Patent Publication Number: US-11042759-B2

Title: Roadside object recognition apparatus

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is based on and claims the benefit of priority from earlier Japanese Patent Application No. 2017-238208, filed Dec. 13, 2017, the description of which is incorporated herein by reference. 
     BACKGROUND 
     Technical Field 
     The present disclosure relates to a technology for recognizing a roadside object that is present on a travel route on which a vehicle travels, the roadside object being used for driving control of the own vehicle. 
     Related Art 
     In automatic driving of a vehicle, a shape (travel road shape) of a travel road on which an own vehicle travels is recognized, and driving control is performed such that the own vehicle travels along the recognized travel road shape. For the travel road shape to be recognized, the shapes of a plurality of objects that can be used to determine the travel road shape are recognized through use of various types of onboard sensors, such as cameras and radars. For example, such objects include travel road boundary lines (lane markers) such as white lines and roadside objects such as guardrails. 
     Japanese Patent Publication No. 5402983 describes a technology for recognizing a roadside object using a radar. A plurality of reflection points are obtained through measurement performed by the radar. Therefore, the shape of the roadside object can be recognized as a result of the plurality of reflection points being successively connected. However, the reflection points from an object other than the roadside object are present as noise points among the reflection points. Therefore, a process for excluding such noise points is desired. Japanese Patent Publication No. 5402983 discloses a technology in which the reflection points that are present between a preceding vehicle recognized by the radar and an own vehicle are excluded as the noise points. 
     In the case in Japanese Patent Publication No. 5402983, the noise points cannot be excluded when the preceding vehicle is not detected by the radar. For recognition of the shape of a roadside object, a technology for removing noise points of a radar under various circumstances is desired. 
     SUMMARY 
     An exemplary embodiment provides a roadside object recognition apparatus that recognizes a roadside object that is present on a travel route on which a vehicle travels, for use in driving control of the own vehicle. The roadside object recognition apparatus includes: a reflection point acquiring unit that acquires, using a radar that emits electromagnetic waves, a reflection-point group of reflection points of the electromagnetic waves reflected by an object that is present on the travel route; an image acquiring unit that acquires an image of the travel route using a camera; a reflection point correcting unit that corrects the reflection-point group by removing an erroneous reflection point that is determined to be highly likely not to be a reflection point of the roadside object from the reflection-point group through image processing of the image; and a shape recognizing unit that recognizes a shape of the roadside object using the corrected reflection-point group. 
     As a result of the roadside object recognition apparatus, the erroneous reflection point that is highly likely not to be the reflection point of the roadside object is removed from the reflection-point group through image processing of the image of the travel route. Therefore, noise points can be appropriately removed. The likelihood of the roadside object being erroneously recognized can be reduced. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the accompanying drawings: 
         FIG. 1  is a block diagram of a configuration of an automatic driving control system according to a first embodiment; 
         FIG. 2  is an explanatory diagram of an example of a plurality of objects associated with a travel road shape according to the first embodiment; 
         FIG. 3  is an explanatory diagram of an example of an image captured by a camera; 
         FIG. 4  is a flowchart of a roadside object recognition process; 
         FIG. 5  is an explanatory diagram of a setting example of a travel road area using travel road boundary lines: 
         FIG. 6  is an explanatory diagram of a setting example of a travel road area using an other vehicle locus: 
         FIG. 7  is an explanatory diagram of a setting example of a travel road area using a road edge; 
         FIG. 8  is an explanatory diagram of a setting example of a travel road area using the travel road boundary line and the other vehicle locus: 
         FIG. 9  is an explanatory diagram of a process for recognizing a roadside object from a corrected reflection-point group: 
         FIG. 10  is an explanatory diagram of an example of a method for setting the travel road area using a recognized overhead object: 
         FIG. 11  is an explanatory diagram of another example of the method for setting the travel road area using a recognized overhead object: 
         FIG. 12  is an explanatory diagram of a road including a temporary boundary line; and 
         FIG. 13  is an explanatory diagram of a method for excluding reflection points using the temporary boundary line. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
     A. First Embodiment 
     As shown in  FIG. 1 , a vehicle  50  according to a first embodiment includes an automatic driving control system  100  that corresponds to a vehicle control system. The automatic driving control system  100  includes an automatic driving electronic control unit (ECU)  200 , a vehicle control unit  300 , a front detection apparatus  410 , a rear detection apparatus  420 , and an assistance information acquiring unit  500 . In the present description, the vehicle  50  is also referred to as an “own vehicle  50 .” 
