Patent Publication Number: US-2023135702-A1

Title: Apparatus for controlling autonomous driving and method thereof

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the benefit of and priority to Korean Patent Application No. 10-2021-0149255, filed in the Korean Intellectual Property Office on Nov. 2, 2021, the entire contents of which are incorporated herein by reference. 
     TECHNICAL FIELD 
     The present disclosure relates to an autonomous driving control apparatus for controlling autonomous driving and a method thereof. 
     BACKGROUND 
     The statements in this section merely provide background information related to the present disclosure and may not constitute prior art. 
     Autonomous driving vehicles require the ability to adaptively respond to surrounding situations that change in real time while the autonomous driving vehicles are driving. 
     First of all, a reliable determination control function is desired to produce autonomous driving vehicles. 
     A semi-autonomous vehicle, which has been recently released, basically performs driving, braking, and steering instead of a driver, thereby reducing the driver&#39;s fatigue. 
     For semi-autonomous driving, unlike fully autonomous driving, a driver needs to keep his/her concentration on driving, such as holding a steering wheel continuously. 
     Nowadays, the semi-autonomous vehicle may be equipped with a highway driving assist (HDA) function, a driver status warning (DSW) function that outputs a warning alarm through a cluster, or the like by determining status abnormalities and the driver&#39;s negligence such as drowsy driving and taking eyes off the road (namely, the driver&#39;s eyes are not directed to the road), a driver awareness warning (DAW) function that determines whether a vehicle is unsafely driving such as a case that a vehicle is crossing the lane, through a front camera, and a forward collision-avoidance assist (FCA) or active emergency brake system (AEBS) function that performs sudden braking when a forward collision is detected; and, the autonomous vehicle may be on sale. 
     However, we have discovered that when a conventional autonomous driving system controls autonomous driving by using precise map information, it is difficult to implement appropriate autonomous driving in a space where there is no line or precise map information. 
     SUMMARY 
     The present disclosure has been made to solve the above-mentioned problems occurring in the prior art while advantages achieved by the prior art are maintained intact. 
     An aspect of the present disclosure provides an autonomous driving control apparatus that controls autonomous driving based on feature points of another vehicle, and a method thereof. 
     An aspect of the present disclosure provides an autonomous driving control apparatus that solves an issue in which existing dynamics information and precise map matching information disappear and normal determination is not made when objects in the blind spot of a LiDAR sensor or in an area adjacent to the blind spot are separated or merged with each other, and a method thereof. 
     An aspect of the present disclosure provides an autonomous driving control apparatus, which is capable of controlling autonomous driving along a safe route in response to driving in cases where it is impossible to rely on a precise map, for example, a free space, a construction/accident section, and the like, and a method thereof. 
     An aspect of the present disclosure provides an autonomous driving control apparatus, which performs autonomous driving by reducing complex calculations without using precise map matching and quickly responding to emergency situations, or the like without delay, and a method thereof. 
     An aspect of the present disclosure provides an autonomous driving control apparatus, which complementarily enhance the completeness of autonomous driving by combining the existing autonomous driving logic, a method thereof. 
     The technical problems to be solved by the present disclosure are not limited to the aforementioned problems, and any other technical problems not mentioned herein should be clearly understood from the following description by those having ordinary skill in the art to which the present disclosure pertains. 
     According to an aspect of the present disclosure, an autonomous driving control apparatus may include a sensor included in an autonomous vehicle and obtaining information about a surrounding object in the area adjacent to the autonomous vehicle. The autonomous driving control apparatus may further include a controller that extracts one or more feature points corresponding to the object through the information about the object, determines whether there is a risk of collision with the object, based on the extracted feature point, and controls autonomous driving of the autonomous vehicle in consideration of the object in which the risk of collision is present. 
     In an embodiment, the controller may extract two or more feature points, which correspond to the object, from among the one or more feature points, may calculate an angle between the two or more feature points based on a current location of the autonomous vehicle or a point on a planned driving route line of the autonomous vehicle. The controller may further determine whether there is the risk of collision with the object, based on the calculated angle. 
     In an embodiment, the controller may extract the one or more feature points of the object through the information about the object based on displacement over time of the one or more feature points, such that the one or more feature points, which are determined to be easy to trace and which are uniquely identified, are selected. 
     In an embodiment, the sensor may obtain the information about the object through at least one sensor among a camera, a lidar, or radar. 
     In an embodiment, the controller may extract the one or more feature points of the object by fusing the information about the object obtained through two or more sensors included in the sensor. 
     In an embodiment, the controller may calculate an occupancy angle weight based on a weight corresponding to a predetermined angle section with respect to a driving direction of the autonomous vehicle and a section occupied by the calculated angle, at the current location of the autonomous vehicle or at the point on the planned driving route line of the autonomous vehicle and may determine whether there is the risk of collision with the object, based on the occupancy angle weight and the calculated angle. 
     In an embodiment, the controller may calculate a weight corresponding to one predetermined angle section as the occupancy angle weight when the section occupied by the calculated angle is included in the one predetermined angle section. And the controller may calculate a weight corresponding to a predetermined angle section including a line bisecting an angle between the two or more feature points as the occupancy angle weight with respect to the current location of the autonomous vehicle or the point on the planned driving route line of the autonomous vehicle, when the section occupied by the calculated angle spans two or more predetermined angle sections. 
     In an embodiment, the controller may determine whether there is the risk of collision with the object, based on a value obtained by multiplying the occupancy angle weight and the calculated angle. 
     In an embodiment, the controller may calculate the angle between the two or more feature points based on a state where the autonomous vehicle is away from the object by a specific distance to the object on the planned driving route line. 
