CN115492493A - Tail gate control method, device, equipment and medium - Google Patents
Tail gate control method, device, equipment and medium Download PDFInfo
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- E—FIXED CONSTRUCTIONS
- E05—LOCKS; KEYS; WINDOW OR DOOR FITTINGS; SAFES
- E05F—DEVICES FOR MOVING WINGS INTO OPEN OR CLOSED POSITION; CHECKS FOR WINGS; WING FITTINGS NOT OTHERWISE PROVIDED FOR, CONCERNED WITH THE FUNCTIONING OF THE WING
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Abstract
The invention provides a tail gate control method, which comprises the following steps: acquiring the current state of the tail gate; acquiring a first tail gate control parameter, wherein the first tail gate control parameter is obtained by detecting through a capacitive sensor; determining a category to which the first tailgate control parameter belongs; and controlling the state of the tail gate according to the belonged category and the current state of the tail gate. By the method, when each user kicks the tail gate to open or close, standard kicking actions are not needed, and the electric tail gate can be opened or closed only by kicking according to personal habits. Meanwhile, the method can also continuously collect the kick action data of the user, update the algorithm parameters, continuously improve the accuracy of model prediction and bring better opening or closing experience of the electric tailgate to the user.
Description
Technical Field
The application relates to the technical field of automobile safety, in particular to a tail gate control method, a tail gate control device, tail gate control equipment and a tail gate control medium.
Background
Automobile tail gate especially large-scale SUV (sports utility vehicle) tail gate is heavier, and bare-handed opens the comparison difficulty, and it is more inconvenient that both hands embrace to transport the east and west and open the tail gate, and when the automobile body was dirty (glues dust, greasy dirt) simultaneously, the hand can inevitably be made dirty when the staff pressed the button, and the response tail gate is suitable for fortune and is given birth. The induction electric tail gate mainly identifies the kicking action through the kicking sensor to realize the opening and closing of the tail gate. However, the existing tail gate opening and closing mode has the problems of poor identification accuracy, easy false triggering, insufficient intelligence in operation and the like.
Disclosure of Invention
In view of the above drawbacks of the prior art, the present invention provides a tailgate control method, apparatus, device and medium to solve the above problems.
The invention provides a tail gate control method, which comprises the following steps:
acquiring the current state of the tail gate;
acquiring a first tail gate control parameter, wherein the first tail gate control parameter is detected by a capacitive sensor;
determining the category of the first tail gate control parameter;
and controlling the state of the tail gate according to the belonged category and the current state of the tail gate.
In an embodiment of the present invention, after determining the category to which the first tailgate control parameter belongs, the method further includes:
judging whether the tail gate meets an opening condition or not, if so, opening the tail gate, and if not, keeping the tail gate closed; wherein the turn-on condition includes:
the vehicle speed is zero;
the gear is P gear;
the distance between the bluetooth key and the nearest antenna is within the valid range.
In an embodiment of the present invention, the classifying the tail gate control parameter includes:
classifying the first tail gate control parameter through a pre-trained classification model, wherein the classification model is obtained by taking a second tail gate control parameter as a model input and taking a category of the classification model as a model output training; wherein the categories include kicking actions and non-kicking actions.
In an embodiment of the present invention, after obtaining the tailgate control parameter, the method further includes:
and carrying out filtering processing on the first tail gate control parameter, wherein the filtering processing comprises amplitude limiting filtering, recursive average filtering, first-order lag filtering and jitter elimination filtering.
In an embodiment of the invention, the classification model is one of a support vector machine model, a logistic regression model, a naive bayes model, and a random forest model.
In an embodiment of the present invention, the method for training the classification model includes:
acquiring a data set;
filtering the data set through amplitude limiting filtering, recursive average filtering, first-order lag filtering and jitter elimination filtering;
performing binary K-means clustering on the filtered data to obtain a plurality of clustering clusters;
and screening a target cluster from the plurality of clusters, and taking the target cluster as the output of a classification model to train the classification model.
