based on the driving information of the vehicle, the power information of the vehicle may be collected at the same time in the brake pedal operation section, so as to obtain first characteristic information for identifying the user according to the first operation information and the power information, where the first operation information and the power information may be specifically detected by corresponding sensors.

Step S104, obtaining first characteristic information according to the first action information and the power information of the vehicle, wherein the first characteristic information is used for identifying the identity of the user.

In this embodiment, because driving habits of different driving users are different, the collected pedal action information and power information are further processed to obtain first feature information capable of reflecting driving features of the driving users, so that the driving users can be identified based on the first feature information.

Step S106, identifying the user identity information of the vehicle according to the first characteristic information.

In this embodiment, each brake pedal action correspondingly generates a set of first feature information, the user identity is identified by adopting the first feature information, the user identity can be determined by detecting whether the feature information of the designated user matched with the first feature information is prestored, and the user identity can be determined based on the output result by inputting the first feature information into a preset identification model.

According to the vehicle user identification method provided by the embodiment, when the brake pedal is detected to generate the action, the first action information of the pedal action and the power information of the vehicle in the execution process of the pedal action are collected, and as different users have different driving habits on the vehicle, different driving habits can be quantitatively reflected through the collected first action information and the collected power information of the vehicle, and therefore the first characteristic information for identifying identity can be generated according to the first action information and the power information.

In some embodiments, one possible implementation of step S102 is: if the brake pedal is detected to act, recording the stroke of the brake pedal; under the condition that the stroke of the brake pedal is detected to be increased to be greater than or equal to a preset stroke threshold value, determining that the brake pedal meets a first preset condition, and acquiring first action information of the brake pedal and power information of a vehicle; and stopping collecting the first action information of the brake pedal and the power information of the vehicle under the condition that the stroke of the brake pedal is detected to be smaller than or equal to a preset stroke threshold value.

The first preset condition may be understood as a brake pedal action that the pedal depth (i.e., the pedal stroke) generated by the pedal action meets a preset condition.

Specifically, the action of the brake pedal can be detected according to a preset detection period, for example, the stroke information of the brake pedal collected in the adjacent three detection periods is analyzed, if the detected stroke of the brake pedal is smaller than or equal to a preset threshold value T (for example, 0.0001, the threshold value can be adjusted according to the loosening degree of a brake pedal spring) in the first detection period, the stroke data in the first detection period is not in accordance with a first preset condition, and if the stroke of the brake pedal in the second detection period is larger than or equal to the preset stroke threshold value T in the second detection period, the action of the brake pedal which is in accordance with the first preset condition is considered to be started in the second detection period, thereby starting to record the stroke data of the brake pedal; if the stroke of the brake pedal is reduced to be smaller than the preset stroke threshold value T, the action of the brake pedal is indicated to be no longer in accordance with the first preset condition, and the recording of the stroke data of the brake pedal is finished. And dividing a single complete brake pedal action interval, acquiring data again if the stroke of the brake pedal is continuously detected to be greater than or equal to a preset stroke threshold value T in a third detection period, and acquiring no data if the stroke of the brake pedal is not greater than or equal to the preset stroke threshold value T.

In this embodiment, by defining the first preset condition and the preset travel threshold corresponding to the first preset condition, information is collected when the pedal travel is detected to be greater than or equal to the preset travel threshold, and compared with the manner of collecting information when the pedal action is detected, the collected information can embody the difference of the driving habits of the users, so that the identification of the identity information of the vehicle user is facilitated, and the reliability of the identification scheme of the application is improved.

In some embodiments, one possible implementation of step S104 is: under the condition that the action generated by the brake pedal is detected and accords with the first preset condition, second action information of the accelerator pedal is acquired; acquiring a first set of time-space domain characteristic information and a first set of frequency domain characteristic information in first action information; acquiring a second set of time-space domain characteristic information and a second set of frequency domain characteristic information in second action information; and configuring a feature matrix according to the first set of time-space domain feature information, the first set of frequency domain feature information, the second set of time-space domain feature information, the second set of frequency domain feature information and the power information, so as to take the feature matrix as first feature information.