     The automatic driving ECU  200  is a circuit that includes a central processing unit (CPU) and a memory. The automatic driving ECU  200  actualizes the respective functions of an automatic driving control unit  210  and a state recognizing unit  220  by running a computer program that is stored in a non-volatile storage medium. A part of the functions of the automatic driving ECU  200  may be implemented by a hardware circuit. 
     The state recognizing unit  220  recognizes the driving states of the own vehicle  50  and an other vehicle  60 , and the surrounding environment using various types of information and detection values that are provided by the front detection apparatus  410 , the rear detection apparatus  420 , the assistance information acquiring unit  500 , and general sensors  340 . 
     According to the present embodiment, the state recognizing unit  220  includes a reflection point acquiring unit  222 , an image acquiring unit  224 , a reflection point correcting unit  226 , a shape recognizing unit  228 , and a travel road shape calculating unit  230 . Of the foregoing, the reflection point acquiring unit  222 , the image acquiring unit  224 , the reflection point correcting unit  226 , and the shape recognizing unit  228  as a whole configure a roadside object recognition apparatus that recognizes a roadside object that is present on a travel route on which the own vehicle  50  travels. In other words, according to the present embodiment, the automatic driving ECU  200  functions as the roadside object recognition apparatus. 
     The reflection point acquiring unit  222  acquires a reflection-point group using a radar  414  of the front detection apparatus  410 . The reflection-point group includes reflection points of electromagnetic waves (e.g., radio waves or lights) reflected by an object that is present on the travel route. The image acquiring unit  224  acquires an image of the travel route using a camera  412 . 
     The reflection point correcting unit  226  corrects the reflection-point group acquired by the reflection point acquiring unit  222  by removing the reflection points (erroneous reflection points, noise points) determined to be highly likely not to be the reflection points (actual reflection points) of a roadside object from the reflection-point group, through image processing performed on the image captured by the camera  412 . Details of this correction will be further described hereafter. The reflection point correcting unit  226  also provides a function for recognizing boundary lines and other objects using the images captured by the camera  412  and the results of measurement by the radar  414 . 
     The shape recognizing unit  228  recognizes the shape of the roadside object using the reflection-point group corrected by the reflection point correcting unit  226 . More specifically, the shape recognizing unit  228  calculates a two-dimensional shape of the roadside object, such as a guardrail. Here, the “two-dimensional shape” refers to a shape that appears in a planar view of the own vehicle  50  and the travel route thereof. 
     The travel road shape calculating unit  230  calculates a shape (travel road shape) of the travel road on which the own vehicle  50  travels using the shape of the roadside object recognized by the shape recognizing unit  228 . However, for calculation of the travel road shape, the recognition results regarding travel road boundary lines, such as white lines, are preferably also used in addition to the roadside object. 
     The vehicle control unit  300  is a section that performs various types of control for driving the vehicle  50 . The vehicle control unit  300  is used for both automatic driving and manual driving. The vehicle control unit  300  includes a drive unit control apparatus  310 , a brake control apparatus  320 , a steering angle control apparatus  330 , and the general sensors  340 . 
     The drive unit control apparatus  310  provides a function for controlling a drive unit (not shown) that drives the wheels of the vehicle  50 . At least one of a plurality of motors including an internal combustion engine and an electric motor can be used as the drive unit for the wheels. 
     The brake control apparatus  320  performs brake control of the vehicle  50 . For example, the brake control apparatus  320  is configured as an electronically controlled brake system (ECB). 
