CN112509361A - Control method and device for remote control parking - Google Patents
Control method and device for remote control parking Download PDFInfo
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- G08G1/141—Traffic control systems for road vehicles indicating individual free spaces in parking areas with means giving the indication of available parking spaces
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Abstract
The application discloses a control method and a control device for remote control parking, wherein the method comprises the following steps: responding to an operation executed in an automatic parking operation interface displayed on a screen by a user, and acquiring real-time operation data of the user from the start of the operation to the current moment in the automatic parking operation interface; determining whether the operation of the user in the automatic parking operation interface is regular or not based on the real-time operation data; if the regularity exists, controlling the target vehicle to automatically park; otherwise, the control target vehicle is suspended from automatic parking. The method and the device automatically determine whether to control the target vehicle to automatically park based on whether the operation of the user in the automatic parking operation interface is regular or not, and the user does not need to preset and follow a fixed screen operation mode or have fixed limitation, so that the method and the device can be adapted to mobile phone screens, palm sizes and operation habits of different users, and the operation of the user is relatively more flexible and convenient.
Description
Technical Field
The application relates to the field of computers, in particular to a control method and a control device for remote control parking.
Background
According to the regulations of the law and legislation and for safety reasons, the current remote parking function is not fully automatic, but rather the driver (hereinafter referred to as the user) is also involved in the control during the entire remote parking process. Specifically, before remote parking, a user is required to set a fixed operation mode (for example, a circle is continuously drawn in a fixed range on a touch screen of a mobile phone), and during the remote parking, the user is required to continuously control in the fixed operation mode, and once the user stops operating, the parking process is also correspondingly suspended.
It is not easy to find that the current control scheme of remote control parking requires that a user presets and follows a fixed operation mode, and the mode is relatively rigid, not flexible and convenient enough, and can not be well adapted to mobile phone screens, palm sizes, operation habits and the like of all users.
Disclosure of Invention
The embodiment of the application provides a method and a device for controlling remote-control parking, and aims to provide a more flexible and more convenient remote-control parking control scheme.
In a first aspect, an embodiment of the present application provides a method for controlling remote-controlled parking, including:
responding to the operation of a user in the automatic parking operation interface, and recording real-time operation data of the user from the start of the operation to the current moment on a screen;
determining whether the operation of the user in the automatic parking operation interface is regular or not based on the real-time operation data;
when the operation of the user in the automatic parking operation interface is regular, controlling the target vehicle to automatically park;
and when the operation of the user in the automatic parking operation interface is not regular, suspending the automatic parking of the control target vehicle.
In a second aspect, an embodiment of the present application further provides a control device for remotely controlling parking, including:
the data recording module is used for responding to the operation of the user in the automatic parking operation interface and recording real-time operation data of the user from the start of the operation to the current moment on the screen;
the regularity detection module is used for determining whether the operation of the user in the automatic parking operation interface is regular or not based on the real-time operation data;
the first control module is used for controlling the target vehicle to automatically park when the operation of the user in the automatic parking operation interface is regular;
and the second control module is used for suspending the automatic parking of the control target vehicle when the operation of the user in the automatic parking operation interface is not regular.
In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor and computer executable instructions stored on the memory and executable on the processor, which when executed by the processor implement the steps of the apparatus as described in the first aspect above.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium for storing computer-executable instructions, which when executed by a processor implement the steps of the apparatus according to the first aspect.
According to the technical scheme, whether the operation of the user in the automatic parking operation interface is regular or not can be automatically detected, and the target vehicle is controlled to automatically park when the operation of the user is regular. That is to say, the operation of the user is regular, the user does not need to preset and follow a fixed screen operation mode, and the fixed limitation is not provided, so that the mobile phone can be adapted to mobile phone screens, palm sizes and operation habits of different users, and the operation of the user is relatively more flexible and more convenient.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flowchart illustrating a method for controlling remote parking according to an embodiment of the present application.
Fig. 2 is a flowchart illustrating a detailed implementation of step 102 shown in fig. 1.
Fig. 3 is a schematic flow chart of another detailed implementation of step 102 shown in fig. 1.
Fig. 4 is a schematic structural diagram of a control device for remotely controlling parking according to an embodiment of the present application.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In order to solve the problems that a control scheme of remote control parking in the related art is relatively hard, inflexible and convenient, the embodiment of the application provides a control method and a control device of remote control parking. The method and the device provided by the embodiment of the application can be executed by electronic equipment, such as terminal equipment. In other words, the method may be performed by software or hardware installed in the terminal device. The terminal devices may include, but are not limited to: smart terminal devices such as smart phones, Personal Computers (PCs), notebook computers, tablet computers, electronic readers, and wearable devices.
A method for controlling remote parking according to an embodiment of the present application will be described first.
Fig. 1 is a flowchart illustrating a method for controlling a remote-controlled parking according to an embodiment of the present disclosure. As shown in fig. 1, the method may include:
step 101, responding to an operation executed in an automatic parking operation interface displayed on a screen by a user, and acquiring real-time operation data of the user from the start of the operation to the current time in the automatic parking operation interface.