In an embodiment of the present invention, if the category of the first tailgate control parameter is non-kicking motion and the state of the tailgate changes from the first state to the second state, the first tailgate control parameter at that time is added to the training set as a negative sample of the training set, and the classification model is updated.
The invention provides a tail gate control device, which comprises:
the state acquisition module is used for acquiring the current state of the tail gate;
the parameter acquisition module is used for acquiring a first tail gate control parameter, and the first tail gate control parameter is obtained by detection of a capacitive sensor;
the category determining module is used for determining the category of the first tail gate control parameter;
and the control module is used for controlling the state of the tail gate according to the belonged category and the current state of the tail gate.
The invention provides an electronic device, comprising:
one or more processors;
a storage device for storing one or more programs that, when executed by the one or more processors, cause the electronic device to implement the steps of the tailgate control method described above.
The present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor of a computer, causes the computer to execute the steps of the tailgate control method described above.
The invention has the beneficial effects that: the tail gate control method comprises the following steps: acquiring the current state of the tail gate; acquiring a first tail gate control parameter, wherein the first tail gate control parameter is obtained by detecting through a capacitive sensor; determining the category of the first tail gate control parameter; and controlling the state of the tail gate according to the belonged category and the current state of the tail gate. By the method, when each user kicks the tail gate to open or close, standard kicking actions are not needed, and the electric tail gate can be opened or closed only by kicking according to personal habits. Meanwhile, the method can also continuously collect the kick action data of the user, update the algorithm parameters, continuously improve the accuracy of model prediction and bring better opening or closing experience of the electric tailgate for the user.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the appended drawings in the following description are only some embodiments of the present application, and that other drawings may be derived from these drawings without inventive effort by a person skilled in the art. In the drawings:
FIG. 1 is a schematic diagram of an implementation environment of a tailgate control method according to an exemplary embodiment of the present application;
FIG. 2 is a flow chart illustrating a tailgate control method according to an exemplary embodiment of the present application;
FIG. 3 is a flowchart illustrating a classification model training method according to an exemplary embodiment of the present application;
FIG. 4 is a block diagram of a tailgate control apparatus, shown in an exemplary embodiment of the present application;
FIG. 5 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the disclosure herein, wherein the embodiments of the present invention are described in detail with reference to the accompanying drawings and preferred embodiments. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, and are not intended to limit the scope of the present invention.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
In the following description, numerous details are set forth to provide a more thorough explanation of embodiments of the present invention, however, it will be apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details, and in other embodiments, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring embodiments of the present invention.
FIG. 1 is a schematic diagram of an exemplary tailgate control method implementation environment of the present application. Referring to fig. 1, the implementation environment includes a terminal device 101 and a server 102, and the terminal device 101 and the server 102 communicate with each other through a wired or wireless network. The terminal equipment can acquire the current state of the tail gate; acquiring a first tail gate control parameter, wherein the first tail gate control parameter is obtained by detection of a capacitive sensor; determining a category to which the first tailgate control parameter belongs; and controlling the state of the tail gate according to the belonged category and the current state of the tail gate.
It should be understood that the number of terminal devices 101 and servers 102 in fig. 1 is merely illustrative. There may be any number of terminal devices 101 and servers 102, as may be desired.
The terminal device 101 corresponds to a client, and may be any electronic device having a user input interface, including but not limited to a smart phone, a tablet, a notebook computer, a vehicle-mounted computer, and the like, where the user input interface includes but not limited to a touch screen, a keyboard, a physical key, an audio pickup device, and the like.
The server 102 corresponds to a server, may be a server providing various services, may be an independent physical server, may also be a server cluster or a distributed system formed by a plurality of physical servers, and may also be a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN (Content Delivery Network), and a big data and artificial intelligence platform, which is not limited herein.
The terminal 101 may communicate with the server 102 through a wireless network such as 3G (third generation mobile information technology), 4G (fourth generation mobile information technology), 5G (fifth generation mobile information technology), and the like, which is not limited herein.
