Patent Publication Number: US-2023152120-A1

Title: Map Information Correction Method, Driving Assistance Method, and Map Information Correction Device

Description:
TECHNICAL FIELD 
     The present invention relates to a map information correction method, a driving assistance method, and a map information correction device. 
     BACKGROUND 
     In JP 2016-161456 A, a map data processing device is disclosed that calculates data for update from map data calculated based on a recognition result of a surrounding environment and a travel state of a vehicle and, when error between map data in a map database and the data for update is larger than a criterion value, updates the map data in the map database. 
     SUMMARY 
     Driving assistance control that, using map information representing positions of lane boundaries, generates a travel route based on lane boundaries in the map information and controls vehicle behavior, such as speed of a vehicle and a steering angle, in such a way as to assist travel of the vehicle along the generated travel route has been known. When a lane boundary in the map information is corrected while such driving assistance control is in execution, there is a possibility that a rapid behavior change is caused to occur to the vehicle due to change in the travel route having been set based on the lane boundary. 
     An object of the present invention is to provide a map information correction method that is capable of preventing behavior change of a vehicle even when map information including position information of lane boundaries used in driving assistance control is corrected while the driving assistance control is in execution. 
     According to an aspect of the present invention, there is provided a map information correction method for correcting map information including information of a lane boundary line, the method including: detecting a position with respect to an own vehicle of a lane boundary line set in place on a road surface around the own vehicle; estimating an own position on a map of the own vehicle; and correcting, depending on the estimated own position and the detected position of the lane boundary line, a position of the lane boundary line included in the map information by, in a first region comparatively close to the own vehicle, a larger rotational correction amount than in a second region comparatively far from the own vehicle and, in the second region, a larger translational correction amount than in the first region. 
     According to an aspect of the present invention, it is possible to prevent behavior change of a vehicle even when map information including position information of lane boundaries is corrected while driving assistance control is in execution. 
     The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a schematic configuration diagram of a driving assistance device of embodiments; 
         FIG.  2 A  is an explanatory diagram of an example of differences between detection results of lane boundary lines and lane boundary lines in map information; 
         FIG.  2 B  is an explanatory diagram of an example of a case where map information is corrected only by rotation; 
         FIG.  3 A  is an explanatory diagram of an example of a case where the map information is corrected only by translation; 
         FIG.  3 B  is an explanatory diagram of an example of a map information correction method of the embodiments; 
         FIG.  4    is a block diagram illustrative of an example of a functional configuration of the driving assistance device of the embodiments; 
         FIG.  5    is an explanatory diagram of an example of matching processing between a lane boundary line and map information; 
         FIG.  6 A  is an explanatory diagram of an example of a calculation method of translational correction parameters; 
         FIG.  6 B  is an explanatory diagram of an example of a calculation method of rotational correction parameters; 
         FIG.  7    is an explanatory diagram of an example of a map information correction method of a first embodiment; 
         FIG.  8    is an explanatory diagram of another example of the map information correction method of the first embodiment; 
         FIG.  9    is a flowchart of an example of the map information correction method of the embodiments; 
         FIG.  10 A  is an explanatory diagram of an example of a map information correction method of a second embodiment; 
         FIG.  10 B  is another explanatory diagram of the example of the map information correction method of the second embodiment; and 
         FIG.  11    is an explanatory diagram of an example of a map information correction method of a third embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     First Embodiment 
     (Configuration) 
       FIG.  1    is now referred to. An own vehicle  1  includes a driving assistance device  10  configured to perform driving assistance of the own vehicle  1 . The driving assistance performed by the driving assistance device  10  is self-driving control that, based on, for example, a travel environment around the own vehicle  1  and map information, causes the own vehicle  1  to self-drive without involvement of a driver. Note that, although the driving assistance device  10  may be a device that executes driving assistance control to assist driving performed by a passenger by controlling some functions of apparatuses involved in traveling of the own vehicle  1 , such as steering, accelerator opening, and a steering angle, based on a travel environment around the own vehicle  1  and map information, the following description will be made assuming, unless otherwise specifically stated, that the self-driving control that causes the own vehicle  1  to self-drive without involvement of a driver is executed. 
     The driving assistance device  10  includes object sensors  11 , vehicle sensors  12 , a positioning device  13 , a map database  14 , a communication device  15 , a controller  16 , and actuators  17 . In the drawings, the map database is denoted as “map DB”. 
     The object sensors  11  include a plurality of object detection sensors of different types that are mounted on the own vehicle  1  and detect objects around the own vehicle  1 , such as a laser radar, a millimeter-wave radar, a camera, and a light detection and ranging or laser imaging detection and ranging (LIDAR). 
     The vehicle sensors  12  are mounted on the own vehicle  1  and detects various information (vehicle signals) that can be acquired from the own vehicle  1 . The vehicle sensors  12  include a vehicle speed sensor to detect traveling speed (vehicle speed) of the own vehicle  1 , wheel speed sensors to detect rotational speeds of respective tires that the own vehicle  1  includes, a triaxial acceleration sensor (G sensor) to detect acceleration (including deceleration) of the own vehicle  1  in three axial directions, a steering angle sensor to detect a steering angle (including a turning angle), a gyro sensor to detect angular velocity generated in the own vehicle  1 , a yaw rate sensor to detect a yaw rate, an accelerator sensor to detect accelerator opening of the own vehicle, and a brake sensor to detect a brake operation amount by the driver. 
     The positioning device  13  includes a global navigation satellite system (GNSS) receiver and, by receiving radio waves from a plurality of navigation satellites, measures a current position of the own vehicle  1 . The GNSS receiver may be, for example, a global positioning system (GPS) receiver or the like. The positioning device  13  may be, for example, an inertial navigation device. 