     In an embodiment, the controller may determine whether the object is an object in which the risk of collision is present, in consideration of time to collision (TTC) for the object. 
     In an embodiment, the controller may determine that the object, of which the risk of collision calculated based on the occupancy angle weight and the calculated angle exceeds a threshold value and of which TTC is less than a threshold time, is an object in which the risk of collision is present. 
     In an embodiment, the controller may control autonomous driving of the autonomous vehicle by generating an autonomous route that minimizes the risk of collision of the object calculated based on the occupancy angle weight and the calculated angle. 
     According to an aspect of the present disclosure, an autonomous driving control method may include: obtaining, by a sensor provided in an autonomous vehicle, information about a surrounding object, extracting, by a controller, one or more feature points corresponding to the object through the information about the object, determining, by the controller, whether there is a risk of collision with the object, based on the extracted feature points, and controlling, by the controller, autonomous driving of the autonomous vehicle in consideration of the object in which the risk of collision is present. 
     In an embodiment, the extracting, by the controller, of the one or more feature points corresponding to the object through the information about the object may include extracting, by the controller, two or more feature points, which correspond to the object, from among the one or more feature points. The determining, by the controller, of whether there is the risk of collision with the object, based on the extracted feature points may include calculating, by the controller, an angle between the two or more feature points based on a current location of the autonomous vehicle or a point on a planned driving route line of the autonomous vehicle and determining, by the controller, whether there is the risk of collision with the object, based on the calculated angle. 
     In an embodiment, the extracting, by the controller, of the one or more feature points corresponding to the object through the information about the object may include extracting, by the controller, the one or more feature points of the object through the information about the object based on displacement over time of the one or more feature points, such that the one or more feature points, which are determined to be easy to trace and which are uniquely identified, are selected. 
     In an embodiment, the determining, by the controller, of whether there is the risk of collision with the object, based on the calculated angle may include calculating, by the controller, an occupancy angle weight based on a weight corresponding to a predetermined angle section with respect to a driving direction of the autonomous vehicle and a section occupied by the calculated angle, at the current location of the autonomous vehicle or at the point on the planned driving route line of the autonomous vehicle and determining, by the controller, whether there is the risk of collision with the object, based on the occupancy angle weight and the calculated angle. 
     In an embodiment, the calculating, by the controller, of the occupancy angle weight may include calculating, by the controller, a weight corresponding to one predetermined angle section as the occupancy angle weight when the section occupied by the calculated angle is included in the one predetermined angle section and calculating, by the controller, a weight corresponding to a predetermined angle section including a line bisecting an angle between the two or more feature points as the occupancy angle weight with respect to the current location of the autonomous vehicle or the point on the planned driving route line of the autonomous vehicle, when the section occupied by the calculated angle spans two or more predetermined angle sections. 
     In an embodiment, the determining, by the controller, of whether there is the risk of collision with the object, based on the extracted feature points may include determining, by the controller, whether the object is an object in which the risk of collision is present, in consideration of TTC for the object. 
     In an embodiment, the determining, by the controller, of whether there is the risk of collision with the object, based on the occupancy angle weight and the calculated angle may include determining, by the controller, the object, of which the risk of collision calculated based on the occupancy angle weight and the calculated angle exceeds a threshold value and of which TTC is less than a threshold time, as an object in which the risk of collision is present. 
     In an embodiment, the method may further include controlling, by the controller, autonomous driving of the autonomous vehicle by generating an autonomous route that minimizes the risk of collision of the object calculated based on the occupancy angle weight and the calculated angle. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other objects, features and advantages of the present disclosure should be more apparent from the following detailed description taken in conjunction with the accompanying drawings: 
         FIG.  1    is a block diagram illustrating an autonomous driving control apparatus, according to an embodiment of the present disclosure; 
         FIG.  2    is a diagram specifically illustrating a configuration of an autonomous driving control apparatus, according to an embodiment of the present disclosure; 
         FIG.  3    is a diagram illustrating that an autonomous driving control apparatus extracts a feature point corresponding to a surrounding object, according to an embodiment of the present disclosure; 
         FIGS.  4 A and  4 B  are diagrams illustrating an angle between two or more feature points calculated by an autonomous driving control apparatus, according to an embodiment of the present disclosure; 
         FIG.  5    is a diagram illustrating that an autonomous driving control apparatus calculates a reference line while a vehicle is driving on a curved road, according to an embodiment of the present disclosure; 
         FIG.  6    is a diagram illustrating that an autonomous driving control apparatus calculates a risk of collision with an object, according to an embodiment of the present disclosure; 
         FIG.  7    is a diagram illustrating that an autonomous driving control apparatus controls autonomous driving of an autonomous vehicle, according to an embodiment of the present disclosure; 
         FIG.  8    is a flowchart illustrating an operation of an autonomous driving control apparatus, according to an embodiment of the present disclosure; and 
         FIG.  9    is a flowchart illustrating an autonomous driving control method, according to an embodiment of the present disclosure. 
     
    
    
     The drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way. 
     DETAILED DESCRIPTION 
     Hereinafter, some embodiments of the present disclosure are described in detail with reference to the accompanying drawings. In adding reference numerals to components of each drawing, it should be noted that the same components have the same reference numerals, although they are indicated on another drawing. Furthermore, in describing the embodiments of the present disclosure, detailed descriptions associated with well-known functions or configurations are omitted when they may make subject matters of the present disclosure unnecessarily obscure. 
