Patent Publication Number: US-11654895-B2

Title: Apparatus for recognizing parking area for autonomous parking and method thereof

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims priority from and the benefit of Korean Patent Application No. 10-2020-0160344, filed on Nov. 25, 2020, which is hereby incorporated by reference for all purposes as if set forth herein. 
     BACKGROUND 
     Field 
     Exemplary embodiments relate to technologies of recognizing a parking area for autonomous parking. 
     Discussion of the Background 
     With the development of technology, an autonomous parking technology for assisting a vehicle to park or exit a parking space has been introduced. For example, in an autonomous parking technology including a remote smart parking assist (RSPA) system, when a user pushes a button on a smart key, a vehicle may park or exit a parking space by itself without user involvement in the parking or the exiting of a parking space. 
     The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and, therefore, it may contain information that does not constitute prior art. 
     SUMMARY 
     To perform autonomous parking, there is a need for a vehicle (or a system of the vehicle) to recognize a parking area. In this case, the system may recognize a parking area using an ultrasonic wave, but, because an object, such as a previously parked vehicle, a wall, or a pillar, around a space where the vehicle wants to park is typically present, it may be impossible for the system using ultrasonic waves to perform autonomous parking in a parking area where there is no object around the space. A technology of recognizing a parking area using an image may recognize a space around a vehicle although there is no object around the vehicle, but it is difficult to detect a parking line designating a boundary of a parking space due to factors such as reflection of light or shadow effects. Particularly, a parking line indicating a parking area may fail to be accurately distinguished due to an auxiliary line that is not a parking space boundary or a merchandise loading mark that may exist on a parking lot. 
     The exemplary embodiments described herein have been made to solve the above-mentioned problems occurring in conventional autonomous vehicle parking technologies. 
     The technical problems to be solved by the present inventive concept are not limited to the aforementioned problems, and any other technical problems not mentioned herein will be clearly understood from the following description by those skilled in the art to which exemplary embodiments pertain. 
     According to an aspect, a vehicle parking assistance device may include an image sensing device, an artificial intelligence learning device, and a controller connected with the image sensing device and the artificial intelligence learning device. The controller may be configured to obtain an image using the image sensing device, detect at least one parking line pair in the obtained image, detect a parking area based on deep learning, detect a parking line based on the detected parking slot and the at least one detected parking line pair, detect an entrance point for the parking area, and generate parking information for autonomous parking based on the parking area and the entrance point. 
     According to another aspect, a vehicle parking assistance device may include an image sensing device, an artificial intelligence learning device, and a controller connected with the image sensing device and the artificial intelligence learning device. The controller may be configured to obtain an image using the image sensing device, detect a parking slot in the obtained image based on deep learning by means of the artificial intelligence learning device, detect a parking line and an entrance point in the obtained image, and generate parking information for autonomous parking based on the detected parking slot, the detected parking line, and the detected entrance point. 
     According to another aspect, a vehicle parking assistance device may include an image sensing device and a controller connected with the image sensing device. The controller may be configured to obtain an image using the image sensing device, detect a parking line in the obtained image, detect an entrance point included in a parking area based on an amount of change in a pixel value of the detected parking line, and generate information about the detected entrance point. 
     According to another aspect, a vehicle parking assistance device may include an image sensing device, an artificial intelligence learning device, and a controller connected with the image sensing device and the artificial intelligence learning device. The controller may be configured to obtain an image using the image sensing device, detect a parking line in the obtained image, detect a plurality of entrance point candidate groups based on an amount of change in a pixel value of the detected parking line, detect an entrance point having high confidence among the entrance point candidate groups based on deep learning using the artificial intelligence learning device, and generate information about the detected entrance point. 
     According to another aspect, a method may include obtaining an image, detecting at least one parking line pair in the obtained image, detecting a parking slot based on deep learning, detecting a parking area based on the detected parking slot and the at least one detected parking line pair, detecting an entrance point for the detected parking area, and generating parking information for autonomous parking based on the detected parking area and the entrance point. 
     According to another aspect, a method may include obtaining an image, detecting a parking slot in the obtained image based on deep learning, detecting a parking line and an entrance point in the obtained image, and generating parking information for autonomous parking based on the detected parking slot, the detected parking line, and the detected entrance point. 
     According to another aspect, a method may include obtaining an image, detecting a parking line in the obtained image, detecting an entrance point included in a parking area based on an amount of change in a pixel value of the detected parking line, and generating information about the detected entrance point. 
     According to another aspect, a method may include obtaining an image, detecting a parking line in the obtained image, detecting a plurality of entrance point candidate groups based on an amount of change in a pixel value of the detected parking line, detecting an entrance point having high confidence among the entrance point candidate groups based on deep learning, and generating information about the detected entrance point. 
     It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention as claimed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention, and together with the description serve to explain the principles of the invention. 
         FIG.  1    is a functional block diagram of a vehicle system device according to various embodiments. 
         FIG.  2    illustrates parameters configuring parking information according to various embodiments. 
         FIG.  3    illustrates an operation for generating parking information according to various embodiments. 
         FIG.  4    illustrates an operational flowchart of a vehicle system device for outputting parking information according to various embodiments. 
         FIG.  5    illustrates another operation for generating parking information according to various embodiments. 
         FIG.  6    illustrates another operational flowchart of a vehicle system device for outputting parking information according to various embodiments. 
         FIG.  7    illustrates an operational flowchart of a vehicle system device for detecting a parking line candidate group according to various embodiments. 
         FIG.  8    illustrates an operation for detecting a parking slot based on a deep learning classifier according to various embodiments. 
         FIG.  9 A  illustrates an operation for obtaining various types of parking slot images according to various embodiments. 
         FIG.  9 B  illustrates an operation for obtaining various types of parking slot images according to various embodiments. 
         FIG.  10    illustrates an operation for learning a parking slot image by way of a deep learning classifier according to various embodiments. 
         FIG.  11    illustrates a type of an entrance point according to various embodiments. 
         FIG.  12    illustrates an operation for detecting an entrance point according to various embodiments. 
         FIG.  13    illustrates an operational flowchart of a vehicle system device for outputting information about an entrance point according to various embodiments. 
         FIG.  14    illustrates an operational flowchart of a vehicle system device for detecting an entrance point according to various embodiments. 
         FIG.  15    illustrates another operation for detecting an entrance point according to various embodiments. 
