CN113780039A - Intelligent vehicle window control method and device, electronic equipment and storage medium - Google Patents
Intelligent vehicle window control method and device, electronic equipment and storage 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
- E05F15/00—Power-operated mechanisms for wings
- E05F15/70—Power-operated mechanisms for wings with automatic actuation
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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
- E05F15/00—Power-operated mechanisms for wings
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- E05F15/73—Power-operated mechanisms for wings with automatic actuation responsive to movement or presence of persons or objects
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- E05Y—INDEXING SCHEME ASSOCIATED WITH SUBCLASSES E05D AND E05F, RELATING TO CONSTRUCTION ELEMENTS, ELECTRIC CONTROL, POWER SUPPLY, POWER SIGNAL OR TRANSMISSION, USER INTERFACES, MOUNTING OR COUPLING, DETAILS, ACCESSORIES, AUXILIARY OPERATIONS NOT OTHERWISE PROVIDED FOR, APPLICATION THEREOF
- E05Y2900/00—Application of doors, windows, wings or fittings thereof
- E05Y2900/50—Application of doors, windows, wings or fittings thereof for vehicles
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Abstract
The embodiment of the application provides a car window intelligent control method: after receiving a user vehicle locking signal, detecting a power mode state, a four-door two-cover switch state and a locking state of a vehicle; if the vehicle windows are in the closed state, acquiring an image of the vehicle window in the initial state by using an in-vehicle camera of the vehicle; determining whether an obstacle exists on the vehicle window and the position of the top end of the glass of the vehicle window through a preset neural network model based on the image of the vehicle window in the initial state; when no obstacle exists on the vehicle window, the vehicle window is controlled to be closed based on the position of the top end of the glass of the vehicle window. According to the method, after the vehicle locking signal of the user is received, the vehicle window image can be automatically acquired, the position of the top end of the glass of the vehicle window is calculated, the vehicle window which is not closed is controlled to be closed, timeliness is good, and the problem that property of a vehicle owner is lost or water enters the vehicle can be avoided.
Description
Technical Field
The present disclosure relates to the field of intelligent control technologies, and in particular, to an intelligent control method and apparatus for a vehicle window, an electronic device, and a storage medium.
Background
Along with the popularization of automobiles, people have more and more requirements on the quality of automobiles in various aspects, and automobile windows are continuously improved as indispensable parts of each automobile.
In life, many users forget to close windows after power-off and locking the automobile, which easily causes the theft of valuables in the automobile and can also cause water inflow in the automobile if raining. In the related art, a main solution to the above problem is to remotely control a window, and the window is remotely controlled, but needs not to be closed based on the thinking of the user, and obviously, this cannot completely solve the problem that the user forgets to close the window. Moreover, when a user thinks to close the window remotely, valuables in the vehicle may be lost early and the timeliness is poor.
Disclosure of Invention
In view of the above, embodiments of the present invention provide an intelligent control method and apparatus for a vehicle window, an electronic device, and a storage medium, so as to overcome the above-mentioned drawbacks.
In a first aspect, an embodiment of the present application provides an intelligent control method for a vehicle window, including:
after receiving a user vehicle locking signal, detecting a power mode state, a four-door two-cover switch state and a locking state of a vehicle;
if the vehicle windows are in the closed state, acquiring an image of the vehicle window in the initial state by using an in-vehicle camera of the vehicle;
determining whether an obstacle exists on the vehicle window and the position of the top end of the glass of the vehicle window through a preset neural network model based on the image of the vehicle window in the initial state;
and when no obstacle exists on the vehicle window, controlling the vehicle window to be closed based on the position of the top end of the glass of the vehicle window.
In one embodiment, the determining, based on the image of the vehicle window in the initial state, whether an obstacle exists on the vehicle window and a position of a glass tip of the vehicle window is located through a preset neural network model includes:
preprocessing the image of the vehicle window in the initial state;
taking the preprocessed image as the input of a two-classification convolutional neural network model, and calculating whether the vehicle window has an obstacle or not through the two-classification convolutional neural network model;
if the position of the top end of the glass of the vehicle window is not the same as the position of the glass top end of the vehicle window, the preprocessed image is used as the input of the position classification convolution neural network model, and the position of the top end of the glass of the vehicle window is calculated through the position classification convolution neural network model.