The power information comprises at least one of lateral acceleration, longitudinal acceleration, vehicle speed, motor rotation speed and vehicle torque of the vehicle.

In some driving environments, the situation that the accelerator pedal is stepped on can also occur in an action interval generated by the brake pedal, if the action of the accelerator pedal is detected, second action information of the accelerator pedal is correspondingly acquired, so that a first set of time-space domain characteristic information and a first set of frequency domain characteristic information are obtained according to the first action information, a second set of time-space domain characteristic information and a second set of frequency domain characteristic information are obtained according to the second action information, and if the action of the accelerator pedal does not occur, the second action information is regarded as 0.

The first set of time-space domain feature information includes: maximum value, average value, standard deviation, duration, number of points of depth change, quantile, waveform factor, peak factor, pulse factor and margin factor of the stroke of the brake pedal.

The first set of frequency domain characteristic information includes: and carrying out frequency domain transformation on the first set of time-space domain characteristic information to obtain maximum value, minimum value, average value, standard deviation, kurtosis, skewness, waveform factor, peak factor, pulse factor and margin factor.

The second set of time-space domain feature information includes: maximum, minimum, mean, standard deviation, quantile, form factor, peak factor, pulse factor, margin factor of accelerator pedal travel.

The second set of frequency domain characteristic information includes: and carrying out frequency domain transformation on the maximum value, the minimum value, the mean value, the standard deviation, the kurtosis, the skewness, the waveform factor, the peak factor, the pulse factor and the margin factor of the second set of time-space domain characteristic information of the pedal action information.

In the characteristic information, the pedal action information is respectively marked by time-space domain characteristics and frequency domain characteristics, numerical statistics of kurtosis reflecting random variable distribution characteristics are respectively adopted under the time-space domain and the frequency domain, digital characteristics of asymmetric degree of deviation statistical data distribution are adopted, the ratio of root mean square value and average value of signals is represented by a waveform factor, the ratio of signal peak value and effective value (RMS) is represented by a peak factor, the ratio of signal peak value and rectification average value (average value of absolute value) is represented by a pulse factor, and the ratio of signal peak value and root mean square value is represented by a margin factor.

The characteristic information is acquired and calculated, so that pedal action information can be reflected from various aspects such as a time-space domain, a frequency domain, data distribution, a data mean value, square root amplitude and the like, and therefore, a characteristic matrix is generated based on the characteristic information, and differentiated characteristics reflected in the braking process of different driving users can be effectively extracted, and therefore, the characteristic matrix is taken as an object of identification, and a high-precision identification effect can be obtained.

In some embodiments, one possible implementation of step S106 is: recording the action quantity of a brake pedal; obtaining multiple groups of first characteristic information matched with the action number under the condition that the action number of the brake pedal is larger than or equal to the preset identification number; and identifying the user identity information of the vehicle according to the plurality of sets of first characteristic information.

In this embodiment, a set of first feature information is generated corresponding to the motion of each brake pedal, and since a single brake pedal motion has higher contingency, in order to ensure accuracy of a recognition result, after a plurality of sets of first feature information are generated, a user identity recognition operation is performed according to the plurality of sets of first feature information, so that probability of abnormal recognition can be reduced, and accuracy of the recognition result is ensured.

In some embodiments, one possible implementation of step S106 is: determining a feature matrix meeting a second preset condition as first feature information, and respectively inputting a plurality of groups of first feature information into a trained classification recognition model to correspondingly output a plurality of recognition identifications; dividing the same identification in the plurality of identification identifications into a group to obtain at least one group of identification; and calculating the duty ratio of each group of identification marks in the plurality of identification marks, and determining the identification mark with the largest duty ratio as the identification mark of the vehicle user.