     The map database  14  may store high-definition map information (hereinafter, simply referred to as “high-definition map”), which is suitable as a map for self-driving. The high-definition map is map data of higher precision than map data for navigation (hereinafter, simply referred to as “navigation map”) and includes information in units of lanes, which is more detailed than information in units of roads. Hereinafter, a map represented by map information in the map database  14  is sometimes simply referred to as “map”. 
     For example, the high-definition map includes, as information in units of lanes, information of lane nodes that indicate reference points on a lane reference line (for example, a line at the center of a lane) and information of lane links that indicate forms of lane sections between lane nodes. 
     Information of each lane node includes an identification number and position coordinates of the lane node, the number of connected lane links, and identification numbers of connected lane links. Information of each lane link includes an identification number of the lane link, the type of the lane, width of the lane, a shape of the lane, shapes and the types of lane boundary lines, and a shape of a lane reference line. 
     The lane boundary line is a pavement marking set in place on a road surface to indicate a lane boundary, and examples of the lane boundary line includes a “roadway center line”, a “lane boundary line”, and a “roadway outside line”. 
     The high-definition map includes, as information of a shape of a lane, information of positions of a plurality of boundary points forming a lane boundary, and the shape of a lane boundary line is represented by a set of such boundary points. As information of the shape of a lane boundary line, point group data representing positions on the edge of a lane-width direction edge portion of the lane boundary line may be used. Although, in the present embodiment, it is assumed that the shape of a lane boundary line in the high-definition map is represented by a set of a plurality of boundary points forming the lane boundary, without being limited to the above, the shape of a lane boundary line may be represented by lines. 
     The high-definition map further includes the types and position coordinates of ground objects, such as a traffic light, a stop line, a road sign, a building, a utility pole, a curb, and a crosswalk, that exist on a lane or in the vicinity of the lane and information of the ground objects, such as identification numbers of lane nodes and identification numbers of lane links that correspond to the position coordinates of the ground objects. 
     Since the high-definition map includes node information and link information in units of lanes, it is possible to specify a lane in which the own vehicle  1  travels in a travel route. The high-definition map has a coordinate system that can represent positions in the extending direction and width direction of each lane. The high-definition map has coordinates (for example, longitude, latitude, and altitude) that can represent positions in the three-dimensional space, and lanes and the above-described ground objects may be described as shapes in the three-dimensional space. 
     The communication device  15  performs wireless communication with a communication device external to the own vehicle  1 . A communication method used by the communication device  15  may be, for example, wireless communication via a public mobile telephone network, vehicle-to-vehicle communication, road-to-vehicle communication, or satellite communication. 
     The controller  16  is an electronic control unit (ECU) that performs driving assistance control of the own vehicle  1 . The controller  16  controls the actuators  17  by outputting control signals to the actuators  17 , based on a travel environment around the own vehicle  1 , which was detected by the object sensors  11 , and the map information in the map database  14 , and thereby executes the self-driving control that causes the own vehicle  1  to self-drive. 
     Specifically, the controller  16  calculates a current position (own position) of the own vehicle  1  on the map from a current position of the own vehicle  1  detected by the positioning device  13  and also sets a travel trajectory (travel trajectory represented by a relative position with respect to a lane boundary line) from the own position to a position a predetermined distance ahead, based on the own position and lane boundary lines on the map. The controller  16  controls, based on the own position on the map and the set travel trajectory, the own vehicle in such a way that the own vehicle  1  travels along the travel trajectory, by outputting control signals to the actuators  17  in such a way that the own position coincides with a position on the travel trajectory. 
     In addition, the controller  16 , as described later, executes correction processing of map information of correcting lane boundary lines included in the map information in the map database  14 , based on relative positions of the lane boundary lines with respect to the own vehicle, which is detected based on detection results by the object sensors  11 , and the current position (own position) of the own vehicle  1 . Details of the correction processing will be described later. 
     The controller  16  includes a processor  18  and peripheral components, such as a storage device  19 . The processor  18  may be, for example, a central processing unit (CPU) or a micro-processing unit (MPU). 
     The storage device  19  may include a semiconductor storage device, a magnetic storage device, an optical storage device, and the like. The storage device  19  may include registers, a cache memory, and a memory used as a main storage device, such as a read only memory (ROM) and a random access memory (RAM). 
     Functions of the controller  16 , which will be described below, may be achieved by, for example, the processor  18  executing computer programs stored in the storage device  19 . 
     Note that the controller  16  may be formed by dedicated hardware for executing information processing that will be described below. 
     For example, the controller  16  may include a functional logic circuit that is implemented in a general-purpose semiconductor integrated circuit. For example, the controller  16  may include a programmable logic device, such as a field-programmable gate array (FPGA), and the like. 
     The actuators  17  operate a steering wheel, accelerator opening, and a braking device of the own vehicle in accordance with control signals output from the controller  16  and thereby generate vehicle behavior of the own vehicle. The actuators  17  include a steering actuator, an accelerator opening actuator, and brake control actuators. The steering actuator controls steering direction and the amount of steering of the own vehicle  1 . That is, the steering actuator controls a steering mechanism. 
     The accelerator opening actuator controls the accelerator opening of the own vehicle. That is, the accelerator opening actuator controls output from a power unit of the own vehicle  1 . The brake control actuators control braking action of the braking devices of the own vehicle  1 . 
     Next, an example of the correction processing of the map information in the map database  14  executed by the controller  16  will be described. The controller  16 , while executing the self-driving control based on the map information in the map database  14 , corrects lane boundary lines in the map information, based on a detection result in which the lane boundary lines were detected by the object sensors  11 . 
