Patent Publication Number: US-2023154205-A1

Title: Virtual traffic line generation apparatus and method thereof

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     The present application claims priority to Korean Patent Application No. 10-2021-0157881, filed on Nov. 16, 2021, the entire contents of which is incorporated herein for all purposes by this reference. 
     BACKGROUND OF THE PRESENT DISCLOSURE 
     Field of the Present Disclosure 
     The present disclosure relates to technologies of generating a virtual traffic line based on reliability of a traffic line. 
     Description of Related Art 
     Due to the wear of the traffic line, the use of defective paint, and the like, it is often the case that the reflectivity of the traffic line does not meet the road regulations. Thus, when driving at night, traffic accidents occur frequently because drivers do not properly recognize traffic lines. 
     Furthermore, when it rains, the reflectivity of the traffic line drops sharply as a water film is formed on the road due to rainwater. Thus, traffic accidents according to no detection of traffic lines occur more frequently when it rains than when it does not rain. 
     When an autonomous vehicle which detects a traffic line based on an image does not detect the traffic line due to the above-mentioned reasons, because it performs autonomous driving without information related to the traffic line, it is unable to ensure safety of passengers. 
     The information included in this Background of the present disclosure section is only for enhancement of understanding of the general background of the present disclosure and may not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art. 
     BRIEF SUMMARY 
     Various aspects of the present disclosure are directed to providing a virtual traffic line generation apparatus of determining reliability of a traffic line detected for each frame, storing the reliability of the traffic line for each frame, and generating a virtual traffic line based on a traffic line with the highest reliability among traffic lines detected in a previous frame when the traffic line is not detected to ensure driving safety of an autonomous vehicle even when the traffic line is not detected and a method thereof. 
     Another aspect of the present disclosure provides a virtual traffic line generation apparatus of determining reliability of a traffic line detected for each frame, storing the reliability of the traffic line for each frame, generating a virtual traffic line based on a traffic line with the highest reliability among traffic lines detected in a previous frame when the traffic line is not detected, and providing the virtual traffic line and reliability corresponding to the virtual traffic line together so that an autonomous vehicle may use the reliability as a parameter when performing various autonomous driving policies, and a method thereof. 
     The purposes of the present disclosure are not limited to the aforementioned purposes, and any other purposes and advantages not mentioned herein will be clearly understood from the following description and may more clearly known by an exemplary embodiment of the present disclosure. Furthermore, it may be easily seen that purposes and advantages of the present disclosure may be implemented by means indicated in claims and a combination thereof. 
     According to an aspect of the present disclosure, a virtual traffic line generation apparatus may include a controller configured for determining reliability of a traffic line detected for each frame and generates a virtual traffic line based on a traffic line with the highest reliability among traffic lines detected in a previous frame when the traffic line is not detected and a storage storing the reliability of the traffic line for each frame. 
     In an exemplary embodiment of the present disclosure, the controller may represent the detected traffic line in a form of a quadratic function and may determine a degree to which the quadratic function is identical to a real traffic line as the reliability of the detected traffic line. 
     In an exemplary embodiment of the present disclosure, the controller may obtain an x2 value on the quadratic function for a y value with respect to any coordinate value (x1, y) on the real traffic line, may determine an error between an x1 value on the real traffic line and the x2 value on the quadratic function, counts a case where the error is less than or equal to a reference value as a valid number of times, and may determine a ratio of the valid number of times to a total number of times as the reliability of the detected traffic line. 
     In an exemplary embodiment of the present disclosure, it may be preferable that the total number of times is greater than the reference number of times. 
     In an exemplary embodiment of the present disclosure, the controller may provide the virtual traffic line and reliability corresponding to the virtual traffic line together to an autonomous vehicle. 
     According to another aspect of the present disclosure, a virtual traffic line generation method may include determining, by a controller, reliability of a traffic line detected for each frame, storing, by a storage, the reliability of the traffic line for each frame, and generating, by the controller, a virtual traffic line based on a traffic line with a highest reliability among traffic lines detected in a previous frame, when the traffic line is not detected. 
     In an exemplary embodiment of the present disclosure, the determining of the reliability may include representing the detected traffic line in a form of a quadratic function and determining a degree to which the quadratic function is identical to a real traffic line as the reliability of the detected traffic line. 