In this embodiment, during the process that the user drives the vehicle, most of pedal actions conform to the driving habit of the user, but it is also impossible to completely avoid occurrence of part of pedal actions which do not conform to the driving habit of the user, if this occurs, there is a probability that the part of pedal actions cannot be identified or are incorrectly identified when the part of characteristic information is individually identified, so by collecting multiple groups of pedal actions, each group of pedal actions forms a group of first characteristic information, and identification operations are respectively performed to obtain multiple identification identifications, where the multiple identification identifications may be the same or different, and in the multiple identification identifications, the characteristic information corresponding to the identification with the largest proportion can reflect the driving habit of the user more, so by determining the identification with the largest proportion as the identity identification of the vehicle user, the accuracy of the user identity identification and the reliability of the identification operations are facilitated to be further improved.

In some embodiments, before determining the feature matrix meeting the second preset condition as the first feature information, and inputting the multiple sets of first feature information into the trained classification recognition model respectively to correspondingly output multiple recognition identifiers, the method further includes: detecting whether a tag is associated with the feature matrix; and under the condition that the feature matrix is not associated with a label, determining the feature matrix as first feature information meeting a second preset condition.

The second preset condition is specifically that a label is not associated with the feature matrix, if the feature matrix is detected to be associated with the label, the fact that the action information and the power information corresponding to the current user are not trained is indicated, namely, if a trained classification recognition model is input for recognition, recognition abnormality is generated, and the feature information associated with the label is recorded as second feature information.

In some embodiments, under the condition that the feature matrix is associated with the tag, determining that the feature matrix does not meet a second preset condition, and marking the feature matrix which does not meet the second preset condition as second feature information, wherein the second feature information is used for training a classification recognition model, the second feature information and the tag are input into a training model of the classification recognition model, and the classification recognition model is updated according to a training result of the training model, so that the updated classification recognition model can recognize the tag as a recognition mark.

In the embodiment, the classification recognition model is obtained through training of a support vector machine (SVM: support Vector Machine), a plurality of groups of feature matrixes obtained through the training are respectively input into the classification recognition model, each group of feature matrixes correspondingly outputs an identity, the recognition identity with the largest proportion is determined as the identity, the recognition result corresponding to the feature matrix generated by abnormal braking operation of a user can be deleted, and the normal recognition result is reserved, so that the recognition accuracy of the identity recognition is ensured.

In addition, it can be appreciated by those skilled in the art that the classification recognition model can also be obtained based on training of a neural network model or a bayesian classifier.

After identifying the user identity information of the vehicle according to the plurality of sets of first characteristic information, the method further comprises the following steps: and inputting the first characteristic information and the identified identity into a training model, wherein the training model is used for training a classification recognition model, and optimizing the classification recognition model according to the training result of the training model.

In this embodiment, the identity obtained by marking the feature matrix after recognition is stored in the training data set for training the next classification recognition model, so that the accuracy of the classification recognition model can be further improved.

Referring to fig. 2, a flowchart illustrating steps of another embodiment of a vehicle user identification method of the present invention may specifically include the steps of:

step S202, receiving feedback information input by a user according to the identity prompt information.

Step S204, whether the user is an identifiable user is determined according to the feedback information.

In step S206, when the user is not identifiable, a tag is generated according to the input operation of the user, so that when the motion information and the power information are acquired, the generated feature matrix is used as the second feature information, and an association relationship is established between the tag and the feature matrix.

If the user is not identifiable and the user indicates that the identity is not generated before, under the condition, the corresponding generated feature matrix can be used as training data to train the classification recognition model, so that the training data are collected in the normal driving process, and after the training data are collected, the training data are input into a support vector machine to train, so that the classification recognition model is perfected, and the recognition efficiency of the classification recognition model is improved.