       FIG.  2 A  is now referred to. The reference sign  20 L indicates a left side lane boundary line that is a lane boundary line on the left side of a lane  2  in which the own vehicle  1  travels, and the reference sign  20 R indicates a right side lane boundary line that is a lane boundary line on the right side of the lane  2 . The left side lane boundary line  20 L and the right side lane boundary line  20 R are sometimes collectively referred to as “lane boundary lines  20 ”. 
     A hatched portion  21 L indicates a left side detection portion that is a portion of the left side lane boundary line  20 L that was detected by the object sensors  11 , and a hatched portion  21 R indicates a right side detection portion that is a portion of the right side lane boundary line  20 R that was detected by the object sensors  11 . The left side detection portion  21 L and the right side detection portion  21 R are sometimes collectively referred to as “detection portions  21 ”. 
     Each of square marks  22 L and square marks  22 R indicate positions on the map of a plurality of boundary points forming one of the lane boundary lines of the lane  2 . 
     Each of the square marks  22 L is a left side boundary point forming the lane boundary line on the left side of the lane  2 , and each of the square marks  22 R is a right side boundary point forming the lane boundary line on the right side of the lane  2 . The left side boundary points  22 L and the right side boundary points  22 R are sometimes collectively referred to as “boundary points  22 ”. Note that, since a lane boundary line is formed by a plurality of boundary points  22 , it may be rephrased that a boundary point  22  is a portion of a lane boundary line. Therefore, hereinafter, a portion of a lane boundary line is sometimes represented as a boundary point  22 . 
     The controller  16 , using the object sensors  11 , detects relative positions of the detection portions  21  with respect to the position of the own vehicle  1 . 
     In addition, the controller  16 , using a measurement result by the positioning device  13 , odometry using detection results from the vehicle sensors  12 , and map matching between detection results of target objects by the object sensors  11  and the map database  14 , estimates an own position of the own vehicle  1  on the map. 
     The controller  16  calculates, based on the relative positions of the detection portions  21  and the own position of the own vehicle  1 , positions on the map of the detection portions  21 . 
     On this occasion, caused by estimation error of the own position of the own vehicle  1  and error of the map information itself in the map database  14 , differences sometimes occur between the positions of the detection portions  21  and the boundary points  22 , as illustrated in  FIG.  2 A . 
     Accordingly, the controller  16  corrects the map in the map database  14  in such a manner as to cause the positions on the map of the boundary points  22  to coincide with the actually detected positions of the detection portions  21 . 
     The correction of a map includes correction of a rotational component and correction of a translational component.  FIG.  2 B  illustrates a case where the boundary points  22  are corrected by rotation. 
     Each of round marks  23 L indicates a left side corrected boundary point that is a boundary point to which a left side boundary point  22 L has been corrected, and each of round marks  23 R indicates a right side corrected boundary point that is a boundary point to which a right side boundary point  22 R has been corrected. The left side corrected boundary points  23 L and the right side corrected boundary points  23 R are sometimes collectively referred to as “corrected boundary points  23 ”. 
     In the example in  FIG.  2 B , the boundary points  22  are rotated using the position of the own vehicle  1  as a reference and the corrected boundary points  23  are calculated in such a manner that differences between the corrected boundary points  23  and the detection portions  21  are minimized. As a result, the positions of the boundary points  22  are corrected in such a manner that the lane boundaries are rotated using the position of the own vehicle  1  as a reference. 
     Thus, even when the map is corrected while the self-driving control is in execution, behavior change of the own vehicle  1  is small because it is only required to adjust the attitude of the own vehicle  1  to a change in the direction of the lane boundaries. 
     However, a correction amount becomes larger as a boundary point  22  is farther from the own vehicle  1 , and, contrary to expectation, there is a possibility that a deviation amount d between the corrected boundary points  23  and the actual lane boundary lines  20  increases. 
     On the other hand,  FIG.  3 A  illustrates a case where the boundary points  22  are corrected by translation. The controller  16  translates the boundary points  22  and calculates corrected boundary points  23  in such a manner as to minimize differences between the corrected boundary points  23  and the detection portions  21 . 
     In this case, the intra-lane position of the own vehicle  1  is caused to instantaneously change by the translation of the lane boundaries. Thus, when the map is corrected while the self-driving control is in execution, there is a possibility that, in order to correct the displacement of the intra-lane position of the own vehicle  1 , a rapid behavior change is caused to occur on the own vehicle  1 . 
     Accordingly, the controller  16  corrects the positions of boundary points  22  by a larger rotational correction amount in a first region  24  that is comparatively close to the own vehicle  1  than in a second region  25  that is comparatively far from the own vehicle  1 , as illustrated in  FIG.  3 B . In addition, the controller  16  corrects the positions of boundary points  22  by a larger translational correction amount in the second region  25  than in the first region  24 . 
     Because of this configuration, in the first region  24 , which is comparatively close to the own vehicle  1 , the translational correction amount becomes small. Thus, even when the positions of the boundary points  22  on the map are corrected while the self-driving control is in execution, it is possible to reduce behavior change of the own vehicle  1 . 
     In addition, in the second region  25 , which is comparatively far from the own vehicle  1 , the rotational correction amount becomes small. Thus, it is possible to reduce the deviation amount d between the corrected boundary points  23  and the actual lane boundary lines  20 . 
     Next, a functional configuration of the controller  16  will be described in detail with reference to  FIG.  4   . The controller  16  includes an object detection unit  30 , an own position estimation unit  31 , a map acquisition unit  32 , a detection integration unit  33 , an object tracking unit  34 , a map information correction unit  35 , a driving action determination unit  36 , a trajectory generation unit  37 , and a vehicle control unit  38 . 