         FIG.  16    illustrates another operational flowchart of a vehicle system device for outputting information about an entrance point according to various embodiments. 
         FIG.  17    illustrates an operational flowchart of a vehicle system device for learning data for an entrance point according to various embodiments. 
         FIG.  18    illustrates an operation for learning data for an entrance point according to various embodiments. 
     
    
    
     With regard to description of drawings, the same or similar denotations may be used for the same or similar components. 
     DETAILED DESCRIPTION OF THE ILLUSTRATED EMBODIMENTS 
     The invention is described more fully hereinafter with reference to the accompanying drawings, in which embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that the description of the various aspects is thorough, and will fully convey the scope of the invention to those skilled in the art. 
     Various embodiments and terms used therein are not intended to limit the technical features described herein to particular embodiments, and it should be construed as including various modifications, equivalents, or alternatives of a corresponding embodiment. With regard to description of drawings, similar denotations may be used for similar or related components. A singular form of a noun corresponding to an item may include one item or a plurality of the items, unless context clearly indicates otherwise. As used herein, each of the expressions “A or B,” “at least one of A and B,” “at least one of A or B,” “A, B, or C,” “at least one of A, B, and C,” and “at least one of A, B, or C,” may include any and all combinations of one or more of the items listed together with a corresponding expression among the expressions. Terms as “1st” and “2nd,” or “first” and “second” may be used to distinguish a corresponding component from another, and do not limit the components in another aspect (e.g., importance or order). If an element (e.g., a first element) is referred to, with or without the term “operatively” or “communicatively”, as “coupled with,” “coupled to,” “connected with,” or “connected to” another element (e.g., a second element), it means that the element may be coupled with the other element directly (e.g., wiredly), wirelessly, or via a third element. 
     As used herein, the term “module” used in various embodiments may include a unit implemented in hardware, software, or firmware, and may be interchangeably used with other terms, such as “logic,” “logic block,” “part,” or “circuitry”. A module may be a single integral component, or a minimum unit or part thereof, adapted to perform one or more functions. For example, according to an embodiment, the module may be implemented in the form of an application-specific integrated circuit (ASIC). 
     As customary in the field, some exemplary embodiments are described and illustrated in the accompanying drawings in terms of functional blocks, units, and/or modules. Those skilled in the art will appreciate that these blocks, units, and/or modules are physically implemented by electronic (or optical) circuits, such as logic circuits, discrete components, microprocessors, hard-wired circuits, memory elements, wiring connections, and the like, which may be formed using semiconductor-based fabrication techniques or other manufacturing technologies. In the case of the blocks, units, and/or modules being implemented by microprocessors or other similar hardware, they may be programmed and controlled using software (e.g., microcode) to perform various functions discussed herein and may optionally be driven by firmware and/or software. It is also contemplated that each block, unit, and/or module may be implemented by dedicated hardware, or as a combination of dedicated hardware to perform some functions and a processor (e.g., one or more programmed microprocessors and associated circuitry) to perform other functions. Also, each block, unit, and/or module of some exemplary embodiments may be physically separated into two or more interacting and discrete blocks, units, and/or modules without departing from the scope of the inventive concepts. Further, the blocks, units, and/or modules of some exemplary embodiments may be physically combined into more complex blocks, units, and/or modules without departing from the scope of the inventive concepts. 
     Various embodiments may be implemented as software (e.g., a program) including instructions that are stored in a machine-readable storage medium (e.g., an internal memory or an external memory). For example, the machine may invoke at least one of one or more instructions stored in the storage medium and may execute the invoked instruction. This may allow the machine to be operated to perform at least one function depending on the at least one invoked instruction. The one or more instructions may contain a code made by a compiler or a code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, the term “non-transitory” simply means that the storage medium is a tangible device and does not include a signal (e.g., an electromagnetic wave), but this term does not differentiate between where data is semi permanently stored in the storage medium and where data is temporarily stored in the storage medium. 
     According to an embodiment, a method according various embodiments disclosed herein may be included and provided in a computer program product. The computer program product may be traded as commodities between sellers and buyers. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., a compact disc read only memory (CD-ROM)) or may be distributed (e.g., downloaded or uploaded) directly or online through an application store or between two user devices. When distributed online, at least part of the computer program product may be at least temporarily stored in a machine-readable storage medium, such as a memory of the manufacturer&#39;s server, a server of the application store, or a relay server, and may be temporarily generated. 
     According to various embodiments, each (e.g., a module or program) of the above-mentioned components may include a single entity or a plurality of entities, and some of the plurality of entities may be separately arranged in another component. According to various embodiments, one or more components of the above-mentioned components or operations may be omitted, or one or more other components or operations may be added. Alternatively or additionally, the plurality of components (e.g., modules or programs) may be integrated into one component. In such a case, the integrated component may one or more functions of each of the plurality of components to be the same or similar to being performed by a corresponding component of the plurality of components before the integration. According to various embodiments, operations performed by modules, programs, or other components may be carried out sequentially, in parallel, repeatedly, or heuristically, or at least one or more of the operations may be executed in a different order or omitted, or other operations may be added. 
       FIG.  1    is a functional block diagram of a vehicle system device  100  according to various embodiments. 
     Referring to  FIG.  1   , the vehicle system device  100  may refer to a system device loaded onto a vehicle. The vehicle system device  100  may perform the overall function (e.g., autonomous driving or autonomous parking) of the vehicle. The vehicle system device  100  may include an image sensing device  110 , a controller  120 , an artificial intelligence learning device  130 , and an output device  140 . According to other embodiments, the vehicle system device  100  may exclude at least some (e.g., the output device  140 ) among the components shown in  FIG.  1    or may further include another component (e.g., a communication interface or a memory) which is not shown in  FIG.  1   . The components included in the vehicle system device  100  may refer to software (e.g., a program) implemented by instructions as well as hardware components. 
     The image sensing device  110  may be used to obtain an image. For example, the image sensing device  110  may be a camera including at least one of one or more lenses, an image sensor, an image signal processor, or a flash imaging device. According to an embodiment, the image sensing device  110  may obtain an image surrounding the vehicle. In this case, the image sensing device  110  may include 4-channel or more cameras mounted on the vehicle. The image surrounding the vehicle may be, for example, a surround view, 360-degree image of an area around the vehicle. The vehicle system device  100  may detect a parking area (or a parking slot) and an entrance point for parking the vehicle in the parking slot, based on the obtained image data or the surround view image. 