Step S208, inputting the second characteristic information and the label into a training model of the classification recognition model, and updating the classification recognition model according to the training result of the training model, so that the updated classification recognition model can recognize the label as a recognition mark.

In step S210, if the user is identifiable, it is directly detected whether the brake pedal is actuated, and the obtained feature matrix is used as the first feature information.

The identifiable user can understand that the pedal action information, the power information and the like of the user are used as training data for model training, namely, the feature matrix generated by the pedal action information and the power information of the user can be identified by the classified identification model so as to output an identification result, and further, the reliability of the identification operation can be ensured.

Step S212, under the condition that a plurality of groups of first characteristic information are obtained, the user identity information of the vehicle is identified according to the plurality of groups of first characteristic information.

In this embodiment, before the identification operation is performed, first prompt information may be generated, where the first prompt information is used to prompt the user to confirm whether the identity is generated, and use the received feedback information of the user as a confirmation result, if the identity is not generated, second prompt information may be generated to prompt the user to input information such as a name or a code, so as to generate the identity according to the information such as the name or the code,

referring to fig. 3, a flowchart illustrating steps of another embodiment of a vehicle user identification method of the present invention may specifically include the steps of:

step S302, feedback information input by a user according to the identity prompt information is received.

Step S304, determining whether the user is an identifiable user according to the feedback information.

In step S306, in the case where the user is not identifiable, a tag is generated according to the input operation of the user.

Step S308, when the brake pedal is detected to generate motion and the first preset condition is met, acquiring first motion information of pedal motion, second motion information of an accelerator pedal and power information of a vehicle corresponding to the first motion information, and acquiring the feature matrix as second feature information according to the motion information and the power information.

Step S310, establishing an association relation between the tag and the feature matrix.

Step S312, inputting the feature matrix and the label into a training model of the classification recognition model, and updating the classification recognition model according to the training result of the training model, so that the updated classification recognition model can recognize the label as a recognition mark.

In step S314, when the user is identifiable and the brake pedal actuation is detected and the first preset condition is met, first actuation information of the pedal actuation, second actuation information of the accelerator pedal, and power information of the vehicle corresponding to the first actuation information are acquired, and the feature matrix is obtained as the first feature information according to the actuation information and the power information.

Step S316, inputting multiple groups of first characteristic information into the trained classification recognition model respectively so as to correspondingly output multiple recognition identifications.

Step S318, dividing the same identification of the plurality of identification identifications into a group to obtain at least one group of identification identifications.

Step S320, the duty ratio of each group of identification marks in the plurality of identification marks is calculated, and the identification mark with the largest duty ratio is determined as the identification mark of the vehicle user.

Step S322, inputting the first characteristic information and the identified identity into a training model, wherein the training model is used for training a classification recognition model, and optimizing the classification recognition model according to the training result of the training model.

In addition, after the identity of the user is identified, the application program is pushed based on the identity, so that the vehicle can provide an accurate and humanized application program for the user, and the user experience is improved.

Referring to fig. 4, a flowchart illustrating steps of yet another embodiment of a vehicle user identification method of the present invention may specifically include the steps of:

step S402, responding to the starting instruction, generating identity prompt information, prompting a driving user to confirm whether the braking data is trained or not, receiving feedback information input by the user according to the identity prompt information, determining whether the user is an identifiable user according to the feedback information, if yes, entering step S412, and if no, entering step S404.

Step S404, prompting the driving user to input the name/code of the driver, and generating a label according to the name/code of the driver.

Step S406, when the brake pedal is detected to generate an action and the first preset condition is met, acquiring first action information of the pedal action, second action information of the accelerator pedal and power information of the vehicle corresponding to the first action information, and obtaining the feature matrix as second feature information according to the action information and the power information.

Step S408, establishing an association relation between the tag and the feature matrix.

Step S410, inputting the second characteristic information and the label into a training model of the classification recognition model, and updating the classification recognition model according to the training result of the training model, so that the updated classification recognition model can recognize the label as a recognition mark.

