Patent Publication Number: US-10789488-B2

Title: Information processing device, learned model, information processing method, and computer program product

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is based upon and claims the benefit of priority from Japanese Patent Application No. 2017-201786, filed on Oct. 18, 2017; the entire contents of which are incorporated herein by reference. 
     FIELD 
     Embodiments described herein relate generally to an information processing device, a learned model, an information processing method, and a computer program product. 
     BACKGROUND 
     A technique for estimating an obstacle, a road, and the like hidden in a position that is not observed by a sensor has been known. For example, a technique for estimating an object that can appear from a blind area on the basis of a position and a size of the blind area and the length of the border between the blind area and a non-blind area has been disclosed. In another disclosed technique, using a result of tracking other vehicles, a future travel direction and a trajectory of the past movement in the blind area are estimated, so that a position of a road where a vehicle can pass is estimated. Moreover, a technique of estimating a position of an object when the object that is observed once is included in a blind area by tracking the position of the object using time-series data has been disclosed. 
     However, in the conventional techniques, when the result of tracking other vehicles, is not used or the object is not included in the time-aeries data, it has been difficult to accurately estimate the object at a non-observation position. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram illustrating one example of a moving body; 
         FIG. 2  is an explanatory diagram of sensing by a sensor; 
         FIG. 3  is a block diagram illustrating one example of a structure of an information processing device; 
         FIG. 4  is an explanatory diagram illustrating one example of a data structure of observation information; 
         FIG. 5  is a schematic diagram illustrating one example of an actual space; 
         FIG. 6  is an explanatory diagram illustrating one example of a process performed by an object mapping unit; 
         FIG. 7  is an explanatory diagram illustrating one example of a process performed by a non-observation mapping unit; 
         FIG. 8  is an explanatory diagram illustrating one example in which a correction unit corrects correlation; 
         FIG. 9  is a flowchart illustrating one example of a procedure of information processing; 
         FIG. 10  is a block diagram illustrating one example of a moving body; 
         FIG. 11  is a flowchart illustrating one example of a procedure of an interrupting process; 
         FIG. 12  is a block diagram illustrating one example of a moving body; 
         FIG. 13  is a flowchart illustrating one example of a procedure of an interrupting process; 
         FIG. 14  is a block diagram illustrating one example of a moving body; 
         FIG. 15  is a block diagram illustrating one example of a moving body; and 
         FIG. 16  is a hardware structure diagram. 
     
    
    
     DETAILED DESCRIPTION 
     According to an embodiment, an information processing device includes one or more processors. The one or more processors is configured to acquire a map in which, for each of grids in a particular space, observation information representing object information on an object or the observation information representing non-observation information on non-observation of the object is correlated; and correct, for each of the grids, correlation of the observation information by using a learned model based on the observation information correlated with other peripheral grids. 
     With reference the attached drawings, an information processing device, a learned model, an information processing method, and a computer program product will hereinafter be described in detail. In embodiments and modifications below, components with the same function are denoted by the same reference symbol and the detailed description may be omitted. 
     First Embodiment 
       FIG. 1  is a diagram illustrating one example of a moving body  10  according to the present embodiment. 
     The moving body  10  includes an information processing device  20 , an output unit  10 A, a sensor  10 B, an input device  100 , a driving control unit  10 G, and a driving unit  10 H. 
     The information processing device  20  is, for example, a dedicated or general-purpose computer. In the example described in the present embodiment, the information processing device  20  is mounted on the moving body  10 . 
     The moving body  10  is a movable object. The moving body  10  is, for example, a vehicle (motorcycle, four-wheel vehicle, bicycle), a carriage, a robot, a ship, a flying object (such as aircraft, unmanned aerial vehicle (UAV), drone), a person, an animal, or the like. The moving body  10  is, specifically, a moving body that travels through a person&#39;s driving operation, or a moving body that can travel automatically (autonomous travel) without a person&#39;s driving operation. The moving body capable of the automatic travel is, for example, an automated driving vehicle. In the example described in the present embodiment, the moving body  10  is a vehicle capable of the autonomous travel. 
     Note that the information processing device  20  is not limited to a mode in which the information processing device  20  is mounted on the moving body  10 . The information processing device  20  may alternatively be mounted on a stationary object. The stationary object is an unmovable object or an object that stands still relative to the ground. Examples of the stationary object include a guardrail, a pole, a building, a road, a sidewalk, an obstacle, a stereoscopic object, a parked vehicle, and a road sign. The information processing device  20  may be mounted on a cloud server that executes processes on the cloud. 
     The sensor  10 B is one example of a sensing unit. The sensor  10 B is an external sensor, and acquires sensing information by sensing an external environment. 
     The sensor  10 B is, for example, a photographing device, a distance sensor (millimeter-wave radar, laser sensor), or the like. The photographing device obtains photograph image data (hereinafter referred to as a camera image) through photographing. The camera image data is digital image data in which a pixel value is defined for each pixel, a depth map in which the distance from the sensor  10 B is defined for each pixel, or the like. The laser sensor is, for example, a two-dimensional laser imaging detection and ranging (LIDAR) sensor or a three-dimensional LIDAR sensor set in parallel to a horizontal plane. 
     In the example described in the present embodiment, the sensor  10 B is a two-dimensional LIDAR sensor. 
       FIG. 2  as an explanatory diagram of sensing by the sensor  10 B. The sensor  10 B mounted on the moving body  10  emits laser light L along an observation plane around the sensor  10 B, and receives reflection light reflected on objects (targets) B. The observation plane is, for example, a horizontal plane. This enables the sensor  10 B to obtain the sensing information of a plurality of points  32  corresponding to a plurality of reflection points along an external shape of the objects B. Note that the position of the objects B, the number of objects B, the number of points  32 , and the positions of the points  32  in  FIG. 2  are just one example and are not limited to those illustrated therein. 
     Note that the sensor  10 B may emit one line of laser light L in a horizontal direction and receive the reflection light of the laser light L, or may emit a plurality of lines of laser light L and receive the reflection light of the laser light L. The sensor  10 B may emit the laser light L along a plane intersecting with the horizontal plane. 
     The sensing information represents the position of each of the plurality of points  32  around the sensor  108 . The position of the point  32  is represented by, for example, a position coordinate representing the relative position based on the sensor  10 B, a position coordinate representing the absolute position of the point.  32 , a vector, or the like. 
     Specifically, the position of the point  32  is represented by an azimuth direction corresponding to a direction in which the laser light is delivered based on the sensor  10 B, and the distance from the sensor  108 . That is to say, when the sensor  108  is a LIDAR, the sensing information is represented by the distance and the azimuth direction using the sensor  10 B as an origin in a polar coordinate space. The polar coordinate space is a space represented by a polar coordinate system. 
     The distance from the sensor  108  is derived from the time passed from the emission of the laser light L to the reception of the reflection light, the intensity of the received light, the attenuation ratio of the light, or the like. The detection intensity of the point.  32  is represented by, for example, the intensity of the reflection light, or the attenuation ratio of the light intensity. 