     The controller  120  may execute, for example, software (e.g., a program) to control at least one other component (e.g., a hardware or software component) of the vehicle system device  100  connected to the controller  120  and may perform a variety of data processing tasks or calculations. According to an embodiment, as at least a part of data processing or calculation performed, the controller  120  may store commands or data received from another component (e.g., the image sensing device  110 , the artificial intelligence learning device  130 , or the output device  140 ) in a volatile memory, may process the commands or data stored in the volatile memory, and may store resultant data in a non-volatile memory. According to an embodiment, the controller  120  may include a main processor (e.g., a central processing unit or an application processor) or an auxiliary processor (e.g., a graphic processing unit, an image signal processor, a sensor hub processor, a communication processor) operable independently or together with each other. For example, when the controller  120  includes the main processor and the auxiliary processor, the auxiliary processor may be configured to use lower power than the main processor or specialize in a specified function. The auxiliary processor may be implemented independently of the main processor or as a part thereof. 
     According to embodiments, the controller  120  may perform the overall function of the vehicle system device  100  for outputting parking information. For example, the controller  120  may obtain an image by way of the image sensing device  110  and may detect a plurality of parking line candidate groups in the obtained image. The controller  120  may detect at least one parking line pair among the plurality of parking line candidate groups. The controller  120  may detect a parking slot in an image obtained based on deep learning by way of the artificial intelligence learning device  130 . The controller  120  may detect a parking area based on the at least one detected candidate parking line pair and the detected parking slot and may detect an entrance point of the detected parking area. The controller  120  may generate parking information for autonomous parking based on the detected parking area and the detected entrance point and may output the generated parking information through the output device  140 . 
     For another example, the controller  120  may obtain an image by way of the image sensing device  110  and may preprocess image data of the obtained image. The controller  120  may detect a parking slot in the image based on deep learning by way of the artificial intelligence learning device  130 . The controller  120  may detect a parking line in the image. The controller  120  may detect an entrance point of the parking area based on the detected parking line and the detected parking slot. The controller  120  may generate parking information for autonomous parking based on the detected parking slot, the detected parking line, and the detected entrance point and may output the generated parking information through the output device  140 . 
     According to embodiments, the controller  120  may perform the overall function of the vehicle system device  100  for outputting information about the entrance point. For example, the controller  120  may obtain an image by way of the image sensing device  110  and may detect a parking line in the obtained image. The controller  120  may detect an entrance point for a parking area based on an amount of change in a pixel value of the detected parking line. The controller  120  may generate information about the detected entrance point and may output the generated information through the output device  140 . 
     For another example, the controller  120  may obtain an image by way of the image sensing device  110  and may detect a parking line in the obtained image. The controller  120  may detect an entrance point candidate group for a parking area based on an amount of change in a pixel value of the detected parking line. The controller  120  may detect an entrance point having high confidence based on deep learning using the artificial intelligence learning device  130 . The controller  120  may output information about the detected entrance point. 
     The above-mentioned example describes that the controller  120  performs each operation for generating parking information in an integrated manner. However, the vehicle system device  100  according to embodiments may separately include a parking line detector  122  for detecting a parking line, a parking slot detector  124  for detecting a parking slot, and an entrance point detector  126  for detecting an entrance point. Each of these components may be a hardware device or software (a program) stored in a memory. As the respective components may operate separately without operating as one module, a change in algorithm, learning of the algorithm, or performance enhancement of the algorithm may be separately performed. 
     The artificial intelligence learning device  130  may include a hardware structure specialized in processing an artificial intelligence model, for example, a neural processing unit (NPU). For another example, the artificial intelligence learning device  130  may be present as a separate program in a memory (not shown) of the vehicle system device  100 . The artificial intelligence model may be generated by machine learning. Such learning may be performed in the vehicle system device  100  itself in which artificial intelligence is performed or may be performed by way of a separate server. A learning algorithm may include, for example, but is not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. The artificial intelligence model may include a plurality of artificial neural network layers. An artificial neural network may be, but is not limited to, one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), deep Q-networks, two or more combinations thereof. Additionally or alternatively, the artificial intelligence model may include a software structure, other than a hardware structure. 
     According to embodiments, the artificial intelligence learning device  130  may learn the image for the parking slot by way of a deep learning classifier and may distinguish the parking slot in the surround view image depending to the learned result. For another example, the artificial intelligence learning device  130  may learn an image for the entrance point by way of the deep learning classifier and may classify one entrance point among entrance point candidate groups depending on the learned result. 
     The output device  140  may include a hardware component for visually or audibly providing information about the parking information or the entrance point. For example, the output device  140  may include a display, a hologram device, or a projector, and a control circuit for controlling the corresponding device. For another example, the output device  140  may include an audio device (e.g., a speaker) capable of converting a sound into an electrical signal or converting an electrical signal into a sound. 
       FIG.  2    illustrates parameters configuring parking information according to various embodiments. 
     Referring to  FIG.  2   , a vehicle system device  100  of  FIG.  1    may obtain an image  200  using an image sensing device  110  of  FIG.  1   . The image  200  may include, for example, a surround view, 360-degree image that surrounds a vehicle. To perform autonomous parking, the vehicle system device  100  may use at least one information among the following types of information: a) an entrance point (e.g.,  210 - 1 ,  210 - 2 ,  210 - 3 , or  210 - 4 ), b) a parking line (e.g.,  220 - 1 ,  220 - 2 ,  220 - 3 , or  220 - 4 ), or c) a parking slot (e.g.,  230 - 1  or  230 - 2 ). 
     The entrance point may be used to control autonomous parking.  FIG.  2    illustrates that the entrance point is represented as a point where the parking line and the parking slot meet, but the entrance point according to various embodiments may be an area including a portion of the parking area. The entrance point may include location coordinates (e.g., x and y coordinates) and direction information (e.g., an angle). For example, first information among the direction information of the entrance point may correspond to a direction of a parking line, and second information among the direction information of the entrance point may correspond to a direction of a parking slot. Thus, the vehicle system device  100  may determine a type (or form) of a parking area depending on a type (or form) of the entrance point. An example of the type of the entrance point will be described with reference to  FIG.  11   . 
       FIGS.  3  and  4    illustrate an operation for generating parking information according to an embodiment. 