     In an exemplary embodiment of the present disclosure, the determining of the degree to which the quadratic function is identical to the real traffic line as the reliability of the detected traffic line may include obtaining an x2 value on the quadratic function for a y value with respect to any coordinate value (x1, y) on the real traffic line, determining an error between an x1 value on the real traffic line and the x2 value on the quadratic function, counting a case where the error is less than or equal to a reference value as a valid number of times, and determining a ratio of the valid number of times to a total number of times as the reliability of the detected traffic line. 
     In an exemplary embodiment of the present disclosure, the virtual traffic line generation method may further include providing the virtual traffic line and reliability corresponding to the virtual traffic line together to an autonomous vehicle. 
     The methods and apparatuses of the present disclosure have other features and advantages which will be apparent from or are set forth in more detail in the accompanying drawings, which are incorporated herein, and the following Detailed Description, which together serve to explain certain principles of the present disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram illustrating an example of a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied; 
         FIG.  2 A  is a drawing illustrating an example of an image captured by a front view camera provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied; 
         FIG.  2 B  is a drawing illustrating an example of an image captured by a left view camera provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied; 
         FIG.  2 C  is a drawing illustrating an example of an image captured by a right view camera provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied; 
         FIG.  3 A  is a drawing illustrating an example of a top view image generated by an SVM system provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied; 
         FIG.  3 B  is a drawing illustrating another example of a top view image generated by an SVM system provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied; 
         FIG.  4 A  is a drawing illustrating an example of a result where a traffic line detection device provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied, classifies an object in a top view image; 
         FIG.  4 B  is a drawing illustrating another example of a result where a traffic line detection device provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied, classifies an object in a top view image; 
         FIG.  5 A  is a drawing illustrating an example of a result where a traffic line detection device provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied, binarizes a top view image; 
         FIG.  5 B  is a drawing illustrating an example of a result where a traffic line detection device provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied, extracts an edge of a traffic line from a binary image; 
         FIG.  5 C  is a drawing illustrating an example of a result where a traffic line detection device provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied, extracts a central point from a traffic line area; 
         FIG.  5 D  is a drawing illustrating an example of a result where a traffic line detection device provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied, clusters central points of a traffic line area; 
         FIG.  5 E  is a drawing illustrating an example of a result where a traffic line detection device provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied, detects a traffic line; 
         FIG.  6    is a block diagram illustrating a configuration of a virtual traffic line generation apparatus according to an exemplary embodiment of the present disclosure; 
         FIG.  7    is a flowchart illustrating a virtual traffic line generation method according to an exemplary embodiment of the present disclosure; and 
         FIG.  8    is a block diagram illustrating a computing system for executing a virtual traffic line generation method according to an exemplary embodiment of the present disclosure. 
     
    
    
     It may be understood that the appended drawings are not necessarily to scale, presenting a somewhat simplified representation of various features illustrative of the basic principles of the present disclosure. The specific design features of the present disclosure as included herein, including, for example, specific dimensions, orientations, locations, and shapes will be determined in part by the particularly intended application and use environment. 
     In the figures, reference numbers refer to the same or equivalent parts of the present disclosure throughout the several figures of the drawing. 
     DETAILED DESCRIPTION 
     Reference will now be made in detail to various embodiments of the present disclosure(s), examples of which are illustrated in the accompanying drawings and described below. While the present disclosure(s) will be described in conjunction with exemplary embodiments of the present disclosure, it will be understood that the present description is not intended to limit the present disclosure(s) to those exemplary embodiments of the present disclosure. On the other hand, the present disclosure(s) is/are intended to cover not only the exemplary embodiments of the present disclosure, but also various alternatives, modifications, equivalents and other embodiments, which may be included within the spirit and scope of the present disclosure as defined by the appended claims. 
     Hereinafter, various exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In adding the reference numerals to the components of each drawing, it should be noted that the identical component is designated by the identical numerals even when they are displayed on other drawings. Furthermore, in describing the exemplary embodiment of the present disclosure, a detailed description of well-known features or functions will be ruled out in order not to unnecessarily obscure the gist of the present disclosure. 
     In describing the components of the exemplary embodiment according to an exemplary embodiment of the present disclosure, terms such as first, second, “A”, “B”, (a), (b), and the like may be used. These terms are merely intended to distinguish one component from another component, and the terms do not limit the nature, sequence or order of the corresponding components. Furthermore, unless otherwise defined, all terms including technical and scientific terms used herein are to be interpreted as is customary in the art to which the present disclosure belongs. Such terms as those defined in a generally used dictionary are to be interpreted as having meanings equal to the contextual meanings in the relevant field of art, and are not to be interpreted as having ideal or excessively formal meanings unless clearly defined as having such in the present application. 