In step S412, when the brake pedal actuation is detected and the first preset condition is met, first actuation information of the pedal actuation, second actuation information of the accelerator pedal, and power information of the vehicle corresponding to the first actuation information are acquired.

Step S414 obtains a first set of time-space domain feature information and a first set of frequency domain feature information in the first action information.

Step S416, a second set of time-space domain feature information and a second set of frequency domain feature information in the second action information are acquired.

Step S418, a feature matrix is configured according to the first set of time-space domain feature information, the first set of frequency domain feature information, the second set of time-space domain feature information, the second set of frequency domain feature information and the power information, so as to take the feature matrix as first feature information.

Wherein the first set of time-space domain feature information comprises: maximum value, average value, standard deviation, duration, number of points of depth change, quantile, waveform factor, peak factor, pulse factor, margin factor of the stroke of the brake pedal, the first set of frequency domain characteristic information comprises: maximum value, minimum value, average value, standard deviation, kurtosis, skewness, waveform factor, peak factor, pulse factor and margin factor after the stroke of the brake pedal is subjected to frequency domain transformation.

The power information includes: lateral acceleration, longitudinal acceleration, vehicle speed, motor speed, and vehicle torque.

The second set of time-space domain feature information includes: maximum, minimum, mean, standard deviation, quantile, form factor, peak factor, pulse factor, margin factor of the travel of the accelerator pedal; the second set of frequency domain characteristic information includes: maximum value, minimum value, average value, standard deviation, kurtosis, skewness, waveform factor, peak factor, pulse factor and margin factor after the stroke of the accelerator pedal is subjected to frequency domain transformation.

Step S420, record the number of actions of the brake pedal.

Step S422, obtaining multiple sets of first feature information matching the number of actions when the number of actions of the brake pedal is greater than or equal to the preset number of identifications.

Step S424, inputting a plurality of groups of first characteristic information into the trained classification recognition model respectively so as to correspondingly output a plurality of recognition identifications, and determining the recognition identifications as the identity of the vehicle user according to the plurality of recognition identifications.

Step S426, inputting the first characteristic information and the identified identity into a training model, wherein the training model is used for training a classification recognition model, and optimizing the classification recognition model according to the training result of the training model.

In this embodiment, by the above scheme, the identity of the driving user can be identified only by relying on the brake data without involving the privacy information (such as face information, iris information, fingerprint information, voiceprint information, etc.) of the driving user. Specifically, the method and the device are used for identifying the identities of the driving users by effectively extracting the differentiation characteristics reflected in the braking action process of different driving users. In addition, a Support Vector Machine (SVM) is used for training the braking characteristic data with the identity marks, and a classification recognition model is obtained for carrying out driving user identity recognition on the braking characteristic data without the identity marks.

It should be noted that, for simplicity of description, the method embodiments are shown as a series of acts, but it should be understood by those skilled in the art that the embodiments are not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred embodiments, and that the acts are not necessarily required by the embodiments of the invention.

Referring to fig. 5, a block diagram of an embodiment of a vehicle user identification device 500 of the present invention is shown, which may specifically include the following modules:

The acquisition module 502 is configured to acquire first motion information of a pedal motion and power information of a vehicle corresponding to the first motion information when a brake pedal motion is detected during running of the vehicle.

The obtaining module 504 is configured to obtain first feature information according to the first motion information and the power information of the vehicle, where the first feature information is used to identify the identity of the user.

The identifying module 506 is configured to identify user identity information of the vehicle according to the first feature information.

As shown in fig. 6, optionally, the acquisition module 502 includes:

the first recording submodule 5022 is configured to record a stroke of the brake pedal when the brake pedal actuation is detected.

And the determining submodule 5024 is used for determining that the brake pedal meets a first preset condition under the condition that the stroke of the brake pedal is detected to be increased to be greater than or equal to a preset stroke threshold value so as to acquire first action information of the brake pedal and power information of the vehicle.