     Referring to  FIG.  3   , in operation  301 , a vehicle system device  100  of  FIG.  1    may obtain a surround view, 360-degree image  300  that surrounds a vehicle  350  using an image sensing device  110  of  FIG.  1   . Embodiments where the vehicle system device  100  uses the surround view, 360-degree image  300  are described for convenience of description, but the form of the image obtained by the image sensing device  110  is not limited to a surround view form. 
     In operation  302 , the vehicle system device  100  may detect at least one parking line pair in the image  300 . The parking line pair may refer to, for example, two parking lines forming one parking slot. For example, the parking line  310 - 1  and the parking line  310 - 2  may form one parking line pair, and the parking line  310 - 3  and the parking line  310 - 4  may form another parking line pair.  FIG.  3    illustrates an example of detecting only two parking line pairs, but the number of parking lines and the number of parking line pairs, which are detected by the vehicle system device  100 , are not limited thereto. 
     According to an embodiment, the vehicle system device  100  may detect a plurality of parking line candidate groups in the image  300  to detect a parking line pair. For example, a controller  120  of  FIG.  1    may perform filtering (e.g., Gaussian filtering) to remove noise due to raw data or the surround view image obtained by way of the image sensing device  110  and may extract edge data from the filtered image. The controller  120  may determine a point determined as being a line in the image  300  as a line feature point. The line feature point may include, for example, location information (e.g., x and y coordinates) and direction information based on a gradient, in the image  300 . The controller  120  may perform line fitting for the determined line feature point. For example, the controller  120  may extract lines by clustering feature points, each of which has a similar direction and location, among the determined line feature points. The extracted lines (i.e., parking lines) may include both end points (e.g., x and y coordinates) and direction information. The controller  120  may determine two lines, which are parallel to each other and are separated from each other by a specified interval, among a plurality of parking line candidate groups determined through the filtering, the feature point detection, and the line fitting as a parking line pair. 
     In operation  303 , the vehicle system device  100  may detect at least one parking slot (e.g.,  320 - 1  or  320 - 2 ) based on deep learning. The parking slot may be, for example, a space between parking line pairs, which may refer to an entrance section of a parking area. According to an embodiment, the controller  120  may learn various types of parking slots by way of an artificial intelligence learning device  130  of  FIG.  1    and may detect an area corresponding to the parking slot in the image  300  based on the learned result. The parking line pair and the parking slot may form one parking area (or a parking area). 
     In operation  304 , the vehicle system device  100  may detect an entrance point (e.g.,  330 - 1 ,  330 - 2 ,  330 - 3 , or  330 - 4 ). The entrance point may refer to a point where an end point of the parking slot and an end point of the parking line pair meet. The entrance point may be represented as, for example, location coordinates (e.g., x and y coordinates) in the image  300 . The vehicle system device  100  may control autonomous parking using a location of the entrance point. 
     In operation  305 , the vehicle system device  100  may output parking information for autonomous parking. The parking information may include at least one information among, for example, identification information (e.g., index 0 or index 1) about a parkable area, a location and an angle of the entrance point (e.g.,  340 - 1 ,  340 - 2 ,  340 - 3 , or  340 - 4 ), or a type (e.g., a parallel type, a perpendicular type, a diagonal type, or a stepped type) of a parking slot. 
       FIG.  4    illustrates an operational flowchart of a vehicle system device for outputting parking information according to an embodiment. In the description below, operations included in the operational flowchart may be performed by a vehicle system device  100  of  FIG.  1    or may be performed by components included in the vehicle system device  100 . For example, a controller  120  of the vehicle system device  100  may control other components (e.g., an image sensing device  110 , an artificial intelligence learning device  130 , and the output device  140 ) to perform operations of the operational flowchart. 
     Referring to  FIG.  4   , in operation  410 , the controller  120  may obtain an image using the image sensing device  110 . The obtained image may include, for example, a surround view, 360-degree image of that surrounds a vehicle including the vehicle system device  100 . 
     In operation  420 , the controller  120  may detect a plurality of parking line candidate groups in the obtained image. The parking line may refer to, for example, a line having a specified direction in the image. 
     In operation  430 , the controller  120  may detect a parking line pair in the obtained image. For example, the controller  120  may determine two parking lines, which are parallel to each other and have a specified interval between them, among the plurality of parking line candidate groups as a parking line pair. According to an embodiment, the controller  120  may perform operation  420  and operation  430  at the same time without separately performing operation  420  and operation  430 . Operation  420  and operation  430  may be performed by a parking line detector  122  of  FIG.  1   . 
     In operation  440 , the controller  120  (e.g., a parking slot detector  124  of  FIG.  1   ) may detect a parking slot in the image based on deep learning. For example, the controller  120  may extract an area between the detected parking line pairs and may classify the extracted area using an artificial intelligence learning device  130  of  FIG.  1   . The classification may be performed by, for example, a DNN-based deep learning classifier. The controller  120  may determine whether the area extracted through the classification is a parking slot. When the area extracted through the classification corresponds to the parking slot, the controller  120  may determine what a type of the parking slot is. The type of the parking slot may include, for example, but is not limited to, a parallel type, a perpendicular type, a diagonal type, and a stepped type. 
     In operation  450 , the controller  120  may detect a parking area. For example, the controller  120  may determine the parking area based on the parking slot and a parking line corresponding to the parking slot among the parking line candidate groups (or parking line pairs). 
     In operation  460 , the controller  120  (e.g., an entrance point detector  126  of  FIG.  1   ) may detect an entrance point of the determined parking area. The entrance point may be represented as, for example, location coordinates and direction information. The direction information of the entrance point may correspond to a direction of the parking line pair. According to an embodiment, the entrance point may be determined together when the parking area is detected. In this case, the controller  120  may omit operation  460 . 
     In operation  470 , the controller  120  may output parking information for autonomous parking. For example, the controller  120  may deliver the parking information to another component of the vehicle system device  100  for autonomous driving. For another example, the controller  120  may display the parking information on an output device  140  of  FIG.  1    such that a user may identify the parking area and the entrance point. 
       FIGS.  5  and  6    illustrate an operation for generating parking information according to another embodiment. 
     Referring to  FIG.  5   , in operation  501 , a vehicle system device  100  of  FIG.  1    may obtain an image  500  including a vehicle  550  using an image sensing device  110  of  FIG.  1   . The image  500  may be, for example, a surround view, 360-degree image that surrounds the vehicle  550 . 