     When the sensing information is a camera image photographed by the sensor  10 B as a photographing camera, the position of the point  32  may be represented by a pixel position in the camera image or a position coordinate in a rectangular coordinate space. The rectangular coordinate space is a space represented by a rectangular coordinate system. 
     The output unit  10 A outputs various kinds of output information. The output unit  10 A includes a function of, for example, a communication function to transmit output information, a display function to display output information, a sound output function to output a sound representing output information, and the like. For example, the output, unit  10 A includes a communication unit  10 D, a display  10 E, and a speaker  10 F. 
     The communication unit  10 D transmits the output information to another device. For example, the communication unit  10 D transmits the output information through a known communication line. The display  10 E displays the output information. The display  10 E is, for example, a known liquid crystal display (LCD), a projection device, a light, or the like. The speaker  10 F outputs a sound representing the output information. 
     The input device  10 C receives various instructions and information input from a user. The input device  10 C is, for example, a pointing device such as a mouse or a track hall, or an input device such as a key board. 
     The driving unit  10 H is a device that drives the moving body  10 . The driving unit  10 H is, for example an engine, a motor, a wheel, or the like. 
     The driving control unit  10 G controls the driving unit  10 H. The driving unit  10 H is driven by the control of the driving control unit  10 G. For example, the driving control unit  10 G determines the peripheral circumstances on the basis of the output information output from the information processing device  20 , the information obtained from the sensor  10 B, and the like, and performs controls over an accelerating amount, a braking amount, a steering angle, and the like. For example, if it is estimated that a road exists in a blind area, the driving control unit  10 G controls the vehicle so that the speed is decreased around that road. 
     Next, detailed description is made of a structure of the information processing device  20 .  FIG. 3  is a block diagram illustrating one example of a structure of the information processing device  20 . 
     The information processing device  20  is, for example, a dedicated or general-purpose computer. The information processing device  20  includes a processing unit  20 A and a storage unit  20 B. 
     The processing unit  20 A, the storage unit  20 B, the output unit  10 A, the sensor  10 B, and the input device  10 C are connected through a bus  20 Z. Note that the storage unit  20 B, the output unit  10 A (communication unit  10 D, display  10 E, speaker  10 F), the sensor  10 B, and the input device  100  may be connected to the processing unit  20 A with or without a wire. At least one of the storage unit  20 B, the output unit  10 A (communication unit  100 , display  10 E, speaker  10 F), the sensor  10 B, and the input device  100  may be connected to the processing unit  20 A through a network. 
     The storage unit  20 B stores various kinds of data. The storage unit  20 B is, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, a hard disk, an optical disk, or the like. The storage unit  20 E may be a storage device provided outside the information processing device  20 . The storage unit  20 B may be a storage medium. Specifically, the storage medium may be a storage medium in which computer programs or various kinds of information obtained by downloading through a local area network (LAN), the Internet, or the like are stored or temporarily stored. The storage unit  20 B may include a plurality of storage media. The storage unit  20 B may be provided in a cloud server connected to the information processing device  20  through a network. 
     The processing unit  20 A includes a reception unit  20 C, a derivation unit  200 , an object mapping unit  20 E, a non-observation mapping unit  20 F, an acquisition unit  20 G, a correction unit  20 H, and an output control unit  20 I. 
     Each of these units (reception unit  20 C, derivation unit  20 D, object mapping unit  20 E, non-observation mapping unit  20 F, acquisition unit  20 G, correction unit  20 H, and output control unit  20 I) is achieved by, for example, one or a plurality of processors. For example, each of these units may be achieved by having a processor such as a central processing unit (CPU) executes computer programs, that is, by using software. Each of these units may alternatively be achieved by a processor such as a dedicated integrated circuit (IC), that is, by using hardware. Each of these units may be achieved by using both software and hardware. In the case of using a plurality of processors, each processor may achieve one of these units or two or more of these units. 
     The reception unit  20 C receives sensing information from the sensor  10 B. The reception unit  20 C outputs the received sensing information to the derivation unit  200 . For example, the sensor  10 B detects an external environment and outputs the sensing information to the reception unit  20 C at every predetermined timing. Every time the reception unit  20 C receives the sensing information, the reception unit  20 C outputs the received sensing information to the derivation unit  20 D sequentially. 
     The derivation unit  20 D derives observation information from the sensing information for each position in an actual space sensed by the sensor  10 B. The observation information is the information representing the observation result around the moving body  10  (sensor  10 B). 
       FIG. 4  is an explanatory diagram illustrating one example of a data structure of observation information  30 . The observation information  30  represents object information  30 A or non-observation information  30 B. 
     The object information  30 A is information do the object B. The object B exists in the external environment around the sensor  10 B, and is observed by the sensor  10 B. 
     The object B may be either a moving body or a stationary object. The definition of the moving object and the stationary object is as described above. The object B may be either a living thing or a non-living thing. The living thing is, for example, a person, an animal, a plant, or the like. The non-living thing is, for example, a vehicle, an object that can fly, a robot, a building, a guard rail, a road surface of a driveway, a sidewalk, or the like, a region in which travel is possible, an obstacle, or the like. The region in which travel is possible corresponds to a region in which the moving, body  10  can travel. The obstacle is an object that interrupts the travel. In the example described in the present embodiment, the object B is an obstacle. 
     In the present embodiment, the object information  30 A represents object presence information  30 C or object absence information  30 D. 
     The object presence information  30 C is information representing the presence of the object B. That is to say, the object presence information  30 C is the information representing that the object B is present. The object absence information  30 D is information representing the absence of the object B. That is to say, the object absence information  300  is the information representing that the object B is absent. Note that the object absence information  30 D is classified into a plurality of kinds by a process to be described below (the details will be described below). The object presence information  30 C may be information representing the presence of one object B or information representing the presence of a plurality of objects B. 
     The non-observation information  303  is information on the non-observation of the object. B. More specifically, the non-observation information  30 B is information representing that the sensing by the sensor  10 B has failed because the laser light L from the sensor  10 B does not reach. That is to say, the non-observation information  30 B is information representing that the presence or absence of the object B is unknown. The case in which the laser light L from the sensor  10 B does not reach corresponds to, for example, a case in which the place is in a blind area due to another object B, the place is out of the viewing angle of a camera, the place is at a position where the reflection light cannot be measured, or the place is out of a range where the sensor  10 B is sensible, or a case in which a reflector does not exist so that the reflection of the laser light L cannot be measured and the distance therefore cannot be measured. 
     The object presence information  30 C may be the information representing the attribute of the object B or the presence probability of the object B. 
     The derivation unit  20 D derives the observation information  30  for each position P in the actual space by using the sensing information representing the position of each of the plurality of points  32  around the sensor  10 B. 