In order to solve the problems of the prior art, embodiments of the present application propose a tailgate control method, a tailgate control device, an electronic device, and a computer-readable storage medium, which will be described in detail below.
Referring to fig. 2, fig. 2 is a flowchart illustrating a tailgate control method according to an exemplary embodiment of the present application. The method may be applied to the implementation environment shown in fig. 1 and specifically executed by the terminal device 101 in the implementation environment. It should be understood that the method may be applied to other exemplary implementation environments and is specifically executed by devices in other implementation environments, and the embodiment does not limit the implementation environment to which the method is applied.
Referring to fig. 2, fig. 2 is a flowchart of an exemplary tailgate control method according to an embodiment of the present application, the method is used for recognizing a specific action of a person outside a vehicle to determine whether to open or close a tailgate, and the tailgate control method at least includes steps S210 to S240, and the following steps are described in detail:
step S210, acquiring the current state of the tail gate;
it should be noted that the current state of the tail gate includes an open state and an open/close state, and the state of the tail gate can be read by an onboard computer or a domain controller. In this embodiment, the tailgate refers to an inductive electric tailgate, and mainly realizes the opening and closing of the tailgate by recognizing a kick action through a kick sensor.
Step S220, acquiring a first tail gate control parameter, wherein the first tail gate control parameter is obtained by detection of a capacitive sensor;
it should be noted that the first tailgate control parameter is detected by the capacitive sensor before the tailgate status changes, for example, the tailgate is opened, a kick operation is required before the tailgate is opened, and the kick operation is performed. According to the principle of the capacitive kick sensor, the change of the dielectric constant between the polar plates can be caused when a user enters and exits the capacitive polar plates, and finally the sensor outputs voltage waveforms with different amplitudes, slopes and frequencies, wherein the voltage waveform is defined as a tail gate control parameter.
In an exemplary embodiment, after acquiring the tailgate control parameters, the method further comprises:
performing analog-to-digital conversion on the first tail gate control parameter; and filtering the first tail gate control parameter subjected to analog-to-digital conversion, wherein the filtering comprises amplitude limiting filtering, recursive average filtering, first-order lag filtering and jitter elimination filtering.
Specifically, a first tail gate control parameter is converted into a digital signal from an analog voltage signal through a high-resolution analog-to-digital converter in the domain controller so as to be preprocessed by subsequent data, and the sampling frequency is set to be 2kHz.
Because the kick sensor works in a complex environment, the output voltage signal inevitably has noise which is unfavorable for model training, such as spike interference, jitter and the like. Therefore, after data are acquired with high precision, spike interference is eliminated by utilizing digital amplitude limiting filtering, and then the data flow is sequentially filtered by a recursive average filter, a first-order lag filter and a jitter elimination filter to filter residual noise, so that data processing is completed.
Step S230, determining the category of the first tail gate control parameter;
in one embodiment, the classifying the tailgate control parameters includes:
classifying the first tail gate control parameter through a pre-trained classification model, wherein the classification model is obtained by taking a second tail gate control parameter as a model input and taking the category of the second tail gate control parameter as a model output training; wherein the categories include kicking actions and non-kicking actions. That is, the tail gate control parameter is input into the classification model to determine whether the tail gate control parameter belongs to the kick action
It should be noted that the classification model is one of a support vector machine model, a logistic regression model, a naive bayes model, and a random forest model.
The induction tail gate mainly relies on the kick sensor to detect the kick action, and the core that the user opened the tail gate through the kick is discernment kick action, consequently can convert the classification problem in the machine learning into the problem. Whether the kicking action is needed to be predicted or not and a sample set with labels is available, so that a classification algorithm is selected for supervised learning, random seeds a are set, the sample set is divided according to the proportion of pseudo-ginseng to obtain the same verification set and training set, classification algorithms such as a support vector machine, logistic regression, naive Bayes and random forests are respectively used for training and verifying, and verification indexes of the respective algorithms, namely accuracy, recall rate, F1 value, AUC and ROC, are obtained.
