Patent Publication Number: US-11648963-B2

Title: Driving control apparatus for automated driving vehicle, stop target, and driving control system

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application claims priority to Japanese Patent Application No. 2019-178781 filed on Sep. 30, 2019, which is incorporated herein by reference in its entirety including the specification, claims, drawings, and abstract. 
     TECHNICAL FIELD 
     The present specification discloses a driving control apparatus for an automated driving vehicle, a stop target, and a driving control system. 
     BACKGROUND 
     Automated driving vehicles that automatically control steering and speed of vehicles are known. An automated driving vehicle includes various sensors to estimate the vehicle location and recognize peripheral environments. 
     An automated driving vehicle includes, for example, a LiDAR (Light Detection and Ranging) sensor for measuring a distance to an obstacle in the vicinity. A LiDAR sensor measures a distance to a peripheral object using laser light such as infrared rays. For example, a LiDAR sensor scans infrared laser light three-dimensionally (in the horizontal and vertical directions) to obtain three-dimensional point group data of the vicinity of the vehicle. 
     An automated driving vehicle further includes a camera that captures an image of the surrounding area. An image captured by the camera is analyzed with a deep learning technique such as SSD (Single Shot Multibox Detector), for example, so that attributes of various objects (vehicles, passengers, structures, etc.) included in the image can be recognized. 
     By combining the three-dimensional point group data obtained by the LiDAR sensor with the image recognition by the camera, it is possible to determine the distance of objects from the vehicle and attributes of the objects. 
     If such an automated driving vehicle is public transportation such as a passenger bus, it is necessary to stop the vehicle beside a stop target such as a bus stop, as disclosed in JP H11-3495 A and JP 2017-196962 A. This stopping control is also referred to as precise docking control. Precise docking control requires driving control for allowing a space between the curb and an entrance step of the vehicle to be within a several centimeters, for example, to enable a wheelchair and the like to board or exit smoothly. To execute precise docking control, it is necessary to recognize a stop target ahead of the vehicle in the travelling direction. 
     Precise docking control of a vehicle starts when a stop target is recognized in a camera image, for example. In precise docking control, the three-dimensional point group data from the LiDAR sensor, for example, are used to determine the distance between the recognized stop target and the vehicle, and the steering angle and the speed of the vehicle are controlled in accordance with the distance. 
     If it is possible to recognize a stop target by a sensor other than the camera, in addition to the camera, an increase in the detection accuracy of the stop target is expected. Embodiments of the present disclosure are therefore directed toward providing an improved driving control apparatus for an automated driving vehicle, a stop target, and a driving control system, capable of recognition of a stop target using a LiDAR sensor. 
     SUMMARY 
     A driving control apparatus for an automated driving vehicle disclosed in the present specification includes a LiDAR sensor, an analyzer, and a determination unit. The LiDAR sensor is configured to three-dimensionally scan laser light outward of the vehicle and to receive reflected light. The analyzer is configured to group three-dimensional points of the reflected light acquired by the LiDAR sensor into clusters. The determination unit is configured to compare shapes and reflectance distributions between at least one of the clusters obtained by the analyzer and a reference cluster representing a stop target to determine whether or not the at least one cluster includes the stop target. 
     The above configuration is used to group the three-dimensional points acquired by the LiDAR sensor into clusters to enable recognition of the stop target by using the shape and the reflectance distribution of the clusters. 
     In the above configuration, the reference cluster may have a stripe configuration including repeated patterns of a plurality of regions in which adjacent regions have different reflectances with respect to the laser light. 
     The above configuration facilitates discrimination of the stop target from other objects, resulting in increased accuracy of recognition of the stop target. 
     In the above configuration, the reference cluster may include, as the stripe configuration, a high reflectance region having a relatively high reflectance with respect to the laser light and a low reflectance region having a relatively low reflectance with respect to the laser light. The high reflectance region and the low reflectance region may be alternately arranged repeatedly. 
     In the above configuration, the stripe configuration includes two types of zones: the high reflectance region and the low reflectance region. This increases a difference in reflectance between the regions (i.e., enhances the contrast), thereby facilitating recognition of the stop target from a distant place. 
     In the above configuration, the reference cluster may include, as the stripe configuration, a horizontal stripe configuration including the high reflectance region and the low reflectance region both extending horizontally. The high reflectance region and the low reflectance region may be alternately disposed repeatedly in a vertical direction. 
     The stop target typically has a vertically elongated configuration with the horizontal dimension smaller than the vertical dimension. When such a stop target and the corresponding reference cluster have a horizontal stripe configuration including the high reflectance region and the low reflectance that are alternately arranged repeatedly in the vertical direction, each stripe can have an increased width. This facilitates recognition of the stop target from a distant place accordingly. 
     In the above configuration, the reference cluster may have a surface shape of 0° to 180° of a circular cylindrical side face. 
     The stop target having a circular cylindrical shape reduces variations of the projection area caused by the imaging angle of the stop target. This requires that the reference cluster corresponding to the stop target should have only a surface shape of 0° to 180° of the circular cylindrical side face; that is, only a surface shape of the circular cylindrical side face as viewed from the front, eliminating the need to change the shape of the reference cluster depending on the imaging angle, for example. 