       FIG. 5  is a schematic diagram illustrating one example of an actual space R around the sensor  10 B. The actual space R is a space represented by a rectangular coordinate system. For example, it is assumed that the sensor  10 B has sensed a group of points  32  on a surface of the object B in the actual space R. In this case, the derivation unit  20 D receives the sensing information representing the position of each of the points  32  in the actual space R from the sensor  10 B through the reception unit  200 . 
     Back to  FIG. 3 , the derivation unit  20 D specifies the point  32  representing the object B among the points  32  indicated by the sensing information. In the present embodiment, the derivation unit  200  determines the point  32  detected by the two-dimensional LIDAR (sensor  10 B) set in parallel to the horizontal plane as an obstacle with a certain height from the road surface, and specifies this point  32  as the point  32  representing the object B. Then, the derivation unit  20 D derives the object presence information  30 C representing the presence of the object B in regard to the position P of the specified point  32 . 
     In addition, in regard to the position P the laser light L has passed from the sensor  10 B to the object B in the actual space R, the derivation unit  20 D assumes that the object B as the obstacle blocking the laser light L does not exist and derives the object absence information  30 D representing that the object B is absent. 
     On the other hand, in regard to the position P in the actual space R farther than the point  32 , which is specified as the point that represents the presence of the object B, from the set position of the sensor  10 B, the derivation unit  20 D derives the non-observation information  30 B representing that the object B is not observed. That is to say, in regard to the position P farther than the object B from the sensor  10 B, the laser light L is blocked by the object B and does not reach (see laser light L′); therefore, the presence or absence of the object B is unknown. Therefore, the derivation unit  20 D derives the non-observation information  30 B in regard to the position P in the actual space R farther from the sensor  10 B than the point  32  that is specified as the point representing the presence of the object B. 
     Note that the derivation unit  20 D may derive the non-observation information  30 B by another method. For example, the derivation unit  20 D sets the detectable distance or angle range of the sensor  10 B in advance. Then, the derivation unit  20 D may derive the non-observation information  30 B in regard to the position P in the distance and angle range where the measurement has failed due to the absorption of the laser light L, for example. 
     In this manner, the derivation unit  20 D derives the observation information  30  for each position P in the actual space R. 
     Note that if the sensor  10 B is a photographing device, the sensing information obtained by the sensor  10 B a camera image. In this case, the derivation unit  20 D may derive the observation information  30  for each position P of the pixel included in the camera image by known template matching. In this case, the derivation unit  20 D may derive the observation information  30  for each position P of the pixel by using semantic segmentation in which the attribute of the object B is estimated for each pixel. Moreover, the derivation unit  20 D may derive the observation information  30  by estimating the distance from the sensor  10 B to the object B. To estimate the distance, the derivation unit  20 D performs a three-dimensional reconstruction by tracking the characteristic point in the camera image. 
     In the case of using the camera image as the sensing information, the derivation unit  20 D may derive the non-observation information  30 B in regard to the position P of the pixel where the template matching is not performed or the position P of the pixel where the attribute is not estimated. 
     Note that the observation information  30  may be the information in which the presence or absence of the object B is represented in binary. 
     As described above, in the present embodiment, the observation information  30  is the information representing the object presence information  30 C, the object absence information  30 D, or the non-observation information  30 B. Therefore, for example, the observation information  30  may be represented by continuous values of the presence probability of the object B or the like, or the attribute of the object B represented by a label of the semantic segmentation. The attribute is, for example, the kind of the object B. The kind of the object B may include, in addition to the definition of the object B described above, the information representing whether the object is the object B to be estimated. 
     Back to  FIG. 3 , the description is continued. The derivation unit  20 D derives the observation information  30  in this manner in regard to each position P in the actual space R. Then, the derivation unit  20 D outputs the observation information  30  at each position P in the actual space R to the object mapping unit  20 E and the non-observation mapping unit  20 F. 
     Next, the object mapping unit  20 E is described.  FIG. 6  is an explanatory diagram illustrating one example of a process performed by the object mapping unit  20 E. 
     The object mapping unit  20 E correlates the object information  30 A, which is represented by the observation information  30  received from the derivation unit  20 D, with corresponding grid G in a particular space S. That is, the object mapping unit  20 E correlates the object presence information  30 C or the object absence information  30 D with the grids G in the particular space S. 
     The particular space S is a space in which the actual space R is represented by a coordinate system. The coordinate system is a polar coordinate system or a rectangular coordinate system. In the example described in the present embodiment, the particular space S is a space in which the actual space R is represented by a polar coordinate system, that is, a polar coordinate space. The polar coordinate space is defined by the azimuth direction (arrow Y direction) using the sensor  10 B (information processing device  20 ) as an origin, and the distance direction from the sensor  10 B (arrow X direction) on an observation plane of the sensor  10 B. The azimuth direction represents the angle at which the laser light L is delivered, on the basis of the direction passing the sensor  10 B and orthogonal to the traveling direction of the moving body  10  including the sensor  10 B. 
     The grid G corresponds to each region obtained by dividing the particular space S into a plurality of regions. Specifically, the grid G is each region obtained by dividing the particular space S into the plurality of regions along coordinate axes of the particular space S. That is to say, the particular space S is represented by the plurality of grids G arrayed in two axial directions along the coordinate axes. Note that the coordinate axes are the coordinate axes of the rectangular coordinate or the polar coordinate. In the present embodiment, since the particular space S is the polar coordinate space as described above, the coordinate axes are the coordinate axes of the polar coordinate. 
     Note that  FIG. 6  illustrates one example in which the row direction represented by the array of the grids G and the azimuth direction (arrow Y direction) are the same and the column direction represented by the array of the grids C and the distance direction (arrow X direction) are the same. However, the present invention is not limited to this example. 
     In fact, the identification numbers of the rows and the columns represented by the array of grids C may be discrete values obtained by equally dividing each of the azimuth and the distance. That is to say, each grid G corresponds to the region around the moving body  10  (information processing device  20 ). 
     The shape of the grid G is not limited to a particular shape. In addition, the size of the grid G is not limited to a particular size. For example, the size of the grid G is more than or equal to the size of the point  32  obtained by the sensor  10 B (see  FIG. 2 ,  FIG. 5 ). Note that the size of the grid G may be adjusted as appropriate in accordance with the object B to be estimated. 
     Note that the size of the grid G may be either constant or different in the particular space S. For example, the grid G may have a larger size as the grid G is away from the sensor  10 B. In the particular space S represented by the polar coordinate space using the sensor  10 B as the origin, the grid G may be smaller in a particular angle range in which the sensor  10 B is the origin than in the angles other than that particular angle range. 
     The particular space S may be either a two-dimensional space or a three-dimensional space. When the particular space S is a three-dimensional space, the grids G may be arrayed in a three-dimensional direction (triaxial directions). 