     In describing elements of exemplary embodiments of the present disclosure, the terms first, second, A, B, (a), (b), and the like may be used herein. These terms are only used to distinguish one element from another element, but do not limit the corresponding elements irrespective of the nature, order, or priority of the corresponding elements. 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. It should be understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of the present disclosure and the relevant art and should not be interpreted in an idealized or overly formal sense unless expressly so defined herein. 
     When a component, device, element, or the like of the present disclosure is described as having a purpose or performing an operation, function, or the like, the component, device, or element should be considered herein as being “configured to” meet that purpose or to perform that operation or function. 
     As publicly known in the art, some of exemplary forms may be illustrated in the accompanying drawings from the viewpoint of function blocks, units and/or modules. Those having ordinary skill in the art should understand that such blocks, units and/or modules are physically implemented by electronic (or optical) circuits such as logic circuits, discrete components, processors, hard wired circuits, memory devices and wiring connections. When the blocks, units and or modules are implemented by processors or other similar hardware, the blocks, units and modules may be programmed and controlled through software (for example, codes) in order to perform various functions discussed in the present disclosure. 
     Hereinafter, various embodiments of the present disclosure are described in detail with reference to  FIGS.  1  to  9   . 
       FIG.  1    is a block diagram illustrating an autonomous driving control apparatus, according to an embodiment of the present disclosure. 
     Referring to  FIG.  1   , an autonomous driving control apparatus  100  may include a sensor  110  and a controller  120 . 
     According to an embodiment of the present disclosure, the autonomous driving control apparatus  100  may be implemented inside or outside of a vehicle. In one form, the autonomous driving control apparatus  100  may be integrated with internal control units of a vehicle and may be implemented with a separate hardware device so as to be connected to control units of the vehicle by means of a connection means. 
     For example, the autonomous driving control apparatus  100  may be implemented integrally with a vehicle or may be implemented in a form installed/attached to the vehicle as a configuration separate from the vehicle. Alternatively, a part of the autonomous driving control apparatus  100  may be implemented integrally with the vehicle, and the other parts may be implemented in a form installed/attached to the vehicle as a configuration separate from the vehicle. 
     The sensor  110  may be provided in an autonomous vehicle so as to obtain information about surrounding objects in an area adjacent to the autonomous vehicle. 
     For example, the sensor  110  may obtain information about a surrounding object(s) through at least one sensor among a camera, lidar, and radar. 
     For example, the sensor  110  may obtain images around the autonomous vehicle through the camera. 
     For example, the sensor  110  may obtain point cloud information through the lidar. 
     For example, the sensor  110  may obtain the distance to an object and the direction of the object through the radar. 
     The controller  120  may extract at least one feature point corresponding to an object through information about the object. 
     For example, the controller  120  may extract two or more feature points corresponding to an object. 
     For example, the controller  120  may extract feature points of an object through the information about the object based on displacement over time of a feature point, such that the feature point that is determined to be easy to trace and which is uniquely identified, is selected. 
     For example, the controller  120  may extract a point having relatively-small displacement over time as a feature point based on raw data obtained through a sensor. 
     For example, the controller  120  may extract a point, at which data corresponding to a feature point is not redundant in a data distribution (a contour point distribution, an object shape, or the like), as a feature point such that other points having similar shapes are not selected on the object. 
     For example, the controller  120  may extract the feature point of an object by fusing information about the object obtained through two or more sensors included in the sensor  110 . 
     For example, the controller  120  may extract feature points by analyzing information in a form of fusion of data, which is associated with the same object and which is obtained through two or more of a camera, lidar and radar. 
     For example, when information, which is obtained by fusing information about an object obtained through two or more sensors, satisfies traceability and uniqueness, the controller  120  may extract the feature point of the object through the fused information. 
     For example, when the sensor  110  uses information obtained through the camera, the controller  120  may extract a feature point, which is distinguished from other portions even after a location of the object in an image is changed and has the smallest displacement over time, from the obtained image. 
     For example, when the sensor  110  uses information obtained through the lidar, the controller  120  may extract feature points, of which the distribution is unique and which are easy to trace due to the smallest change over time, from among a set of points having a unique distribution, in consideration of the movement of an object at a cloud point level. 
     For example, when the sensor  110  uses information obtained through the radar, the controller  120  may extract a feature point, which has a location and velocity, at which it is assured that a location change of the longitudinal location data is the same object, and at which the location change obtained through dead reckoning through integration of a longitudinal velocity vector may explain the movement of the same object, in consideration of an accurate longitudinal resolution. 
     The controller  120  may determine whether there is a risk of collision with an object, based on the extracted feature point. 
     For example, the controller  120  may calculate an angle between two or more feature points based on an autonomous vehicle&#39;s current location or a point on a planned driving route line of the autonomous vehicle. 
     For example, the controller  120  may obtain an angle at which two or more feature points of the same object occupy a current location of the autonomous vehicle or a point on the planned driving route line of the autonomous vehicle as a reference point. 
     For example, the controller  120  may determine whether there is a risk of collision with an object, based on the calculated angle. 
     For example, as the angle at which two or more feature points of the same object occupy a current location of the autonomous vehicle or a point on the planned driving route line of the autonomous vehicle as a reference point increases, the controller  120  may determine that there is a risk of collision with an object, because the object is close to the autonomous vehicle. 
     For example, the controller  120  may calculate a planned driving route line corresponding to a driving direction of the autonomous vehicle. 
     For example, the controller  120  may calculate the planned driving route line corresponding to the driving direction for the future location of the vehicle in consideration of the autonomous vehicle&#39;s driving route. 
     For example, when the autonomous vehicle&#39;s driving route corresponds to a straight line, the controller  120  may calculate the straight line in the current autonomous vehicle&#39;s traveling direction as a planned driving reference line. 