In one embodiment, the height of the vehicle window is divided into at least one section in advance, each section corresponds to one position type, and each position type corresponds to different time length for closing the vehicle window;
the controlling the window to close based on the position of the glass top end of the window comprises:
determining a first position category of the vehicle window according to the height of the position of the top end of the glass of the vehicle window;
and controlling the car window to be closed within the time length corresponding to the first position type according to the first position type.
In one embodiment, after controlling the window to close within the time period corresponding to the first position category, the method further includes:
counting the closing times of the car window;
and when the closing times of the vehicle window reach a preset value, checking whether the vehicle window is completely closed by using a camera controller of the vehicle.
In one embodiment, the verifying, with a camera controller of the vehicle, whether the window is completely closed includes:
acquiring an image of the vehicle window after closing by using a camera controller of the vehicle;
determining a second position category of the vehicle window based on the image of the vehicle window after the closing action is performed;
and if the window is determined not to be completely closed through the second category, controlling the window to be closed again.
In one embodiment, the method further comprises:
taking the time when the vehicle window is controlled to be closed again as an initial time point, and when the motor current value of the vehicle window is detected to be increased, calculating the time length T between the initial time point and the time point when the motor current value of the vehicle window is increased, wherein the time length T is used for closing the vehicle window again;
and sending the time length T for closing the vehicle window again, the image of the vehicle window after closing action and the second position category to a cloud end as parameters for optimizing the preset neural network model.
In a second aspect, an embodiment of the present application provides an intelligent vehicle window control device, including:
the detection module is used for detecting the power mode state, the four-door two-cover switch state and the locking state of the vehicle after receiving the vehicle locking signal of the user;
the acquisition module is used for acquiring an image of a vehicle window in an initial state by using an in-vehicle camera of the vehicle if the vehicle window is in a closed state;
the position determining module is used for determining whether an obstacle exists on the vehicle window and the position of the top end of the glass of the vehicle window through a preset neural network model based on the image of the vehicle window in the initial state;
and the control module is used for controlling the window to be closed based on the position of the top end of the glass of the window when no obstacle exists on the window.
In one embodiment, the location determination module comprises:
the preprocessing unit is used for preprocessing the image of the vehicle window in the initial state;
the first calculation unit is used for taking the preprocessed image as the input of a two-classification convolutional neural network model, and calculating whether the vehicle window has an obstacle or not through the two-classification convolutional neural network model;
and the second calculation unit is used for taking the preprocessed image as the input of the position classification convolution neural network model if no obstacle exists on the vehicle window, and calculating the position of the glass top end of the vehicle window through the position classification convolution neural network model.
In one embodiment, the height of the vehicle window is divided into at least one section in advance, each section corresponds to one position type, and each position type corresponds to different time length for closing the vehicle window;
the control module is specifically used for determining a first position type of the vehicle window according to the height of the position of the top end of the glass of the vehicle window; and controlling the car window to be closed within the time length corresponding to the first position type according to the first position type.
In one embodiment, the apparatus further comprises:
the counting module is used for counting the closing times of the car window;
and the checking module is used for checking whether the car window is completely closed or not by utilizing the camera controller of the car when the closing times of the car window reach a preset value.
In one embodiment, the verification module comprises:
an image acquisition unit configured to acquire an image of the window after a closing operation by using a camera controller of the vehicle;
a position type determination unit configured to determine a second position type of the window based on the image of the window after the closing operation;
and the control unit is used for controlling the window to be closed again if the window is determined not to be completely closed through the second category.
In one embodiment, the apparatus further comprises:
the time length calculation module is used for calculating the time length T between the starting time point and the time point when the motor current value of the car window is increased when the car window is controlled to be closed again as the starting time point, and the time length T is used for closing the car window again;
and the sending module is used for sending the time length T for closing the vehicle window again, the image of the vehicle window after the closing action and the second position category to a cloud end to be used as parameters for optimizing the preset neural network model.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor; and a memory configured to store computer-executable instructions that, when executed, cause the processor to implement the method described in any of the embodiments of the present application.
In a fourth aspect, embodiments of the present application provide a storage medium storing computer-executable instructions that, when executed, implement a method described in any of the embodiments of the present application.