In general, the general process of remote parking is as follows: firstly, a vehicle searches for available parking spaces and displays the available parking spaces on a screen (generally a touch screen) of intelligent terminal equipment (such as a mobile phone of a user); then, the user selects a target parking space to be parked on the screen through clicking or other operations; and then, the user gets off the vehicle, enters an automatic parking operation interface by clicking a screen or executing other operations, and controls the vehicle to park in the target parking space by operating in the automatic parking operation interface.
According to the method and the device, the operation made by the user in the automatic parking operation interface displayed on the screen is responded, the operation data generated when the user operates in the automatic parking operation interface is continuously recorded, and the real-time operation data from the start of the user to the current time in the automatic parking operation interface is obtained. Suppose that the time at which the operation is started is t0If the current time is t, then t is obtained0T operating data generated during this period. It is understood that the current time is also moving backwards as time goes on, so the real-time operation data is also increasing. The operation performed by the user in the automatic parking interface may be a touch operation such as clicking, sliding, and the like.
In some examples, the operation data of the user in the automatic parking operation interface includes one of the following data:
first type data: and the change data of the abscissa and/or the ordinate of the operation of the user in the automatic parking operation interface along with the time. When the abscissa and the ordinate are respectively represented by x and y (the same applies below), the operation data of the user in the automatic parking operation interface includes: data for x over time t: x (t), and/or, y is a function of time t: y (t). It will be appreciated that such data is a discrete function of x and/or y as time t. Alternatively, if the user does not touch the screen at a certain time, the abscissa and ordinate may be agreed to a certain special value (similar in the other classes of data), such as-1 or 0.
Second-class data: the abscissa of the user's operation in the automatic parking operation interface is a function of the ordinate, such as a function of x with respect to y: x (y), it is understood that the data may be a discrete function of x with respect to y, or a continuous function obtained by fitting after the data of x and y are acquired.
Data of the third type: the ordinate of the user's operation in the automatic parking operation interface is a function of the abscissa, such as y is a function of x: y (x), it is also understood that the data may be a discrete function of y with respect to x, or a continuous function obtained by fitting after the data for x and y are acquired.
Data of the fourth type: at least one (which may be expressed as an nth order, n being an integer greater than or equal to 1) derivative of one of the first type of data, the second type of data, and the third type of data. Specifically, when the fourth type of data is the nth order derivative of the first type of data, after the first type of data is acquired, the nth order derivative of the first type of data may be obtained to obtain the fourth type of data; when the fourth type of data is the nth derivative of the second type of data, the nth derivative of the second type of data may be obtained after the second type of data is obtained, and so on, which is not described again.
Step 102, determining whether the operation of a user in the automatic parking operation interface is regular or not based on the real-time operation data; if there is regularity, go to step 103, otherwise go to step 104.
There are many embodiments for determining whether there is regularity in the operation of the user in the automatic parking operation interface based on the real-time operation data, and two types are roughly listed below, wherein each type may have various modifications, which are described below.
First embodiment
The step 102 may include: and circularly executing the first specified step until a first preset condition is met, wherein the first preset condition can comprise that the user finishes the operation, such as the operation of the user on the automatic parking operation interface is not detected for a long time (the specified time is exceeded). Alternatively, if the user's operation on the automatic parking operation interface is detected after the specified time period, the execution of step 101 is restarted.
Specifically, as shown in fig. 2, the step 102 may include:
substep 201, selecting data in a first preset time period from the real-time operation data according to a first preset time interval as a first group of operation data.
Wherein the ending time of the first preset time period is the current time or before the current time, optionally, the length of the first preset time period is fixed. As described above, assume that the time at which the operation is started is t0If the current time is t, the real-time operation data is t0T, assuming that the length of the first preset time period is T, when the ending time of the first preset time period is the current time T, the data in the first preset time period selected from the real-time operation data may be represented as: the operation data from time T-T to time T (first set of operation data).
And a sub-step 202 of determining whether regularity exists in the operation of the user in the automatic parking operation interface based on the first group of operation data.
As can be seen with reference to fig. 2, the first specifying step includes the above sub-step 201 and sub-step 202. It will be appreciated that after the first prescribed step described above has been performed once, the following sub-step 203 may continue to be performed to determine whether to exit the loop.
It is also understood that, although not shown in fig. 2, after the first step is executed once, step 103 or step 104 shown in fig. 1 may be selected to be executed according to the result of determining whether there is regularity in the operation of the user in the automatic parking operation interface.
Optionally, after it is determined that there is regularity in the operation of the user in the automatic parking operation interface based on the first set of operation data, setting a first preset time interval as a cycle period corresponding to the regularity (if the regularity is specifically a cycle period corresponding to the periodicity, this is a cycle period corresponding to the periodicity), and then performing the first specifying step in a recycling manner. That is, if it is determined that there is regularity in the operation of the user in one cycle, in the next cycle, the cycle period corresponding to the regularity is used as a first preset time interval to collect a first set of operation data to determine whether there is regularity in the operation of the user again, and it is not necessary to search for the regularity each time, until no regularity is detected, the automatic parking of the control target vehicle is suspended, and then the execution step 101 is returned to restart the method shown in fig. 1. And after determining that the operation of the user in the automatic parking operation interface is not regular based on the first group of operation data, the first preset time interval may not be changed, and the first specified step is continuously and circularly executed.