     In this case, the controller  120  does not perform calculations that take into account a change in the autonomous vehicle&#39;s traveling direction, and thus may benefit from the calculation process. 
     For example, when the autonomous vehicle&#39;s driving route is curved, the controller  120  may calculate a planned driving route line in consideration of the change in the traveling direction of autonomous vehicle. 
     For example, the controller  120  may calculate a planned driving route line based on the traveling direction of the vehicle traveling on the driving route through line information grasped by the camera or precise map information of an autonomous driving system. 
     In detail, the controller  120  may basically determine the traveling direction of the driving vehicle on a driving route through line information determined through the camera, and may calculate a reference line by selectively using the precise map information, thereby reducing computational loads. 
     For example, the controller  120  may determine a change in the traveling direction of the autonomous vehicle based on a line connecting the center of lines. 
     For example, assuming that a location of a left line where the autonomous vehicle is driving according to time is τ LeftLS (t), and a location of a right line where the autonomous vehicle is driving according to time is τ RightLS (t), the controller  120  may determine a change in the traveling direction of the autonomous vehicle through following Equation 1. 
       Traveling direction reference angle=τ′ LeftLS (t 1 )+τ′ RightLS (t 1 /2)   [Equation 1]
 
     Here, τ′ LeftLS (t 1 ) may denote a direction of a left line on a future driving route at time tl as an angle. τ′ RightLS (t 1 ) may denote a direction of a right line on a future driving route at time tl as an angle. 
     For example, the controller  120  may calculate an angle between two or more feature points based on a location of the autonomous vehicle that is away from the object by a specific distance to an object on a planned driving route. 
     For example, the controller  120  may calculate an occupancy angle weight based on a weight corresponding to a predetermined angle section with respect to the autonomous vehicle&#39;s driving direction and a section occupied by the calculated angle, at the autonomous vehicle&#39;s current location or at a point on the autonomous vehicle&#39;s driving route line, and may determine whether there is a risk of collision with an object, based on the occupancy angle weight and the calculated angle. 
     For example, as a predetermined angle section is close to the autonomous vehicle&#39;s current location or at a point on the autonomous vehicle&#39;s driving route line in the autonomous vehicle&#39;s driving direction, the controller  120  may determine to have a high risk of collision because an object is located on the autonomous vehicle&#39;s driving route. 
     For example, when a section occupied by the calculated angle is included in one predetermined angle section, the controller  120  may calculate a weight corresponding to the one predetermined angle section as an occupancy angle weight. When the section occupied by the calculated angle spans two or more predetermined angle sections, the controller  120  may calculate a weight corresponding to a predetermined angle section including a line bisecting an angle between two or more feature points as an occupancy angle weight based on the autonomous vehicle&#39;s current location or a point on the autonomous vehicle&#39;s planned driving route line. 
     For example, the controller  120  may determine whether the object is an object with a risk of collision in consideration of time to collision (TTC) for an object. 
     For example, the controller  120  may calculate the collision reference point, which is an orthographic projection of the nearest point, from among points constituting an object on a reference line and may calculate TTC through a period during which the autonomous vehicle drives to the collision reference point. 
     For example, the controller  120  may determine that the risk of collision increases as the TTC for the object decreases. 
     For example, the controller  120  may determine whether the object is an object with a risk of collision, depending on the risk of collision with the object calculated based on the occupancy angle weight and the calculated angle. 
     For example, when the calculated risk of collision exceeds a threshold value, the controller  120  may determine that there is a risk of collision with an object. 
     The controller  120  may control autonomous vehicle&#39;s autonomous driving in consideration of the object with the risk of collision. 
     For example, the controller  120  may control autonomous vehicle&#39;s autonomous driving by generating an autonomous route that minimizes the risk of collision with the object calculated based on the occupancy angle weight and the calculated angle. 
       FIG.  2    is a diagram specifically illustrating a configuration of an autonomous driving control apparatus, according to an embodiment of the present disclosure. 
     Referring to  FIG.  2   , a sensor  210  may include a lidar  211 , a camera  212  and radar  213 . 
     For example, the sensor  210  may detect information about at least one of cloud pointer information about an object, image information, or location and direction of the object through at least one of the lidar  211 , the camera  212 , or the radar  213 . 
     For example, the sensor  210  may transmit information about the detected object to a location recognition module  221  and an object fusion module  223 . 
     A controller  220  may be directly or indirectly connected to transmit and receive various types of information through wireless or wired communication with a precise map transmission module  201 , a vehicle to everything (V2X) communication module  202 , a controller area network (CAN) communication module  203 , and a global positioning system (GPS)  204  of an autonomous vehicle. 
     The controller  220  may include the location recognition module  221 , a road information fusion module  222 , the object fusion module  223 , an object feature point extraction module  224 , a feature point angle calculation module  225 , a line-based reference line output module  226 , an object location and traveling direction calculation module  227 , a risk determination module  228 , a safe route calculation module  229 , a speed profile generation module  230 , a driving route generation module  231 , a control parameter output module  232 , and a control part  233 . 
     The precise map transmission module  201  may be included in the autonomous vehicle&#39;s autonomous driving system so as to transmit information about a precise map of a space, in which a vehicle is driving, to the road information fusion module  222  and the location recognition module  221 . 
     The V2X communication module  202  may transmit road information, location information of another vehicle, or precise map information, which are received through another vehicle or a communication device, to the location recognition module  221  and the road information fusion module  222 . 
     The CAN communication module  203  may transmit information about a location of an autonomous vehicle obtained through the GPS  204  to the location recognition module  221 . 