     The steering angle control apparatus  330  controls a steering angle of the wheels of the vehicle  50 . The “steering angle” refers to an average steering angle of the two front wheels of the vehicle  50 . For example, the steering angle control apparatus  330  is configured as an electric power steering system (EPS). 
     The general sensors  340  include a vehicle speed sensor  342 , a steering angle sensor  344 , and a yaw rate sensor  346 . The general sensors  340  are general sensors that are required for driving the vehicle  50 . The general sensors  340  include sensors that are used in either of automatic driving and manual driving. 
     The front detection apparatus  410  acquires information related to various types of objects, such as objects and road facilities (such as traffic lanes, intersections, and traffic lights), that are present ahead of the own vehicle  50 . The front detection apparatus  410  uses onboard sensors to acquire the information. According to the present embodiment, the front detection apparatus  410  includes the camera  412  and the radar  414 . 
     A monocular camera or a stereo camera can be used as the camera  412 . In addition, the camera  412  is preferably a color camera to enable differentiation between the colors of the objects (such as differentiation between a white travel road boundary line and a yellow travel road boundary line). Various types of radars that emit electromagnetic waves (e.g., radio waves or lights), such as a light detection and ranging (LIDAR) apparatus that emits light or a radar (such as a millimeter-wave radar) that emits radio waves, can be used as the radar  414 . 
     The rear detection apparatus  420  acquires information related to various types of objects, such as objects and road facilities, that are present to the rear of the own vehicle  50 . The rear detection apparatus  420  can also be configured to include onboard sensors similar to those of the front detection apparatus  410 . 
     The assistance information acquiring unit  500  acquires various types of assistance information for automatic driving. The assistance information acquiring unit  500  includes a global navigation satellite system (GNSS) receiver  510 , a navigation apparatus  520 , and a wireless communication apparatus  530 . 
     The GNSS receiver  510  determines a current position (longitude and latitude) of the own vehicle  50  based on navigation signals received from satellites configuring the GNSS. The navigation apparatus  520  provides a function for determining a predicted travel route for automatic driving based on a destination and the own vehicle position detected by the GNSS receiver  510 . In addition to the GNSS receiver  510 , other sensors, such as a gyro sensor, may be used to determine and correct the predicted travel route. 
     The wireless communication apparatus  530  is capable of exchanging state information related to the state of the own vehicle  50  and the state of the surrounding environment through wireless communication with an intelligent transport system  70 . The wireless communication apparatus  530  is also capable of exchanging the state information through inter-vehicle communication with the other vehicle  60 , and road-vehicle communication with a roadside transceiver that is set in a road facility. 
     The assistance information acquiring unit  500  may acquire some pieces of information related to the driving state of the own vehicle  50  using the state information acquired through such wireless communication. The various types of assistance information acquired by the assistance information acquiring unit  500  are transmitted to the automatic driving ECU  200 . 
     In the present description, “automatic driving” refers to driving in which all of drive unit control, brake control, and steering angle control are automatically performed without the driver performing driving operations. Therefore, in automatic driving, an operation state of the drive unit, an operation state of the brake mechanism, and the steering angle of the wheels are automatically determined. “Manual driving” refers to driving in which the driver performs an operation (stepping on an accelerator pedal) for drive unit control, an operation (stepping on a brake pedal) for brake control, and an operation (rotation of a steering wheel) for steering angle control. 
     The automatic driving control unit  210  performs control for automatic driving of the own vehicle  50  using the various states recognized by the state recognizing unit  220 . Specifically, the automatic driving control unit  210  transmits a drive indicator value to the drive unit control apparatus  310 . The drive indicator value indicates the operation state of the drive unit (engine and motor). 
     The automatic driving control unit  210  also transmits a brake indicator value to the brake control apparatus  320 . The brake indicator value indicates the operation state of the brake mechanism. The automatic driving control unit  210  also transmits a steering angle indicator value to the steering angle control apparatus  330 . The steering angle indicator value indicates the steering angle of the wheels. The control apparatuses  310 ,  320 , and  330  perform control of the respective mechanisms to be controlled based on the provided indicator values. For example, the various functions of the automatic driving control unit  210  can be implemented through artificial intelligence using machine learning such as deep learning. 