The embodiment of the application provides a car window intelligent control method: after receiving a user vehicle locking signal, detecting a power mode state, a four-door two-cover switch state and a locking state of a vehicle; if the vehicle windows are in the closed state, acquiring an image of the vehicle window in the initial state by using an in-vehicle camera of the vehicle; determining whether an obstacle exists on the vehicle window and the position of the top end of the glass of the vehicle window through a preset neural network model based on the image of the vehicle window in the initial state; when no obstacle exists on the vehicle window, the vehicle window is controlled to be closed based on the position of the top end of the glass of the vehicle window. According to the method, after the vehicle locking signal of the user is received, the vehicle window image can be automatically acquired, the position of the top end of the glass of the vehicle window is calculated, the vehicle window which is not closed is controlled to be closed, timeliness is good, and the problem that property of a vehicle owner is lost or water enters the vehicle can be avoided.
Drawings
Some specific embodiments of the present application will be described in detail hereinafter by way of illustration and not limitation with reference to the accompanying drawings. The same reference numbers in the drawings identify the same or similar elements or components. Those skilled in the art will appreciate that the drawings are not necessarily drawn to scale. In the drawings:
fig. 1 is a schematic structural diagram of an intelligent vehicle window control system provided in an embodiment of the present application;
fig. 2 is a flowchart of an intelligent vehicle window control method provided in an embodiment of the present application;
FIG. 3 is a schematic diagram of a two-class convolutional neural network model provided in an embodiment of the present application;
FIG. 4 is a schematic diagram of a position classification convolutional neural network model provided in an embodiment of the present application;
fig. 5 is another flowchart of a vehicle window intelligent control method provided in the embodiment of the present application;
FIG. 6-1 is a schematic view of an intelligent control device for a vehicle window provided in an embodiment of the present application;
fig. 6-2 is a further schematic diagram of the intelligent control device for vehicle windows according to the embodiment of the present application;
6-3 are still another schematic diagrams of the intelligent vehicle window control device provided by the embodiment of the application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to more clearly understand the technical features, objects, and effects of the embodiments of the present application, specific embodiments of the present application will now be described with reference to the accompanying drawings.
The embodiment of the application provides a door window intelligence control system, as shown in fig. 1, this system includes: the system comprises a four-door two-cover switch 11, a window motor relay 12, a window motor 13, a camera motor 14, a camera 15, a vehicle body controller 16, a power mode controller 17 and a camera controller 18, wherein the vehicle body controller 16, the camera controller 17, the power mode controller 18 and a remote information processor 19 are communicated through a CAN network.
The window motor relay 12 is closed or opened under the control of the vehicle body controller 16, so that the connection or disconnection of the power supply of the window motor 13 is realized, and the movement of the window glass is controlled. Alternatively, the window motor relay 12 may be a pair, one is respectively installed at the positive and negative power supply ends of the window motor 13, and the rotation direction of the window motor 13 can be changed by different attraction states of the two window motor relays 12, so as to realize the ascending and descending of the window glass. The camera 15 is installed on the camera motor 14, can realize carrying out image acquisition to each door window respectively under the drive of camera motor 14. The controllers are connected and communicated through a CAN network, the power mode controller 17 CAN manage the power mode of the whole vehicle and send the power mode of the whole vehicle to the vehicle body controller 16 and the camera controller 17, the camera controller 17 CAN control the camera motor 14 and analyze and process images collected by the camera 15 and send an analysis result to the vehicle body controller 16, and the vehicle body controller 16 CAN determine whether to control the corresponding vehicle window motor relay 12 to suck according to the received result so as to close the vehicle window.
The embodiment of the application provides an intelligent control method for a vehicle window, which is applied to the intelligent control system for the vehicle window, as shown in fig. 2, and fig. 2 is a flowchart of the intelligent control method for the vehicle window provided by the embodiment of the application.
The method comprises the following steps:
In this embodiment, after the user gets OFF and locks the vehicle, the vehicle body controller 16 may receive the power mode state signal sent by the power mode controller 18, optionally, the power mode signal may be represented by "ON" or "OFF", or may be represented by "0" or "1", and the specific representation mode may be set according to actual requirements, which is not limited in this embodiment; if the power mode status signal indicates that the vehicle is locked, the vehicle body controller 16 may detect a four-door two-cover switch state, wherein the four-door two-cover switch 11 may include a left front door switch, a right front door switch, a left rear door switch, a right rear door switch, a front hatch switch, and a tailgate switch; the vehicle body controller 16 detects whether the switches are in the on state or the off state, and if all the switches are in the off state, the vehicle body controller 16 can detect whether the vehicle is in the locked outside state.