     The GPS  204  may transmit the information about the location of the autonomous vehicle to the location recognition module  221 . 
     The location recognition module  221  may identify a precise location of an autonomous vehicle based on information obtained through the precise map transmission module  201 , the V2X  202 , the CAN communication module  203 , and the GPS  204  and the sensor  210  and then may transmit information about the precise location of the autonomous vehicle and reliability information about location recognition to the road information fusion module  222 . 
     The road information fusion module  222  may transmit line information of the driving autonomous vehicle, which is detected by the line recognition camera, to the object feature point extraction module  224 . 
     The road information fusion module  222  may transmit information about a surrounding precise map of the autonomous vehicle to the object fusion module  223 . 
     The object fusion module  223  may calculate and output a relative location of an object with respect to the autonomous vehicle based on information detected by the sensor  210 , line recognition camera, or precise map information and then may transmit information about a location of the object to the feature point angle calculation module  225 . 
     The object feature point extraction module  224  may extract a feature point, for which tracking and identification of an object are easy, and then may transmit information about the extracted feature point to the feature point angle calculation module  225 . 
     The feature point angle calculation module  225  may calculate an angle between extracted feature points based on the traveling direction of the autonomous vehicle and then may transmit the calculated angle to the line-based reference line output module  226 . 
     The line-based reference line output module  226  may utilize line information obtained through the camera or line information obtained through the precise map, may calculate a reference line corresponding to the traveling direction of an autonomous vehicle in consideration of the autonomous vehicle&#39;s driving route, and may transmit information about the calculated reference line to the object location and traveling direction calculation module  227 . 
     The object location and traveling direction calculation module  227  may determine the location of an object based on the angle between the reference line and a line connecting the object&#39;s feature point to the autonomous vehicle. 
     The object location and traveling direction calculation module  227  may calculate the traveling direction of an object based on the change in an angle between the feature points of the object by using the location of the autonomous vehicle as the reference point. 
     The object location and traveling direction calculation module  227  may transmit information about the location and traveling direction of the object to the risk determination module  228 . 
     The risk determination module  228  may determine a risk of collision with an object based on an angle between the reference line and a line connecting the object&#39;s feature point to the autonomous vehicle, an angle between the feature points of the object by using the autonomous vehicle&#39;s current location or a point on the autonomous vehicle&#39;s planned driving route line as a reference point, a location of the object, information about a traveling direction, and TTC, and then may transmit information about the risk of collision with the object to the safe route calculation module  229 . 
     The safe route calculation module  229  may calculate a safe route, which minimizes a risk, based on the feature points of objects in a drivable region in consideration of an object determined to be dangerous and then may transmit information about the calculated safe route to the speed profile generation module  230 . 
     The speed profile generation module  230  may calculate a speed profile, which is capable of following the calculated safe route, and then may transmit information about the speed profile to the driving route generation module  231 . 
     The driving route generation module  231  may calculate a final driving route, which is determined to be most suitable during driving, based on the safe route and the speed profile and then may transmit information about the final driving route to the control parameter output module  232 . 
     The control parameter output module  232  may output a control parameter for autonomous driving control and may transmit the control parameter to the control part  233 . 
     The control part  233  may control autonomous driving depending on the received control parameter. 
       FIG.  3    is a diagram illustrating that an autonomous driving control apparatus extracts a feature point corresponding to a surrounding object, according to an embodiment of the present disclosure. 
     An autonomous vehicle  301  may extract feature points of a first object  302  and a second object  303  based on information about a surrounding object obtained through a sensor included in the autonomous driving control apparatus  100 . 
     For example, through the autonomous driving control apparatus  100 , the autonomous vehicle  301  may extract two or more feature points  304  of the first object  302  and may extract two or more feature points  305  of the second object  303 . 
     For example, the autonomous vehicle  301  may extract feature points having uniqueness and traceability through the autonomous driving control apparatus  100 . 
     For example, when the autonomous driving control apparatus  100  uses information obtained through the camera, the autonomous driving control apparatus  100  included in the autonomous vehicle  301  may extract a feature point, which is distinguished from another portion so as to have the uniqueness even after a location of the object in an image is changed and has the traceability because the feature point has the smallest displacement over time, from the obtained image. 
     For example, when the autonomous driving control apparatus  100  uses information obtained through the lidar, the autonomous driving control apparatus  100  included in the autonomous vehicle  301  may extract feature points, of which the distribution is unique so as to have the uniqueness and which have the traceability due to the smallest change over time, from among a set of points having a unique distribution, in consideration of the movement of an object at a cloud point level. 
     For example, when the autonomous driving control apparatus  100  uses information obtained through the radar, the autonomous driving control apparatus  100  included in the autonomous vehicle  301  may extract a feature point, which has a location and velocity, at which it is assured that a location change of the longitudinal location data is the same object, and at which the location change obtained through dead reckoning through integration of a longitudinal velocity vector may explain the movement of the same object, in consideration of an accurate longitudinal resolution. 
       FIGS.  4 A and  4 B  are diagrams illustrating an angle between two or more feature points calculated by an autonomous driving control apparatus, according to an embodiment of the present disclosure. 
     Referring to  FIG.  4 A , on the basis of a current location of the autonomous vehicle  401  or a point on a planned driving route line of the autonomous vehicle  401 , the autonomous driving control apparatus  100  included in the autonomous vehicle  401  may calculate an angle  404  between two feature points of a first object  402  and may calculate an angle  405  between two feature points of a second object  403 . 
     The autonomous driving control apparatus  100  included in the autonomous vehicle  401  may determine whether a risk of collision with the first object  402  is present, based on the angle  404  between two feature points of the first object  402  and may determine whether a risk of collision with the second object  403  is present, based on the angle  405  between two feature points of the second object  403 . 