     A stop target disclosed in the specification is a stop target for an automated driving vehicle comprising a LiDAR sensor configured to three-dimensionally scan laser light outward of the vehicle and to receive reflected light. The stop target has a stripe configuration including repeated patterns of a plurality of regions in which adjacent regions have different reflectances with respect to the laser light. 
     A driving control system disclosed in the specification includes a stop target, and an automated driving vehicle to stop beside the stop target. The stop target has a stripe configuration including repeated patterns of a plurality of regions in which adjacent regions have different reflectances with respect to laser light. The automated driving vehicle includes a LiDAR sensor, an analyzer, and a determination unit. The LiDAR sensor is configured to three-dimensionally scan the laser light outward of the vehicle and to receive reflected light. The analyzer is configured to group three-dimensional points of the reflected light acquired by the LiDAR sensor into clusters. The determination unit is configured to compare shapes and reflectance distributions between at least one of the clusters obtained by the analyzer and a reference cluster representing a stop target to determine whether or not the at least one cluster includes the stop target. 
     The driving control apparatus for an automated driving vehicle, the stop target, and the driving control system disclosed in the specification enable recognition of a stop target using a LiDAR sensor. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       Embodiments of the present disclosure will be described by reference to the following figures, wherein: 
         FIG.  1    is a perspective view illustrating an outer appearance of a vehicle; 
         FIG.  2    is a plan view illustrating an outer appearance of a vehicle; 
         FIG.  3    is an enlarged perspective view of a sensor unit and its periphery on a front face of the vehicle; 
         FIG.  4    illustrates an irradiation range of a LiDAR unit; 
         FIG.  5    is a hardware structural view illustrating a configuration of a driving control apparatus for an automated driving vehicle according to an embodiment; 
         FIG.  6    illustrates functional blocks of a control unit; 
         FIG.  7    illustrates a stop target according to an embodiment; 
         FIG.  8    illustrates a reference cluster corresponding to a stop target according to an embodiment; 
         FIG.  9    illustrates another example stop target according to the present embodiment; 
         FIG.  10    is a diagram for explaining a change in the projection area of the stop target depending on the angle of the stop target; 
         FIG.  11    illustrates another example stop target according to an embodiment; 
         FIG.  12    illustrates another example stop target according to an embodiment; 
         FIG.  13    illustrates a precise docking control start determination flow performed by a driving control apparatus for an automated driving vehicle according to an embodiment; 
         FIG.  14    illustrates an example image captured by a camera; 
         FIG.  15    illustrates three-dimensional point group data provided by a LiDAR sensor, which correspond to the scene illustrated in  FIG.  14   ; 
         FIG.  16    illustrates a clustering process of three-dimensional point group data; 
         FIG.  17    illustrates an image after recognition of objects; 
         FIG.  18    illustrates another example stop target according to an embodiment; and 
         FIG.  19    illustrates another example stop target according to an embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Vehicle Configuration 
     The configuration of a vehicle  10  including a driving control apparatus according to the present embodiment will be described by reference to the drawings. In  FIGS.  1  to  4   , a direction along the length of a vehicle body is indicated by an axis denoted with a sign FR, a direction along the width of the vehicle is indicated by an axis denoted with a sign LH (Left Hand), and a direction along the vehicle height is indicated by an axis denoted with a sign UP. 
     The lengthwise axis FR and the widthwise axis LH both extend along the horizontal direction, and the vehicle height axis UP extends along the vertical direction. The forward direction of the vehicle body lengthwise axis FR corresponds to the frontward direction of the vehicle body; the forward direction of the widthwise axis LH corresponds to the leftward in the vehicle width direction; and the forward direction of the vehicle height axis UP corresponds to the upward direction. These three axes are orthogonal to each other. 
     In the following description, unless otherwise specified, frontward in the vehicle body lengthwise direction is simply referred to as “frontward” or “front”, rearward in the vehicle body lengthwise direction is simply referred to as “rearward” or “rear”, upward in the vehicle height direction is simply referred to as “upward”, and downward in the vehicle height direction is simply referred to as “downward”. 
       FIG.  1    is a perspective view illustrating an outer appearance of the vehicle  10 . Specifically,  FIG.  1    illustrates a front face (front) and a left side face of the vehicle  10 . 
     The vehicle  10  is an automated driving vehicle having automated driving functions. The vehicle  10  is capable of automated driving from level 0 (fully manual) to level 5 (fully autonomous) based on the automated driving standard of the Society of Automotive Engineers (SAE), for example. According to the SAE standard, for example, a driver&#39;s manipulation is at least partially required from level 0 to level 3. In level 4 (highly automated driving), full automated driving which does not require driver&#39;s manipulation operation is executed within a limited area (e.g., within a bus operation route). In level 5, automated driving (full autonomous operation) which does not require drivers under any conditions is executed. 
     The vehicle  10  is used as a passenger bus which travels along a predetermined route according to automated driving within a specific site with passengers being on board in the vehicle interior. During operation, the automated driving level is set to SAE level 4, for example. 
     The vehicle  10  is an electric car that uses a rotary electric machine as a drive source, for example. The vehicle  10  includes a main battery (not shown) installed to supply electric power to the rotary electric machine. The vehicle  10  is not limited to an electric car, and may be a hybrid vehicle including an internal combustion engine (engine) and a rotary electric machine as drive sources. 