     The automatic driving control system  100  has numerous electronic apparatuses including the automatic driving ECU  200 . The plurality of electronic apparatuses are connected to each other via an onboard network such as a controller area network (CAN). 
     As shown in  FIG. 2 , when the own vehicle  50  travels along a travel route SDR, the state recognizing unit  220  can recognize a plurality of objects that are associated with the travel road shape. Here, a left edge LE and a right edge RE of the road, travel road boundary lines WL 1 , WL 2 , and WL 3 , and a roadside object RSO are shown as the objects that can be recognized by the state recognizing unit  220 . The two travel road boundary lines WL 1  and WL 3  are solid white lines. The travel road boundary line WL 2  in the center is a broken white line. For example, the roadside object RSO is a guardrail. These objects can be recognized through use of images captured by the camera  412  and detection results from the radar  414 . 
     A preceding other vehicle  60  may be driving ahead of the own vehicle  50 . The presence and travel locus (travel trajectory) of the other vehicle  60  such as this can also be recognized through use of the images captured by the camera  412  and the detection results from the radar  414 . A false boundary line FWL that is easily erroneously recognized as a travel road boundary line is present on the road surface near the center travel road boundary line WL 2 . 
     Hereafter, a process for recognizing the roadside object RSO as the object used to calculate the shape (travel road shape) of the travel road on which the own vehicle  50  travels will be described. In addition to the guardrail, other objects that are present on the shoulder of the road, such as a curbstone or a row of poles on the shoulder of the road, can be recognized as the roadside object. 
     As shown in  FIG. 3 , the image captured by the camera  412  includes the left edge LE and the right edge RE of the road, the travel road boundary lines WL 1 , WL 2 , and WL 3 , and the roadside object RSO shown in  FIG. 2 . The image further includes an overhead object (upper object) UOB that is present above the own vehicle  50 . In this example, the overhead object UOB is a road sign. However, the image may include an elevated structure, such as an overpass or a pedestrian bridge, as the overhead object UOB. 
     Here, “above the own vehicle  50 ” means that the position of the overhead object UOB in a vertical direction is higher than the own vehicle  50  and does not mean that the overhead object UOB is required to be present directly above the own vehicle  50 . The radar  414  is capable of detecting the reflection points of the electromagnetic waves on the roadside object RSO and the overhead object UOB. Therefore, the reflection point acquiring unit  222  may acquire a reflection-point group that includes not only the reflection points of the roadside object RSO, but also the reflection points of the overhead object UOB. 
     The reflection points of the overhead object UOB become noise when the roadside object RSO is being recognized. Therefore, the reflection points of the overhead object UOB should be removed from the reflection-point group. According to the first embodiment, a travel road area that includes the travel road on which the own vehicle  50  travels is set in the image captured by the camera  412 . The reflection points present within the travel road area are removed from the reflection-point group. The details of this process will be described hereafter. 
     As shown in  FIG. 4 , in the process for recognizing the roadside object RSO, first, at step S 110 , the reflection point acquiring unit  222  acquires the reflection-point group from the detection results of the radar  414 . As described above, the reflection-point group may include not only the reflection points of the roadside object RSO, but also the reflection points of the overhead object UOB. 
     At step S 120 , the image acquiring unit  224  acquires an image of the travel route using the camera  412 . For example, the image is an image such as that shown in  FIG. 3 , described above. 
     At step S 130 , the reflection point correcting unit  226  corrects the reflection-point group by removing the reflection points that are highly likely not to be the reflection points of the roadside object RSO through image processing of the image captured by the camera  412 . The positions of the reflection points acquired through measurement by the radar  414  are converted to positions in the image captured by the camera  412  by a predetermined coordinate transformation matrix. 