     The object detection unit  30  detects, based on detection signals from the object sensors  11 , positions, attitudes, sizes, speeds, and the like of objects around the own vehicle  1 , such as a vehicle, a motorcycle, a pedestrian, and an obstacle. The object detection unit  30  outputs detection results representing two-dimensional positions, attitudes, sizes, speeds, and the like of objects in, for example, a zenith view (also referred to as a plan view) in which the own vehicle  1  is viewed from the air. 
     The own position estimation unit  31  measures, based on a measurement result by the positioning device  13  and odometry using detection results from the vehicle sensors  12 , the own position (position on the map) of the own vehicle  1 , that is, the position of the own vehicle  1  with respect to a predetermined reference point, attitude, and speed of the own vehicle  1 . 
     The map acquisition unit  32  acquires map information indicating a structure of a road on which the own vehicle  1  is to travel, from the map database  14 . The map acquisition unit  32  may acquire map information from an external map data server through the communication device  15 . 
     The detection integration unit  33  integrates a plurality of detection results that the object detection unit  30  respectively acquired from a plurality of object detection sensors and outputs one detection result with respect to each of the respective objects. 
     Specifically, the detection integration unit  33  calculates, from behavior of objects respectively acquired from the object detection sensors, the most reasonable behavior of the objects that minimizes error, in consideration of error characteristics of the respective object detection sensors. 
     Specifically, by using a known sensor fusion technology, the detection integration unit  33  comprehensively evaluates detection results acquired by a plurality of types of sensors and acquires a more accurate detection result. 
     The object tracking unit  34  tracks objects detected by the object detection unit  30 . Specifically, based on the detection results integrated by the detection integration unit  33 , the object tracking unit  34  performs verification of identity (association) of objects between different times from behaviors of the objects output at different times and predicts, based on the association, behavior of the objects, such as speed. 
     The map information correction unit  35  determines a degree of reliability of a detection result of the detection portions  21  that was detected by the detection integration unit  33  and the object tracking unit  34 . 
     For example, the map information correction unit  35  may determine the degree of reliability according to deviation between the positions of the detection portions  21  detected in the previous detection and the positions of the detection portions  21  detected in the current detection. The map information correction unit  35  may also determine the degree of reliability according to deviation between the positions of the boundary points  22  in the map information and the positions of the detection portions  21 . For example, the map information correction unit  35  may determine that the degree of reliability is high when a deviation amount is less than a threshold value and determine that the degree of reliability is low when the deviation amount is greater than or equal to the threshold value. The map information correction unit  35  may also determine the degree of reliability according to a degree of clarity of the detection portions  21  detected by the object sensors  11 . 
     When determining that the degree of reliability of a detection result of the detection portions  21  is high, the map information correction unit  35  corrects, based on the detection result of the detection portions  21  and the own position of the own vehicle  1  estimated by the own position estimation unit  31 , map information acquired from the map database  14 . When the map acquisition unit  32  acquired map information from an external map data server, the map information correction unit  35  corrects the map information acquired from the external map data server. 
     On the contrary, when determining that the degree of reliability of the detection result of the detection portions  21  is not high, the map information correction unit  35  prohibits correction of the map information. 
     The map information correction unit  35  includes a lane matching unit  35   a , a correction parameter calculation unit  35   b , and a lane correction unit  35   c.    
     The lane matching unit  35   a  identifies positions on the map corresponding to the positions of points that the object sensors  11  detected as the detection portions  21 . The points that the object sensors  11  detected as the detection portions  21  are referred to as “detection points”. The points on the map corresponding to the detection points are referred to as “corresponding points”. The lane matching unit  35   a  independently identifies corresponding points with respect to each of the left side detection portion  21 L and the right side detection portion  21 R. 
       FIG.  5    is now referred to. White triangle marks  40  are detection points in a detection portion  21  detected by the object sensors  11 . Hatched triangle marks  41  are a history of detection points detected in the past. An alternate long and short dash line  42  is a lane boundary formed by boundary points  22  in the map information. The lane boundary  42  may, for example, be calculated by interpolation of the boundary points  22 . 
     The lane matching unit  35   a  drops perpendiculars  43  from the detection points  40  and  41  to the lane boundary  42  and calculates feet  44  (round marks) of the perpendiculars  43  as corresponding points corresponding to the detection points  40  and  41 . 
       FIG.  4    is now referred to. The correction parameter calculation unit  35   b  calculates translational correction parameters M, which are correction amounts by which boundary points  22  are corrected by translation, and rotational correction parameters R, which are correction amounts by which boundary points  22  are corrected by rotation. The correction parameter calculation unit  35   b  independently calculates, with respect to each of the left side boundary points  22 L and the right side boundary points  22 R, translational correction parameters M and rotational correction parameters R. 
     The translational correction parameters M are translational components (tx, ty) in an affine transformation matrix expressed by, for example, the formula (1) below: 
     
       
         
           
             
               
                 
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                         tx 
                       
                     
                     
                       
                         
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     and the rotational correction parameters R are rotational components (cos θ, −sin θ, sin θ, cos θ) in the affine transformation matrix. 
     Note that the translational correction parameters M and the rotational correction parameters R are examples of a “translational correction amount” and a “rotational correction amount” described in the claims, respectively. 
       FIG.  6 A  is now referred to. Hatched round marks  45  indicate points obtained by translating the corresponding points  44 . The correction parameter calculation unit  35   b  calculates a translation amount that minimizes error between the corresponding points  45  after translation and the detection points  40  and  41  as the translational correction parameters M. 