It should be noted that there is no certain sequence for detecting the power mode state, the four-door two-cover switch state, and the locked state of the vehicle, and the detection sequence may be flexibly adjusted according to the actual situation, and if any one of the states is not closed, the process is ended, and the vehicle body controller 16 does not control the vehicle window to be closed.
And step 202, if the vehicle windows are in the closed state, acquiring an image of the vehicle window in the initial state by using an in-vehicle camera of the vehicle.
In this embodiment, if the power mode state, the four-door two-door open/close state, and the locked state of the vehicle are all the closed states, the image of the window in the initial state can be acquired.
Specifically, the camera motor 14 can be controlled through the camera controller 17, and the camera 15 is driven to collect the image of the vehicle window in the initial state through the rotation of the camera motor 14. Optionally, before the image of the window is collected, an image of a seat in the vehicle can also be collected, so as to judge whether a person is in the vehicle.
And step 203, determining whether an obstacle exists on the vehicle window and the position of the glass top end of the vehicle window through a preset neural network model based on the image of the vehicle window in the initial state.
In this embodiment, optionally, the preset neural network model may be a convolutional neural network model, and the acquired image of the vehicle window in the initial state may be used as an input, and the image is processed by the convolutional neural network model in the camera controller 17, so that whether an obstacle exists on the vehicle window may be determined, and a position of a glass top end of the vehicle window may be determined.
As an alternative embodiment, in particular, the presence or absence of an obstacle on the window and the position of the glass tip of the window can be determined by:
and preprocessing the image of the vehicle window in the initial state.
In this embodiment, optionally, the image of the vehicle window in the initial state may be preprocessed to achieve the purposes of denoising and improving the operation efficiency.
And taking the preprocessed image as the input of a two-classification convolutional neural network model, and calculating whether the vehicle window has the obstacle or not through the two-classification convolutional neural network model.
In this embodiment, as shown in fig. 3, fig. 3 is a schematic diagram of a two-class convolutional neural network model, and the preprocessed image is subjected to feature extraction and filtering through a convolutional layer 1, a pooling layer 1, a convolutional layer 2, and a pooling layer 2, where the image is three-dimensional data, and is planar two-dimensional pixel point data and RGB channel data, respectively. The convolutional layer mainly functions in extracting features, the size of a convolution kernel can be 3 x 3, the convolution kernel can scan and calculate the previous layer of data according to a specific step length during work, and a specific formula is as follows:
wherein b is the deviation amount, ZlAnd Zl+1Representing the L +1 th layer of convolutional input and output, Ll+1Is Zl+1Z (i, j) is the corresponding pixel, K is the number of channels, f, s0And p is the convolution kernel size, convolution step size and number of layers filled, respectively. After the above calculation is completed, an activation function is needed to help express the complex feature, and a linear rectification function ReLU is used in the embodiment of the present application, which can be expressed as follows:
the pooling layer can filter and select the extracted features, and the calculation formula is as follows:
wherein p is a pre-specified parameter of the pooling layer, and other parameters have the same meaning as the parameter of the convolutional layer.
The input preprocessed image is subjected to convolution layer and pooling layer to obtain extracted and filtered features, then nonlinear combination is carried out on the features through full-connection 1 layer and full-connection 2 layer, and the learning target is completed by utilizing the features. And finally realizing final classification of the output result of the fully-connected 2-layer through a Softmax function, wherein the expression of the Softmax function is as follows:
wherein y isiValue, z, representing the final classification resultiThe value of i is 1 to n, which is the output result of the fully connected layer 2. The final classification results of fig. 3 are of two kinds: with or without obstacles.
And if no obstacle exists, taking the preprocessed image as the input of the position classification convolution neural network model, and calculating the position of the glass top end of the vehicle window through the position classification convolution neural network model.
In this embodiment, after detecting that there is no obstacle on the window glass, the position of the glass top end of the window glass can be calculated by using the position classification convolutional neural network model.