     Referring to  FIG.  1   , the vehicle  10  includes entrance/exit doors  12  in the center of the left side face. The entrance/exit doors  12  are, for example, slidable double doors that move lengthwise of the vehicle in a slidable manner to open and close. The vehicle  10  is configured to travel on the left side of the road. 
     The vehicle  10  includes, on its front face, a pair of head lamps  14  (headlights). The vehicle  10  further includes, between the pair of head lamps  14 , a destination and mode display  16  showing letters indicating a destination and an operation mode (e.g., automated driving or manual driving), for example. The destination and mode display  16  further shows message to passengers, such as “After You”, when the vehicle  10  stops before a pedestrian crossing. 
     The vehicle  10  further includes a plurality of sensors to enable automated driving. Referring to  FIG.  1    and  FIG.  2   , the vehicle  10  includes, on its front face, rear face, and side faces, sensor units  30  ( 30 A to  30 D). The vehicle  10  further includes clearance sensors  20  at the four corners of the vehicle  10 . 
     The clearance sensors  20  may be sonar sensors, for example, and measure the distance between the vehicle  10  and surrounding objects. In executing precise docking which will be described below, for example, the clearance sensors  20  measure the distance between the vehicle  10  and the curb. 
       FIG.  3    illustrates the sensor unit  30 A disposed on the front face of the vehicle  10 . The other sensor units  30 B,  30 C, and  30 D are similarly configured. The sensor unit  30 A protrudes from the vehicle front face toward outside the vehicle; that is, protrudes frontward of the vehicle. The sensor unit  30  includes a camera  34 , a LiDAR sensor  36 , and a casing  32  that houses these elements. 
     The casing  32  protects the camera  34  and the LiDAR sensor  36  against wind and rain or obstacles, for example, while securing their field of view. The casing  32  is composed of an optically transparent resin material, for example. The casing  32  is configured in a semicylindrical shape protruding from the vehicle front face toward the vehicle outside (frontward of the vehicle), for example. 
     The LiDAR sensor  36  is a sensor unit for automated driving which employs LiDAR; that is, a technique for measuring a distance to a surrounding object using laser light. The LiDAR sensor  36  includes an emitter  36 A that emits infrared laser light outward, a receiver  36 B that receives the reflected light, and a motor  36 C that rotates the emitter  36 A and the receiver  36 B. 
     An irradiation plane of the emitter  36 A and a receiving plane of the receiver  36 B are parallel to each other and are aligned in the vehicle width direction and the vehicle length direction. The emitter  36 A emits infrared laser light outward of the vehicle; that is, toward the region ahead of the vehicle  10 . The emitter  36 A may include a pulse laser light source that emits laser light of around 905 nm. The laser light emitted from the emitter  36 A impinges onto an object located ahead of the vehicle  10 , and the reflected light is received by the receiver  36 B. The distance between the reflection point and the receiver  36 B is determined based on time elapsed from light emission from the emitter  36 A to light reception by the receiver  36 B. 
     The motor  36 C allows the emitter  36 A and the receiver  36 B to rotate about its vertical axis (UP axis) serving as a rotary axis. The motor  36 C may be a servo motor, for example. The motor  36 C allows the emitter  36 A and the receiver  36 B to scan horizontally, so that range for measuring the distance to an object in the region ahead of the vehicle  10  expands in the horizontal direction. An electro-magnetic mirror may be used, in place of the motor  36 C, to perform the horizontal scanning. 
     The emitter  36 A and the receiver  36 B may also be line sensors extending in the vertical direction (height direction). For example, a plurality of light sources (e.g., laser elements) of the emitter  36 A are aligned in the vertical direction and a plurality of light receiving elements of the receiver  36 B are also aligned in the vertical direction. The emitter  36 A and the receiver  36 B are arranged radially in the vertical direction such that laser light can be emitted radially and the reflected light can be received. Such an arrangement enables acquisition of the range data by vertical scanning. 
     As described above, the LiDAR sensor  36  scans laser light outward of the vehicle in the horizontal and vertical directions; that is, three-dimensionally, and receives the reflected light, as illustrated in  FIG.  4   . Based on the irradiation angle and the light receiving angle (including the horizontal and vertical angles) and the time elapsed from laser light irradiation to reception of light, three-dimensional information of the reflection points; that is, the distance from the vehicle to the reflection points, can be obtained three-dimensionally, as the range data. Then, three-dimensional points as illustrated in  FIG.  15    are determined by two-dimensionally obtaining the reflection points including the three-dimensional information. In  FIG.  15   , the reflection points are indicated with circles. 
     Each reflection point includes, in addition to the three-dimensional information, its reflectance data. As will be described below, in recognizing a stop target  70 , a reflectance distribution of clustered three-dimensional points is compared with a reflectance distribution of a reference cluster  80  representing the stop target  70 . 
     The camera  34  captures an image of the same view as the LiDAR sensor  36 . The camera  34  includes an image sensor such as a CMOS sensor or a CCD sensor, for example. The image captured by the camera  34  (captured image) is used for automated driving control. For example, the image captured by the camera  34  is analyzed to detect objects included in the image and recognize the attribute of the objects, as described below. 