     For example, the correction parameter calculation unit  35   b  calculates a translation amount that minimizes the square sum of differences in positions between the corresponding points  45  after translation and the detection points  40  and  41  as the translational correction parameters M. 
       FIG.  6 B  is now referred to. Hatched round marks  46  indicate points obtained by rotating the corresponding points  44  about a rotation center C as the center. The correction parameter calculation unit  35   b  sets the rotation center C on a line representing the lane boundary  42  on the map. For example, the correction parameter calculation unit  35   b  may set the rotation center C in such a way that the longitudinal position of the rotation center C along the lane  2  coincides with the longitudinal position of a current position O of the own vehicle  1 . The current position O of the own vehicle  1  is only required to be, for example, any point of the own vehicle  1  and may be, for example, the central position of the own vehicle  1  or the central position of the front edge of the own vehicle  1 . 
     The correction parameter calculation unit  35   b  calculates a rotation amount θ that minimizes error between the corresponding points  46  after moving and the detection points  40  and  41  and calculates rotational components (cos θ, −sin θ, sin θ, cos θ) according to the rotation amount θ, which are elements in the above-described formula (1), as the rotational correction parameters R. 
     For example, the correction parameter calculation unit  35   b  calculates a rotation amount θ that minimizes the square sum of differences in positions between the corresponding points  46  after moving and the detection points  40  and  41  and calculates rotational components according to the rotation amount θ in the above-described formula (1) as the rotational correction parameters R. 
       FIG.  4    is now referred to. The lane correction unit  35   c  corrects, according to the translational correction parameters M and the rotational correction parameters R calculated by the correction parameter calculation unit  35   b , the positions of the boundary points  22  included in the map information (that is, the positions of the boundary points  22  on the map). 
     The lane correction unit  35   c  independently corrects the left side boundary points  22 L and the right-side boundary points  22 R. 
       FIG.  7    is now referred to. The lane correction unit  35   c  corrects the positions of boundary points  22  (that is, the positions of the lane boundary lines) by a larger translational correction amount in the second region  25 , which is comparatively far from the own vehicle  1 , than in the first region  24 , which is comparatively close to the own vehicle  1 . In addition, the lane correction unit  35   c  corrects the positions of boundary points  22  (that is, the positions of the lane boundary lines) by a larger rotational correction amount in the first region  24  than in the second region  25 . 
     For example, the lane correction unit  35   c  may set a region located ahead of the own vehicle  1  and within a first predetermined distance d 1  from the current position O of the own vehicle  1  as the first region  24  and set a region located ahead of the own vehicle  1  and farther than the first predetermined distance d 1  from the current position O of the own vehicle  1  as the second region  25 . 
     In the second region  25 , the lane correction unit  35   c  corrects the positions of boundary points  22  by the translational correction parameters M and does not rotate the boundary points  22 . 
     In contrast, in the first region  24 , the lane correction unit  35   c  corrects boundary points  22  by the weighted sum of the rotational correction parameters R and the translational correction parameters M. 
     The lane correction unit  35   c , for example, determines, according to longitudinal distance x between the own vehicle  1  and a boundary point  22 L 1 , weights of the rotational correction parameters R and the translational correction parameters M by which the boundary point  22 L 1  is corrected. 
     The lane correction unit  35   c  determines the weight of the rotational correction parameters R to be larger as the longitudinal distance x is shorter. 
     The lane correction unit  35   c  may correct a boundary point  22 L 1  by, for example, a correction amount calculated by the formula (2) below. 
       Correction amount=(( d 1− x )/ d 1)× R +( x/d 1)× M   (2)
 
     Performing weighting in this way causes a boundary point  22  to be corrected by a smaller translation amount as the boundary point  22  is located closer to the own vehicle  1 . Thus, even when the boundary points  22  in the map information are corrected while the self-driving control is in execution, it is possible to reduce behavior change of the own vehicle  1 . 
     Note that, in a region  26 , within the second region  25 , that is located ahead of and farther than a range in which the positions of the lane boundary lines were detected (that is, the detection portions  21 ), the lane correction unit  35   c  may reduce a correction amount by which the position of a boundary point  22  is translated by a larger amount as the longitudinal distance between the own vehicle  1  and the boundary point  22  is farther. Because of this configuration, it is possible to preferentially use map information in a distant region where no detection result of the lane boundary lines can be acquired. 
     The lane correction unit  35   c  stores corrected map information in the map database  14 . That is, the lane correction unit  35   c  updates the map database  14  with the corrected map information. When the map information is acquired from an external map data server, the lane correction unit  35   c  updates the map information in the external map data server with the corrected map information. 
     The map information correction unit  35  executes the above-described correction processing iteratively at a predetermined processing period. That is, the map information correction unit  35  acquires map information corrected and stored in the previous period from the map database  14  (or acquires the map information from the external map data server) and further corrects the acquired map information by the above-described correction processing. 
     Note that an upper limit may be set for a correction amount by which map information can be corrected in one processing period. That is, the correction parameter calculation unit  35   b  may limit the magnitudes of the rotational correction parameters R and the translational correction parameters M to less than or equal to a predetermined upper limit. Since, because of this configuration, excessive correction of the boundary points  22  can be avoided, even when the boundary points  22  in the map information are corrected while the self-driving control is in execution, it is possible to reduce behavior change of the own vehicle  1 . 