       FIG.  1    is a block diagram illustrating an example of a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied.  FIG.  2 A  is a drawing illustrating an example of an image captured by a front view camera provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied.  FIG.  2 B  is a drawing illustrating an example of an image captured by a left view camera provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied.  FIG.  2 C  is a drawing illustrating an example of an image captured by a right view camera provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied.  FIG.  5 A  is a drawing illustrating an example of a result where a traffic line detection device provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied, binarizes a top view image.  FIG.  5 B  is a drawing illustrating an example of a result where a traffic line detection device provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied, extracts an edge of a traffic line from a binary image.  FIG.  5 C  is a drawing illustrating an example of a result where a traffic line detection device provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied, extracts a central point from a traffic line area.  FIG.  5 D  is a drawing illustrating an example of a result where a traffic line detection device provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied, clusters central points of a traffic line area.  FIG.  5 E  is a drawing illustrating an example of a result where a traffic line detection device provided in a system for detecting a traffic line based on an image, to which an exemplary embodiment of the present disclosure is applied, detects a traffic line. 
     As shown in  FIG.  1   , the system for detecting the traffic line based on the image, to which an exemplary embodiment of the present disclosure is applied, may include a camera  100 , a surround view monitoring (SVM) system  200 , a traffic line detection device  300 , and a virtual traffic line generation apparatus  400 . Herein, the virtual traffic line generation apparatus  400  may be implemented to merge into the traffic line detection device  300  so that the traffic line detection device  300  performs all functions of the virtual traffic line generation apparatus  400 . 
     Seeing the respective components, first of all, the camera  100  may be an image capture device which captures an image around a vehicle, which may include a front view camera, a rear view camera, a left view camera, and a right view camera. Such cameras may be tuned so that an optimal surround view (SV) image (e.g., a top view image) may be generated. 
     The front view camera may be located in front of the vehicle and may be used to obtain an image in front of the vehicle. The front view camera may be located on a center portion between both headlamps of the vehicle, but not limited thereto. An example of an image captured by such a front view camera is shown in  FIG.  2 A . 
     The rear view camera may be located on the rear of the vehicle and may be used to obtain an image behind the vehicle. The rear view camera may be located on a center portion between both rear lamps of the vehicle, but not limited thereto. 
     The left view camera may be located on the left side of the vehicle and may be used to obtain an image at the left of the vehicle. The left view camera may be located on a lower end portion of the left side mirror of the vehicle, but not limited thereto. An example of an image captured by such a left view camera is shown in  FIG.  2 B . 
     The right view camera may be located on the right side of the vehicle and may be used to obtain an image at the right of the vehicle. The right view camera may be located on a lower end portion of the right side mirror of the vehicle, but not limited thereto. An example of an image captured by such a right view camera is shown in  FIG.  2 C . 
     The SVM system  200  may convert the image captured by the camera  100  into a top view image. The top view image generated by such an SVM system  200  is shown in  FIG.  3 A  and  FIG.  3 B .  FIG.  3 A  illustrates the result of converting a front view image captured by a front view camera into a top view.  FIG.  3 B  illustrates a top view image generated using a left view image captured by a left view camera and a right view image captured by a right view camera. 
     A traffic line detection device  300  of  FIG.  1    may detect a traffic line for each frame based on a top view image generated by an SVM system  200  of  FIG.  1   . 
     Hereinafter, the operation of the traffic line detection device  300  will be described in detail. 
     First of all, the traffic line detection device  300  may have a traffic line detection model, deep learning of which is completed, and may classify an object in the top view image generated by the SVM system  200  based on the traffic line detection model. In other words, the traffic line detection device  300  may detect class information of each pixel for each frame of the top view image. The detected class information of each pixel is shown in  FIG.  4 A  and  FIG.  4 B .  FIG.  4 A  illustrates the result of detecting class information of each pixel from a front top view image of a vehicle.  FIG.  4 B  illustrates the result of detecting class information of each pixel from left and right top view images of a vehicle. 
     A traffic line detection device  300  of  FIG.  1    may binarize pixels corresponding to a traffic line into white ( 255 ) and may binarize the remaining pixels into black ( 0 ) in a top view image. The binarized image (hereinafter referred to as a “binary image”) is shown in  FIG.  5 A . 