And a substep 203, judging whether a first preset condition is met, if so, executing a substep 204, otherwise, returning to execute the substep 201.
Substep 204, exit the loop.
In the embodiment shown in fig. 2, the manner in which the sub-step 202 determines whether there is regularity in the operation of the user in the automatic parking operation interface based on the first set of operation data may be subdivided into two ways, which are described below.
Firstly, detecting whether periodicity exists in the first group of operation data; if the periodicity exists, determining that the operation of the user in the automatic parking operation interface is regular; and if the periodicity does not exist, determining that the regularity does not exist in the operation of the user in the automatic parking operation interface.
Specifically, data in a second preset time period may be selected from the real-time operation data as a second group of operation data, wherein the start time of the second preset time period is earlier than the start time of the first preset time period by a preset time period, the length of the second preset time period is consistent with that of the first preset time period, and the preset time period is equal to a preset period; then comparing the similarity of the change rules of the first group of operation data and the second group of operation data based on a preset mode; if the similarity meets a second preset condition, determining that the first group of operation data has periodicity; and if the similarity does not meet a second preset condition, determining that the periodicity does not exist in the first group of operation data.
The preset mode may include, but is not limited to, one of the following modes: a covariance based approach and a cross entropy based approach between the two sets of data. The second preset condition may be that the degree of similarity is greater than a preset degree, such as the similarity value being greater than a preset threshold value.
For example, assume that the real-time operation data is embodied as the first type of data (x and y at t)0The change data in the time period t) is selected from the real-time operation data, and the data in the first preset time period selected from the real-time operation data is: x and y data (a first group of operation data) from T-T moment to T moment, wherein the data in a second preset time period selected from the real-time operation data is as follows: x and y data (a second set of operation data) from time T-c to time T-c, where c is a preset period, then whether the first set of operation data has periodicity may be determined based on the following preset manner:
1) the way based on the covariance between the two sets of data (here, the first set of operational data and the second set of operational data) is as follows:
a) calculating the covariance c _ x of x data (x data from T-T moment to T moment) in the first group of operation data and x data (x data from T-T-c moment to T-c moment) in the second group of operation data;
b) calculating the covariance c _ y of the y data (from the T-T moment to the T moment) in the first group of operation data and the covariance c _ y of the y data (from the T-T-c moment to the T-c moment) in the second group of operation data;
c) calculating a weighted sum of the covariance c _ x and the covariance c _ y: c _ xy _ x _ c _ x + (1-w _ x) c _ y, wherein w _ x is a preset weight, and 0< w _ x < 1.
d) Finding a proper preset period c to maximize the dereferencing of c _ xy, and particularly finding a proper c by adopting a grid searching mode in a fixed dereferencing interval;
e) if the found maximum value of c _ xy is larger than a preset threshold value, the similarity exists between the first group of operation data and the second group of operation data, namely a second preset condition is met, so that the periodicity of the first group of operation data is determined, and otherwise, the periodicity of the first group of operation data is determined.
2) The manner based on the cross entropy between the two sets of data (here, the first set of operation data and the second set of operation data) is as follows:
a) calculating the cross entropy e _ x between the two groups of x data after normalizing the x data (x data from T-T moment to T moment) in the first group of operation data and the x data (x data from T-T-c moment to T-c moment) in the second group of operation data (namely, the values of the x data are both larger than 0 and the sum is 1 through linear transformation);
b) after y data (y data from time T-T to time T) in the first set of operation data and y data (y data from time T-T-c to time T-c) in the second set of operation data are normalized (namely, the values of the y data are both larger than 0 and the sum is 1 through linear transformation), cross entropy e _ y between the two sets of y data is calculated;
c) calculating a weighted sum of the cross entropy e _ x and the cross entropy e _ y: e _ xy _ x _ e _ x + (1-w _ x) e _ y, wherein w _ x is a preset weight, and 0< w _ x < 1;
d) and finding a proper preset period c to minimize the value of e _ xy, and particularly finding the proper c by adopting a grid searching mode in a fixed value interval.
e) If the found minimum value of e _ xy is smaller than a preset threshold, the similarity between the first group of operation data and the second group of operation data is considered, namely a second preset condition is met, so that the periodicity of the first group of operation data is determined, and otherwise, the periodicity of the first group of operation data is determined.
It should be noted that the above real-time operation data is only the first kind of data (x and y at t)0The variation data in the time period up to t) is taken as an example, and it is not necessarily the case that only the first type of data is suitable for determining whether the first group of operation data has periodicity based on the covariance or cross entropy of the two groups of data, and the second type of data, the third type of data, and the fourth type of data are all suitable for the two methods. In a specific implementation, the first type of data may be replaced with one of the second type of data, the third type of data, and the fourth type of data. For example, when the real-time operation data is specifically the fourth type of data, similarity detection may be performed on the n-th order derivatives (e.g., 0 th order, i.e., raw data, 1 st order and 2 nd order derivatives, etc.) of the first group of operation data and the second group of operation data by using the above method, that is, covariance or cross entropy of the n-th order derivatives of the two groups of data is calculated, and finally, a weighted sum of the covariance or cross entropy of the n-th order derivatives is calculated by using a preset weight as an index finally used for determining similarity, and so on, which is not described herein again.