     The object mapping unit  20 E specifies the observation information  30  representing the object information  30 A in the observation information  30  for each position P in the actual space R received from the derivation unit  20 D. Then, the object mapping unit  20 E correlates the object information  30 A with the grid G in the particular space S at the position P of the observation information  30  representing the object information  30 A. As described above, the object information  30 A represents the object presence information  30 C or the object absence information  30 D. Therefore, the object mapping unit  20 E correlates the object presence information  30 C or the object absence information  30 D as the object information  30 A with each grid G. By this correlation, the object mapping unit  20 E generates an object map M 1 . 
     The correlation by the object mapping unit  20 E is specifically described. For example, the object mapping unit  20 E correlates the position P in the actual space R and the grid G at the corresponding position in the map M for each direction (azimuth direction) in which the laser light L is delivered by the sensor  10 B. Specifically, the object mapping unit  20 E specifies, among the rows of the grids G in each azimuth direction of the laser light L by the sensor  10 B, the row where the object B has been sensed. The row of the grids G in each azimuth direction refers to the row including the grids G arrayed along the distance direction (arrow X direction) and each of the rows disposed along the azimuth direction (arrow Y direction). 
     Then, the object mapping unit  20 I correlates the object presence information  30 C with the grid G at the position P where the object B is sensed among the grids G constituting the specified row. The position P where the object B is sensed is the position P where the derivation unit  20 D has derived the object presence information  30 C. 
     The object mapping unit  20 I correlates the object absence information  30 D with the grid G positioned closer to the sensor  10 B than the grid G at the position P where the object B is sensed in the grids G constituting the specified row, because in this grid G, the object B does not exist and the laser light has passed. 
     In this manner, the object mapping unit  20 B correlates the object presence information  30 C or the object absence information  30 D with each row of grids G in each direction for each direction in which the laser light is delivered by the sensor  10 B. 
     In the present embodiment, the object mapping unit  20 E correlates the presence probability of the object B as the object information  30 A with the grid G. In the present embodiment, the presence probability of the object B is represented by values from “0.0” to “1.0”. A presence probability of “1.0” represents that the object B exists at the position of that grid G. A presence probability of “0.0” represents that the object B does not exist at the position of that grid G. As the presence probability is closer to “1.0”, it is more probable that the object B exists. As the presence probability is closer to “0.0”, it is less probable that the object B exists. 
     Specifically, the object mapping unit  20 E correlates a presence probability of “1.0” as the object presence information  30 C with the grid G at the position P where the object B is sensed. Moreover, the object mapping unit  20 E correlates a presence probability of “0.0” as the object absence information  30 D with the grid closer to the sensor  10 B than the grid G at the position P where the object B is sensed. 
     Next, in regard to the grid G with which the object absence information  300  with a presence probability of “0.0” is correlated, the object mapping unit  20 E adjusts the presence probability so that the presence probability decreases in a direction to the sensor  10 B from the grid G with which the object presence information  30 C with a presence probability of “1.0” is correlated. 
     That is to say, using as a center the grid G at the position P where the object B is sensed, the object mapping unit  20 E adjusts the presence probability so that the presence probability decreases as away from the center to the sensor  10 B in accordance with a Gauss distribution representing predetermined dispersion. 
     For example, the object mapping unit  20 E correlates the object absence information  30 D representing a presence probability of “0.5” with the grid G, among the grids G with which the object absence information  30 D is correlated, that is adjacent on the sensor  10 B side to the grid G with which the object presence information  30 C is correlated. Then, in regard to the grid G with which the object absence information  30 D is correlated other than the adjacent grid G, the object mapping unit  20 E correlates the object absence information  30 D representing a presence probability of “0.0”. 
     In the description below, the object absence information  30 D representing a presence probability of “0.0” may be referred to as object absence information  30 E. In addition, the object absence information  30 D representing a presence probability of “0.5” may be referred to as object absence information  30 F (see  FIG. 4 ). 
     Therefore, the object mapping unit  20 E correlates the object presence information  30 C representing a presence probability of “1.0” with the grid G at the position P where the object B is sensed. The object mapping unit  20 E correlates any of the object absence information  30 E representing a presence probability of “0.0” or the object absence information  30 F representing a presence probability of “0.5” with the grid G at the position P where the object absence information  30 D is derived in the particular space S. 
     In regard to the grids G constituting the row where the object B is not sensed among the rows of the grids G in the azimuth direction of the laser light L, the of mapping unit  20 E does not correlate the observation information  30 . 
     In the above correlation, the object mapping unit  20 E generates the object map M 1  in which the object presence information  30 C, the object absence information  30 E, or the object absence information  30 F is correlated with each grid G. 
     In this manner, the object mapping unit  20 E can adjust the sensing error of the distance in the sensor  10 B by correlating the grid G and the presence probability in accordance with the distance from the sensor  10 B as the object information  30 A. That is to say, at a position away from the object B by a distance corresponding to the sensing error of the sensor  10 B, it may be difficult to determine whether the object B exists because of the sensing error of the sensor  10 B. Therefore, in regard to the grids G around the grid G with which the object presence information  30 C is correlated among the grids G with which the object absence information  30 D is correlated, the object mapping unit  20 E preferably correlates the object absence information  30 F representing the intermediate presence probability between “0.0” and “1.0” (for example, “0.5”) as the object absence information  30 D. 
     Note that the object mapping unit  203  may correlate, in addition to the presence probability of the object B, at least one of binary information representing the presence or absence of the object B, the discrete value such as the number of times of sensing the object B, the presence probability of each of a plurality of different kinds of objects B, and the label or likelihood representing the attribute of the object B as the object information  30 A with the grids C in the particular space S. 
     The object mapping unit  20 B may generate the object map M 1  using a method different from the aforementioned one. For example, in regard to the grids G in the particular space S, the object mapping unit  205  derives from the observation information  30  the information on the plurality of laser light L passing the corresponding positions P in the actual space R. Then, the object mapping unit  20 E may calculate the presence probability of the object B for each grid G on the basis of the information on the plurality of laser light L, and correlate the presence probability with the grids G. 
     If the sensing information is the camera image, the object mapping unit  20 E calculates a formula used for a projective transformation between the photograph surface of the photographing device as the sensor  10 B that has obtained the sensing information, and the two-dimensional plane of the particular space S. By performing the projective transformation on the position P of each pixel in the camera image to the two-dimensional plane in the particular space S using the formula, the grid G at the position P may be specified and with this grid G, the object information  30 A may be correlated. 
     Next, the non-observation mapping unit  20 F is described.  FIG. 7  is an explanatory diagram illustrating one example of a process performed by the non-observation mapping unit  20 F. 
     The non-observation mapping unit  205  correlates the non-observation information  30 B, which is represented by, the observation information  30  received from the derivation unit  20 D, with the corresponding grid G in the particular space S. In other words, the non-observation mapping unit  20 F correlates the non-observation informs ion  30 B with the grid G at the position P where the non-observation information  30 B is derived as the observation information  30  among the positions P where the observation information  30  is derived. By this correlation, the non-observation mapping unit  20 F generates a non-observation map M 2 . 