     In operation  502 , the vehicle system device  100  may detect at least one parking slot (e.g.,  510 - 1  or  510 - 2 ) based on deep learning. The parking slot may refer to, for example, a section a vehicle enters in a parking area (or a parking slot). According to an embodiment, a controller  120  of  FIG.  1    may learn various types of parking slots by way of an artificial intelligence learning device  130  of  FIG.  1    and may detect an area corresponding to the parking slot and a type of the parking slot indicated by the area in the image  500  based on the learned result. The controller  120  may generate parking slot information indicating the detected type of the parking slot and location information (e.g., x and y coordinates). 
     In operation  503 , the vehicle system device  100  may detect at least one parking line (e.g.,  520 - 1 ,  520 - 2 ,  520 - 3 , or  520 - 4 ) in the image  500 . For example, the controller  120  may perform filtering (e.g., Gaussian filtering) to remove noise due to raw data or the surround view image obtained by way of the image sensing device  110  and may extract edge data from the filtered image. The controller  120  may determine a point determined as being a line in the image  500  as a line feature point. The line feature point may include, for example, location information (e.g., x and y coordinates) and direction information based on a gradient, in the image  500 . The controller  120  may perform line fitting for the determined line feature point. For example, the controller  120  may extract lines by clustering feature points, each of which has a similar direction and location, among the determined line feature points. The extracted lines (i.e., parking lines) may include both end points (e.g., x and y coordinates) and direction information. The controller  120  may determine two lines, which are parallel to each other and are separated from each other at a specified interval, among the plurality of parking lines determined through the filtering, the feature point detection, and the line fitting as a parking line pair (e.g.,  520 - 1  and  520 - 2 , or  520 - 3  and  520 - 4 ). The parking line pair and the parking slot may form one parking area, which is designated by its respective pair of parking lines. 
     In operation  504 , the vehicle system device  100  may detect an entrance point (e.g.,  530 - 1 ,  530 - 2 ,  530 - 3 , or  530 - 4 ) for the parking slot. The entrance point may refer to a point where an end point of the parking slot and an end point of the parking line pair meet. The entrance point may be represented as, for example, location coordinates (e.g., x and y coordinates) in the image  500 . The vehicle system device  100  may control autonomous parking using a location of the entrance point. 
     In operation  505 , the vehicle system device  100  may output parking information for autonomous parking. The parking information may include at least one information among, for example, identification information (e.g., index 0 or index 1) about a parkable area, a location and an angle of the entrance point (e.g.,  540 - 1 ,  540 - 2 ,  540 - 3 , or  540 - 4 ), or a type (e.g., a parallel type, a perpendicular type, a diagonal type, or a stepped type) of the parking slot. 
       FIG.  6    illustrates an operational flowchart of a vehicle system device for outputting parking information according to another embodiment. In the description below, operations included in the operational flowchart may be performed by a vehicle system device  100  of  FIG.  1    or may be performed by components included in the vehicle system device  100 . For example, a controller  120  of the vehicle system device  100  may control other components (e.g., an image sensing device  110 , an artificial intelligence learning device  130 , and an output device  140  of  FIG.  1   ) to perform operations of the operational flowchart. 
     Referring to  FIG.  6   , in operation  610 , the controller  120  may obtain an image using the image sensing device  110 . The obtained image may include, for example, a surround view, 360-degree image that surrounds a vehicle including the vehicle system device  100 . 
     In operation  620 , the controller  120  may preprocess image data. The image data may refer to, for example, raw data of the image obtained in operation  610  of  FIG.  6    or data of the surround view image. For example, the controller  120  may filter the image data to remove noise in the image data. The controller  120  may perform a task for extracting edge data from the filtered image data. 
     In operation  630 , the controller  120  (e.g., a parking slot detector  124  of  FIG.  1   ) may detect a parking slot in the image based on deep learning. For example, the controller  120  may recognize an area corresponding to the parking slot in the image using the artificial intelligence learning device  130  and may classify a type of the parking slot indicated by the recognized area. The classification may be performed by, for example, a DNN-based deep learning classifier. The type of the parking slot may include, for example, but is not limited to, a parallel type, a perpendicular type, a diagonal type, and a stepped type. 
     In operation  640 , the controller  120  (e.g., a parking line detector  122  of  FIG.  1   ) may detect a parking line in the obtained image. The parking line may refer to, for example, a line having a specified direction in the image. The controller  120  may extract feature points from the obtained image and may detect a parking line through line fitting for the extracted feature points. The controller  120  may determine two parking lines, which are parallel to each other and have a specified interval by which these parking lines are separated from each other, among the plurality of detected parking lines as a parking line pair. 
     According to another embodiment, the controller  120  or the parking line detector  122  may detect a parking line based on the detected parking slot. For example, the controller  120  may detect a parking line extended from the parking slot, depending on the type of the parking slot. In this case, the direction of the parking line may be based on the detected type of the parking slot. 
     In operation  650 , the controller  120  (e.g., an entrance point detector  126  of  FIG.  1   ) may detect an entrance point. The entrance point may be represented as, for example, location coordinates and direction information. The direction information of the entrance point may correspond to a direction of the parking line. In an embodiment, the controller  120  may detect an entrance point using at least one of the detected parking slot or the detected parking line. For example, the entrance point may be a point where the parking slot and the parking line meet. For another example, the entrance point may be an end point of the parking slot or the parking line meet. 
     In operation  660 , the controller  120  may output parking information for autonomous parking. For example, the controller  120  may deliver the parking information to another component of the vehicle system device  100  for autonomous driving. For another example, the controller  120  may display the parking information on an output device  140  of  FIG.  1    such that a user may identify the parking slot and the entrance point. 
       FIG.  7    illustrates an operational flowchart of a vehicle system device for detecting a parking line candidate group according to various embodiments. Operations shown in  FIG.  7    may be performed as an example of, for example, operation  420  of  FIG.  4    or operation  640  of  FIG.  6   . Operations  720  to  740  among the operations shown in  FIG.  7    may be performed by a parking line detector  122  of  FIG.  1   . 
     Referring to  FIG.  7   , in operation  710 , a controller  120  of  FIG.  1    may preprocess image data. The image data may refer to, for example, raw data of an image obtained in operation  310  of  FIG.  3    or operation  610  of  FIG.  6    or data of a surround view image. For example, the controller  120  may filter the image data to remove noise in the image data. The controller  120  may perform a task for extracting edge data from the filtered image data. 