Support Vector Machines (SVMs) are generalized linear classifiers (generalized linear classifiers) that binary classify data in a supervised learning manner, and their decision boundaries are maximum-margin hyperplane (maximum-margin hyperplane) that solve for a learning sample. The SVM calculates an empirical risk (empirical risk) using a hinge loss function (change loss) and adds a regularization term to a solution system to optimize a structural risk (structural risk), which is a classifier with sparsity and robustness. SVMs can be classified non-linearly by a kernel method, which is one of the common kernel learning methods.
Logistic regression, also called logistic regression analysis, is a generalized linear regression analysis model, and belongs to supervised learning in machine learning. The derivation process and calculation method is similar to the regression process, but in practice, the derivation process and calculation method are mainly used for solving the two-classification problem (the multi-classification problem can also be solved). The model is trained by a given set of n data sets (training set) and after training is complete, the given set or sets of data (test set) are classified. Wherein each group of data is composed of p indexes.
The Bayesian method is based on Bayesian principle, and the sample data set is classified by using the knowledge of probability statistics. Because of the solid mathematical foundation, the misjudgment rate of the Bayes classification algorithm is very low. The Bayesian method is characterized by combining the prior probability and the posterior probability, thereby avoiding the subjective bias of only using the prior probability and avoiding the over-fitting phenomenon of singly using the sample information. The Bayesian classification algorithm shows higher accuracy under the condition of larger data set, and the algorithm is simpler. The naive Bayes method is correspondingly simplified on the basis of Bayes algorithm, namely that the attributes are mutually independent under the condition when a target value is given. That is, neither attribute variable has a large weight for the decision result nor attribute variable has a small weight for the decision result.
In machine Learning, there is a large class called Ensemble Learning (Ensemble Learning), and the basic idea of Ensemble Learning is to combine a plurality of classifiers, thereby implementing an Ensemble classifier with better prediction effect. The integrated algorithms can be broadly divided into: bagging, boosting and Stacking. There are two tasks in machine learning, regression and classification, and random forests can be competent for both tasks simultaneously. The classification task is to predict the dispersion value (for example, classify the types of vegetation, buildings, water bodies and other ground objects in a scene image); the regression task is to predict the continuous value (for example, predict how much the temperature is in tomorrow based on the existing data, and predict the price of a fund in tomorrow).
Referring to fig. 3, fig. 3 is a flowchart illustrating a method for training a classification model according to an exemplary embodiment of the present application, and as shown in fig. 3, the step of training the classification model includes:
step S310, acquiring a data set;
step S320, filtering the data set through amplitude limiting filtering, recursive average filtering, first-order lag filtering and jitter elimination filtering;
step S330, performing binary K-means clustering on the filtered data to obtain a plurality of clustering clusters;
and step S340, screening target cluster clusters from the cluster clusters, and taking the target cluster clusters as the output of a classification model to train the classification model.
The collected data is concentrated, and the action data of 1000 users kicking the tail door in different vehicle types (SUV/car). According to the principle of the capacitive kick sensor, when a user enters and exits from a capacitor plate, the dielectric constant between the plates is changed, and finally the sensor outputs voltage waveforms with different amplitudes, slopes and frequencies. During data acquisition, standard kicking actions do not need to be specified, namely factors such as the distance between a user and the tail of the vehicle, the leg lifting angle, the leg lifting height and the action time do not need to be limited. During collection, the user only needs to perform kicking actions according to personal habits to finish data collection.
The data preprocessed in step S320 is kicking actions that are accustomed to by different users. In order to facilitate subsequent classification model training, a data set needs to be established. The data after finishing and processing need to use the data after processing to carry out the kick action and build a database, and in view of the fact that the data is non-standard kick action collection, clustering in unsupervised learning can be used for realizing data set kick action classification. Firstly, the whole data set is used as a cluster, then the cluster is divided into two parts, the error square sum from each point of the two clusters to the center of mass is respectively calculated, and the error square sum is used as a parameter for measuring the clustering effect. And performing K-mean classification on the large error square sum cluster, calculating the total error after dividing the large error square sum cluster into two, selecting the cluster with the minimum error to perform one-to-two operation, and repeating the steps until the specified cluster number is obtained, thereby finally completing the kick action basic standard library.