Secondly, inputting a preset model to obtain a target output result by taking the first group of operation data as target input data, wherein the preset model is obtained by training based on sample operation data, and the output result of the preset model is used for determining whether regularity exists in the input data input into the preset model; and determining whether regularity exists in the operation of the user in the automatic parking operation interface based on the target output result.
The second method may be regarded as a judgment method relying on machine learning, and specifically, a preset model is obtained by performing machine learning based on sample operation data, then the first group of operation data is input into the preset model to obtain an output result, and then whether the operation of the user in the automatic parking operation interface is regular or not is judged according to the output result. As some examples, the preset model includes one of a self-encoder, a boltzmann machine test (prmd), a Recurrent Neural Network (RNN), and a Convolutional Neural Network (CNN), in this embodiment, the self-encoder and the boltzmann machine test may be regarded as an unsupervised machine learning model, and the cyclic Neural Network and the Convolutional Neural Network may be regarded as a supervised machine learning model, which will be separately described below.
1) When the preset model is detected by a self-encoder or a Boltzmann machine, the first group of operation data is used as target input data, the target input data is input into the self-encoder or the Boltzmann machine detection model to obtain a target output result, then a reconstruction error between the target input data and the target output result is calculated, if the reconstruction error between the target input data and the target output result is smaller than a set threshold value, the operation of the user in the automatic parking operation interface is determined to have regularity, otherwise, the operation of the user in the automatic parking operation interface is determined not to have regularity.
For example, assuming that the first set of operation data is x and y data from T-T time to T time, the target output result obtained by inputting the first set of operation data into the encoder or the Boltzmann machine detection model is x ' and y ' data from T-T time to T time, the reconstruction error r _ x between the x data and the x ' data from T-T time to T time is calculated, and calculating the reconstruction error r _ y of the y data and the y' data from the time T-T to the time T, then calculating the weighted sum r _ xy of the reconstruction error r _ x and the reconstruction error r _ y, w _ x r _ x + (1-w _ x) r _ y, and w _ x is a weight, if r _ xy is smaller than a set threshold value, the operation of the user in the automatic parking operation interface is considered to have regularity, otherwise, the operation of the user in the automatic parking operation interface is considered to have no regularity.
2) When the preset model is a recurrent neural network, inputting the first group of operation data serving as target input data into the recurrent neural network to obtain a target output result: whether the first group of operation data has regular binary classification results or not; and then, determining whether the operation of the user in the automatic parking operation interface is regular or not directly according to the classification result.
For example, assuming that the first set of operation data is x and y data from time T-T to time T, the time series of x and y may be input into the recurrent neural network to obtain whether there is a regular binary result in the first set of operation data.
3) When the preset model is a convolutional neural network, the first group of operation data may be drawn on a two-dimensional plane to obtain an image, and then the image is input into the convolutional neural network as target input data to obtain a target output result: whether the track in the image has a regular binary classification result or not; and then, determining whether the operation of the user in the automatic parking operation interface is regular or not directly according to the classification result.
For example, assuming that the first set of operation data is x and y data from time T-T to time T, the x and y trajectories can be drawn on a two-dimensional plane to obtain an image, and the image is input to a convolutional neural network to obtain whether the trajectory in the image has a regular binary classification result.
It should also be noted that the above real-time operation data alone is specifically the first type of data (x and y at t)0The change data in the period of time t) is taken as an example, and it is not limited that only the first type of data is suitable for determining whether regularity exists by using the preset model, and the second type of data, the third type of data and the fourth type of data are applicable to both methods. In a specific implementation, the first type of data is replaced with one of the second type of data, the third type of data, and the fourth type of data, and details are not repeated herein.
Second embodiment
The step 102 may include: and circularly executing the second specified step within the specified time length until a third preset condition is met, wherein the third preset condition can comprise that the operation mode of using the memory is not detected at the end of the specified time length (T _ m) or the user ends the operation, such as that the operation of the user on the automatic parking operation interface is not detected for a long time (exceeding the specified time length). Alternatively, if the user's operation on the automatic parking operation interface is detected after the specified time period, the execution of step 101 is restarted.
Specifically, as shown in fig. 3, the step 102 may include:
and a substep 301 of selecting data in a third preset time period from the real-time operation data according to a second preset time interval as a third group of operation data.
Wherein the ending time of the third preset time interval is the current timeAt or before the current time, optionally, the length of the third preset period is fixed. As described above, assume that the time at which the operation is started is t0If the current time is t, the real-time operation data is t0T, assuming that the length of the third preset time period is T _ m, when the ending time of the third preset time period is the current time T, the data in the third preset time period selected from the real-time operation data may be represented as: operation data from time T-T _ m to time T (third set of operation data).