     In operation  720 , the controller  120  may detect a line feature point based on the preprocessed image data. The line feature point may be plural in number. The line feature point may include location information and direction information. 
     In operation  730 , the controller  120  may perform line fitting for the detected line feature point. For example, the controller  120  may generate a line by clustering feature points, each of which has a similar direction and location, among the determined line feature points. The generated line may include location coordinates (e.g., x and y coordinates) and direction information (e.g., an angle) for both end points. 
     In operation  740 , the controller  120  may detect a parking line candidate group in the image through the generated line. For example, the controller  120  may determine lines, each of which has a specified length or is parallel to another line, among the previously determined lines as a parking line candidate group. 
       FIG.  8    illustrates an operation for detecting a parking slot based on a deep learning classifier according to various embodiments. 
     Referring to  FIG.  8   , a vehicle system device  100  (e.g., a controller  120 ) of  FIG.  1    may use an image (e.g.,  810 - 1  or  810 - 2 ) including a parking slot as an input of a DNN-based parking slot classifier. The image including the parking slot may be extracted from an image (e.g.,  200  of  FIG.  2   ) previously obtained by an image sensing device  110  of  FIG.  1    or may be obtained additionally by the image sensing device  110  after the vehicle system device  100  detects a parking line candidate group (or a parking line pair). 
     Because the DNN-based parking slot classifier is in a state learned through an image including various types of parking slots, it may identify whether the input image includes a parking slot and may identify whether the input image indicates any type of parking slot when the input image includes the parking slot. For example, when the first image  810 - 1  is input to the DNN-based parking slot classifier, the controller  120  (or an artificial intelligence learning device  130  of  FIG.  1   ) may recognize that the first image  810 - 1  indicates a parking slot and may recognize that a type of the recognized parking slot indicates a diagonal type  820 - 1 . As a similar example, when the second image  810 - 2  is input to the DNN-based parking slot classifier, the controller  120  may recognize that the second image  810 - 2  indicates a parking slot and may recognize that a type of the recognized parking slot indicates a perpendicular type  820 - 2 . 
       FIGS.  9 A,  9 B, and  10    illustrate an operation for learning a parking slot according to various embodiments.  FIGS.  9 A and  9 B  illustrate an operation for obtaining a parking slot image.  FIG.  10    illustrates an operation for learning a parking slot image by way of a deep learning classifier. A method for learning a parking slot image, which will be described below, may be performed by way of an artificial intelligence learning device  130  of a vehicle system device  100  of  FIG.  1    or may be performed by way of a separate server linked with the vehicle system device  100 . In this case, the vehicle system device  100  may detect a parking slot by receiving a learned result from the server. 
     Referring to  FIG.  9 A , the vehicle system device  100  (e.g., the artificial intelligence learning device  130 ) may obtain an image  900  including at least one parking slot by way of an image sensing device  110  of  FIG.  1   . The artificial intelligence learning device  130  may obtain a plurality of images, each of which includes a parking slot, while changing a place (e.g., a parking lot), weather, and a type of a parking slot. The artificial intelligence learning device  130  may generate a local patch image to be used for learning in the image including the parking slot. In an embodiment, the artificial intelligence learning device  130  may generate a plurality of local patch images including a parking slot (e.g.,  910 - 1 ) through pairing of entrance points (e.g.,  920 - 1  and  920 - 2 ). The local patch image may include information about location coordinates (e.g., x and y), a width, a height, or a type (e.g., a perpendicular type, a parallel type, a diagonal type, a stepped type, and an open/closed type) of a parking slot. 
     Referring to  FIG.  9 B , the type of the parking slot may include, for example, but is not limited to, a closed perpendicular type ( 930 - 1 ), an open perpendicular type ( 930 - 2 ), a closed parallel type  930 - 3 , a closed diagonal type  930 - 4 , an open diagonal type  930 - 5 , and a stepped type  930 - 6 . 
     Referring to  FIG.  10   , the artificial intelligence learning device  130  may learn a local patch image  1005  by way of a deep learning classifier to accurately recognize a type of a parking slot in various situations (e.g., a place, weather, and the like). The artificial intelligence learning device  130  may first determine whether the local patch image  1005  obtained before learning is a parking slot and may then perform learning when the local patch image  1005  corresponds to the parking slot. The deep learning classifier may learn a parking slot based on various types of artificial intelligence models. For example, the deep learning classifier may enhance accuracy of image recognition by learning a type of a parking slot using a CNN structure. For example, an input  1010  of the deep learning classifier may be local patch images indicating various types of parking slots and a parking slot type indicated by each image. The deep learning classifier may repeatedly perform convolution and sub-sampling for data of the input  1010 . The convolution may be understood as an operation of applying a mask to an input image, multiplying a weight of the mask for each pixel value of the input image, and setting the sum thereof to a pixel value of an output image. The input  1010  of the deep learning classifier may be output as a plurality of images  1020  through the convolution. The plurality of images  1020  may be referred to as a feature map. The sub-sampling may be an operation of reducing a size of a screen, which may be referred to as a pooling operation. The sub-sampling may be understood as an operation of reducing a feature map of an M×M size to a feature map of an N×N size. A plurality of images  1030 , each of which has the reduced size through the sum-sampling, may be generated. When the above-mentioned convolution and the above-mentioned sub-sampling are repeated, the image may increase in number and the image may decrease in size ( 1040  and  1050 ). The reduced images may have only features. The feature may be to distinguish, for example, a type of a parking slot, which may include a location of an entrance point, a direction of the entrance point, an angle of the entrance point, whether the entrance point is open/closed, or the like. The deep learning classifier may learn a plurality of images  1050  generated through the convolution and the sub-sampling by applying the plurality of images  1050  to at least one hidden layer  1060  (e.g., a DNN). The deep learning classifier may classify a type of a parking slot in an image obtained thereafter using a learned result  1070 . 
       FIGS.  11  to  18    illustrate an operation for outputting information about an entrance point according to various embodiments. 
       FIG.  11    illustrates a type of an entrance point according to various embodiments. 