     The first predetermined distance d 1 , which defines length of the first region  24 , may, for example, be set according to an upper limit of lateral acceleration that is allowed to be generated in the own vehicle  1  and a correction amount by which the positions of the boundary points  22  are corrected in the lateral direction. Specifically, the first predetermined distance d 1  may be set according to vehicle speed of the own vehicle  1 , an upper limit of lateral acceleration, and a lateral component of the translational correction parameters M. 
     When an upper limit of the correction amount by which map information can be corrected in one processing period is set, the first predetermined distance d 1  may be set according to, in place of the lateral component of the translational correction parameters M, an upper limit of a lateral correction amount. 
     In addition, the first predetermined distance d 1  may be set depending on the positions of the lane boundary lines  20  detected by the object sensors  11 , that is, the positions of the detection portions  21 . 
       FIG.  8    is now referred to. For example, at an intersection or the like, the lane boundary lines  20  are interrupted. Thus, the lane boundary lines  20  are not set in place in a range closer to the own vehicle  1  than a longitudinal position  50 , and the object sensors  11  do not detect the lane boundary lines  20  in a region closer to the own vehicle  1  than the longitudinal position  50 . 
     For example, the lane correction unit  35   c  sets a longitudinal distance from the longitudinal position  50 , which is the closest position that allows the lane boundary lines  20  to be detected, to the current position O of the own vehicle  1  as the first predetermined distance d 1 . 
     Because of this configuration, when the own vehicle  1  is located in a region where the lane boundary lines  20  are not set in place, the region where the lane boundary lines  20  are not set in place can be set as the first region  24 . 
     As a result, in the region where the lane boundary lines  20  are not set in place, it is possible to reduce the translational correction amount and thereby reduce behavior change of the own vehicle  1 . 
       FIG.  4    is now referred to. The driving action determination unit  36  determines, based on detection results acquired by the detection integration unit  33  and the object tracking unit  34  and map information corrected by the map information correction unit  35 , a schematic driving action of the own vehicle  1  that the driving assistance device  10  is to execute. 
     Examples of the driving action that the driving action determination unit  36  determines include, for example, stopping, temporary stopping, traveling speed, deceleration, acceleration, course change, right turn, left turn, traveling straight, lane change at a merging section or between a plurality of lanes, lane keeping, overtaking, response to an obstacle of the own vehicle  1 , and the like. 
     The driving action determination unit  36  generates, based on the map information corrected by the map information correction unit  35  and the positions and attitudes of objects around the own vehicle  1 , a route space map that represents existence or nonexistence of a route and an object around the own vehicle  1  and a risk map in which a degree of risk of a traveling field is quantified. The driving action determination unit  36  generates, based on the route space map and the risk map, a driving action plan of the own vehicle  1 . 
     The trajectory generation unit  37  generates, based on a driving action determined by the driving action determination unit  36 , motion characteristics of the own vehicle  1 , and the route space map, candidates of a travel trajectory (a travel trajectory represented by relative positions with respect to the lane boundary lines  20 ) along which and a speed profile in accordance with which the own vehicle  1  is caused to travel. 
     The trajectory generation unit  37  evaluates future risks of the respective candidates, based on the risk map, selects an optimum travel trajectory and speed profile, and sets the selected travel trajectory and speed profile as a target travel trajectory along which and a target speed profile in accordance with which the own vehicle  1  is caused to travel, respectively. 
     The vehicle control unit  38  drives the actuators  17  in such a way that the own vehicle  1  travels along the target travel trajectory at a speed in accordance with the target speed profile generated by the trajectory generation unit  37 . 
     (Operation) 
     Next, an example of operation of the driving assistance device  10  in the embodiment will be described with reference to  FIG.  9   . 
     In step S 1 , the object detection unit  30 , using a plurality of object detection sensors, detects relative positions with respect to the own vehicle  1 , attitudes, sizes, speeds, and the like of objects, including lane boundary lines, around the own vehicle  1 . 
     In step S 2 , the detection integration unit  33  integrates a plurality of detection results acquired respectively from a plurality of object detection sensors and outputs one detection result with respect to each of the respective objects. The object tracking unit  34  tracks the respective objects that were detected and integrated and predicts behavior of the objects around the own vehicle  1 . In this processing, the positions of the lane boundary lines  20 , which are set in place on the road surface around the own vehicle  1 , are detected. 
     In step S 3 , the map acquisition unit  32  acquires map information indicating a structure of a road on which the own vehicle  1  is to travel. 
     In step S 4 , the own position estimation unit  31  measures, based on a measurement result by the positioning device  13  and odometry using detection results from the vehicle sensors  12 , the position, attitude, and speed of the own vehicle  1  with respect to a predetermined reference point. 
     In step S 5 , the map information correction unit  35  determines whether or not the degree of reliability of a detection result of the detection portions  21  of the lane boundary lines  20  is high. When the degree of reliability is high (step S 5 : Y), the process proceeds to step S 6 . When the degree of reliability is not high (step S 5 : N), the process proceeds to step S 9 . 
     In step S 6 , the lane matching unit  35   a  executes matching processing. In the matching processing, the lane matching unit  35   a  identifies positions on the map (corresponding points  44 ) corresponding to points that the object sensors  11  detected as the detection portions  21 . 
     In step S 7 , the correction parameter calculation unit  35   b  calculates translational correction parameters M and rotational correction parameters R. 
     In step S 8 , the lane correction unit  35   c  corrects, according to the translational correction parameters M and the rotational correction parameters R, the positions of the boundary points  22  included in the map information (that is, the positions of the boundary points  22  on the map). 