     This causes the non-observation information  30 B to be correlated with the grid G at the position P where the object B is not observed by the sensor  10 B. 
     The non-observation mapping unit  20 F correlates the non-observation information  30 B with the grid G for each direction in which the laser light L is delivered by the sensor  10 B (azimuth direction), which is similar to the object mapping unit  20 I. 
     Specifically, the non-observation mapping unit  20 F specifies the row of grids G where the object B is sensed among the rows of grids G in the azimuth directions of the laser light L from the sensor  10 B. 
     The non-observation mapping unit  20 F correlates the non-observation information  30 B with the grid G positioned farther from the sensor  10 B than the grid G at the position P where the object B is sensed in the grids G constituting the specified row. 
     Here, as described above, the non-observation information  30 B is the information representing that the presence or absence of the object B is unknown. Therefore, for example, the non-observation mapping unit  20 F may correlate the grid G and the presence probability representing that whether the presence or absence of the object B is unknown as the non-observation information  30 B. In the present embodiment, the non-observation mapping unit  20 F correlates the intermediate value of the presence probability “0.5” as the non-observation information  30 B with the grid G at the position P where the derivation unit  20 D derives the non-observation information  30 B. 
     Note that in a manner similar to the object mapping unit  20 E, the non-observation mapping unit  20 F may generate the non-observation map M 2  using a method different from the aforementioned one. 
     The non-observation mapping unit  20 F may correlate with the grid G, a value other than “0.5”, which is more than “0.0” and less than “1.0”, as the intermediate value of the presence probability as the non-observation information  30 B. In addition, the non-observation mapping unit  20 F may correlate, in addition to the presence probability of the object B, other variable as the non-observation information  30 B with the grid G. 
     In this manner, the non-observation mapping unit  20 F generates the non-observation map M 2 . 
     Back to  FIG. 3 , the description is continued. Next, the acquisition unit  200  is described. The acquisition unit  200  acquires the map M. In the map M, the object information  30 A or the non-observation information  30 B on the object B represented by the observation information  30  is correlated with each grid G in the particular space S. Specifically, on the map M, the observation information  30  of any of the object presence information  30 C, the object absence information  30 E, the object absence information  30 F, and the non-observation information  30 B is correlated with each grid G in the particular space S. 
     In the present embodiment, the acquisition unit  20 G acquires the object map M 1  generated by the object mapping unit  20 E and the non-observation map M 2  generated by the non-observation mapping unit  20 G as the map M. Note that the acquisition unit  20 G acquires as the map M, a pair of the object map M 1  and the non-observation map M 2  that are generated based on the observation information  30  derived from the sensing information sensed at the same sensing timing. 
     Note that the object mapping unit  20 E and the non-observation mapping unit  20 F may generate the map M by correlating the object information  30 A and the non-observation information  30 B with each grid G in one particular space S. That is to say, the object mapping unit  20 E and the non-observation mapping unit  20 F may directly correlate the object information  30 A and the non-observation information  30 B with each grid G of the same map M. 
     In the case in which the particular space S is a two-dimensional space, the map M, may be either a two-dimensional space coinciding with the observation plane of the sensor  10 B or a two-dimensional space represented by a two-dimensional plane tilted relative to the observation plane. The map M may include a plurality of kinds of maps M with different resolutions of the grids G. 
     The acquisition unit  20 G outputs the acquired map M to the correction unit  20 H. 
     For each grid G in the map M, the correction unit  20 H corrects the correlation of the observation information  30  by using a learned model on the basis of the observation information  30  correlated with other peripheral grids G. 
     The correction of the correlation of the observation information  30  refers to the correction of the observation information  30  correlated with the grid G in the map M to another observation information  30  as illustrated in  FIG. 4 . For example, it is assumed that the non-observation information  30 B is correlated as the observation information  30  with a certain grid G. In this case, correcting the correlation means that the object presence information  30 C, the object absence information  30 E, or the object absence information  30 F is correlated with the grid G instead of the non-observation information  30 B. 
     The learned model is a model used in a correction process performed by the correction unit  20 H. In the present embodiment, for each grid G in the map M, the learned model corrects the correlation of the observation information  30  on the basis of the observation information  30  correlated with other peripheral grids G. In the map M, as described above, the observation information  30  representing the object information  30 A on the object B or the observation information  30  representing the non-observation information  30 B on the non-observation of the object is correlated with each grid G in the particular space S. 
     In the present embodiment, the processing unit  20 A generates the learned model in advance. 
     For example, the processing unit  20 A prepares a plurality of pairs of a map M (referred to as a first map) before the correction, in which the observation information  30  is correlated with each grid G, and a map M (referred to as a second map) after at least a part of the correlation of the observation information  30  is corrected. Then, the processing unit  20 A learns parameters of the model in advance for the input of the first map and the output of the second map. By this learning, the processing unit  20 A generates the learned model in which the learned parameters are set. 
     The processing unit  20 A uses, for example, a convolutional neural network (CNN) as a model. 
     The first map is the map in which the observation information  30  of any of the object information  30 A (object presence information  30 C, object absence information  30 E, or object absence information  30 F) and the non-observation information  30 B is correlated with each grid G. The second map is the map in which the object information  30 A is correlated with the grid G at the positions of at least a part of the grids G with which the non-observation information  30 B is correlated in the first map constituting the pair. 
     Note that the second map is preferably the map in which the object presence information  30 C as the object information  30 A is correlated with the grid G at the positions of at least a part of the grids G with which the non-observation information  30 B is correlated in the first map constituting the pair. 
     For example, the sensing information in time series of the sensor  10 B is held in advance, and the map in which the object information  30 A or the non-observation information  30 B on the basis of the sensing information sensed by the sensor  10 B at a single sensing timing is correlated with each grid G is used as the first map. As the second map, the map in which the object information  30 A or the non-observation information  30 B on the basis of the sensing information sensed by the sensor  10 B at a plurality of sensing timings is correlated with each grid G is used. Specifically, the processing unit  20 A correlates the object information  30 A or the non-observation information  30 B with the second map on the basis of the sensing information at the sensing timing before and after the sensing timing of the first map. 
     Note that the processing unit  20 A may use the map in which the sensing information till a certain time is correlated as the first map, and use the map in which the sensing information after that time is additionally correlated as the second map. Furthermore, the processing unit  20 A may use the map in which the sensing information of a certain sensor  10 B is correlated as the first map, and use the map in which the sensing information of another sensor  10 B is correlated as the second map. 
       FIG. 8  is an explanatory diagram illustrating one example in which the correction unit  20 H corrects the correlation. 
     As described above, for each grid G in the map M to be corrected, the correction unit  20 H corrects the correlation of the observation information  30  for the grid G by using the learned model on the basis of the observation information  30  correlated with other peripheral grids G. 