Substep 302, determining whether the user uses the memorized operation mode based on the third group of operation data and the preset historical operation data; if so, perform substep 303, otherwise perform substep 304.
The preset historical operation data is pre-stored regular operation data of a user in a preset historical time period, and the length of the preset historical time period is consistent with that of the third preset time period.
Specifically, in sub-step 302, similarity of change rules of the third group of operation data and the preset historical operation data may also be compared based on the preset manner; if the similarity meets a fourth preset condition, determining that the user uses a memorized operation mode; and if the similarity does not meet the fourth preset condition, determining the operation mode of the user without using memory.
As mentioned above, the preset mode may include, but is not limited to, one of the following modes: a covariance based approach and a cross entropy based approach between the two sets of data. The fourth preset condition may also be that the similarity degree is greater than a preset degree, such as the similarity value is greater than a preset threshold value.
For example, assume that the real-time operation data is embodied as the first type of data (x and y at t)0The change data in the time period t) is obtained, and the data in the third preset time period selected from the real-time operation data is as follows: x and y data (third group of operation data) from time T-T _ m to time T, the change rule of the third group of operation data and the preset historical operation data can be determined based on the following preset modeSimilarity of law:
1) the way based on the covariance between the two sets of data (here, the third set of operation data and the upper preset historical operation data) is as follows:
a) calculating the covariance c _ x of x data (x data from T-T _ m to T) in the third group of operation data and x data in the preset historical operation data (x data in the T _ m period of historical memory);
b) calculating the covariance c _ y of the y data (from T-T _ m to T) in the first group of operation data and the y data in the preset historical operation data (the y data in the T _ m period of the historical memory);
c) calculating a weighted sum of the covariance c _ x and the covariance c _ y: c _ xy _ x _ c _ x + (1-w _ x) c _ y, wherein w _ x is a preset weight, and 0< w _ x < 1.
d) And if the value of c _ xy is larger than a preset threshold value, the similarity exists between the third group of operation data and the preset historical operation data, namely the fourth preset condition is met, so that the fact that the user uses a memorized operation mode is determined, and the user operates regularly.
2) The manner based on the cross entropy between the two sets of data (here, the third set of operation data and the preset historical operation data) is as follows:
a) calculating the cross entropy e _ x between the two groups of x data after normalizing the x data (x data from T-T _ m to T) in the third group of operation data and the x data (x data in T _ m period of history memory) in the preset historical operation data (namely, the values of the x data are all larger than 0 and the sum is 1 through linear transformation);
b) after y data (y data from T-T _ m to T) in the third set of operation data and y data (y data in a history memory T _ m period) in the preset history operation data are normalized (namely, values of the y data are all larger than 0 and the sum is 1 through linear transformation), cross entropy e _ y between the two sets of y data is calculated;
c) calculating a weighted sum of the cross entropy e _ x and the cross entropy e _ y: e _ xy _ x _ e _ x + (1-w _ x) e _ y, wherein w _ x is a preset weight, and 0< w _ x < 1;
d) and if the value of e _ xy is smaller than a preset threshold value, the similarity between the third group of operation data and the preset historical operation data is considered, namely a fourth preset condition is met, so that the fact that the user uses a memorized operation mode is determined, and the user operates regularly.
It should be noted that the above real-time operation data is only the first kind of data (x and y at t)0The change data in the period up to t) is taken as an example, and it is not limited to that only the first type of data is suitable for determining whether the third group of operation data has similarity with the preset historical operation data in a manner of using the covariance or cross entropy based on the two groups of data, and the second type of data, the third type of data, and the fourth type of data are all applied to the two methods.
And a substep 303 of determining that the operation of the user in the automatic parking operation interface has regularity.
And a sub-step 304 of determining that no regularity exists in the operation of the user in the automatic parking operation interface.
As can be seen with reference to fig. 3, the second designated step includes the sub-steps 301, 302, 303 and 304 described above. It will be appreciated that after the second prescribed step described above has been performed once, the sub-step 305 described below may continue to be performed to determine whether to exit the loop.
It is understood that, during the period T _ m, the third set of operation data is continuously selected from the real-time operation data at the second preset time interval, and the second step is executed in a loop (repeated) manner, so as to continuously detect whether the user uses the memorized operation manner. If a third group of operation data with the similarity to the preset historical operation data meeting a fourth preset condition can not be detected all the time within the T _ m period, the user is considered not to use a memorized operation mode, and the operation of the user is irregular; if a third group of operation data, the similarity of which with the preset historical operation data meets a fourth preset condition, is detected at the time T _ s in the T _ m period, the user is considered to use a memorized operation mode, the user operation is regular, the third group of operation data can be continuously selected from the real-time operation data from the time T _ s by taking the T _ m as a period (a second preset time interval), and the similarity of the newly selected third group of operation data with the preset historical operation data is judged, so that whether the user uses the memorized operation mode is continuously detected.
It is also understood that, although not shown in fig. 3, after the second specifying step is executed once, step 103 or step 104 shown in fig. 1 may be selected to be executed according to the result of whether the user operates in the automatic parking operation interface regularly.