And the control submodule 5026 is used for stopping collecting the first action information of the brake pedal and the power information of the vehicle under the condition that the stroke of the brake pedal is detected to be smaller than or equal to the preset stroke threshold value.

Optionally, the obtaining module 504 includes:

the collecting submodule 5042 is configured to collect second motion information of the accelerator pedal when a brake pedal motion is detected and a first preset condition is met.

A first obtaining submodule 5044, configured to obtain a first set of time-space domain feature information and a first set of frequency domain feature information in the first action information;

the first acquisition submodule 5044 is further configured to: acquiring a second set of time-space domain characteristic information and a second set of frequency domain characteristic information in second action information;

the configuration submodule 5046 is configured to configure a feature matrix according to the first set of time-space domain feature information, the first set of frequency domain feature information, the second set of time-space domain feature information, the second set of frequency domain feature information and the power information, so as to take the feature matrix as the first feature information, wherein the power information comprises at least one of lateral acceleration, longitudinal acceleration, vehicle speed, motor rotation speed and vehicle torque of the vehicle.

Optionally, the identifying module 506 includes:

a second recording sub-module 5062 for recording the number of brake pedal actions.

The second obtaining sub-module 5064 is configured to obtain, when the number of actions of the brake pedal is greater than or equal to a preset number of identifications, a plurality of sets of first feature information that matches the number of actions.

An identification sub-module 5066 is configured to identify user identity information of the vehicle according to the plurality of sets of first characteristic information.

Optionally, the identification module 506 further includes:

the input submodule 5068 is configured to determine a feature matrix meeting a second preset condition as first feature information, and input multiple sets of first feature information into the trained classification recognition model respectively, so as to correspondingly output multiple recognition identifiers.

A dividing sub-module 5070, configured to divide the same identifier of the plurality of identifiers into a group to obtain at least one group of identifiers.

A calculating submodule 5072, configured to calculate a duty ratio of each group of identification identifiers among the plurality of identification identifiers, and determine an identification identifier with the largest duty ratio as an identification identifier of the vehicle user.

Optionally, the vehicle user identification device 500 further includes:

a first detection module 508, configured to detect whether the feature matrix is associated with a tag.

The determining module 510 is configured to determine, when the feature matrix is not associated with a tag, that the feature matrix is first feature information that meets a second preset condition.

The determining module 510 is further configured to: under the condition that the feature matrix is associated with the tag, determining that the feature matrix is second feature information which does not meet a second preset condition, wherein the second feature information is used for training a classification recognition model, inputting the second feature information and the tag into the training model of the classification recognition model, and updating the classification recognition model according to a training result of the training model so that the updated classification recognition model can recognize the tag as a recognition mark.

Optionally, the vehicle user identification device 500 further includes:

the receiving module 512 is configured to receive feedback information input by the user according to the identity prompt information.

The determining module 510 is further configured to: and determining whether the user is an identifiable user according to the feedback information.

A generating module 514, configured to generate a tag according to an input operation of the user when the user is not the identifiable user, so as to establish an association relationship between the tag and the feature matrix when the feature matrix is used as the second feature information.

The second detection module 516 is configured to directly detect whether the brake pedal is actuated when the user is an identifiable user, so as to use the feature matrix as the first feature information.

Optionally, the vehicle user identification device 500 further includes:

the input module 518 is configured to input the first feature information and the identified identity into a training model, where the training model is used to train the classification recognition model, and optimize the classification recognition model according to a training result of the training model.

For the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points.

As shown in fig. 7, a car machine 70 according to an embodiment of the present invention includes: the user identification device 500 of the vehicle described in the above embodiment.