     Referring to  FIG.  11   , a vehicle system device  100  of  FIG.  1    may obtain or learn and store data  1101  to  1111  for various entrance point types. The entrance point type may be determined based on whether there is a parking slot and an angle where the parking slot and a parking line meet. The entrance point type may include, for example, an open type  1101 , a T type  1102 , a left L type  1103 , a right L type  1104 , a T type  1105  slanted to the left, a left L type  1106  to be slanted to the left, a right L type  1107  slanted to the left, a T type  1108  slanted to the right, a left L type  1109  slanted to the right, a right L type  1110  slanted to the right, and a U type  1111 . 
       FIGS.  12  to  14    illustrate an operation for outputting information about an entrance point according to an embodiment. 
     Referring to  FIG.  12   , in operation  1201 , a controller  120  of  FIG.  1    may detect a parking line  1210  from an obtained image  1200 . In operation  1202 , the controller  120  may extract a profile  1220  for pixel values of the detected parking line  1210 . An x-axis of a graph indicating the profile  1220  may indicate an x-axis distance in the image  1200 , and a y-axis of the graph may refer to a pixel value. An interval  1230  where the amount of change is large on the graph (or an interval where the amount of change is greater than a threshold) may refer to an interval  1240  where the parking line is ended. The controller  120  may know that there is an entrance point at a location adjacent to the interval  1240  where the parking line  1210  is ended, but a result for a location and type of the entrance point may fail to be accurate when the interval  1230  where the amount of change is large in width or when a difference of the amount of change is not large. 
     To more accurately recognize the location and type of the entrance point, in operation  1203 , the controller  120  may detect an entrance point through mask filtering for the interval (e.g.,  1230  of  FIG.  12   ) where the amount of change is large. The mask filtering may refer to a scheme of comparing a form for the interval where the amount of change is large with data previously stored in a vehicle system device  100  of  FIG.  1   . The previously stored data may refer to, for example, data for various entrance point types indicated in  FIG.  11   . The controller  120  may compare the interval  1210  in the image  1200  corresponding to the interval  1230  where the amount of change is large with previously stored data to determine an entrance point type with the highest matching score as an entrance point type of the interval. The controller  120  may more accurately detect a location of the entrance point based on the previously detected parking line and the determined entrance point type. For example, in operation  1204 , the controller  120  may determine a first point  1250  on the graph as a location of an entrance point  1260  in the image  1200 . 
       FIG.  13    illustrates an operational flowchart of a vehicle system device for outputting information about an entrance point according to an embodiment. In the description below, operations included in the operational flowchart may be performed by a vehicle system device  100  of  FIG.  1    or may be performed by components included in the vehicle system device  100 . For example, a controller  120  of the vehicle system device  100  may control other components (e.g., an image sensing device  110  and an output device  140  of  FIG.  1   ) to perform operations of the operational flowchart. For another example, in operation  1320  may be performed by a parking line detector  122  of  FIG.  1   , and operation  1330  and operation  1340  may be performed by an entrance point detector  126  of  FIG.  1   . 
     Referring to  FIG.  13   , in operation  1310 , the controller  120  may obtain an image using the image sensing device  110 . The obtained image may include, for example, a surround, 360-degree view image that surrounds a vehicle including the vehicle system device  100 . 
     In operation  1320 , the controller  120  may detect a parking line pair from the obtained image. For example, the controller  120  may perform filtering (e.g., Gaussian filtering) to remove noise due to raw data or the surround view image obtained by way of the image sensing device  110  and may extract edge data from the filtered image. The controller  120  may determine a point determined as being a line in the image as a line feature point. The line feature point may include, for example, location information (e.g., x and y coordinates) and direction information based on a gradient, in the image. The controller  120  may perform line fitting for the determined line feature point. For example, the controller  120  may extract lines by clustering feature points, each of which has a similar direction and location, among the determined line feature points. The extracted lines (i.e., parking lines) may include both end points (e.g., x and y coordinates) and direction information. 
     In operation  1330 , the controller  120  may detect an entrance point based on an amount of change in a pixel value of the detected parking line. For example, an amount of change between pixel values corresponding to the parking line is not large, whereas a pixel value at a point where the parking line is ended has a large difference with the pixel value corresponding to the parking line. Thus, the controller  120  may determine a point, where the amount of change is large, as an entrance point. 
     In operation  1340 , the controller  120  may output information about the entrance point. The information about the entrance point may include at least one of, for example, a location, an angle, a direction, or a type of the entrance point. In an embodiment, the controller  120  may deliver the information about the entrance point to another component of the vehicle system device  100  for autonomous driving. For another example, the controller  120  may display the information about the entrance point on an output device  140  of  FIG.  1    such that a user may identify the entrance point. 
       FIG.  14    illustrates an operational flowchart of a vehicle system device for detecting an entrance point according to an embodiment. Operations shown in  FIG.  14    may be implemented by a controller  120  or an entrance point detector  126  of  FIG.  1   . 
     Referring to  FIG.  14   , in operation  1410 , the controller  120  may extract a profile for pixel values indicating a parking line from an image. 
     In operation  1420 , the controller  120  may measure an amount of change on the profile. 
     In operation  1430 , the controller  120  may detect an interval where the amount of change is large. In an embodiment, the controller  120  may detect an interval where the amount of change in pixel value is greater than a specified threshold. 
     In operation  1440 , the controller  120  may detect an entrance point through mask filtering for the interval (e.g.,  530  of  FIG.  5   ) where the amount of change is large. The mask filtering may refer to a scheme of comparing a form for the interval where the amount of change is large with data previously stored in a vehicle system device  100  of  FIG.  1   . 
       FIGS.  15  to  18    illustrate an operation for outputting information about an entrance point according to another embodiment. 
     Referring to  FIG.  15   , in operation  1501 , a controller  120  of  FIG.  1    may detect a parking line  1510  from an obtained image  1500 . For example, the controller  120  may preprocess the obtained image  1500  and may detect the parking line  1510  through feature point extraction and line fitting of the preprocessed image  1500 . 
     In in operation  1502 , the controller  120  may extract a profile  1520  for pixel values of the detected parking line  1510 . An x-axis of a graph indicating the profile  1520  may indicate an x-axis distance in the image  1500 , and a y-axis of the graph may refer to a pixel value. The controller  120  may measure an amount of change in a pixel value and may detect an interval (e.g.,  1530 ) where the amount of change is greater than a specified threshold. The interval where the amount of change is greater than the threshold may be plural in number on the profile  1520 . In this case, the controller  120  may determine an interval as an entrance point candidate group. 