And step 204, when no obstacle exists on the window, controlling the window to be closed based on the position of the top end of the glass of the window.
In this embodiment, optionally, the height of the window may be divided into at least one section in advance, each section corresponds to one position category, and each position category corresponds to a different time length for closing the window.
For example, the height of the window may be divided into 8 segments, when the top glass end of the window is at the 1 st segment height, the window is fully opened, when the top glass end of the window is at the 8 th segment height, the window is fully closed, the top glass end of the window is at different segment heights, and the corresponding time periods for closing the window are different, it being understood that when the top glass end of the window is at the 1 st segment height, the time period for closing the window is the longest, and when the top glass end of the window is at the 8 th segment height, the time period for closing the window is 0.
In this embodiment, as shown in fig. 4, a schematic diagram of a position classification convolutional neural network model is shown, where the structures of the convolutional layer, the pooling layer, the fully-connected layer, and Softmax may be consistent with those in fig. 3, but the weight parameters need to be adjusted according to the training situation. When no obstacle is detected on the window glass, the position of the top end of the window glass is calculated and then classified by using the position classification convolutional neural network model shown in fig. 4, and the classification can be understood as the segmentation. Specifically, the preprocessed image may be input, feature extraction and filtering are performed on the convolutional layer 1, the pooling layer 1, the convolutional layer 2 and the pooling layer 2 in the position classification convolutional neural network model, regression calculation is performed on the extracted and filtered features through the fully-connected layer 1 and the fully-connected layer 2, and finally, an eight-classification result is obtained from a calculation result through Softmax. If the classification result is 8, the vehicle window is in a closed state, and if the classification result is 3, the vehicle window can be controlled to be closed according to the preset time length corresponding to the height.
According to the intelligent control method for the vehicle window, after a user gets off and locks the vehicle, the camera controller 17 can control the camera 15 to collect images on front and back seats, whether people exist on the seats or not is identified, if no people exist on the seats, the images of the vehicle window glass are collected continuously, and then the position of the top end of the glass of the vehicle window is calculated and classified through a pre-trained neural network model. The camera controller 17 sends the calculated position category to the vehicle body controller 16 through the CAN network, and the vehicle body controller 16 controls the pull-in time of the window motor relay 12 according to the position category, so that the protection of the window motor is realized, the service life of the storage battery and the window motor is prolonged, wherein the pull-in time is pre-calculated and stored in the vehicle body controller 16. And the car windows at different positions and heights have different closing time, so that the electric energy is saved, and the car windows can be automatically closed in time after a user locks the car.
In this application embodiment, optionally, camera controller 17 can check whether automobile body controller 16 can control the door window and close completely according to certain frequency, if according to the relay actuation time that calculates, the door window can not close completely, and telematics unit 19 can upload this sample to the high in the clouds and add the training set and carry out the degree of accuracy of model training in order to improve the model.
As shown in fig. 5, the verification may be performed in the following manner:
and step 501, counting the closing times of the vehicle window.
In this embodiment, the number of times of closing the window may be counted by using a counter, and the window is closed once, and the corresponding counter is incremented by 1.
And step 502, when the closing times of the vehicle window reach a preset value, checking whether the vehicle window is completely closed by using the camera controller 17 of the vehicle.
In this embodiment, the preset threshold may be flexibly set, and may be 20, for example, that is, after the window is closed 20 times, the camera controller 17 of the vehicle may be used to check whether the window is completely closed. When the counter value reaches 20, the counter may be cleared to restart counting.
Specifically, the verification may be performed in the following manner:
acquiring an image of the window after the closing action by using a camera controller 17 of the vehicle; then determining a second position type of the vehicle window based on the image of the vehicle window after the closing action is carried out; and if the window is determined not to be completely closed through the second category, controlling the window to be closed again.
And taking the time when the vehicle window is controlled to be closed again as an initial time point, and when the motor current value of the vehicle window is detected to be increased, calculating the time length T between the initial time point and the time point when the motor current value of the vehicle window is increased, wherein the time length is used for closing the vehicle window again.
And sending the time length T for closing the vehicle window again, the image of the vehicle window after the closing action and the second position category to the cloud end as parameters for optimizing the preset neural network model.