       FIG.  5    illustrates a driving control apparatus of an automated driving vehicle according to the present embodiment. The driving control apparatus includes the camera  34 , the LiDAR sensor  36 , a dynamic map  22 , a navigation system  24 , a steering mechanism  26 , a braking mechanism  27 , a driving mechanism  28 , and a control unit  40 . The driving control apparatus is mounted on the vehicle  10 . 
     The dynamic map  22  is a three-dimensional map that stores locations and three-dimensional configurations of roads, footpaths, surrounding structures, traffic signals, and stop lines, for example. The navigation system  24  performs positioning using an artificial satellite, and a GNSS (Global Navigation Satellite System) is used, for example. As will be described below, the navigation system  24  and the dynamic map  22  are used to enable estimation of the location of the vehicle with accuracy within a range of positioning errors of the artificial satellite. 
     The steering mechanism  26  is used to manipulate a steering wheel, for example, and includes a steering motor, for example. When a steering instruction is supplied from the control unit  40  to the steering motor, the steering angle of the vehicle  10  is controlled. 
     The braking mechanism  27  is used to manipulate a brake mechanism, and includes, for example, an actuator for a master cylinder that controls the oil pressure of the brake. Upon receiving a braking instruction from the control unit  40  by the actuator, the vehicle  10  decelerates. 
     The driving mechanism  28  is used to control driving force of a rotary electric machine which is a drive source of the vehicle  10 , and includes, for example, an inverter that controls the driving force of the rotary electric machine. When a driving instruction is supplied from the control unit  40  to the inverter, the driving force of the vehicle  10  is controlled. 
     The control unit  40  may be an electronic control unit (ECU) of the vehicle  10 , for example, and is composed of a computer. The control unit  40  includes an input/output controller  41  that controls input/output of data. The control unit  40  further includes, as operation elements, a CPU  42 , a GPU (Graphics Processing Unit)  43 , and a DLA (Deep Learning Accelerator)  44 . The control unit  40  also includes, as storage units, a ROM  45 , a RAM  46 , and a hard disk drive  47  (HDD). These structural elements are coupled to an internal bus  48 . 
     At least one of the ROM  45  and the hard disk drive  47  which are storage devices stores a program for executing automated driving control including precise docking control. At least one of the ROM  45  and the hard disk drive  47  further stores a program for executing a precise docking control start determination flow as illustrated in  FIG.  13   . 
     To execute programs of the automated driving control and the precise docking control start determination flow, the control unit  40  includes function blocks as illustrated in  FIG.  6   , including an imaging data analyzer  50 , a LiDAR data analyzer  52 , a vehicle location estimator  54 , a precise docking control start determination unit  56 , a reference cluster memory unit  58 , and an automated driving controller  60 . Operation of these function blocks will be described below. 
     Stop Target 
       FIG.  7    to  FIG.  12   ,  FIG.  18   , and  FIG.  19    illustrate stop targets  70  and corresponding reference clusters  80 . The stop target  70  and the driving control apparatus mounted on the vehicle  10  constitute a driving control system for an automated driving vehicle according to the present embodiment. 
       FIG.  7   ,  FIG.  8   ,  FIG.  9   ,  FIG.  11   ,  FIG.  12   ,  FIG.  18   , and  FIG.  19    each show a vertical axis V and a horizontal axis H. The vertical axis V is parallel to the vehicle height axis UP of the vehicle, and the horizontal axis H extends within the same plane (within the horizontal plane) as the lengthwise axis FR and the widthwise axis LH. 
     Referring to  FIG.  7   , the stop target  70  is a so-called bus stop and is a target which indicates a stop position of the vehicle  10 . The outer face (exposed face) of the stop target  70  may have a stripe configuration including repeated patterns of a plurality of regions in which adjacent regions have different reflectances with respect to laser light. 
     The outer face (exposed face) of the stop target  70  includes, as a stripe configuration, high reflectance regions  72  with relatively high reflectance with respect to the infrared laser light emitted from the LiDAR sensor  36  and low reflectance regions  74  with relatively low reflectance, which are alternately arranged in a repeated manner, for example. In other words, the outer face (exposed face) includes a plurality of pairs, each including the high reflectance region  72  and the low reflectance region  74 . 
     The stripe configuration formed of two types of zones, the high reflectance region  72  and the low reflectance region  74 , can assume a significant difference in reflectance among the regions (that is, can have high contrast). This facilitates recognition of the stop target from a distant place. 
     The exposed face of the stop target  70  may include a horizontal stripe configuration, for example. More specifically, the exposed face includes the high reflectance region  72  and the low reflectance region  74  both extending in the horizontal direction and arranged alternately in the vertical direction. 
     Typically, a bus stop has a vertical dimension L 1  which is greater than a horizontal dimension R 1 , as illustrated in  FIG.  7   . The horizontal stripe configuration in which stripe patterns are deployed along the longitudinal direction has a greater number of patterns than the number of patterns in the vertical stripe configuration in which stripe patterns are deployed along the lateral direction, and can have an increased width (stripe width) for each stripe. 
     The stop target having an increased number of stripe patterns can be easily extracted from among surrounding objects. Further, the stop target  70  with a greater stripe width can be recognized from a distant place. 
     The stripe width is determined in light of the specifications of the LiDAR sensor  36  (see  FIG.  6   ) and the start point of the precise docking control. For example, the vertical angle resolution θ of the LiDAR sensor  36  and the distance r with respect to the stop target  70  at which the precise docking control starts are used to determine a space z between the laser light beams in the vertical direction at the distance r from the following equation: z=2r×tan(θ/2). 