     According to the first embodiment, in the process at step S 130 , the travel road area including the travel road on which the own vehicle  50  travels is set by image processing. The reflection points present in the travel road area are determined to be highly likely not to be the reflection points of the roadside object RSO and are removed from the reflection-point group. For example, this process can be performed through use of at least one of three methods, described below. 
     &lt;Method 1 ( FIG. 5 )&gt; A travel road area RLA 1  of the own vehicle  50  is set with reference to the travel road boundary lines WL 1  to WL 3 . Reflection points RP 8  and RP 9  that are present in the travel road area RLA 1  are then removed from the reflection-point group. 
     &lt;Method 2 ( FIG. 6 )&gt; A travel road area RLA 2  of the own vehicle  50  is set with reference to the locus of a preceding other vehicle  61 . The reflection points RP 8  and RP 9  that are present in the travel road area RLA 2  are then removed from the reflection-point group. 
     &lt;Method 3 ( FIG. 7 )&gt; A travel road area RLA 3  of the own vehicle  50  is set with reference to the road edge RE. The reflection points RP 8  and RP 9  that are present in the travel road area RLA 3  are then removed from the reflection-point group. 
     Specific examples of the above-described methods 1 to 3 in a case in which the roadside object RSO to be recognized is present on a right side of the own vehicle  50  will be described below. In a case in which the roadside object RSO is present on a left side of the own vehicle  50 , the description below is made similarly applicable by “left” and “right” in the description below being reversed. 
     In the example in  FIG. 5 , the travel road boundary lines WL 1  to WL 3  are recognized through image processing of the image captured by the camera  412 . In this case, an area on the left side of the travel road boundary line WL 1  that is present furthest to the right among the travel road boundary lines recognized on the right side of the own vehicle  50  is set as the travel road area RLA 1 . The travel road area RLA 1  is shaded with dots. At this time, the reflection point correcting unit  226  removes the reflection points RP 8  and RP 9  that are present in the travel road area RLA 1  from the reflection-point group. As a result, the reflection-point group of the reflection points RP 1  to RP 7  of the roadside object RSO can be accurately recognized. 
       FIG. 5  shows a two-dimensional coordinate system in which a vehicle-width direction is an X axis and a direction perpendicular to the X axis is a Y axis, with a reference position of the own vehicle  50  as a point of origin. The vehicle-width direction X is also referred to as a “lateral direction.” The position of the reflection point is prescribed by an XY-coordinate value. In addition, the travel road boundary lines WL 1  to WL 3  are recognized as two-dimensional shapes on the XY coordinate system. This similarly applies to the other-vehicle travel locus and the road edge described hereafter. 
     In the example in  FIG. 6 , at least one of preceding other vehicles  61  and  62  are recognized through image processing on the image captured by the camera  412 . In this case, an area on the left side of a right-edge position of the locus of the other vehicle  61  that is present furthest to the right among the other vehicles recognized on the right side of the own vehicle  50  is set as the travel road area RLA 2 . 
     Here, “the other vehicles  61  recognized on the right side of the own vehicle  50 ” refers to a vehicle of which a left-right (lengthwise) center line of the vehicle is present on the right side of the left-right center line of the own vehicle  50 . In addition, “the locus of the other vehicle  61 ” refers to a locus of a right edge OVE of the other vehicle  61  that travels, and a locus that is recognized from a plurality of images that are captured in time-series. 
     When the travel road area RLA 2  is set using the locus of the other vehicle  61 , the range of the travel road area RLA 2  along an advancing direction of the own vehicle  50  is preferably set so as to extend to a position at the rear end of the other vehicle  61 . The reflection point correcting unit  226  removes the reflection points RP 8  and RP 9  that are present in the travel road area RLA 2  from the reflection-point group. As a result, the reflection-point group of the reflection points RP 1  to RP 7  of the roadside object RSO can be accurately recognized. 