     For example, because the object gets close to the autonomous vehicle  401  as the angle between the object&#39;s feature points increases, the autonomous driving control apparatus  100  included in the autonomous vehicle  401  may determine that a risk of collision is high. 
     Referring to  FIG.  4 B , the autonomous driving control apparatus  100  included in the autonomous vehicle  401  may calculate an angle  406  between two feature points of the first object  402  and may calculate an angle  407  between two feature points of the second object  403 . 
     Because the change in the angle  406  between two feature points of the first object  402  present in a traveling direction line of the autonomous vehicle  401  is great, when the first object  402  is close to the autonomous vehicle  401 , the autonomous driving control apparatus  100  included in the autonomous vehicle  401  may determine that there is a risk of collision with the first object  402 . 
     Because the change in the angle  407  between two feature points of the second object  403  that are not present in a traveling direction line of the autonomous vehicle  401  is not great, when the second object  403  is close to the autonomous vehicle  401 , the autonomous driving control apparatus  100  included in the autonomous vehicle  401  may determine that there is not a risk of collision with the second object  403 . 
       FIG.  5    is a diagram illustrating that an autonomous driving control apparatus calculates a reference line while a vehicle is driving on a curved road, according to an embodiment of the present disclosure. 
     The autonomous driving control apparatus  100  included in an autonomous vehicle  501  may obtain information about surrounding lines  502  of the autonomous vehicle  501  through a camera. 
     The autonomous driving control apparatus  100  included in the autonomous vehicle  501  may calculate a planned driving route line  503  corresponding to a driving route of a vehicle based on the information about the surrounding lines  502  obtained through the camera. 
     For example, the autonomous driving control apparatus  100  included in the autonomous vehicle  501  may obtain information about the surrounding lines  502  by selectively using precise map information together with line information obtained through the camera. 
     To determine whether there is a risk of collision with an object in front of the autonomous vehicle  501 , the autonomous driving control apparatus  100  included in the autonomous vehicle  501  may determine the location of an object according to the driving of the autonomous vehicle  501  at a future time point. 
     For example, the autonomous driving control apparatus  100  included in the autonomous vehicle  501  may calculate the planned driving route line  503  corresponding to the driving direction of the autonomous vehicle based on a line connecting the center of the surrounding lines  502 . 
     For example, the autonomous driving control apparatus  100  included in the autonomous vehicle  501  may calculate a point, which is present on a line connecting the reference line and is away from the object by a predetermined specific distance. 
     For example, the autonomous driving control apparatus  100  included in the autonomous vehicle  501  may calculate an angle formed by the reference line corresponding to the traveling direction of the autonomous vehicle  501  and a line connecting a feature point of an object to the calculated point. 
     For example, the autonomous driving control apparatus  100  included in the autonomous vehicle  501  may calculate a risk of collision with an object based on the calculated angle and may determine whether there is a risk of collision with an object, based on the risk of collision. 
       FIG.  6    is a diagram illustrating that an autonomous driving control apparatus calculates a risk of collision with an object, according to an embodiment of the present disclosure. 
     Referring to  FIG.  6   , the autonomous driving control apparatus  100  may calculate an angle  603  between two feature points of a first object  601  and an angle  604  between two feature points of a second object  602 . 
     Furthermore, the autonomous driving control apparatus  100  may calculate an occupancy angle weight ( 605 ,  606 ) based on a weight corresponding to a predetermined angle section with respect to the driving direction of an autonomous vehicle and a section occupied by the calculated angle, at the autonomous vehicle&#39;s current location or at a point on the autonomous vehicle&#39;s driving route line. 
     The angle section between lines connecting the feature points of the first object  601  to the autonomous vehicle may be included in a first angle section, and thus the autonomous driving control apparatus  100  may calculate a W_ 1   605  corresponding to the angle section as an occupancy angle weight of the first object  601 . 
     The angle section between lines connecting the feature points of the second object  602  to the autonomous vehicle may be included in a second angle section, and thus the autonomous driving control apparatus  100  may calculate a W_ 2   606  corresponding to the angle section as an occupancy angle weight of the second object  602 . 
     The autonomous driving control apparatus  100  may calculate a risk of collision with an object by multiplying the angle between two feature points on the object and the occupancy angle weight. 
     For example, a weight corresponding to the predetermined angle section based on the driving direction may be set to gradually decrease for each specific angle interval based on the driving direction. 
     For example, when the angle  603  between the two feature points of the first object  601  is 15 degrees, and W 1 , which is a weight calculated based on a section occupied by the angle calculated for the feature points of the first object  601  and a weight corresponding to a predetermined angle section with respect to the driving direction of the autonomous vehicle, is  1 , the autonomous driving control apparatus  100  may calculate a risk of collision with the first object  601  as “15*1=15”. 
     For example, when the angle  604  between the two feature points of the second object  602  is 20 degrees, and W 2 , which is a weight calculated based on a section occupied by the angle calculated for the feature points of the second object  602  and a weight corresponding to a predetermined angle section with respect to the driving direction of the autonomous vehicle, is 0.5, the autonomous driving control apparatus  100  may calculate a risk of collision with the second object  602  as “20*0.5=10”. 
     Here, because the first angle section is closer to the driving direction than the second angle section, W 1  may be set to be greater than W 2 . 
     Also, although not shown, the autonomous driving control apparatus  100  may calculate a risk of collision that is higher as the TTC for an object is lower. 