     To obtain the reflection points in the high reflectance region  72  and the low reflectance region  74  of the stop target  70  from the distance r, the stripe width needs to have the minimum value w min  that is equal to or greater than z. Assuming that the vertical angle resolution θ is 0.1°, and the distance r from the vehicle  10  (more specifically, the LiDAR sensor  36 ) to the stop target  70 , where the precise docking control starts, is 100 m, for example, z≈17 cm. Therefore, the minimum value w min  of the stripe width is 17 cm. The high reflectance region  72  and the low reflectance region  74  may have an equal stripe width, and the stripe widths may also be equal among the patterns. 
     The maximum value W max  of the stripe width is determined based on the height L 1  of a pattern plane of the stop target  70  where the stripe configuration can be formed and the number m of repetition of patterns formed by pairs of the high reflectance region  72  and the low reflectance region  74 . For example, the stripe width w, the pattern plane height L 1 , and the pattern repetition number m have a relationship of 2w×m≤L1. Assuming that the pattern repetition number is 5 and the pattern plane height L 1  is 2 m, w≤20 cm, so that the maximum value W max  of the stripe width is 20 cm. 
     The stripe width of the high reflectance region  72  and the low reflectance region  74  may vary for each pair of the high reflectance region  72  and the low reflectance region  74 , or may be equal for all the patterns. However, if it is difficult to recognize the difference of the stipe widths due to the resolution of the LiDAR sensor  36 , the stripe widths of all of the high reflectance regions  72  and the low reflectance regions  74  may be equal. 
     The stop target  70  is made of a resin plate, for example, to which a tape reflector (reflective tape) is attached. A resin reflector may be used in place of the resin plate. A region of the stop target  70  where the tape reflector is attached is the high reflectance region  72 , and a region without the tape reflector is the low reflectance region  74 . 
     The reflectance of the high reflectance region  72  and the reflectance of the low reflectance region  74  are determined based on the difference in reflectance at the distance r to the stop target  70 , where the precise docking control starts, for example. The difference in reflectance between adjacent reflectance regions, such as the difference in reflectance between the high reflectance region  72  and the low reflectance region  74  in the example illustrated in  FIG.  7   , should be 10% or greater, for example, when the LiDAR sensor  36  emits infrared laser light to the stop target  70  at the distance r. 
     Each of the reflectance regions (the high reflectance region  72  and the low reflectance region  74  in the example illustrated in  FIG.  7   ) may be in a color other than black which is known to have high absorptance of infrared ray. If the LiDAR data analyzer  52  (see  FIG.  6   ) determines reflectance 0%, as will be described below, there is a possibility that the stop target  70  is segmented into a plurality of clusters. For example, if the reflectance of the low reflectance region  74  is around 0%, it is possible that the high reflectance regions  72  may be grouped into different clusters. The high reflectance regions  72  and the low reflectance regions  74  having colors other than black inhibit segmentation of the stop target  70  into a plurality of clusters as described above. 
     The stop target  70  may have a circular cylindrical shape, as illustrated in  FIG.  7   . The stop target  70  having a circular cylindrical shape inhibits variation in a projection area caused by the imaging angle of the stop target  70 . Each of the high reflectance region  72  and the low reflectance region  74  may be disposed along the entire circumference of the side face of the circular cylinder. 
       FIG.  8    illustrates a reflectance distribution obtained when the stop target  70  illustrated in  FIG.  7    is irradiated with infrared laser. This corresponds to the shape of the stop target  70 ; that is, the frontal shape of the stop target  70  viewed from the LiDAR sensor  36 , and to the reflectance distribution on the plane, when the infrared laser light is scanned three-dimensionally (horizontally and vertically) by the LiDAR sensor  36 . The reflectance distribution and the shape illustrated in  FIG.  8    are prestored in the reference cluster memory unit  58  (see  FIG.  6   ) as the reference cluster  80  representing the stop target. 
       FIG.  8    shows the reflection points by circles; an unhatched circle represents a reflection point with a relatively high reflectance and a hatched circle represents a reflection point with a relatively low reflectance. A high reflectance region  82  in the reference cluster  80  corresponds to the high reflectance region  72  in the stop target  70 . Similarly, a low reflectance region  84  in the reference cluster  80  corresponds to the low reflectance region  74  in the stop target  70 . 
     Thus, the reference cluster  80  also has a stripe configuration including repetition of patterns of a plurality of regions with the reflectance with respect to the laser light being different between adjacent regions.  FIG.  8   , for example, includes a stripe configuration including the high reflectance regions  82  and the low reflectance regions  84  that are alternately arranged repeatedly. More specifically, in the cluster  80 , the high reflectance regions  82  and the low reflectance regions  84  that extend horizontally are disposed alternately in the vertical direction in a repeated manner. 
     The reference cluster  80  also has, corresponding to the stop target  70  illustrated in  FIG.  7   , a surface shape of a side face of the circular cylinder as viewed from the front. Referring to  FIG.  7   , for example, the surface shape as viewed from front represents a surface shape of the side face of 0° to 180° of the circular cylinder, as indicated by a region A  1 . 