     In the example in  FIG. 7 , the road edges LE and RE are recognized through image processing of the image captured by the camera  412 . In this case, an area on the left side of the road edge RE recognized on the right side of the own vehicle  50  is set as the travel road area RLA 3 . The reflection point correcting unit  226  removes the reflection points RP 8  and RP 9  that are present in the travel road area RLA 3  from the reflection-point group. As a result, the reflection-point group of the reflection points RP 1  to RP 7  of the roadside object RSO can be accurately recognized. 
     In the example in  FIG. 8 , the travel road boundary line WL 1  that is the reference object used in  FIG. 5  (method 1) and the locus of the other vehicle  61  that is the reference object used in  FIG. 6  (method 2) are recognized. In cases in which a plurality of reference objects that can be used to set the travel road area are present in this manner, the travel road area is preferably set using the reference object with which the widest travel road area can be obtained, among the plurality of reference objects. 
     That is, in the example in  FIG. 8 , of the area (RLA 1  in  FIG. 5 ) on the left side of the travel road boundary line WL 1  present furthest to the right among the travel road boundary lines recognized on the right side of the own vehicle  50  and the area (RLA 2  in  FIG. 6 ) on the left side of the locus of the other vehicle  61  present furthest to the right among the other vehicles  61  recognized on the right side of the own vehicle  50 , the wider area is set as the travel road area RLA 4 . In this case as well, the reflection point correcting unit  226  removes the reflection points RP 8  and RP 9  that are present in the travel road area RLA 4  from the reflection-point group. As a result, the reflection-point group of the reflection points RP 1  to RP 7  of the roadside object RSO can be accurately recognized. 
     The travel road area corresponding to each reference object is determined based on predetermined rules for each reference object. In the example in  FIG. 8 , instead of the widest area among the plurality of travel road areas RLA 1  and RLA 2  set using a plurality of reference objects being selected as the travel road area RLA 4 , a sum area of the areas RLA 1  and RLA 2  may be set as the travel road area to be used for removal of the reflection points. 
     In  FIG. 5  to  FIG. 8 , described above, the travel road area that includes the travel road on which the own vehicle  50  travels is set through image processing of the image captured by the camera  412 . The reflection points present in the travel road area are determined to be highly likely not to be the reflection points of the roadside object RSO and are removed from the reflection-point group. However, the reflection points determined to be highly likely not to be the reflection points of the roadside object RSO may be selected and removed through a method other than the method in which the travel road area is set. 
     For example, as can be understood from  FIG. 3 , in the image captured by the camera  412 , the roadside object RSO is often an object that extends from the left side or the right side of the screen towards a vanishing point in the image. Therefore, the reflection points that are unlikely to be the reflection points of such an object may be removed from the reflection-point group. 
     As shown in  FIG. 9 , when the reflection-point group is corrected as a result of the unnecessary reflection points RP 8  and RP 9  being removed from the reflection-point group RP 1  to RP 9 , at step S 140  in  FIG. 4 , the shape recognizing unit  228  recognizes the shape of the roadside object RSO using the corrected reflection-point group RP 1  to RP 7 . 
     Specifically, the shape recognizing unit  228  recognizes the two-dimensional shape of the roadside object RSO by successively connecting the reflection points in the corrected reflection-point group RP 1  to RP 7 . A technology for recognizing the shape of the roadside object RSO from the reflection-point group is known. Therefore, a detailed description thereof is omitted herein. 
     At step S 150 , the travel road shape calculating unit  230  calculates the travel road shape using the recognized roadside object RSO. Specifically, the travel road shape calculating unit  230  calculates the shape (travel road shape) of the travel road on which the own vehicle  50  travels from the shapes of the roadside object RSO and other objects (such as the travel road boundary lines WL 1  to WL 3 ). The automatic driving control unit  210  performs automatic driving of the own vehicle  50  using the travel road shape calculated in this manner. 
     As described above, according to the first embodiment, the reflection-point group is corrected by the reflection points determined to be highly likely not to be the reflection points of the roadside object RSO being removed from the reflection-point group through image processing of the image captured by the camera  412 . The shape of the roadside object RSO is then recognized through use of the corrected reflection-point group. 