     Other peripheral grids G include other grids G disposed adjacently in the periphery of the grid G to be corrected in the map M. The grid G disposed adjacently in the periphery of the grid G to be corrected corresponds to the grid G disposed in contact with (adjacent to) the grid G to be corrected. 
     Note that other peripheral grids G include at least other grids G disposed adjacently in the periphery of the grid G to be corrected. Therefore, other peripheral grids G may include a plurality of other grids G arrayed successively in a direction away from the grid G adjacent to the grid G to be corrected. 
     For example, the correction unit  20 H inputs to the learned model, the map M in which the observation information  30  (any of object presence information  30 C, object absence information  30 E, object absence information  30 F, and non-observation information  30 B) is correlated with each of the grids G. By the input to this learned model, for each grid G, the correction unit  20 H derives the corrected observation information  30  (i.e., presence probability) in which the observation information of other peripheral grids G is considered. 
     For example, it is assumed that, as the observation information  30 , the presence probability representing the object presence information  30 C, the object absence information  30 E, the object absence information  30 F, or the non-observation information  30 B is correlated with each of the grids G in the map M. 
     In this case, the correction unit  20 H inputs the presence probability correlated with each grid G as the map M in the learned model. By the input to the learned model, the correction unit  20 H derives a corrected map M′ in which the presence probability after the correction is correlated with each grid G. 
     By the process described above, in regard to at least the grid G with which the non-observation information  30 B in the map M is correlated, the correction unit  20 H corrects the correlation of the observation information  30  on the basis of the observation information  30  of other peripheral grids G and the learned model. That is to say, the correction unit  20 H corrects the correlation of at least the grid G with which the non-observation information  30 B is correlated in the map M. 
     Therefore, in the map M before the correction, the correction unit  203  can correct the correlation so that the presence probability representing the observation information  30  other than the non-observation information  30 B (object information  30 A (object presence information  30 C, object absence information  30 D (object absence information  30 E, object absence information  30 F))) is newly correlated with at least a part of the grids G with which the non-observation information  30 B is correlated. 
     Furthermore, by correcting the correlation using the learned model, the correction unit  20 H can correct the correlation of the observation information  30  with the grid G in accordance with the result estimated from the distribution of the pair of the first map and the second map in the past. 
     As described above, in the present embodiment, the intermediate value of the presence probability “0.5” is correlated with the grid G as the non-observation information  30 B in the map M before the correction. 
     Therefore, the correction unit  20 H can correlate the object information  30 A representing the presence or absence of the object B (object presence information  30 C, object absence information  30 E) with the grid G at the position P where the presence of the object B can be estimated in the map M. The correction unit  20 H can correct the correlation so that the object absence information  30 F representing that the presence or absence of the object B is uncertain can be correlated with the grid G at the position P where the presence of the object B cannot be predicted. 
     Note that the correction unit  20 H may also correct the correlation of the grid G with which the object information  30 A is correlated in the map M. That is to say, in regard to the grid G with which the object information  30 A is correlated in the map M, the correction unit  20 H corrects the correlation of the observation information  30  by using the learned model on the basis of the observation information  30  of other peripheral grids G. 
     In this case, the correction unit  20 H preferably corrects the correlation of the observation information  30  while changing the parameter of the learned model so that the correction of the correlation of the grid G with which the object information  30 A is correlated is suppressed as compared with the correction of the correlation of the grid G with which the non-observation information  30 B is correlated. 
     For example, the difference between the presence probability before the correction and the presence probability after the correction is referred to as a correction amount. In this case, the correction unit  20 H may correct the presence probability so that the correction amount of the grid G with which the object information  30 A is correlated is smaller than that of the grid G with which the non-observation information  30 B is correlated. The correction unit  20 H may skip the correction of the correlation of the grid G with which the object information  30 A is correlated. 
     Back to  FIG. 3 , the description is continued. Next, the output control unit  20 I is described. The output control unit  20 I outputs the output information to at least one of the output unit  10 A and the driving control unit  10 G. 
     The output information is the information representing the map M after being corrected by the correction unit  20 H. For example, the output information is the map M after being corrected by the correction unit  20 H. 
     For example, the output control unit  20 I outputs the output information to the output unit  10 A. Having received the output information, the communication unit  10 D of the output unit  10 A transmits the output information to the external device or the like. For example, the display  10 E of the output unit  10 A displays the output information. In another example, the speaker  10 E of the output unit  10 A outputs a sound in accordance with the output information. The sound in accordance with the output information may be a voice representing the output information or a warning sound in accordance with the output information. 
     For example, the output control unit  20 I outputs the output information to the driving control unit  10 G. As described above, the driving control unit  10 G controls the driving unit  10 H of the moving body  10 . The driving control unit  10 G having received the output information determines the peripheral circumstances on the basis of the output information, the information obtained from the sensor  10 B, and the like, and controls the accelerating amount, the braking amount, the steering angle, and the like. For example, the driving control unit  10 G controls the vehicle so that the vehicle travels in the current lane avoiding the obstacle and keeps a predetermined distance or more from the preceding vehicle. 
     Specifically, the driving control unit  10 G controls the driving unit  10 H using the corrected map M. That is to say, the driving control unit  10 G controls the driving unit  10 H using the corrected map M also in regard to the position where the detection information of the sensor  10 B does not clarify the presence or absence of the object B. 
     For example, the driving control unit  10 G controls the speed of the moving body  10  using the corrected map M. Specifically, the driving control unit  10 G performs controls so that a dangerous region is specified from the corrected map M and the speed of the moving body  10  is decreased when the moving body  10  travels near that dangerous region. The dangerous region is, for example, a region in which the object B may exist in a blind area. 
     For example, in a case in which the grid G with which the object information  30 A is correlated exists in a region in which the plurality of grids G with which the non-observation information  30 S is correlated are successively disposed in the corrected map M, the driving control unit  10 G specifies the region including the grid G with which the object information  30 A is correlated, as the dangerous region. In another example, in a case in which the grid G with which the attribute representing the road or the space is correlated exists in a region in which a predetermined number or more of grids G with which the non-observation information  30 B is correlated are successively disposed, the driving control unit  10 G specifies the region including the grid G with which the attribute is correlated, as the dangerous region. 
     By such a control, the driving control unit  10 G can achieve the safe travel of the moving body  10 . 
     Note that the output control unit  20 I may cause the storage unit  20 B to store the output information. The output control unit  20 I may output the output information to another processing functional unit (for example, the function to determine the collision or predict the movement). 
     Next, one example of a procedure of the information processing performed by the processing unit  20 A is described.  FIG. 9  is a flowchart of one example of the procedure of the information processing performed by the processing unit  20 A. 
     First, the reception unit  20 C receives the sensing information from the sensor  10 B (step S 100 ). Next, from the sensing information received in step S 100 , the derivation unit  20 D derives the observation information  30  at each position P in the actual space R (step S 102 ). 