The embodiment of the invention can be applied to a vehicle machine, a control module of other vehicles or a server with a communication link between the vehicle machine and the vehicle, wherein the vehicle machine refers to short for vehicle-mounted information entertainment products arranged in the vehicle, the vehicle machine can be used for realizing information communication with the vehicle and the outside (vehicle-to-vehicle) in function, the vehicle machine comprises a host, a display module, a microphone and a network module, the display module can be integrated with a touch module, and the vehicle machine can be electrically connected with a loudspeaker module of the vehicle.

A computer-readable storage medium according to an embodiment of the present invention includes the user identification method of the vehicle of any one of the above embodiments.

In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described by differences from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.

It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the invention may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the invention.

Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.

The foregoing has described in detail a vehicle user identification method and a vehicle user identification device provided by the present invention, and specific examples have been applied herein to illustrate the principles and embodiments of the present invention, and the above examples are only for aiding in the understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.

Claims

1. A method for identifying a user of a vehicle, comprising:

identifying user identity information of the vehicle according to the first characteristic information;

wherein, according to the first motion information and the power information of the vehicle, obtaining first feature information includes:

Under the condition that the action generated by the brake pedal is detected and accords with the first preset condition, second action information of the accelerator pedal is acquired; the first preset condition is that the stroke of the brake pedal is increased to be greater than or equal to a preset stroke threshold value;

and configuring a feature matrix according to the first set of time-space domain feature information, the first set of frequency domain feature information, the second set of time-space domain feature information, the second set of frequency domain feature information and the power information, so as to take the feature matrix as the first feature information.

2. The method according to claim 1, wherein the step of collecting first motion information of a brake pedal motion and power information of the vehicle corresponding to the first motion information in a case where the brake pedal motion is detected during running of the vehicle, includes:

if the brake pedal is detected to act, recording the stroke of the brake pedal;

3. The method of claim 2, wherein the power information includes at least one of a lateral acceleration, a longitudinal acceleration, a vehicle speed, a motor speed, and a vehicle torque of the vehicle.

4. A method according to claim 3, wherein said identifying user identity information of the vehicle based on the first characteristic information comprises:

recording the action quantity of the brake pedal;

5. The method of claim 4, wherein said identifying user identity information of said vehicle based on said plurality of sets of first characteristic information comprises:

6. The method of claim 5, wherein before determining the feature matrix satisfying a second preset condition as the first feature information, and inputting a plurality of sets of the first feature information into the trained classification recognition model respectively to correspondingly output a plurality of recognition identifications, the method further comprises:

detecting whether the feature matrix is associated with a label or not;

7. The method of claim 6, wherein the method further comprises:

8. The method according to claim 7, wherein, in the case where a brake pedal generation motion is detected, before collecting first motion information of the pedal motion and the power information of the vehicle corresponding to the first motion information, the method further comprises:

9. The method according to any one of claims 5 to 8, further comprising:

10. A user identification device of a vehicle, comprising:

the identification module is used for identifying the user identity information of the vehicle according to the first characteristic information;

wherein, the acquisition module includes:

the acquisition sub-module is used for acquiring second action information of the accelerator pedal when the action generated by the brake pedal is detected and accords with the first preset condition; the first preset condition is that the stroke of the brake pedal is increased to be greater than or equal to a preset stroke threshold value;

The first acquisition sub-module is used for acquiring a first set of time-space domain characteristic information and a first set of frequency domain characteristic information in the first action information;

the first acquisition sub-module is further configured to: acquiring a second set of time-space domain characteristic information and a second set of frequency domain characteristic information in the second action information;

and the configuration submodule is used for configuring a feature matrix according to the first set of time-space domain feature information, the first set of frequency domain feature information, the second set of time-space domain feature information, the second set of frequency domain feature information and the power information so as to take the feature matrix as first feature information.

11. A vehicle machine, comprising:

the user identification device of a vehicle as claimed in claim 10.

12. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when executed by a processor, implements the user identification method of a vehicle according to any one of claims 1 to 8.