     Thereafter, the traffic line detection device  300  may extract an edge of a traffic line area from the binary image. In the instant case, a portion where the pixel value changes from 0 to 255 is referred to as a rising edge, and a portion where the pixel value changes from 255 to 0 is referred to as a falling edge. The extracted edge of the traffic line area is shown in  FIG.  5 B . 
     Thereafter, the traffic line detection device  300  may extract points located in the center portion of the traffic line area (hereinafter referred to as “central points”). In the instant case, the traffic line detection device  300  may regard an area where width is greater than a reference value in the traffic line area as noise to exclude the area from the traffic line area. The extracted central points are shown in  FIG.  5 C . 
     Thereafter, the traffic line detection device  300  may cluster adjacent central points. In the instant case, the traffic line detection device  300  may cluster points where the square of the distance between points is less than or equal to 9 (where one pixel is 1). The clustered result (hereinafter referred to as a “cluster”) is shown in  FIG.  5 D . 
     Thereafter, the traffic line detection device  300  may connect the clusters to detect a traffic line. In the instant case, the traffic line detection device  300  may connect the clusters with regard to a distance and an angle between the clusters. For example, when a distance between a first cluster and a second cluster is less than a reference distance and when an angle between the first cluster and the second cluster is less than a reference angle, the traffic line detection device  300  may connect the first cluster with the second cluster. The detected traffic line is shown in  FIG.  5 E . 
     A virtual traffic line generation apparatus  400  of  FIG.  1    may determine reliability of the traffic line detected for each frame by the traffic line detection device  300 , may store the reliability of the traffic line for each frame, and may generate a virtual traffic line based on a traffic line with the highest reliability among traffic lines detected in a previous frame when the traffic line is not detected. 
     The virtual traffic line generation apparatus  400  may determine reliability of the traffic line detected for each frame by the traffic line detection device  300 , may store the reliability of the traffic line for each frame, may generate a virtual traffic line based on a traffic line with the highest reliability among the traffic lines detected in the previous frame when the traffic line is not detected, and may provide the virtual traffic line and reliability corresponding to the virtual traffic line together. 
     The virtual traffic line generation apparatus  400  may represent the traffic line detected from each frame by the traffic line detection device  300  in a form of a quadratic function and may determine a degree to which the quadratic function is identical to a real traffic line as reliability. 
     Hereinafter, a detailed configuration of the virtual traffic line generation apparatus  400  will be described with reference to  FIG.  6   . 
       FIG.  6    is a block diagram illustrating a configuration of a virtual traffic line generation apparatus according to an exemplary embodiment of the present disclosure. 
     As shown in  FIG.  6   , a virtual traffic line generation apparatus  400  according to an exemplary embodiment of the present disclosure may include a storage  10 , a reception device  20 , an output device  30 , and a controller  40 . In the instant case, the respective components may be combined into one component and some components may be omitted, depending on a manner which executes the virtual traffic line generation apparatus  400  according to an exemplary embodiment of the present disclosure. 
     Seeing the respective components, first of all, the storage  10  may store various logics, algorithms, and programs required in a process of determining reliability of a traffic line detected for each frame by a traffic line detection device  300  of  FIG.  1   , storing the reliability of the traffic line for each frame, and generating a virtual traffic line based on a traffic line with the highest reliability among traffic lines detected in a previous frame when the traffic line is not detected. 
     The storage  10  may store various logics, algorithms, and programs required in a process of determining reliability of a traffic line detected for each frame by the traffic line detection device  300 , storing the reliability of the traffic line for each frame, generating a virtual traffic line based on a traffic line with the highest reliability among traffic lines detected in a previous frame when the traffic line is not detected, and providing the virtual traffic line and reliability corresponding to the virtual traffic line together. 
     The storage  10  may store information related to a traffic line detected for each frame by the traffic line detection device  300 . 
     The storage  10  may store reliability of the traffic line for each frame. In the instant case, the storage  10  may store reliability of the traffic line for every a reference number of frames. For example, when the current frame is n, a reference number of frames may be “ . . . , n−4, n−3, n−2, n−1, and n”. 
     Such a storage  10  may include at least one type of storage medium, such as a flash memory type memory, a hard disk type memory, a micro type memory, a card type memory (e.g., a secure digital (SD) card or an extreme digital (XD) card), a random access memory (RAM), a static RAM (SRAM), a read-only memory (ROM), a programmable ROM (PROM), an electrically erasable PROM (EEPROM), a magnetic RAM (MRAM), a magnetic disk, and an optical disk. 