     According to an embodiment, the controller  120  may set the number of entrance point candidate groups to N (where N is a natural number). In this case, the controller  120  may use N entrance point candidate groups, each of which has the largest amount of change, among the plurality of entrance point candidate groups, each of which has the amount of change greater than the threshold. 
     In in operation  1503 , the controller  120  may extract an image (e.g.,  1540 - 1 ,  1540 - 2 ,  1540 - 3 , or  1540 - 4 ) for each of the entrance point candidate groups. In in operations  1504  to  1506 , the controller  120  may classify an image for each of the entrance point candidate groups by way of a classifier. For example, the controller  120  may compare the image for each of the entrance point candidate groups with data previously learned by the artificial intelligence learning device  130  to determine an image  1550  having the highest confidence as an image corresponding to the entrance point. The classifier may determine a location and type of an entrance point  1560  included in the image  1500  by comparing the image of the entrance point candidate group with previously learned data. 
       FIG.  16    illustrates an operational flowchart of a vehicle system device for outputting information about an entrance point according to another embodiment. In the description below, operations included in the operational flowchart may be performed by a vehicle system device  100  of  FIG.  1    or may be performed by components included in the vehicle system device  100 . For example, a controller  120  of the vehicle system device  100  may control other components (e.g., an image sensing device  110 , an artificial intelligence learning device  130 , and an output device  140  of  FIG.  1   ) to perform operations of the operational flowchart. For another example, operation  1620  may be performed by a parking line detector  122  of  FIG.  1   , and operations  1630  to  1650  may be performed by an entrance point detector  126  of  FIG.  1   . 
     Referring to  FIG.  16   , in operation  1610 , the controller  120  may obtain an image using the image sensing device  110 . The obtained image may include, for example, a surround view 360-degree image that surrounds a vehicle including the vehicle system device  100 . 
     In operation  1620 , the controller  120  may detect a parking line from the obtained image. For example, the controller  120  may perform filtering (e.g., Gaussian filtering) to remove noise due to raw data or the surround view image obtained by way of the image sensing device  110  and may extract edge data from the filtered image. The controller  120  may determine a point determined as being a line in the image as a line feature point. The line feature point may include, for example, location information (e.g., x and y coordinates) and direction information based on a gradient, in the image. The controller  120  may perform line fitting for the determined line feature point. For example, the controller  120  may extract lines by clustering feature points, each of which has a similar direction and location, among the determined line feature points. The extracted lines (i.e., parking lines) may include both end points (e.g., x and y coordinates) and direction information. 
     In operation  1630 , the controller  120  may detect an entrance point candidate group based on an amount of change in a pixel value of the detected parking line. For example, an amount of change between pixel values corresponding to the parking line is not large, whereas a pixel value at a point where the parking line is ended has a large difference with the pixel value corresponding to the parking line. Thus, the controller  120  may determine a point, where the amount of change is large, as an entrance point candidate group. 
     In operation  1640 , the controller  120  may detect an entrance point having high confidence among the entrance point candidate groups based on deep learning. For example, the controller  120  may compare data determined as the entrance point candidate group with data learned by the artificial intelligence learning device  130  and may select an entrance point candidate group having high confidence as a result of the compared result. 
     In operation  1650 , the controller  120  may output information about the entrance point. The information about the entrance point may include at least one of, for example, a location, an angle, a direction, or a type of the entrance point. In an embodiment, the controller  120  may deliver the information about the entrance point to another component of the vehicle system device  100  for autonomous driving. For another example, the controller  120  may display the information about the entrance point on an output device  140  of  FIG.  1    such that a user may identify the entrance point. 
       FIG.  17    illustrates an operational flowchart of a vehicle system device for learning data for an entrance point according to various embodiments. 
     Referring to  FIG.  17   , in operation  1710 , an artificial intelligence learning device  130  of  FIG.  1    may collect an image including an entrance point. 
     In operation  1720 , the artificial intelligence learning device  130  may learn an image collected through a deep learning classifier. The deep learning classifier may use at least one scheme among, for example, multilayer perception (MLP), support vector machine (SVM), or a deep neural network (DNN). 
       FIG.  18    illustrates an operation for learning data for an entrance point. 
     A method for learning data for an entrance point, which will be described below, may be performed by way of an artificial intelligence learning device  130  of a vehicle system device  100  of  FIG.  1    or may be performed by way of a separate server linked with the vehicle system device  100 . In this case, the vehicle system device  100  may detect an entrance point by receiving a learned result from the server. 
     The artificial intelligence learning device  130  may obtain and collect images, each of which includes the entrance point, by way of an image sensing device  110  of  FIG.  1   . The artificial intelligence learning device  130  may obtain a plurality of images, each of which includes an entrance point, while changing a place (e.g., a parking lot), weather, and a type of the entrance point. The entrance point type may be an example shown in  FIG.  11   , but not limited thereto. 
     The artificial intelligence learning device  130  may generate a local patch image to be used for learning in the image including the entrance point and may learn the local patch image by means of the deep learning classifier. The deep learning classifier may learn data for the entrance point based on various types of artificial intelligence models. For example, the deep learning classifier may enhance accuracy of image recognition by learning a type of the entrance point using a CNN structure. A process ( 1810  to  1870 ) where the data for the entrance point is learned by the deep learning classifier may be similar in principle to the process ( 1010  to  1070 ) where the data for the parking slot is learned in  FIG.  10   . In this case, an input  1810  of the deep learning classifier may be local patch images indicating various types of entrance points and an entrance point type indicated by each image. Furthermore, a feature indicated by reduced images may be to distinguish an entrance point, which may include a location, a direction, an angle, a form, or the like of the entrance point. The deep learning classifier may classify an entrance point with high confidence among the entrance point candidate groups using a learned result  1870 . 
     According to embodiments disclosed herein, the vehicle system device may more accurately recognize a parking area where there are no objects around the parking area. 
     According to embodiments disclosed herein, the vehicle system device may more accurately recognize an entrance point in a parking area where there are no objects around the parking area. 
     In addition, various effects ascertained directly or indirectly through the embodiments disclosed herein may be provided. 
     Hereinabove, although exemplary embodiments have been described with reference to the accompanying drawings, these embodiments are not limited thereto, but may be variously modified and altered by those skilled in the art to which the exemplary embodiments pertain without departing from the spirit and scope of the invention as set forth in the following claims.