     Next, the object mapping unit  20 E correlates the object information  30 A with each grid G (performs mapping) (step S 104 ). At step S 104 , the object mapping unit  20 E correlates the object information  30 A with the grid G at the position P where the object information  30 A (object presence information  30 C or object absence information  30 D) is derived as the observation information  30  among the positions P where the observation information  30  is derived at step S 102 . By the process at step S 104 , the object map M 1  is generated. 
     Next, the non-observation mapping unit  20 F correlates the non-observation information  30 B (performs mapping) (step S 106 ). At step S 106 , the non-observation mapping unit  20 F correlates the non-observation information  30 B with the grid G at the position P where the non-observation information  30 B is derived as the observation information  30  among the positions P where the observation information  30  is derived at step S 102 . By the process in step S 106 , the non-observation map M 2  is generated. 
     Next, the acquisition unit  20 G acquires the object map M 1  generated at step S 104  and the non-observation map M 2  generated at step S 106  as the map M (step S 108 ). 
     Next, for each grid G in the map M acquired at step S 108 , the correction unit  20 H corrects the correlation of the observation information  30  by using the learned model on the basis of the observation information  30  correlated with other peripheral grids G (step S 110 ). 
     Next, the output control unit  20 I performs the output control to output the map M after being corrected at step S 110  to the output unit  10 A and the driving control unit  10 G (step S 112 ). Then, the present routine ends. 
     As described above, the information processing device  20  according to the present embodiment includes the acquisition unit  20 G and the correction unit  20 H. The acquisition unit  20 G acquires the map M. In the map M, the observation information  30  representing the object information  30 A on the object B or the observation information  30  representing the non-observation information  30 B on the non-observation of the object is correlated with each grid G in the particular space S. For each grid G, the correction unit  20 H corrects the correlation of the observation information  30  by using the learned model on the basis of the observation information  30  correlated with other peripheral grids G. 
     In this manner, for each grid G included in the map M, the information processing device  20  according to the present embodiment corrects the correlation of the observation information  30  on the basis of the observation information  30  correlated with other peripheral grids G and the learned model. Therefore, in regard to the grid G with which the observation information  30  representing the non-observation information  30 B on the non-observation of the object is correlated, the correlation of the observation information  30  can be corrected by using the learned model on the basis of the observation information  30  correlated with other peripheral grids G. 
     Here, conventionally, it has been difficult to accurately estimate the object at the non-observation position when the result of tracking other vehicle is not used or in regard to the object that has never been observed before. The non-observation position corresponds to the position where the laser light L from the sensor  105  does not reach and the sensing by the sensor  105  is impossible. That is to say, the non-observation position is the position where the presence or absence of the object B is unknown. 
     On the other hand, in regard to the grid G with which the observation information  30  representing the non-observation information  30 B on the non-observation of the object is correlated, the information processing device  20  according to the present embodiment can correct the correlation of the observation information  30  by using the learned model and the observation information  30  correlated with other peripheral grids G. 
     Therefore, in regard to the grid G at the non-observation position where the presence or absence of the object B is unknown, the information processing device  20  according to the present embodiment can perform the correction so that the object information  30 A or the non-observation information  30 B is correlated by using the observation information  30  correlated with other peripheral grids G or the learned model. 
     Therefore, the information processing device  20  according to the present embodiment can accurately predict the object B at the non-observation position. 
     Further, since the information processing device  20  according to the present embodiment can obtain the corrected map M, in addition to the advantageous effect, the efficient information that can be used in the prediction of a collision risk in a blind region or the early control can be provided. 
     In the present embodiment, moreover, the driving control unit  10 G controls the driving unit  10 H by using the corrected map M. 
     Here, in a case in which the driving control unit  10 G controls the driving of the driving unit  10 H without using the corrected map M, it has been difficult to secure the safety of the travel of the moving body  10  before the moving body  10  travels to the position where the object B, which has not been sensed by the sensor  103 , can be sensed. 
     On the other hand, the driving control unit  10 G of the information processing device  20  according to the present embodiment controls the driving unit  10 H by using the corrected map M as the output information. Therefore, the information processing device  20  according to the present embodiment can secure the safety of the travel of the moving body  10  because the risk can be avoided earlier. 
     Based on the corrected map M, the output control unit  20 I may generate the travel route that enables the moving body  10  to avoid the obstacle existing in a blind area or to smoothly travel on the road existing in the blind area. Then, the output control unit  20 I may output the generated travel route to the driving control unit  10 G as the output information. In this case, the driving control unit  10 G may control the driving unit  10 H so that the moving body  10  travels autonomously along the travel route. 
     The output control unit  20 I may predict the moving route of other moving body on the basis of the corrected map M. Then, the output control unit  20 I may output the predicted prediction moving route to the driving control unit  10 G as the output information. In this case, the driving control unit  10 G may control the driving unit  10 H on the basis of the prediction moving route so that the moving body  10  travels on the travel route avoiding the collision with other moving body. 
     Note that the present embodiment has described the example in which the correction unit  20 H outputs to the output control unit  20 I, the map M in which the correlation of the observation information  30  is correct d with each of the grids G included in the map M received from the acquisition unit  20 G. 
     However, for each grid G to be corrected, the correction unit  20 H may correct the correlation of the grid G to be corrected, by using the local map M in which other peripheral grids G are extracted. The correction unit  20 H may alternatively correct, for each grid G in the map M, the correlation in the order from the grid G disposed near the sensor  10 B to the grid G disposed at the far position. 
     Note that the present embodiment has described the example in which the learned CNN is used as the learned model. However, other model than CNN may be used as the learned model. 
     For example, the correction unit  20 H prepares a plurality of pairs of a first map and a second map, each map showing a local region, as a dictionary. Then, the correction unit  20 H selects from the dictionary, the pair that is the most similar to the local region extracted from the map M to be corrected. Then, the correction unit  20 H may correct the correlation by overlapping the selected pair of the first map and the second map on the map M to be corrected. 
     The correction unit  20 H may use the region in a certain range as the local region, or may use the region in a different range depending on the position in the map M to be corrected. 
     The correction unit  20 H may correct the correlation of the observation information  30  by approximating the grid G with which the object presence information  30 C is correlated in the map M to be corrected into a structure such as a line, and by extending the structure to the grid G with which the non-observation information  30 B is correlated in the map M. 
     For each grid G with which the non-observation information  30 B is correlated in the map M to be corrected, the correlation may be corrected by using the object information  30 A of the grid G with which the object presence information  30 C is correlated at the closest position. 
     First Modification 
     As described above, the coordinate system of the particular space S is a polar coordinate system, and in the map M, the observation information  30  is correlated with the grids G of the polar coordinate space. However, a rectangular coordinate system may be employed instead of the polar coordinate system of the particular space S. 
     In this case, the coordinate system of the map M coincides with the coordinate system of the actual space R. For example, by using the sensor  10 B (information processing device  20 ) as the origin, the map M is represented by the rectangular coordinate whose coordinate axes are the traveling direction of the moving body  10  including the sensor  10 B and the direction orthogonal to the traveling direction. 