     In step S 9 , the driving action determination unit  36  determines a driving action, based on detection results acquired by the detection integration unit  33  and the object tracking unit  34  and the map information corrected by the map information correction unit  35 . The trajectory generation unit  37  generates, based on the driving action determined by the driving action determination unit  36 , motion characteristics of the own vehicle  1 , and the map information corrected by the map information correction unit  35 , a target travel trajectory along which and a target speed profile in accordance with which the own vehicle  1  is caused to travel. 
     When the degree of reliability of the detection result of the detection portions  21  is not high (step S 5 : N), since steps S 6  to S 8 , in which the map information is corrected, are skipped, map information that had been corrected up until the previous period is used in step S 9 . 
     In step S 10 , the vehicle control unit  38  drives the actuators  17  in such a way that the own vehicle  1  travels along the target travel trajectory at a speed in accordance with the target speed profile generated by the trajectory generation unit  37 . 
     In step S 11 , whether or not an ignition switch (IGN) of the own vehicle  1  has been turned off is determined. When the ignition switch has not been turned off (step S 11 : N), the process returns to step S 1 . When the ignition switch has been turned off (step S 11 : Y), the process is terminated. 
     (Advantageous Effects of First Embodiment) 
     (1) The controller  16  executes a map information correction method for correcting map information including information of lane boundary lines, which are formed by the boundary points  22 . The object detection unit  30 , the detection integration unit  33 , and the object tracking unit  34  detect relative positions with respect to the own vehicle of the lane boundary lines  20 , which are set in place on a road surface around the own vehicle  1 . The own position estimation unit  31  estimates an own position of the own vehicle  1 . The map information correction unit  35  corrects, depending on the estimated own position and the detected relative positions of the lane boundary lines  20 , the positions of the lane boundary lines formed by the boundary points  22  included in the map information by, in the first region  24 , which is comparatively close to the own vehicle  1 , a larger rotational correction amount than in the second region  25 , which is comparatively far from the own vehicle  1 , and, in the second region  25 , a larger translational correction amount than in the first region  24 . 
     In the first region  24 , which is comparatively close to the own vehicle  1 , since, because of this configuration, the translational correction amount becomes small, even when the lane boundary lines formed by the boundary points  22  in the map information are corrected while the self-driving control is in execution, it is possible to reduce behavior change of the own vehicle  1 . 
     In the second region  25 , which is comparatively far from the own vehicle  1 , since the rotational correction amount becomes small, it is possible to reduce a deviation amount between the corrected boundary points  23  and the actual lane boundary lines  20 . 
     (2) In the first region  24 , the rotational correction amount and the translational correction amount may be weighted according to longitudinal distance in such a manner that the weight of the rotational correction amount becomes larger as the longitudinal distance between the own vehicle  1  and a boundary point  22  is shorter (for a part of a lane boundary line having a shorter longitudinal distance from the own vehicle  1 ). 
     Because of this configuration, a boundary point  22  closer to the own vehicle  1  (a part of a lane boundary line closer to the own vehicle  1 ) is corrected by a smaller translation amount. Thus, even when the lane boundary lines in the map information are corrected while the self-driving control is in execution, it is possible to reduce behavior change of the own vehicle  1 . 
     (3) The lane correction unit  35   c  may set a region located ahead of the own vehicle  1  and within the first predetermined distance d 1  from the position of the own vehicle  1  as the first region  24  and set a region located ahead of the own vehicle  1  and farther than the first predetermined distance d 1  from the position of the own vehicle  1  as the second region  25 . 
     Because of this configuration, it is possible to appropriately set the first region  24  that is comparatively close to the own vehicle  1  and the second region  25  that is comparatively far from the own vehicle  1 . 
     (4) The lane correction unit  35   c  may set the first predetermined distance d 1  according to an upper limit of lateral acceleration that is generated in the own vehicle  1  and a correction amount by which the positions of the boundary points  22  (lane boundary lines) are corrected in the lateral direction. 
     Because of this configuration, it is possible to extend the length of the first region  24 , in which the translational correction amount is comparatively small, according to the upper limit of lateral acceleration and the lateral correction amount. Therefore, it is possible to reduce behavior change of the own vehicle  1 . 
     (5) The lane correction unit  35   c  may set the first predetermined distance d 1  depending on the positions of the detected lane boundary lines  20 . 
     Because of this configuration, when the own vehicle  1  is located in a region where the lane boundary lines  20  are not set in place, it is possible to set the region where the lane boundary lines  20  are not set in place as the first region  24 . Therefore, in the region where the lane boundary lines  20  are not set in place, it is possible to reduce the translational correction amount and thereby reduce behavior change of the own vehicle  1 . 
     (6) The lane correction unit  35   c  may, in a region located ahead of and farther than a range in which the positions of the lane boundary lines  20  are detected, reduce a correction amount of the position of a boundary point  22  by a larger amount as the longitudinal distance between the own vehicle  1  and the boundary point  22  is farther. 
     Because of this configuration, it is possible to preferentially use map information in a distant region where no detection result of the lane boundary lines  20  can be acquired. 
     (7) The lane correction unit  35   c  may independently correct the positions of boundary points  22  on the right side of the lane and the positions of boundary points  22  on the left side of the lane. Because of this configuration, even when distance between the actually detected left side lane boundary line  20 L and right side lane boundary line  20 R differs from a lane width on the map, it is possible to appropriately correct the left side boundary points  22 L and the right side boundary points  22 R depending on detection positions that are actually detected as the left side lane boundary line  20 L and the right side lane boundary line  20 R. 
     Second Embodiment 
     Next, a second embodiment will be described. A map information correction unit  35  of the second embodiment corrects positions of lane boundary lines (boundary points  22 ) by only a rotational correction amount in a first region  24  and only a translational correction amount in a second region  25 . 