After training is completed, the classification model deploys the model to a vehicle body domain controller, kick actions are predicted in real time under certain conditions, and if the kick actions exist, the tail gate is controlled to be opened or closed according to the current tail gate state, gear, vehicle speed and key information. Specifically, the classification model is engineered and deployed and integrated into a domain controller environment, the domain controller supports gigabit Ethernet, and the subsequent model supports OTA to perform model iteration.
It should be noted that, a false detection situation of the classification model cannot be avoided, so that a situation of a classification error can be recorded, and the specific method is that if the voltage waveform classification result is detected to be not the kick action, and meanwhile, within a period of time after the waveform is sent out, a user manually opens or closes a tail gate to indicate that the kick action is detected by a false detection, and the voltage waveform is recorded to be the edge scene Corner case of the model. This data is uploaded to the background, followed by training the updated model using this edge scene Corner case as a training set (kick action standard library), and after the model training is completed, the model is deployed to the vehicle controller via an Over-the-Air Technology (OTA).
In an exemplary embodiment, after determining the category to which the first tailgate control parameter belongs, the method further comprises:
judging whether the tail gate meets an opening condition or not, if so, opening the tail gate, and if not, keeping the tail gate closed; wherein the turn-on condition includes:
the vehicle speed is zero; the gear is P gear; the distance between the bluetooth key and the nearest antenna is within a valid range.
Specifically, in order to prevent the tail gate from being opened during the movement of the vehicle and causing injury to the user, the vehicle speed is set to be zero, and the gear is set to be the P gear, so that the safety of the user is ensured. And because the false recognition is caused by the fact that a cat, a dog and other moving objects run through the proximity of the sensor, the range detection of the Bluetooth key is increased, namely the distance between the third condition key and the nearest antenna is within the effective range.
Step S240, controlling the state of the tail gate according to the belonged category and the current state of the tail gate.
Specifically, if the tail gate is in a closed state at the moment, a kicking action is detected, and the domain controller unlocks the tail gate lock body and controls the electric stay bar to open the electric tail gate; if the tail gate is in an open state, the kicking action is detected, and the domain controller controls the electric stay bar to put down the tail gate and finally lock the tail gate.
By the method, when each user kicks the tail gate to open or close, the electric tail gate can be opened or closed only by kicking according to personal habits without standard kicking actions. Meanwhile, the method can also continuously collect the kick action data of the user, update the algorithm parameters, continuously improve the accuracy of model prediction and bring better opening or closing experience of the electric tailgate to the user.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Fig. 4 is a block diagram of a tailgate control device shown in an exemplary embodiment of the present application. The device can be applied to the implementation environment shown in fig. 1 and is specifically configured in a terminal device. The apparatus may also be applied to other exemplary implementation environments and be specifically configured in other devices, and the embodiment does not limit the implementation environment to which the apparatus is applied.
As shown in fig. 4, the present application provides a tailgate control apparatus, comprising:
a state obtaining module 410, configured to obtain a current state of the tail gate;
a parameter obtaining module 420, configured to obtain a first tailgate control parameter, where the first tailgate control parameter is obtained by detecting with a capacitive sensor;
the category determining module 430 is configured to determine a category to which the first tailgate control parameter belongs;
a control module 440, configured to control a state of the tail gate according to the category to which the tail gate belongs and the current state of the tail gate.
It should be noted that the tail gate control device based on machine learning provided in the foregoing embodiment and the tail gate control method provided in the foregoing embodiment belong to the same concept, and specific ways for each module and unit to perform operations have been described in detail in the method embodiment, and are not described herein again. In practical applications, the tail gate control device provided in the above embodiment may distribute the above functions by different functional modules according to needs, that is, divide the internal structure of the device into different functional modules to complete all or part of the above described functions, which is not limited herein.
An embodiment of the present application further provides an electronic device, including: one or more processors; a storage device, configured to store one or more programs, which when executed by the one or more processors, cause the electronic device to implement the tailgate control method provided in the above-described embodiments.