And a substep 305, judging whether a third preset condition is met, if so, executing the substep 306, otherwise, returning to execute the substep 301.
Substep 306, the loop is exited.
It is understood that in the related art, the reason why the user is required to follow a certain operation manner in the remote control of the vehicle is that it is clear that the user is consciously controlling the vehicle, not unconsciously random and carelessly drawing in the automatic parking operation interface. The method and the device for remote control parking are used for detecting that the user consciously operates in the automatic parking operation interface and judging the operation regularity of the user in the automatic parking operation interface.
And 103, controlling the target vehicle to automatically park.
And step 104, suspending the control target vehicle for automatic parking.
Optionally, after the step 104 is executed, the process may also return to the step 101, so that after the control target vehicle is suspended for automatic parking, the control method for remote parking shown in fig. 1 is restarted, the real-time operation data of the user is obtained again, whether regularity exists in the operation of the user is detected based on the real-time operation data, and whether automatic parking of the control target vehicle is performed again is determined.
According to the control method for remote control parking, whether the operation of a user in the automatic parking operation interface is regular or not can be automatically detected, and the target vehicle is controlled to automatically park when the operation of the user is regular. That is, the operation of the user is regular, and the user does not need to preset and follow a fixed screen operation mode, so that the mobile phone can be adapted to mobile phone screens of different users, the sizes of palms and operation habits; there is no fixed limit, if the user chooses to draw a circle in the automatic parking operation interface, there is no special requirement for the surrogates and positions of the circle; the operation of the user is relatively more flexible and more convenient, such as sliding gestures or rhythmic clicking, and different laws can be changed during the operation, such as changing from drawing circles to sliding left and right, so that the user can use any touch operation mode for control during remote control parking.
In a word, the remote control parking control method provided by the embodiment of the application is more flexible and more convenient.
The above description provides a method for controlling remote parking according to an embodiment of the present application, and accordingly, a control device for remote parking according to the above method for controlling remote parking is also provided in an embodiment of the present application, which is described below.
As shown in fig. 4, a control device 400 for remotely controlling parking according to an embodiment of the present application may include: a data acquisition module 401, a regularity detection module 402, a first control module 403 and a second control module 404.
The data obtaining module 401 is configured to, in response to an operation performed in an automatic parking operation interface displayed on a screen by a user, obtain real-time operation data of the user starting to operate to a current time in the automatic parking operation interface.
According to the method and the device, the operation made by the user in the automatic parking operation interface displayed on the screen is responded, the operation data generated when the user operates in the automatic parking operation interface is continuously recorded, and the real-time operation data from the start of the user to the current time in the automatic parking operation interface is obtained.
In some examples, the operation data of the user in the automatic parking operation interface includes one of the following data: the first type of data is data of the change of the abscissa and/or the ordinate of the operation of a user in the automatic parking operation interface along with time; the second type of data is a function of the abscissa and the ordinate of the operation of the user in the automatic parking operation interface; the third type of data is a function of the ordinate of the operation of the user in the automatic parking operation interface relative to the abscissa; data of the fourth type: at least one (which may be expressed as an nth order, n being an integer greater than or equal to 1) derivative of one of the first type of data, the second type of data, and the third type of data.
And a regularity detecting module 402, configured to determine whether there is regularity in the operation of the user in the automatic parking operation interface based on the real-time operation data.
There are many embodiments for determining whether there is regularity in the operation of the user in the automatic parking operation interface based on the real-time operation data, and two types are roughly listed below, wherein each type may have various modifications, which are described below.
In a first embodiment, the regularity detecting module 402 is specifically configured to: executing a first designated step in a loop until a first preset condition is met, wherein the first preset condition can comprise that a user finishes the operation, and the first designated step comprises: selecting data in a first preset time period from the real-time operation data according to a first preset time interval as a first group of operation data; the end time of the first preset time interval is the current time or before the current time, and the length of the first preset time interval is fixed; and determining whether the operation of the user in the automatic parking operation interface is regular or not based on the first group of operation data.
The above-mentioned manner for determining whether there is regularity in the operation of the user in the automatic parking operation interface based on the first set of operation data may be subdivided into a plurality of manners, and two manners are described below.
Firstly, detecting whether periodicity exists in the first group of operation data; if the periodicity exists, determining that the operation of the user in the automatic parking operation interface is regular; and if the periodicity does not exist, determining that the regularity does not exist in the operation of the user in the automatic parking operation interface.
Specifically, data in a second preset time period may be selected from the real-time operation data as a second group of operation data, wherein the start time of the second preset time period is earlier than the start time of the first preset time period by a preset time period, the length of the second preset time period is consistent with that of the first preset time period, and the preset time period is equal to a preset period; then comparing the similarity of the change rules of the first group of operation data and the second group of operation data based on a preset mode; if the similarity meets a second preset condition, determining that the first group of operation data has periodicity; and if the similarity does not meet a second preset condition, determining that the periodicity does not exist in the first group of operation data.