     As described above, the stop target  70  having a circular cylindrical shape reduces variation of the projection area depending on the imaging angle of the stop target  70 . Therefore, the surface shape of the stop target  70  of 0° to 180° of the circular cylinder side face; that is, the surface shape of the circular cylinder side face as viewed from the front, is itself sufficient for the reference cluster  80  of the stop target  70 ; it is not necessary to alter the shape of the reference cluster  80  for each imaging angle, for example. 
       FIG.  9    illustrates another example stop target  70  according to the present embodiment. In this example, the stop target  70  has a prism pillar shape. The stop target  70  is a rectangular parallelepiped, for example, and has a plane with a relatively larger area (a plane in parallel to axis H 1 ) facing the vehicle  10 . 
     When such a stop target  70  having a prism pillar shape is set on a footpath, the projection width (W 1 , W 2 ) of the stop target  70  varies depending on the angle (imaging angle) of the LiDAR sensor  36  with respect to the stop target  70 , as illustrated in  FIG.  10   . Therefore, a plurality of reference clusters  80  having different projection widths in accordance with the imaging angle are stored in the reference cluster memory unit  58  (see  FIG.  6   ). 
       FIG.  11    illustrates an example stop target  70  with a time table plate  76  attached thereto. The time table plate  76  shows times indicating scheduled arrival time of the vehicle  10 . The time table plate  76  is disposed to avoid the high reflectance region  72  and the low reflectance region  74  of the stop target  70 . The position of the time table plate  76  is determined such that the high reflectance region  72  and the low reflectance region  74  do not overlap the time table plate  76  along a direction in which the footpath extends. 
       FIG.  12    illustrates a still another example stop target  70  according to the present embodiment. In this example, the stop target  70  includes, in its center part in the height direction, a narrow part  78  having a relatively smaller diameter as compared to regions above and below. Such a unique shape makes it easy to discriminate the stop target  70  from surrounding structures, passengers, and other objects. 
     When the vehicle  10  approaches the stop target  70 , for example, the location of the stop target  70  can be detected with higher accuracy by using the narrow part  78  with a relatively smaller diameter as a reference than by using a part with a relatively larger diameter as a reference. Further, the narrow part  78  may be set at a height level which is equal to that of the LiDAR sensor  36  of the vehicle  10 . 
     The diameter R 2  of the narrow part  78  is determined, using the distance r from the vehicle  10  (more specifically, from the LiDAR sensor  36 ) where the precise docking control starts to the stop target  70  and the horizontal resolution α of the LiDAR sensor  36 , as R2≥2r×tan(α/2). 
     While  FIG.  7   ,  FIG.  9   ,  FIG.  11   , and  FIG.  12    illustrate the stripe configuration of the stop target  70  including the high reflectance region  72  and the low reflectance region  74 , and the corresponding reference cluster  80  includes the stripe configuration having the high reflectance region  82  and the low reflectance region  84 , the stop target  70  and the reference cluster  80  are not limited to these examples. 
     Specifically, as the stop target  70  and the reference cluster  80  are only required to have a stripe configuration including repeated patterns of a plurality of regions with adjacent regions having different reflectances with respect to laser light, they may have a stripe configuration of three-color stripes, as illustrated in  FIG.  18   , for example. In this example, the stop target  70  has a stripe configuration including an intermediate reflectance region  73  between the high reflectance region  72  and the low reflectance region  74 . The relationship among the reflectances of these regions is as follows: high reflectance region  72 &gt;intermediate reflectance region  73 &gt;low reflectance region  74 . Accordingly, the reference cluster  80  also has a stripe configuration including an intermediate reflectance region between the high reflectance region  82  and the low reflectance region  84 . 
     In the stop target  70 , a single pattern may include an identical reflectance region a plurality of times. For example, as illustrated in  FIG.  19   , the stop target may have a stripe configuration including repeated patterns each formed of four reflectance regions including the high reflectance region  72 , the intermediate reflectance region  73 , the low reflectance region  74 , and the intermediate reflectance region  73  in this order. The reference cluster  80  may accordingly have a stripe configuration including repeated patterns each formed of four reflectance regions including the high reflectance region  82 , the intermediate reflectance region  73 , the low reflectance region  84 , and the intermediate reflectance region  73  in this order. 
     Automated Driving Control 
     Referring to  FIG.  6   , automated driving control performed by the driving control apparatus of the vehicle  10  will be described. As automated driving control is known, the following description especially refers to features related to the precise docking control start determination flow which will be described below. 
     The dynamic map  22  which is a three-dimensional map and the navigation system  24  are used to perform location estimation of the vehicle  10 . The navigation system  24  which is a satellite positioning system transmits to the vehicle location estimator  54  information concerning latitude and longitude of vehicle  10 . Further, the dynamic map  22  transmits to the vehicle location estimator  54  map data of a location corresponding to the information of the latitude and longitude of the vehicle  10 . Consequently, the location of the vehicle  10  within a range of errors of satellite positioning (e.g., ±10 cm) is estimated. 
     The vehicle location estimator  54  further acquires three-dimensional point group data (scan data) of the vehicle  10  and its peripheral region from the LiDAR sensor  36 .  FIG.  15    illustrates example three-dimensional point group data. The location of the vehicle  10  is estimated with an error which is less than an error of satellite positioning, by matching the three-dimensional point group data and the three-dimensional map data from the dynamic map  22 . 