FIG. 5 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application. It should be noted that the computer system 500 of the electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for system operation are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other through a bus 504. An Input/Output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output section 507 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN (Local area network) card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. A drive 510 is also connected to the I/O interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted as necessary on the drive 510, so that a computer program read out therefrom is mounted as necessary in the storage section 508.
In particular, according to embodiments of the present application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 505, and/or installed from the removable medium 511. When the computer program is executed by a Central Processing Unit (CPU) 501, various functions defined in the system of the present application are executed.
It should be noted that the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM), a flash Memory, an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer-readable signal medium may comprise a propagated data signal with a computer-readable computer program embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
Another aspect of the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor of a computer, causes the computer to execute the tailgate control method as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist separately without being incorporated in the electronic device.
Another aspect of the application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the tailgate control method provided in the above embodiments.
The foregoing embodiments are merely illustrative of the principles of the present invention and its efficacy, and are not to be construed as limiting the invention. Any person skilled in the art can modify or change the above-described embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A tailgate control method, characterized in that the method comprises:
acquiring the current state of the tail gate;
acquiring a first tail gate control parameter, wherein the first tail gate control parameter is obtained by detection of a capacitive sensor;
determining the category of the first tail gate control parameter;
and controlling the state of the tail gate according to the belonged category and the current state of the tail gate.
2. The tailgate control method according to claim 1, characterized in that after determining the category to which the first tailgate control parameter belongs, the method further comprises:
judging whether the tail gate meets an opening condition or not, if so, opening the tail gate, and if not, keeping the tail gate closed; wherein the turn-on condition includes:
the vehicle speed is zero;
the gear is P gear;
the distance between the bluetooth key and the nearest antenna is within the valid range.
3. The tailgate control method according to claim 1, wherein the classifying the tailgate control parameters comprises:
classifying the first tail gate control parameter through a pre-trained classification model, wherein the classification model is obtained by taking a second tail gate control parameter as model input and taking the category of the second tail gate control parameter as model output training; wherein the categories include kicking actions and non-kicking actions.
4. The tailgate control method according to claim 1, wherein after acquiring the tailgate control parameters, the method further comprises:
and carrying out filtering processing on the first tail gate control parameter, wherein the filtering processing comprises amplitude limiting filtering, recursive average filtering, first-order lag filtering and jitter elimination filtering.
5. The tailgate control method according to claim 3, wherein the classification model is one of a support vector machine model, a logistic regression model, a naive Bayes model, and a random forest model.
6. The tailgate control method according to claim 3, characterized in that the training method of the classification model comprises:
acquiring a data set;
filtering the data set through amplitude limiting filtering, recursive average filtering, first-order lag filtering and jitter elimination filtering;
performing binary K-means clustering on the filtered data to obtain a plurality of clustering clusters;
and screening target cluster clusters from the cluster clusters, and taking the target cluster clusters as the output of a classification model to train the classification model.
7. A tailgate control method according to claim 6, characterized in that if the category to which the first tailgate control parameter belongs is a non-kick action and the tailgate status changes from a first status to a second status, the first tailgate control parameter at that time is added to a training set as a negative sample of the training set, and the classification model is updated.
8. A tailgate control apparatus, characterized in that the apparatus comprises:
the state acquisition module is used for acquiring the current state of the tail gate;
the parameter acquisition module is used for acquiring a first tail gate control parameter, and the first tail gate control parameter is obtained by detection of a capacitive sensor;
the category determining module is used for determining the category of the first tail gate control parameter;
and the control module is used for controlling the state of the tail gate according to the belonged category and the current state of the tail gate.
9. An electronic device, characterized in that the electronic device comprises:
one or more processors;
storage means for storing one or more programs that, when executed by the one or more processors, cause the electronic device to implement the steps of the tailgate control method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which, when being executed by a processor of a computer, causes the computer to carry out the steps of the tailgate control method according to any of claims 1 to 7.
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