The preset mode may include, but is not limited to, one of the following modes: a covariance based approach and a cross entropy based approach between the two sets of data. The second preset condition may be that the degree of similarity is greater than a preset degree, such as the similarity value being greater than a preset threshold value.
Secondly, inputting a preset model to obtain a target output result by taking the first group of operation data as target input data, wherein the preset model is obtained by training based on sample operation data, and the output result of the preset model is used for determining whether regularity exists in the input data input into the preset model; and determining whether regularity exists in the operation of the user in the automatic parking operation interface based on the target output result.
The second method may be regarded as a judgment method relying on machine learning, and specifically, a preset model is obtained by performing machine learning based on sample operation data, then the first group of operation data is input into the preset model to obtain an output result, and then whether the operation of the user in the automatic parking operation interface is regular or not is judged according to the output result.
In a second embodiment, the regularity detecting module 402 is specifically configured to: executing a second designated step in a loop until a third preset condition is met within the designated time length, wherein the third preset condition can comprise that the operation mode of using the memory is not detected at the end of the designated time length (T _ m) or the user ends the operation, and the second designated step comprises the following steps: selecting data in a third preset time period from the real-time operation data according to a second preset time interval as a third group of operation data, wherein the ending time of the third preset time period is the current time or before the current time, and the length of the third preset time period is fixed; determining whether the user uses a memorized operation mode or not based on the third group of operation data and preset historical operation data; if so, determining that the operation of the user in the automatic parking operation interface has regularity; otherwise, determining that the operation of the user in the automatic parking operation interface is not regular.
And a first control module 403, configured to control the target vehicle to perform automatic parking when there is regularity in the operation of the user in the automatic parking operation interface.
And the second control module 404 is configured to suspend the control target vehicle from automatically parking when there is no regularity in the operation of the user in the automatic parking operation interface.
The control device for remote control parking, provided by the embodiment of the application, can automatically detect whether the operation of a user in the automatic parking operation interface is regular or not, and control a target vehicle to automatically park when the operation of the user is regular. That is, the operation of the user is regular, and the user does not need to preset and follow a fixed screen operation mode, so that the mobile phone can be adapted to mobile phone screens of different users, the sizes of palms and operation habits; there is no fixed limit, if the user chooses to draw a circle in the automatic parking operation interface, there is no special requirement for the surrogates and positions of the circle; the operation of the user is relatively more flexible and more convenient, such as sliding gestures or rhythmic clicking, and different laws can be changed during the operation, such as changing from drawing circles to sliding left and right, so that the user can use any touch operation mode for control during remote control parking.
It should be noted that, since the control device for remote parking provided by the embodiment of the present application corresponds to the control method for remote parking provided by the embodiment of the present application, the description of the control device for remote parking in the present specification is relatively simple, and in the relevant places, reference is made to the above description of the control method for remote parking.
Fig. 5 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application. Referring to fig. 5, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 5, but this does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads a corresponding computer program from the nonvolatile memory into the memory and then runs the computer program, and forms a control device for remotely controlling parking on a logic level, and is specifically used for executing the following operations:
responding to an operation executed in an automatic parking operation interface displayed on a screen by a user, and acquiring real-time operation data of the user from the start of the operation to the current moment in the automatic parking operation interface;
determining whether the operation of the user in the automatic parking operation interface is regular or not based on the real-time operation data;
when the operation of the user in the automatic parking operation interface is regular, controlling the target vehicle to automatically park;
and when the operation of the user in the automatic parking operation interface is not regular, suspending the automatic parking of the control target vehicle.
The method executed by the control method for remotely controlling parking according to the embodiment shown in fig. 1 of the present application may be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
Therefore, the electronic device executing the method provided by the embodiment of the present application can execute the methods described in the foregoing method embodiments, and implement the functions and beneficial effects of the methods described in the foregoing method embodiments, which are not described herein again.
The electronic device of the embodiments of the present application exists in various forms, including but not limited to the following devices.
(1) The mobile network device features mobile communication function and mainly aims at providing voice and data communication. Such terminals include smart phones (e.g., iphones), multimedia phones, functional phones, and low-end phones, among others.
(2) The ultra-mobile personal computer 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, such as ipads.
(3) And other electronic devices with data interaction functions.
An embodiment of the present application further provides a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which when executed by an electronic device including a plurality of application programs, enable the electronic device to execute the method for controlling remote parking in the embodiment shown in fig. 1, and are specifically configured to perform the following operations:
responding to an operation executed in an automatic parking operation interface displayed on a screen by a user, and acquiring real-time operation data of the user from the start of the operation to the current moment in the automatic parking operation interface;
determining whether the operation of the user in the automatic parking operation interface is regular or not based on the real-time operation data;
when the operation of the user in the automatic parking operation interface is regular, controlling the target vehicle to automatically park;
and when the operation of the user in the automatic parking operation interface is not regular, suspending the automatic parking of the control target vehicle.
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.