       FIG.  10 A  is now referred to. First, in the second region  25 , a lane correction unit  35   c  corrects the positions of boundary points  22  by translational correction parameters M and does not rotate the boundary points  22 . 
     Next, as illustrated in  FIG.  10 B , the lane correction unit  35   c  determines rotation angles of boundary points  22  in the first region  24  in such a way that lane boundaries in the first region  24  and lane boundaries in the second region  25 , which are formed by corrected boundary points  23 , connect to each other at a boundary position between the first region  24  and the second region  25 . 
     Note that, in place of determining rotation angles in such a way that the lane boundaries in the first region  24  and the lane boundaries in the second region  25  connect to each other, the boundary points  22  in the first region  24  may be rotated by rotational correction parameters R. In this case, first predetermined distance d 1  (that is, length of the first region  24 ) may be adjusted in such a way that the lane boundaries in the first region  24  and the lane boundaries in the second region  25  connect to each other at the boundary position between the first region  24  and the second region  25 . 
     (Advantageous Effects of Second Embodiment) 
     The lane correction unit  35   c  corrects the positions of the boundary points  22  by only the rotational correction amount in the first region  24  and only the translational correction amount in the second region  25 . 
     For example, the lane correction unit  35   c  may correct the positions of the boundary points  22  by rotating the positions of boundary points  22  in the first region  24  in such a way that the lane boundaries in the first region  24  and the lane boundaries in the second region  25  connect to each other. 
     Even when the positions of the boundary points  22  are corrected as described above, it is, as with the first embodiment, possible to reduce the translational correction amount in the first region  24 , which is comparatively close to an own vehicle  1 . In addition, it is possible to reduce the rotational correction amount in the second region  25 , which is comparatively far from the own vehicle  1 . 
     Third Embodiment 
     Next, a third embodiment will be described. In the third embodiment, a region that is comparatively close to an own vehicle  1  and a region that is comparatively far from the own vehicle  1  are defined in a first region  24 , and positions of boundary points  22  are, as with the second embodiment, corrected by only a rotational correction amount in the comparatively close region and the rotational correction amount and a translational correction amount are, as with the first embodiment, weighted in the comparatively far region. 
       FIG.  11    is now referred to. The first region  24  includes a third region  27  that is a region located ahead of the own vehicle  1  and within a second predetermined distance d 2  from the position of the own vehicle  1  and a fourth region  28  that is a region located ahead of the own vehicle  1  and farther than the second predetermined distance d 2  from the position of the own vehicle  1 . 
     In the third region  27 , a lane correction unit  35   c  corrects the positions of boundary points  22  by only rotation using rotational correction parameters R. 
     On the other hand, in the fourth region  28 , the lane correction unit  35   c  corrects boundary points  22  using the weighted sum of the rotational correction parameters R and translational correction parameters M. 
     The lane correction unit  35   c  may, for example, determine weights of the rotational correction parameters R and the translational correction parameters M by which a boundary point  22 L 1  is corrected, in such a manner that the weight of the rotational correction parameter R becomes larger as longitudinal distance between the own vehicle  1  and the boundary point  22 L 1  is shorter. 
     For example, when length of the fourth region  28  is denoted by d 3  and longitudinal distance from a boundary position between the third region  27  and the fourth region  28  to the boundary point  22 L 1  is denoted by x, the lane correction unit  35   c  may correct the boundary point  22 L 1  by, for example, a correction amount calculated by the formula (3) below. 
       Correction amount=(( d 3 −x )/ d 3)× R +( x/d 3)× M   (3)
 
     (Advantageous Effects of Third Embodiment) 
     The first region  24  includes the third region  27  that is a region located ahead of the own vehicle  1  and within the second predetermined distance d 2  from the position of the own vehicle  1  and the fourth region  28  that is a region located ahead of the own vehicle  1  and farther than the second predetermined distance d 2  from the position of the own vehicle  1 . In the third region  27 , the lane correction unit  35   c  corrects the positions of boundary points  22  by only the rotational correction amount. In the fourth region  28 , the rotational correction amount and the translational correction amount are weighted according to longitudinal distance in such a manner that the weight of the rotational correction amount becomes larger as the longitudinal distance between the own vehicle  1  and a boundary point  22  is shorter. 
     Because of this configuration, it is possible to set the third region  27  where the positions of boundary points  22  are corrected by only the rotational correction amount and a second region  25  where the positions of boundary points  22  are corrected by only the translation amount, and it is also possible to set, between the third region  27  and the second region  25 , the fourth region  28  where the weights of the rotational correction parameters R and the translational correction parameters M gradually change as a buffer region. 
     All examples and conditional language provided herein are intended for the pedagogical purposes of aiding the reader in understanding the invention and the concepts contributed by the inventor to further the art, and are not to be construed as limitations to such specifically recited examples and conditions, nor does the organization of such examples in the specification relate to a showing of the superiority and inferiority of the invention. Although one or more embodiments of the present invention have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention. 
     REFERENCE SIGNS LIST 
     
         
         
           
               1  Own vehicle 
               10  Driving assistance device 
               11  Object sensor 
               12  Vehicle sensor 
               13  Positioning device 
               14  Map database 
               15  Communication device 
               16  Controller 
               17  Actuator 
               18  Processor 
               19  Storage device 
               30  Object detection unit 
               31  Own position estimation unit 
               32  Map acquisition unit 
               33  Detection integration unit 
               34  Object tracking unit 
               35  Map information correction unit 
               35   a  Lane matching unit 
               35   b  Correction parameter calculation unit 
               35   c  Lane correction unit 
               36  Driving action determination unit 
               37  Trajectory generation unit 
               38  Vehicle control unit