In this embodiment, before the neural network model is trained, the image data may be preprocessed to achieve the purpose of denoising and improving the operation efficiency. And dividing the preprocessed picture data into a training set and a testing set, wherein the training set can account for 75%, and the testing set can account for 25%. The training set is used for training the model, the test set is used for verifying the accuracy of the model obtained through training, and when the accuracy of the neural network model on the test set is higher than 95%, the training is finished. The required threshold of the accuracy may be set according to actual requirements, and this embodiment is not limited. The trained neural network model parameters are migrated and stored in a camera controller 17, when a user unlocks the vehicle and the four-door two-cover switch 11 is closed, a camera 15 sends the acquired image to the camera controller 17, the camera controller 17 analyzes and calculates the two-classification convolutional neural network model parameters which are migrated and stored locally, so that whether barriers exist on the window glass can be obtained, when no barrier exists on the window glass, the camera controller 17 calculates the position type of the top end of the glass of each window through the position classification convolutional neural network model parameters, after receiving the position type of the top end of the glass of each window, a vehicle body controller 16 controls the corresponding window motor relay 12 to suck corresponding time for the first time according to the types, and adds 1 to a counter. For the above operation, the camera controller 17 controls the camera 15 to check and verify the final position of the window after the window moves every time the counter value reaches 20, and clears the counter to 0, and detects whether the counter reaches the fully closed position, that is, the position category is 8, and if the counter reaches the fully closed position, no data is uploaded through the telematics processor 19; if the window does not reach the completely closed position, the vehicle body controller 16 controls the window motor relay 12 to close the window for the second time, the window is judged to be completely closed by monitoring the current value of the window motor in the process, and when the current value is increased, the window is closed tightly. Calculating the time T of the second attraction of the vehicle window motor relay 12, wherein the time of the complete closing of the vehicle window is T + T, wherein T is the time of the first attraction of the relay, according to the time T + T, the real position of the vehicle window in the image CAN be determined to be in the third category by a table look-up method, so that the image and the position category are packaged into a sample, the sample is transmitted to a training set in a cloud end through a remote information processor 19, after the number of the training set in the cloud end is increased to a certain value, a new round of model training is started to optimize parameters, and when the parameter optimization is completed, the sample is transmitted to a camera controller 17 through the remote information processor 19 and a CAN network to be stored, so that the continuous optimization and updating of the parameters are realized, and the intelligent vehicle window control system is more intelligent, accurate and energy-saving.
Based on the same inventive concept, the embodiment of the present application further provides an intelligent vehicle window control device, as shown in fig. 6-1, including:
the detection module 61 is used for detecting the power mode state, the four-door two-cover switch state and the locking state of the vehicle after receiving the vehicle locking signal of the user;
the acquisition module 62 is configured to acquire an image of a vehicle window in an initial state by using an in-vehicle camera of the vehicle if the vehicle window is in a closed state;
the position determining module 63 is configured to determine whether an obstacle exists on the vehicle window and a position of a glass top end of the vehicle window through a preset neural network model based on the image of the vehicle window in the initial state;
and a control module 64 for controlling the window to close based on the position of the glass tip of the window when there is no obstacle on the window.
In one embodiment, the position determination module 63 includes:
the preprocessing unit is used for preprocessing the image of the vehicle window in the initial state;
the first calculation unit is used for taking the preprocessed image as the input of the two-classification convolutional neural network model, and calculating whether the vehicle window has the obstacle or not through the two-classification convolutional neural network model;
and the second calculation unit is used for taking the preprocessed image as the input of the position classification convolution neural network model if no barrier exists on the vehicle window, and calculating the position of the top end of the glass of the vehicle window through the position classification convolution neural network model.
In one embodiment, the height of the vehicle window is divided into at least one section in advance, each section corresponds to one position type, and each position type corresponds to different time length for closing the vehicle window;
the control module 64 is specifically used for determining a first position type of the vehicle window according to the height of the position of the top end of the glass of the vehicle window; and controlling the car window to be closed within the time length corresponding to the first position type according to the first position type.
In one embodiment, as shown in fig. 6-2, the apparatus further comprises:
a counting module 65 for counting the number of times of closing the window;
and the checking module 66 is used for checking whether the vehicle window is completely closed or not by utilizing the camera controller of the vehicle when the closing times of the vehicle window reach a preset value.