     The imaging data analyzer  50  acquires a captured image illustrated in  FIG.  14   , which is captured by the camera  34 . The imaging data analyzer  50  further detects objects within the image according to a known deep learning method such as SSD (Single Shot Multibox Detector) and YOLO (You Only Look Once) using supervised learning, and recognizes their attributes (vehicles, passengers, structures, and the like). For example, as illustrated in  FIG.  17   , the imaging data analyzer  50  recognizes from the captured image the stop target  70 , vehicles  90 , a footpath surface  92 , and a traffic lane  94 . 
     The LiDAR data analyzer  52  acquires the three-dimensional point group data (see  FIG.  15   ) from the LiDAR  36 . The LiDAR data analyzer  52  further executes clustering for grouping the three-dimensional points into at least one cluster. 
     While  FIG.  15    shows the vehicles and the structures with dashed lines so that the correspondence between the point group and the object can be understood, these dashed lines are not actually shown. The LiDAR data analyzer  52  therefore segment the three-dimensional points into certain point groups for clustering. For clustering, known methods may be used; for example, there is used Euclidean clustering that uses the Euclidean distance of each reflection point for grouping points with similar Euclidean distances into a cluster. In the example illustrated in  FIG.  16   , for example, the three-dimensional points are grouped into clusters CL 1  to CL 13  by clustering. 
     The automated driving controller  60  uses the captured image analyzed by the imaging data analyzer  50  and object information included in the image, the clustered three-dimensional point group data analyzed by the LiDAR data analyzer  52 , and the vehicle location information estimated by the vehicle location estimator  54 , to perform driving control of the vehicle  10 . 
     By superposing the captured image and the three-dimensional point group data, for example, there can be determined information concerning an object, such as what type of attribute it has and its distance from the vehicle  10 . Using this superposing information, the automated driving controller  60  controls the steering mechanism  26 , the braking mechanism  27 , and the driving mechanism  28 . 
     In precise docking control, for example, the steering angle and speed of the vehicle  10  are determined based on the distance to the stop target  70 , and the automated driving controller  60  transmits a corresponding steering instruction, braking instruction, and driving instruction to the steering mechanism  26 , the braking mechanism  27 , and the driving mechanism  28 , respectively. This control finally enables the vehicle  10  to stop beside the stop target  70 . 
     Precise Docking Control Start Determination Flow 
       FIG.  13    illustrates a precise docking control start determination flow according to the present embodiment. Precise docking control of a vehicle is triggered by recognition of the stop target  70 . According to the illustrated flow, whether to start the precise docking control is determined based on whether or not the stop target  70  has been recognized. 
     Further, as will be described below, according to the illustrated flow, in recognizing the stop target  70 , analysis of the three-dimensional point group data using the LiDAR sensor  36 , unlike image recognition using the captured image from the camera  34 , is used to recognize the stop target  70 . 
     The flow illustrated in  FIG.  13    starts when the vehicle  10  enters a “bus stop vicinity area”, which is an area in the vicinity of the stop target  70 . The bus stop vicinity area may be a region within a 500 m radius of the stop target  70 . 
     Under automated driving, for example, a destination and a route to the destination are determined in advance. The location of the stop target  70  along the route is further determined using the dynamic map  22 . In addition, based on the determined location of the stop target  70  (its location on the dynamic map) and the vehicle location estimation described above, the distance between the vehicle  10  and the stop target  70  can be estimated. 
     However, as this estimation is a rough location estimation including errors based on the position information accuracy of the dynamic map  22  and errors of satellite positioning, the bus stop vicinity area is set to the order greater than the order of these errors. For example, if the errors of the dynamic map  22  and the satellite positioning are of the order of 10 cm, the bus stop vicinity area is set to the order of 100 m, which can disregard these errors. 
     Upon starting the precise docking control start determination flow, the LiDAR data analyzer  52  acquires three-dimensional point group data from the LiDAR sensor  36  (S 10 ). The LiDAR data analyzer  52  further executes clustering of the acquired three-dimensional point group data (S 12 ). The clustered three-dimensional point group data are transmitted to the precise docking control start determination unit  56 . 
     The precise docking control start determination unit  56  compares shapes and reflectance distributions between at least one cluster acquired by the LiDAR data analyzer  52  and the reference cluster  80  that represents the stop target  70  to determine whether or not the at least one cluster includes the stop target  70 . Specifically, the precise docking control start determination unit  56  confirms the number n of the obtained clusters (S 14 ), and extracts the first cluster (k=1) from the plurality of clusters (S 16 ). For example, cluster CL 1  in  FIG.  16    is extracted. 
     The precise docking control start determination unit  56  then compares the k-th cluster CL_k with the reference cluster  80  (see  FIG.  8   ) stored in the reference cluster memory unit  58  to determine whether or not these clusters match (S 18  to S 24 ). 
     The degree of match based on this comparison may be determined using a known method such as template matching, for example. Specifically, the reference cluster  80  is used as a template. 
     First, the shapes are compared between the cluster CL_k and the reference cluster  80  (S 18  and S 20 ). In this comparison and in comparing the reflectance distributions in the later stage, the reference cluster  80  may be corrected. For example, the precise docking control start determination unit  56 , based on the distance between the cluster CL_k and the LiDAR sensor  36  when the three-dimensional point group data were acquired, enlarges or reduces the reference cluster  80 . At this time, the reflection points which may be included in the stripe widths of the high reflectance region  82  and the low reflectance region  84 , respectively, of the reference cluster  80  are increased or decreased. 