     The reception device  20  may be a module for receiving information related to a traffic line detected for each frame from the traffic line detection device  300 . When the virtual traffic line generation apparatus  400  is implemented to merge into the traffic line detection device  300 , the reception device  20  is not necessarily an essential component. 
     The output device  30  may include a display device configured for displaying the traffic line detected by the traffic line detection device  300  or a virtual traffic line generated by the virtual traffic line generation apparatus  400  and a communication device configured for transmitting information related to the traffic line detected by the traffic line detection device  300  or information related to the virtual traffic line generated by the virtual traffic line generation apparatus  400  to an autonomous vehicle. 
     Herein, the display device may include a cluster provided in the vehicle. The communication device may include at least one of a mobile communication module, a wireless Internet module, or a short-range communication module. 
     The mobile communication module may communicate over a mobile communication network constructed according to technical standards for mobile communication or a communication scheme (e.g., global system for mobile communication (GSM), code division multi access (CDMA), code division multi access 2000 (CDMA2000), enhanced voice-data optimized or enhanced voice-data only (EV-DO), wideband CDMA (WCDMA), high speed downlink packet access (HSDPA), high speed uplink packet access (HSUPA), long term evolution (LTE), long term evolution-advanced (LTE-A), or the like). 
     The wireless Internet module may be a module for wireless Internet access, which may communicate through wireless LAN (WLAN), wireless-fidelity (Wi-Fi), Wi-Fi Direct, digital living network alliance (DLNA), wireless broadband (WiBro), Worldwide Interoperability for Microwave Access (WiMAX), high speed downlink packet access (HSDPA), high speed uplink packet access (HSUPA), long term evolution (LTE), long term evolution-advanced (LTE-A), or the like. 
     The short-range communication module may support short-range communication using at least one of Bluetooth™, radio frequency identification (RFID), infrared data association (IrDA), ultra wideband (UWB), ZigBee, near field communication (NFC), and wireless universal serial bus (USB) technologies. 
     The controller  40  may perform the overall control so that respective components may normally perform their own functions. Such a controller  40  may be implemented in a form of hardware, may be implemented in a form of software, or may be implemented in a form of a combination thereof. The controller  40  may be implemented as, but not limited to, a microprocessor. 
     The controller  40  may perform a variety of control in a process of determining reliability of the traffic line detected for each frame by the traffic line detection device  300 , storing the reliability of the traffic line for each frame, and generating a virtual traffic line based on a traffic line with the highest reliability among traffic lines detected in a previous frame when the traffic line is not detected. 
     Furthermore, the controller  40  may perform a variety of control in a process of determining reliability of a traffic line detected for each frame by the traffic line detection device  300 , storing the reliability of the traffic line for each frame, generating a virtual traffic line based on a traffic line with the highest reliability among traffic lines detected in a previous frame when the traffic line is not detected, and providing the virtual traffic line and reliability corresponding to the virtual traffic line together. 
     Hereinafter, the operation of the controller  40  will be described in detail. 
     The controller  40  may determine reliability of the traffic line detected for each frame by the traffic line detection device  300  and may store the reliability of the traffic line for each frame in the storage  10 . 
     When the traffic line detection device  300  does not detect a traffic line of a current frame, the controller  40  may search the storage  10  for a traffic line with the highest reliability among traffic lines detected in a previous frame and may generate a virtual traffic line based on the found traffic line. 
     The controller  40  may represent the traffic line detected from each frame by the traffic line detection device  300  in a form of a quadratic function and may determine a degree to which the quadratic function is identical to a real traffic line as reliability. Hereinafter, the process of determining the reliability of the traffic line will be described in detail with reference to  FIG.  5 E . 
     In  FIG.  5 E , reference numeral  510  indicates a real traffic line, and reference numeral  520  indicates a traffic line detected by the controller  40 . The controller  40  may obtain an x2 value on a quadratic function of the traffic line  520  for a y value with respect to any coordinate value (x1, y) on the real traffic line  510 , may determine an error between an x1 value on the real traffic line  510  and the x2 value on the quadratic function of the traffic line  520 , and may count the case where the error is less than or equal to a reference value as the valid number of times. Thereafter, the controller  40  may determine the ratio of the valid number of times to the total number of times as reliability of the traffic line  520 . In the instant case, it is advantageous in terms of accuracy that the total number of times is greater than the reference number of times (e.g., 1000 times). 