In one embodiment, the verification module 66 includes:
an image acquisition unit for acquiring an image of the window after the closing action by using a camera controller of the vehicle;
a position type determination unit for determining a second position type of the window based on the image of the window after the closing action;
and the control unit is used for controlling the window to be closed again if the window is determined not to be completely closed through the second category.
In one embodiment, as shown in fig. 6-3, the apparatus further comprises:
the duration calculation module 67 is configured to calculate a duration T between the starting time point and a time point when the motor current value of the window increases when it is detected that the motor current value of the window increases, as a duration used for closing the window again, with the time point when the window is controlled to be closed again as the starting time point;
and the sending module 68 is configured to send the time length T for closing the vehicle window again, the image of the vehicle window after the closing action, and the second position category to the cloud end as parameters for optimizing the preset neural network model.
Based on the intelligent control method for the vehicle window described in the foregoing embodiment, an embodiment of the present application provides an electronic device, which is configured to execute the intelligent control method for the vehicle window described in any of the foregoing embodiments, and as shown in fig. 7, the electronic device provided in the embodiment of the present application includes: a processor (processor) 402; and a memory (memory)404 configured to store computer-executable instructions that, when executed, cause the processor 402 to implement the methods described in any of the embodiments of the present application.
Optionally, the electronic device may further include a bus 406 and a communication Interface (Communications Interface)408, wherein the processor 402, the communication Interface 408, and the memory 404 are configured to communicate with each other via the communication bus 406.
A communication interface 408 for communicating with other devices.
The processor 402 may be a central processing unit CPU or an application Specific Integrated circuit asic or one or more Integrated circuits configured to implement embodiments of the present invention. The electronic device comprises one or more processors, which can be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
The memory 404 may comprise a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
Based on the intelligent control method for the vehicle window described in the above embodiments, an embodiment of the present application provides a storage medium, where the storage medium stores computer-executable instructions, and the computer-executable instructions, when executed, implement the method described in any embodiment of the present application.
The electronic device of the embodiments of the present application exists in various forms, including but not limited to:
(1) a mobile communication device: such devices are characterized by mobile communications capabilities and are primarily targeted at providing voice, data communications. Such terminals include: smart phones (e.g., iphones), multimedia phones, functional phones, and low-end phones, among others.
(2) Ultra mobile personal computer device: the equipment belongs to the category of personal computers, has calculation and processing functions and generally has the characteristic of mobile internet access. Such terminals include: PDA, MID, and UMPC devices, etc., such as ipads.
(3) A portable entertainment device: such devices can display and play multimedia content. This type of device comprises: audio, video players (e.g., ipods), handheld game consoles, electronic books, and smart toys and portable car navigation devices.
(4) And other electronic equipment with data interaction function.
Thus, particular embodiments of the present subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may be advantageous.
The method illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, 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.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular transactions or implement particular abstract data types. The application may also be practiced in distributed computing environments where transactions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (14)
1. An intelligent control method for a vehicle window is characterized by comprising the following steps:
after receiving a user vehicle locking signal, detecting a power mode state, a four-door two-cover switch state and a locking state of a vehicle;
if the vehicle windows are in the closed state, acquiring an image of the vehicle window in the initial state by using an in-vehicle camera of the vehicle;
determining whether an obstacle exists on the vehicle window and the position of the top end of the glass of the vehicle window through a preset neural network model based on the image of the vehicle window in the initial state;
and when no obstacle exists on the vehicle window, controlling the vehicle window to be closed based on the position of the top end of the glass of the vehicle window.
2. The method according to claim 1, wherein the determining whether the obstacle exists on the vehicle window and the position of the glass top end of the vehicle window is determined through a preset neural network model based on the image of the vehicle window in the initial state comprises the following steps:
preprocessing the image of the vehicle window in the initial state;
taking the preprocessed image as the input of a two-classification convolutional neural network model, and calculating whether the vehicle window has an obstacle or not through the two-classification convolutional neural network model;
if the position of the top end of the glass of the vehicle window is not the same as the position of the glass top end of the vehicle window, the preprocessed image is used as the input of the position classification convolution neural network model, and the position of the top end of the glass of the vehicle window is calculated through the position classification convolution neural network model.