     That is, according to the first embodiment, the reflection points that are highly likely not to be the reflection points of the roadside object RSO are removed from the reflection-point group through use of the results of image processing performed on the image of the travel route. Therefore, the noise points can be appropriately removed. The likelihood of the shape of the roadside object RSO being erroneously recognized can be reduced. 
     B. Second Embodiment 
     According to a second embodiment, when an object in an image captured by the camera  412  is recognized as being the overhead object UOB ( FIG. 3 ) through image processing of the image, a process shown in  FIG. 10 , described below, is performed in addition to the above-described processes ( FIG. 5  to  FIG. 8 ) according to the first embodiment. 
     For example, the method for recognizing the overhead object UOB of the own vehicle  50  in an image includes pattern matching between the object in the image and numerous template images of overhead objects registered in advance. Alternatively, an object in the image can be recognized through use of artificial intelligence to which machine learning has been applied, and the object can be recognized as an overhead object should the object be present above the vanishing point in the image. 
     Moreover, when the GNSS signal cannot be detected, the likelihood of an overhead structure, such as an overpass or the ceiling of a tunnel, being present is high. Therefore, a determination that an overhead structure is present may be made. However, in the latter case, as described hereafter, an area that is beyond a distance (such as a value ranging from 50 meters to 60 meters) set in advance is preferably set as an overhead object area UA 1 . 
     As shown in  FIG. 10 , when an object in the image is recognized as being the overhead object UOB of the own vehicle  50 , an overhead object area UA 1  in which the overhead object UOB is assumed to be present is set. The reflection points RPa, RP 8 , and RP 9  that are present in the overhead object area UA 1  are then removed from the reflection-point group.  FIG. 10  also shows the travel road area RLA 1  that has been set in the process in  FIG. 5 . Among the three reflection points RPa, RP 8 , and RP 9  removed from the reflection-point group in the process in  FIG. 10 , the reflection point RPa is outside of the travel road area RLA 1 . Therefore, the reflection point RPa cannot be removed in the process in  FIG. 5 . 
     For example, the reflection point RPa corresponds to the reflection of electromagnetic waves from a column of the overhead object UOB in the image shown in  FIG. 3 . In such cases as well, should the overhead object area UA 1  in which the overhead object UOB is assumed to be present be set, and the reflection points RPa, RP 8 , and RP 9  present in the overhead object area UA 1  be removed from the reflection-point group, the shape of the roadside object RSO can be more accurately recognized. 
     The overhead object area UA 1  in which the overhead object UOB is assumed to be present can be set through various methods. According to the second embodiment, the overhead object area UA 1  is set as an area beyond a position at which the overhead object UOB is calculated or estimated to be present. For example, when the camera  412  is a stereo camera, the distance to the overhead object UOB from the own vehicle  50  can be calculated from the image captured by the stereo camera. The area extending beyond the calculated distance can be set as the overhead object area UA 1 . 
     In addition, when the camera  412  is a single-lens camera, the distance to the overhead object UOB can be estimated based on the coordinates of the overhead object UOB in the image captured by the camera  412 , the coordinates of the reflection points of the overhead object UOB measured by the radar  414 , and the distance to the reflection points. The area extending beyond this distance can be set as the overhead object area UA 1 . Alternatively, in cases in which the position at which the overhead object UOB is present cannot be calculated or estimated, an area beyond a distance (such as a value ranging from 50 meters to 60 meters) set in advance may be set as the overhead object area UA 1 . 
     As described above, according to the second embodiment, when the overhead object UOB (such as a destination guidance sign or an elevated road) is recognized as being present above the travel road on which the own vehicle  50  travels, the overhead object area UA 1  in which the overhead object UOB is assumed to be present is set. The reflection points present in the overhead object area UA 1  are then removed from the reflection-point group. As a result, the likelihood of the overhead object UOB being erroneously recognized as the roadside object RSO can be reduced. 