       FIG.  7    is a flowchart illustrating a virtual traffic line generation method according to an exemplary embodiment of the present disclosure. 
     First of all, in operation  701 , a controller  40  of  FIG.  6    may determine reliability of a traffic line detected for each frame. 
     Thereafter, in operation  702 , a storage  10  of  FIG.  6    may store the reliability of the traffic line for each frame, which is determined by the controller  40 . 
     Thereafter, in operation  703 , when the traffic line is not detected, in operation  703 , the controller  40  may generate a virtual traffic line based on a traffic line with the highest reliability among traffic lines detected in a previous frame. 
       FIG.  8    is a block diagram illustrating a computing system for executing a virtual traffic line generation method according to an exemplary embodiment of the present disclosure. 
     Referring to  FIG.  8   , the above-mentioned virtual traffic line generation method according to an exemplary embodiment of the present disclosure may be implemented by the computing system. A computing system  1000  may include at least one processor  1100 , a memory  1300 , a user interface input device  1400 , a user interface output device  1500 , storage  1600 , and a network interface  1700 , which are connected to each other via a system bus  1200 . 
     The processor  1100  may be a central processing unit (CPU) or a semiconductor device that processes instructions stored in the memory  1300  and/or the storage  1600 . The memory  1300  and the storage  1600  may include various types of volatile or non-volatile storage media. For example, the memory  1300  may include a Read-Only Memory (ROM)  1310  and a Random Access Memory (RAM)  1320 . 
     Thus, the operations of the method or the algorithm described in connection with the exemplary embodiments included herein may be embodied directly in hardware or a software module executed by the processor  1100 , or in a combination thereof. The software module may reside on a storage medium (that is, the memory  1300  and/or the storage  1600 ) such as a RAM, a flash memory, a ROM, an EPROM, an EEPROM, a register, a hard disk, an SSD (Solid State Drive), a removable disk, and a CD-ROM. The exemplary storage medium may be coupled to the processor  1100 . The processor  1100  may read out information from the storage medium and may write information in the storage medium. Alternatively, the storage medium may be integrated with the processor  1100 . The processor and the storage medium may reside in an application specific integrated circuit (ASIC). The ASIC may reside within a user terminal. In another case, the processor and the storage medium may reside in the user terminal as separate components. 
     The present disclosure may determine reliability of a traffic line detected for each frame, may store the reliability of the traffic line for each frame, and may generate a virtual traffic line based on a traffic line with the highest reliability among traffic lines detected in a previous frame when the traffic line is not detected, thus ensuring driving safety of an autonomous vehicle even when the traffic line is not detected. 
     Furthermore, the present disclosure may determine reliability of a traffic line detected for each frame, may store the reliability of the traffic line for each frame, may generate a virtual traffic line based on a traffic line with the highest reliability among traffic lines detected in a previous frame when the traffic line is not detected, and may provide the virtual traffic line and reliability corresponding to the virtual traffic line together so that an autonomous vehicle may use the reliability as a parameter when performing various autonomous driving policies. 
     Hereinabove, although the present disclosure has been described with reference to exemplary embodiments and the accompanying drawings, the present disclosure is not limited thereto, but may be variously modified and altered by those skilled in the art to which the present disclosure pertains without departing from the spirit and scope of the present disclosure claimed in the following claims. 
     Furthermore, the terms such as “unit”, “module”, etc. Included in the specification mean units for processing at least one function or operation, which may be implemented by hardware, software, or a combination thereof. 
     For convenience in explanation and accurate definition in the appended claims, the terms “upper”, “lower”, “inner”, “outer”, “up”, “down”, “upwards”, “downwards”, “front”, “rear”, “back”, “inside”, “outside”, “inwardly”, “outwardly”, “interior”, “exterior”, “internal”, “external”, “forwards”, and “backwards” are used to describe features of the exemplary embodiments with reference to the positions of such features as displayed in the figures. It will be further understood that the term “connect” or its derivatives refer both to direct and indirect connection. 
     The foregoing descriptions of specific exemplary embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teachings. The exemplary embodiments were chosen and described to explain certain principles of the present disclosure and their practical application, to enable others skilled in the art to make and utilize various exemplary embodiments of the present disclosure, as well as various alternatives and modifications thereof. It is intended that the scope of the present disclosure be defined by the Claims appended hereto and their equivalents.