     As another example, the autonomous driving control apparatus  100  may determine whether there is a risk of collision with an object, based on whether the TTC for object exceeds a threshold value. 
       FIG.  7    is a diagram illustrating that an autonomous driving control apparatus controls autonomous driving of an autonomous vehicle, according to an embodiment of the present disclosure. 
     Referring to  FIG.  7   , the autonomous driving control apparatus  100  included in an autonomous vehicle  701  may calculate a safe route  704  so as to minimize a risk of collision with a front vehicle  702  and a surrounding object  703 . 
     For example, while the autonomous driving control apparatus  100  modifies a driving route in real time, the autonomous driving control apparatus  100  may calculate the safe route  704  that minimizes a risk of collision with an object proportional to the product of an occupancy angle weight, which is determined depending on an angle section occupied by a feature point, and an angle between two feature points of an object. 
     For example, to calculate the safe route  704  that minimizes a risk of collision with an object proportional to the product of an occupancy angle weight, which is determined depending on an angle section occupied by a feature point, and an angle between two feature points of an object, while the autonomous driving control apparatus  100  modifies the driving route by a predetermined specific increment such that a risk of collision on the driving route is reduced, the autonomous driving control apparatus  100  may calculate the driving route at a point a time when the risk of collision converges, as the safe route  704 . 
       FIG.  8    is a flowchart illustrating an operation of an autonomous driving control apparatus, according to an embodiment of the present disclosure. 
     Referring to  FIG.  8   , the autonomous driving control apparatus  100  may initialize pieces of information calculated in a previous feature point-based autonomous driving control (S 801 ). 
     For example, the autonomous driving control apparatus  100  may initialize information about a feature point, a reference line, a safe route, a weight, a surrounding line, and the like, which are calculated in the previous feature point-based autonomous driving control. 
     The autonomous driving control apparatus  100  may initialize pieces of information calculated in the previous feature point-based autonomous driving control (S 801 ) and then may calculate the traveling direction of a host vehicle (S 802 ). 
     For example, the autonomous driving control apparatus  100  may calculate the traveling direction of the host vehicle based on line information obtained through a camera or precise map information. 
     The autonomous driving control apparatus  100  may calculate the traveling direction of the host vehicle (S 802 ) and then may extract a feature point of an object (S 803 ). 
     For example, the autonomous driving control apparatus  100  may extract the feature point determined to be easy to trace and to uniquely identify an object. 
     The autonomous driving control apparatus  100  may extract the feature point of the object (S 803 ) and then may calculate an angle between the feature points based on the traveling direction (S 804 ). 
     For example, the autonomous driving control apparatus  100  may extract feature points of two or more objects and then may calculate an angle between the two or more feature points based on a current location of the host vehicle or a point on a planned driving route line of an autonomous vehicle. 
     The autonomous driving control apparatus  100  may calculate the angle between the feature points based on the traveling direction (S 804 ) and then may calculate a planned driving route line based on a line (S 805 ). 
     For example, the autonomous driving control apparatus  100  may calculate a planned driving route line corresponding to the traveling direction of the host vehicle calculated based on the line. 
     The autonomous driving control apparatus  100  may calculate the planned driving route line based on the line (S 805 ) and then may calculate the location and traveling direction of the object (S 806 ). 
     For example, the autonomous driving control apparatus  100  may calculate the location and traveling direction of the object based on the planned driving route line and the angle between two or more feature points calculated with respect to the host vehicle. 
     The autonomous driving control apparatus  100  may calculate the location and traveling direction of the object (S 806 ), and then may determine a risk of the object (S 807 ). 
     For example, the autonomous driving control apparatus  100  may determine the risk of the object in consideration of TTC for the object. 
     For example, the autonomous driving control apparatus  100  may determine the risk of the object based on an angle between two or more feature points and an occupancy angle weight corresponding to a section occupied by an angle between the host vehicle and the feature point of the object. 
     The autonomous driving control apparatus  100  may determine the risk of the object (S 807 ) and then may generate a safe route of the host vehicle (S 808 ). 
     For example, the autonomous driving control apparatus  100  may generate a safe route of the host vehicle that minimizes a risk. 
     The autonomous driving control apparatus  100  may generate the safe route of the host vehicle (S 808 ) and then may control autonomous driving by following a route (S 809 ). 
     For example, the autonomous driving control apparatus  100  may generate a speed profile of the generated safe route of the host vehicle, and may control autonomous driving according to the safe route and the speed profile. 
       FIG.  9    is a flowchart illustrating an autonomous driving control method, according to an embodiment of the present disclosure. 
     Referring to  FIG.  9   , an autonomous driving control method may include obtaining information about a surrounding object (S 910 ), extracting a feature point of the object through the information about the surrounding object (S 920 ), determining whether there is a risk of collision with the object, based on the extracted feature point (S 930 ), and controlling autonomous driving of an autonomous vehicle in consideration of the object in which the risk of collision is present (S 940 ). 
     The obtaining (S 910 ) of the information about the surrounding object may be performed by the sensors  110  and  210  including at least one of a camera, lidar, or radar. 
     The extracting (S 920 ) of the feature point of the object through the information about the surrounding object may be performed by the controllers  120  and  220 . 
     For example, the extracting (S 920 ) of the feature point of the object through the information about the surrounding object may include extracting, by the controller ( 120 ,  220 ), two or more feature points corresponding to the object. 
     For example, the extracting (S 920 ) of the feature point of the object through the information about the surrounding object may include extracting, by the controller ( 120 ,  220 ), feature points of an object through the information about the object based on displacement over time of the feature points, such that the feature points, which are determined to be easy to trace and which are uniquely identified, are selected. 