     C. Third Embodiment 
     According to a third embodiment, when an object in an image captured by the camera  412  is recognized as being the overhead object UOB ( FIG. 3 ) through image processing the image, a process shown in  FIG. 11 , described below, is performed in addition to the above-described processes ( FIG. 5  to  FIG. 8 ) according to the first embodiment. In a manner similar to the process in  FIG. 10 , the process in  FIG. 11 , described hereafter, is a process in which an overhead object area UA 2  in which the overhead object UOB is assumed to be present is set. The reflection points RPa, RP 8 , and RP 9  present in the overhead object area UA 2  are then removed from the reflection-point group. The third embodiment differs from the second embodiment in terms of the setting method of the overhead object area UA 2 . 
     As shown in  FIG. 11 , according to the third embodiment, the overhead object area UA 2  is set as an area within a predetermined distance from the position at which the overhead object UOB is calculated or estimated to be present. For example, when the camera  412  is a stereo camera, the position of the overhead object UOB can be calculated from the image captured by the stereo camera. The area within a predetermined distance (such as 2 meters) from the calculated position can then be set as the overhead object area UA 2 . 
     In addition, when the camera  412  is a single-lens camera, a three-dimensional position of the overhead object UOB can be estimated based on the coordinates of the overhead object UOB in the image captured by the camera  412 , the coordinates of the reflection points of the overhead object UOB measured by the radar  414 , and the distance to the reflection points. The area within a predetermined distance from the estimated position can then be set as the overhead object area UA 2 . According to the third embodiment as well, effects similar to those according to the second embodiment can be achieved. 
     D. Fourth Embodiment 
     In an example shown in  FIG. 12 , the travel route SDR includes a temporary boundary line TLM. The temporary boundary line TLM is a boundary line that indicates that the section of road is a temporary travel section. For example, the temporary boundary line TLM is drawn on the road surface as a composite line including a white line and a yellow line. 
     In addition, a row of poles PL is often set within the area of the temporary boundary line TLM. When the reflection point correcting unit  226  recognizes the temporary boundary line TLM, the recognition can be performed through use of other features (such as the left side being a solid white line, the lane width being narrow, or a vertically aligned edge-point group [a point group of the row of poles PL or the like] being continuously present), in addition to the feature that is the boundary line being the composite line including the white line and the yellow line. According to the fourth embodiment, a process that is performed when the row of poles PL is recognized as the roadside object will be described. 
     In the example shown in  FIG. 13 , the reflection-point group obtained by the radar  414  includes reflection points RP 11  to RP 18  that correspond to the row of poles PL in  FIG. 12 , and reflection points RP* that are obtained from another roadside object. The reflection point correcting unit  226  determines whether or not the temporary boundary line TLM is included in the image captured by the camera  412 . When determined that the temporary boundary line TLM is included in the image, the reflection point correcting unit  226  sets an area RLA 7  other than a temporary boundary line area TLA that includes the temporary boundary line TLM. 
     For example, the temporary boundary line area TLA can be set as an area that circumscribes the temporary boundary line TLM. Alternatively, the temporary boundary line area TLA may be set as an area obtained by a margin of a predetermined width (such as 40 centimeters to 60 centimeters) being provided on the outer side of the area circumscribing the temporary boundary line TLM. The reflection point correcting unit  226  removes the reflection points RP* that are present in the area RLA 7  from the reflection-point group. 
     As described above, when the temporary boundary line TLM is included in the image captured by the camera  412 , should the reflection points RP* present in the area RLA 7  other than the temporary boundary line TLM be removed from the reflection-point group, the roadside object such as the row of poles PL included in the area of the temporary boundary line TLM can be accurately recognized. 
     In particular, this process is greatly effective in terms of enabling the roadside object included in the area of the temporary boundary line TLM to be accurately recognized in a location in which significant noise is present among the reflection points, such as inside a tunnel. Correction of the reflection-point group according to the fourth embodiment is also a type of process for removing the reflection points determined to be highly likely not to be the reflection points of a roadside object from the reflection-point group through image processing of the image. 
     The present disclosure is not limited to the above-described embodiments. Various modes are possible without departing from the spirit of the present disclosure.