     If in step S 20  the shape of the cluster CL_k and the shape of the reference cluster  80  do not match, it is determined that the cluster CL_k does not correspond to the stop target  70 . Therefore, after determining whether or not the cluster counter k reaches the maximum value n (S 26 ), the cluster counter k, not reaching the maximum value n, is incremented (S 28 ) and the next cluster is set as a comparison target. 
     If in step S 20  it is determined that the shape of the cluster CL_k and the shape of the reference cluster  80  match, the two-dimensional reflectance distributions of these clusters are further compared (S 22  and S 24 ). For example, the arrangement pattern of the reflection point regions having relatively high reflectance and the reflection point regions having relatively low reflectance in the cluster CL_k and the arrangement pattern of the high reflectance region  82  and the low reflectance region  84  in the reference cluster  80  are compared. 
     If it is determined that the cluster CL_k and the reference cluster  80  do not match according to comparison of the reflectance distribution, it is then determined that the cluster CL_k does not correspond to the stop target  70 . Therefore, after determining whether or not the cluster counter k reaches the maximum value n (S 26 ), the cluster counter k, not reaching the maximum value n, is incremented (S 28 ), and the next cluster is set as a comparison target. 
     If in step S 24  it is determined that the reflectance distribution of the cluster CL_k and the reflectance distribution of the reference cluster  80  match, the precise docking control start determination unit  56  outputs a start instruction for the precise docking control to the automated driving controller  60  (S 34 ). This changes the driving control for the vehicle  10  from normal travelling control to the precise docking control to the stop target  70 . 
     If in step S 26  it is determined that all of the clusters, clusters CL_ 1  to CL_n, in the three-dimensional point group data do not correspond to the reference cluster  80 , the precise docking control start determination unit  56  determines whether or not the vehicle  10  has passed the bus stop vicinity area (S 36 ). This determination is performed using the vehicle location estimation described above, for example. 
     If in step S 36  it is determined that the vehicle  10  stays in the bus stop vicinity area, the precise docking control start determination unit  56  resets the number n of clusters and the cluster counter k (n=0, and k=1, for example) (S 30 ). Further, the LiDAR data analyzer  52  acquires new three-dimensional point group data from the LiDAR sensor  36  to update the three-dimensional point group data to be analyzed (S 32 ). The updated three-dimensional point group data are clustered by the LiDAR data analyzer  52  as described above (S 12 ). 
     If in step S 36  it is determined that the vehicle  10  has passed the bus stop vicinity area, this means that the stop target  70  has not been detected (recognized) over the entire region of the bus stop vicinity area. In this case, the precise docking control start determination unit  56  executes fault processing (S 38 ). For example, the precise docking control start determination unit  56  notifies occupants within the vehicle or a remotely located operator of a message indicating that the stop target  70  could not be recognized. 
     As described above, the driving control apparatus according to the present embodiment enables recognition of the stop target  70  using the LiDAR sensor  36  alone; that is, without using image recognition by the camera  34 . 
     The driving control apparatus according to the present embodiment is capable of both recognition of the stop target  70  by the camera  34  and recognition of the stop target  70  by the LiDAR sensor  36 . Therefore, in addition to the recognition of the stop target  70  by the LiDAR sensor  36  alone as illustrated in  FIG.  13   , the recognition of the stop target  70  by the camera  34  may also be used to increase the detection accuracy of the stop target  70 . 
     For example, the recognition of the stop target  70  by the LiDAR sensor  36  and the recognition of the stop target  70  by the camera  34  may be used under AND condition or OR condition. When the AND condition is used, for example, the fault processing described above may be executed if the stop target  70  cannot be recognized by at least one of the LiDAR sensor  36  or the camera  34 . On the other hand, when the OR condition is used, the precise docking control may be started if the stop target  70  can be recognized by at least one of the LiDAR sensor  36  or the camera  34 . 
     While, in the above example, the exposed face of the stop target  70  has a stripe configuration, this may raise a possibility that a passenger wearing a shirt with horizontal stripe patterns, for example, is confused with the stop target  70 . In such a case, cluster tracking of the automated driving technology is used to determine whether or not the cluster which is recognized as the stop target  70  is moving. If determined to not be moving, the cluster is determined to be the stop target  70 . A determination flow using such a tracking may be added to the precise docking control start determination flow illustrated in  FIG.  13   . 
     In addition to the stop target  70 , an auxiliary target may be disposed along the curb of the footpath. The auxiliary target, similar to the stop target  70 , may have a configuration which is recognizable by the LiDAR sensor  36 . Specifically, the auxiliary target may have a horizontal stripe configuration including repeated patterns of the high reflectance region  72  and the low reflectance region  74  which are disposed alternately in the vertical direction. 
     Recognizing the stop target  70  and the auxiliary target disposed along the curb enables recognition of the borderline between the footpath and the roadway. For example, a straight line connecting the stop target  70  and the auxiliary target can be used to recognize the curb which is the borderline between the footpath and the roadway. The present disclosure is not limited to the embodiments described above, and includes all changes and modifications without departing from the technical scope or the essence of the present disclosure defined by the claims.