3. The method according to claim 1, characterized in that the height of the window is pre-divided into at least one section, each section corresponding to a position category, each position category corresponding to a different length of time taken to close the window;
the controlling the window to close based on the position of the glass top end of the window comprises:
determining a first position category of the vehicle window according to the height of the position of the top end of the glass of the vehicle window;
and controlling the car window to be closed within the time length corresponding to the first position type according to the first position type.
4. The method of claim 3, further comprising, after controlling the window to close for a duration corresponding to the first position category:
counting the closing times of the car window;
and when the closing times of the vehicle window reach a preset value, checking whether the vehicle window is completely closed by using a camera controller of the vehicle.
5. The method of claim 4, wherein the verifying with the camera controller of the vehicle that the window is fully closed comprises:
acquiring an image of the vehicle window after closing by using a camera controller of the vehicle;
determining a second position category of the vehicle window based on the image of the vehicle window after the closing action is performed;
and if the window is determined not to be completely closed through the second category, controlling the window to be closed again.
6. The method of claim 5, further comprising:
taking the time when the vehicle window is controlled to be closed again as an initial time point, and when the motor current value of the vehicle window is detected to be increased, calculating the time length T between the initial time point and the time point when the motor current value of the vehicle window is increased, wherein the time length T is used for closing the vehicle window again;
and sending the time length T for closing the vehicle window again, the image of the vehicle window after closing action and the second position category to a cloud end as parameters for optimizing the preset neural network model.
7. An intelligent control device for vehicle windows, characterized in that the device comprises:
the detection module is used for detecting the power mode state, the four-door two-cover switch state and the locking state of the vehicle after receiving the vehicle locking signal of the user;
the acquisition module is used for acquiring an image of a vehicle window in an initial state by using an in-vehicle camera of the vehicle if the vehicle window is in a closed state;
the position determining module is used for determining whether an obstacle exists on the vehicle window and the position of the top end of the glass of the vehicle window through a preset neural network model based on the image of the vehicle window in the initial state;
and the control module is used for controlling the window to be closed based on the position of the top end of the glass of the window when no obstacle exists on the window.
8. The apparatus of claim 7, wherein the position determining module comprises:
the preprocessing unit is used for preprocessing the image of the vehicle window in the initial state;
the first calculation unit is used for taking the preprocessed image as the input of a two-classification convolutional neural network model, and calculating whether the vehicle window has an obstacle or not through the two-classification convolutional neural network model;
and the second calculation unit is used for taking the preprocessed image as the input of the position classification convolution neural network model if no obstacle exists on the vehicle window, and calculating the position of the glass top end of the vehicle window through the position classification convolution neural network model.
9. The device according to claim 7, characterized in that the height of the window is pre-divided into at least one section, each section corresponding to a position category, each position category corresponding to a different duration of time for closing the window;
the control module is specifically used for determining a first position type of the vehicle window according to the height of the position of the top end of the glass of the vehicle window; and controlling the car window to be closed within the time length corresponding to the first position type according to the first position type.
10. The apparatus of claim 9, further comprising:
the counting module is used for counting the closing times of the car window;
and the checking module is used for checking whether the car window is completely closed or not by utilizing the camera controller of the car when the closing times of the car window reach a preset value.
11. The apparatus of claim 10, wherein the verification module comprises:
an image acquisition unit configured to acquire an image of the window after a closing operation by using a camera controller of the vehicle;
a position type determination unit configured to determine a second position type of the window based on the image of the window after the closing operation;
and the control unit is used for controlling the window to be closed again if the window is determined not to be completely closed through the second category.
12. The apparatus of claim 11, further comprising:
the time length calculation module is used for calculating the time length T between the starting time point and the time point when the motor current value of the car window is increased when the car window is controlled to be closed again as the starting time point, and the time length T is used for closing the car window again;
and the sending module is used for sending the time length T for closing the vehicle window again, the image of the vehicle window after the closing action and the second position category to a cloud end to be used as parameters for optimizing the preset neural network model.
13. An electronic device, comprising: a processor; and a memory configured to store computer-executable instructions that, when executed, cause the processor to implement the method of any of claims 1-6 above.
14. A storage medium storing computer-executable instructions that, when executed, implement the method of any of claims 1-6.
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