It should be noted that all the embodiments in the present application are described in a related 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, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
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 identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are 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 (10)
1. A method of controlling a remotely controlled parking, the method comprising:
responding to an operation executed in an automatic parking operation interface displayed on a screen by a user, and acquiring real-time operation data of the user from the start of the operation to the current moment in the automatic parking operation interface;
determining whether the operation of the user in the automatic parking operation interface is regular or not based on the real-time operation data;
when the operation of the user in the automatic parking operation interface is regular, controlling the target vehicle to automatically park;
and when the operation of the user in the automatic parking operation interface is not regular, suspending the automatic parking of the control target vehicle.
2. The method according to claim 1, wherein the determining whether the operation of the user in the automatic parking operation interface is regular based on the real-time operation data comprises:
circularly executing the first designated step until a first preset condition is met;
wherein the first specifying step includes:
selecting data in a first preset time period from the real-time operation data according to a first preset time interval as a first group of operation data, wherein the ending time of the first preset time period is the current time or before the current time;
determining whether the operation of the user in the automatic parking operation interface is regular or not based on the first group of operation data;
if the regularity exists, the first appointed step is executed in a recycling mode after the first preset time interval is set as the circulation period corresponding to the regularity.
3. The method of claim 2, wherein determining whether there is regularity in the operation of the user in the automatic parking operation interface based on the first set of operation data comprises:
detecting whether periodicity exists in the first set of operation data;
if the periodicity exists, determining that the operation of the user in the automatic parking operation interface is regular;
and if the periodicity does not exist, determining that the regularity does not exist in the operation of the user in the automatic parking operation interface.
4. The method of claim 3, wherein the detecting whether the first set of operational data is periodic comprises:
selecting data in a second preset time period from the real-time operation data as a second group of operation data, wherein the starting time of the second preset time period is earlier than the starting time of the first preset time period by a preset time period, the length of the second preset time period is consistent with that of the first preset time period, and the preset time period is equal to a preset period;
comparing the similarity of the change rules of the first group of operation data and the second group of operation data based on a preset mode;
if the similarity meets a second preset condition, determining that the first group of operation data has periodicity;
and if the similarity does not meet a second preset condition, determining that the periodicity does not exist in the first group of operation data.
5. The method of claim 2, wherein determining whether there is regularity in the operation of the user in the automatic parking operation interface based on the first set of operation data comprises:
inputting a preset model to obtain a target output result by taking the first group of operation data as target input data, wherein the preset model is obtained by training based on sample operation data, and the output result of the preset model is used for determining whether regularity exists in the input data input into the preset model;
and determining whether the operation of the user in the automatic parking operation interface is regular or not based on the target output result.
6. The method of claim 5,
the preset model comprises one of a self-encoder, Boltzmann machine detection, a recurrent neural network and a convolutional neural network.
7. The method according to claim 1, wherein the determining whether the operation of the user in the automatic parking operation interface is regular based on the real-time operation data comprises:
circularly executing the second specified step within the specified time length until a third preset condition is met;
wherein the second specifying step includes:
selecting data in a third preset time period from the real-time operation data according to a second preset time interval as a third group of operation data, wherein the ending time of the third preset time period comprises the current time or is before the current time;
determining whether the user uses a memorized operation mode or not based on the third group of operation data and preset historical operation data, wherein the preset historical operation data is pre-stored regular operation data of the user in a preset historical time period, and the length of the preset historical time period is consistent with that of the third preset time period;
if the memorized operation mode is used, determining that the operation of the user in the automatic parking operation interface is regular;
and if the memorized operation mode is not used, determining that no regularity exists in the operation of the user in the automatic parking operation interface.
8. The method of claim 7, wherein determining whether the user has used a remembered mode of operation based on the third set of operational data and historical operational data comprises:
comparing the similarity of the change rule of the third group of operation data with the preset historical operation data based on a preset mode;
if the similarity meets a fourth preset condition, determining that the user uses a memorized operation mode;
and if the similarity does not meet the fourth preset condition, determining the operation mode of the user without using memory.
9. The method according to claim 4 or 8,
the operation data of the user in the automatic parking operation interface comprises one of first class data, second class data, third class data and fourth class data, wherein the first class data is data of change of an abscissa and/or an ordinate of the operation of the user in the automatic parking operation interface along with time, the second class data is a function of the abscissa of the operation of the user in the automatic parking operation interface relative to the ordinate, and the third class data is a function of the ordinate of the operation of the user in the automatic parking operation interface relative to the abscissa; the fourth class of data is at least a first derivative of one of the first class of data, the second class of data, and the third class of data;
the preset mode comprises one of the following modes: a covariance based approach and a cross entropy based approach between the two sets of data.
10. A control device for remotely controlling parking of a vehicle, the device comprising:
the data acquisition module is used for responding to the operation executed in the automatic parking operation interface displayed on the screen by the user and acquiring real-time operation data of the user from the start of the operation to the current moment in the automatic parking operation interface;
the regularity detection module is used for determining whether the operation of the user in the automatic parking operation interface is regular or not based on the real-time operation data;
the first control module is used for controlling the target vehicle to automatically park when the operation of the user in the automatic parking operation interface is regular;
and the second control module is used for suspending the automatic parking of the control target vehicle when the operation of the user in the automatic parking operation interface is not regular.
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