     The determining (S 930 ) of whether there is the risk of collision with the object, based on the extracted feature point may be performed the controller ( 120 ,  220 ). 
     For example, the determining (S 930 ) of whether there is the risk of collision with the object, based on the extracted feature point may include calculating, by the controller ( 120 ,  220 ), an angle between the two or more feature points based on a current location of the autonomous vehicle or a point on a planned driving route line of the autonomous vehicle and determining, by the controller ( 120 ,  220 ), of whether there is the risk of collision with the object, based on the calculated angle. 
     For example, the determining (S 930 ) of whether there is the risk of collision with the object, based on the extracted feature point may include calculating, by the controller ( 120 ,  220 ), an occupancy angle weight based on a weight corresponding to a predetermined angle section with respect to a driving direction of the autonomous vehicle and a section occupied by the calculated angle, at the current location of the autonomous vehicle or at the point on the planned driving route line of the autonomous vehicle and determining, by the controller ( 120 ,  220 ), whether there is the risk of collision with the object, based on the occupancy angle weight and the calculated angle. 
     For example, the calculating, by the controller ( 120 ,  220 ), of the occupancy angle weight may include calculating, by the controller ( 120 ,  220 ), a weight corresponding to one predetermined angle section as the occupancy angle weight when the section occupied by the calculated angle is included in the one predetermined angle section and calculating, by the controller ( 120 ,  220 ), a weight corresponding to a predetermined angle section including a line bisecting an angle between the two or more feature points as the occupancy angle weight with respect to the current location of the autonomous vehicle or the point on the planned driving route line of the autonomous vehicle, when the section occupied by the calculated angle spans two or more predetermined angle sections. 
     For example, the determining, by the controller ( 120 ,  220 ), of whether there is the risk of collision with the object, based on the occupancy angle weight and the calculated angle may include determining, by the controller ( 120 ,  220 ), the object, of which the risk of collision calculated based on the occupancy angle weight and the calculated angle exceeds a threshold value and of which TTC is less than a threshold time, as an object in which the risk of collision is present. 
     For example, the determining (S 930 ) of whether there is the risk of collision with the object, based on the extracted feature point may include determining, by the controller ( 120 ,  220 ), whether the object is an object in which the risk of collision is present, in consideration of TTC for the object. 
     For example, the determining (S 930 ) of whether there is the risk of collision with the object, based on the extracted feature point may include determining, by the controller ( 120 ,  220 ), whether the object is an object with a risk of collision, depending on the risk calculated based on an angle between two or more feature points and a weight corresponding to an angle between the reference line and a line connecting the object&#39;s feature point to the autonomous vehicle. 
     The controlling (S 940 ) of the autonomous driving of the autonomous vehicle in consideration of the object in which the risk of collision is present may be performed by the controller ( 120 ,  220 ). 
     For example, the autonomous driving control method may further include controlling, by the controller ( 120 ,  220 ), autonomous vehicle&#39;s autonomous driving by generating an autonomous route that minimizes the risk of collision with the object calculated based on the occupancy angle weight and the calculated angle. 
     The operations of the method or algorithm described in connection with the embodiments disclosed in the specification may be directly implemented with a hardware module, a software module, or a combination of the hardware module and the software module, which is executed by the processor. The software module may reside on a storage medium (i.e., the memory and/or the storage) such as a random access memory (RAM), a flash memory, a read only memory (ROM), an erasable and programmable ROM (EPROM), an electrically EPROM (EEPROM), a register, a hard disk drive, a removable disc, or a compact disc-ROM (CD-ROM). 
     The exemplary storage medium may be coupled to the processor. The processor 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. The processor and storage medium may be implemented with an application specific integrated circuit (ASIC). The ASIC may be provided in a user terminal. Alternatively, the processor and storage medium may be implemented with separate components in the user terminal. 
     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 having ordinary skill 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. 
     Therefore, embodiments of the present disclosure are not intended to limit the technical spirit of the present disclosure, but provided only for the illustrative purpose. The scope of protection of the present disclosure should be construed by the attached claims, and all equivalents thereof should be construed as being included within the scope of the present disclosure. 
     Descriptions of an autonomous driving control apparatus according to an embodiment of the present disclosure, and a method thereof are as follows. 
     According to at least one of embodiments of the present disclosure, it is possible to provide an autonomous driving control apparatus that controls autonomous driving based on feature points of another vehicle, and a method thereof. 
     Furthermore, according to at least one of embodiments of the present disclosure, it is possible to provide an autonomous driving control apparatus that solves an issue in which existing dynamics information and precise map matching information disappear and normal determination is not made when objects in the blind spot of a LiDAR sensor or in an area adjacent to the blind spot are separated or merged with each other, and a method thereof. 
     Moreover, according to at least one of embodiments of the present disclosure, it is possible to provide an autonomous driving control apparatus, which is capable of controlling autonomous driving along a safe route in response to driving in cases where it is impossible to rely on a precise map, for example, a free space, a construction/accident section, and the like, and a method thereof. 
     Besides, according to at least one of embodiments of the present disclosure, it is possible to provide an autonomous driving control apparatus, which performs autonomous driving by reducing complex calculations without using precise map matching and quickly responding to emergency situations, or the like without delay, and a method thereof. 
     Also, according to at least one of embodiments of the present disclosure, it is possible to provide an autonomous driving control apparatus, which complementarily enhance the completeness of autonomous driving by combining the existing autonomous driving logic, a method thereof. 
     Besides, a variety of effects directly or indirectly understood through the specification may be provided. 
     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 having ordinary skill in the art to which the present disclosure pertains without departing from the spirit and scope of the present disclosure.