     In this case, the correction unit  20 H or the output control unit  20 I may convert the corrected map M from the polar coordinate system to the rectangular coordinate system, and output the converted map M. 
     Alternatively, the object map M 1  and the non-observation map M 2  of the rectangular coordinate system may be generated by having the object mapping unit  20 E and the non-observation mapping unit  20 F correlate the observation information  30  with the particular space S of the rectangular coordinate system. 
     In this case, the object mapping unit  20 E converts the position P in the actual space R represented by the azimuth and the distance from the sensor  10 B, into the position P represented by the rectangular coordinate system. That is to say, the object mapping unit  20 E converts the position P, which is represented by the azimuth and the distance from the sensor  10 B, into the position P represented by the distance in the traveling direction using the sensor  10 B as the origin and the distance in the direction orthogonal to the traveling direction. Then, with the grid G at the position P after the conversion, that is, the grid G in the map M represented by the rectangular coordinate system, the object mapping unit  20 E correlates the object information  30 A at the corresponding position P before the conversion. 
     The object mapping unit  20 E may correlate the presence probability as the object absence information  30 D in consideration of the sensing error of the sensor  10 B. Specifically, by using the grid G, which is at the position P where the object B is sensed in the particular space S of the rectangular coordinate system (i.e., the rectangular coordinate space), as a center, the object mapping unit  20 E may correlate the presence probability so that the presence probability decreases as away from the grid G as the center. 
     Note that if the sensor  10 B is a LIDAR, the conversion from the polar coordinate system to the rectangular coordinate system as described above is necessary; however, if the sensor  10 B acquires the sensing information of the rectangular coordinate space such as the camera image, the above conversion is unnecessary. 
     The object mapping unit  20 E may similarly correlate the non-observation information  30 B with the grid G at the position P where the non-observation information  30 B is derived in the map M represented by the rectangular coordinate system. 
     Then, in a manner similar to the above embodiment, the correction unit  20 H may correct, for each grid G in the map M to be corrected, the correlation of the observation information  30  with the grid G on the basis of the observation information  30  correlated with other peripheral grids G and the learned model. 
     In the above embodiment, the map M represented by the polar coordinate system is used. Therefore, in the above embodiment, when the same number of grids G are used as other peripheral grids G between the grids G in the map M, the peripheral region used in the correction of the grid G away from the sensor  10 B by a predetermined distance or more in the actual space R has a different size. 
     On the other hand, in the present modification, the map M represented by the rectangular coordinate system is used. Therefore, the correction unit  20 H can perform the correction using, the regions with the same size in the actual space R as other peripheral grids G not depending on the distance from the sensor  10 B. 
     In the present modification, the map M in the rectangular coordinate system is used; therefore, the driving control unit  100  can perform the control while correlating the driving of the moving body  10  and the position in the map M easily. 
     Second Modification 
     As described above, in the above embodiment, the correction unit  20 H performs the process on the map M based on the sensing information sensed by the sensor  10 B at a single sensing timing. However, the correction unit  20 H may alternatively perform the process on the map M in which the sensing information at a plurality of sensing timings is correlated. 
     In the present modification, the acquisition unit  20 G acquires the map M including the object map M 1  and the non-observation map M 2  that are generated based on the observation information  30  derived from the sensing information sensed at the same sensing timing. The acquisition unit  20 G integrates the maps M that are sequentially generated in accordance with the sensing information received in time series, specifically integrates every two maps M into one map M along the time series, and then outputs the one map M to the correction unit  20 H. 
     For example, the acquisition unit  20 G integrates, with the map M generated at a certain timing (timing A), the object information  30 A correlated with the grid G in the map M generated at the next timing (timing B). 
     Note that with each grid G in the map M generated at the timing A, the object information  30 A or the non-observation information  30 B is already correlated by the object mapping unit  20 E or the non-observation mapping unit  20 F. Therefore, along the time change by the movement of the moving body  10  or the movement of the peripheral object B that can occur between the timing A and the timing B, contradiction may occur between the observation information  30  correlated with the grid G at the timing A and the observation information  30  correlated with the grid G at the timing B. 
     Therefore, the acquisition unit  20 G correlates with the grid G in the map M generated at the timing A, the observation information  30  in consideration of the observation information  30  correlated with each grid G in the map M generated at the timing A and the observation information  30  correlated with each grid G in the map M generated at the timing B. This process enables the acquisition unit  20 G to integrate these maps M into one. 
     Specifically, the acquisition unit  20 G correlates the object information  30 A with the grid G with which the object information  30 A is correlated, at the corresponding position in the map M at the timing B among the grids G with which the non-observation information  30 B is correlated in the map M at the timing A. 
     The acquisition unit  20 G holds the object information  30 A as the prior probability in regard to the grid G with which the object information  30 A is correlated in the map M at the timing A. Then, the acquisition unit  20 G calculates the posterior probability by using the observation information  30  correlated with the grid G at the corresponding position in the map M at the timing B. Then, the acquisition unit  20 G correlates the posterior probability with the corresponding grid G in the map M at the timing A. 
     The maps M may be integrated in a manner that the non-observation information  30 B is correlated with the grid G with which the observation information  30  is not correlated, without changing the observation information  30  in the grid G with which the observation information  30  is correlated in the map M at the timing A. 
     Then, the acquisition unit  20 G outputs the one map M, in which the two maps M are integrated, to the correction unit  20 H. 
     In the present modification, the maps M that are generated sequentially in accordance with the sensing information received in time series are integrated, specifically every two maps M are integrated into one map M along the time series, and the one map M is output to the correction unit  20 H. However, the acquisition unit  20 G may integrate every three maps M along the time series into one map M and output the one map M to the correction unit  20 H. 
     The acquisition unit  20 G may derive the correlation between the position of the grid G in the map M at the timing A and the position of the grid G in the map M at the timing B from the moving status of the moving body  10 , or may estimate the correlation by specifying the position of the same object B included in these two maps M. 
     The moving body  10  may include two sensors  10 B. In this case, the acquisition unit  20 G may integrate two maps N generated based on two pieces of detection information detected by the two sensors  10 B at the same timing into one map M. 
     In the above embodiment, the sensing information at a single sensing timing is correlated with the map M. Therefore, in the above embodiment, only the observation information  30  that can be observed from a certain position is correlated with the map M. 
     In the present modification, however, the sensing information at the plural sensing timings can be correlated with the map M. Therefore, in the present modification, the observation information  30  that can be observed at the different positions can be correlated. In addition, in the present modification, when the correction unit  20 H performs the correction, the correlation can be corrected based on more pieces of observation information  30 . Therefore, in the present modification, the object at the non-observation position can be estimated more accurately. 
     Second Embodiment 
     Next, description is made of an embodiment in which the parameter of the learned model is updated. 
       FIG. 10  is a block diagram illustrating one example of a moving body  11 B including an information processing device  21 B. The moving body  11 B is similar to the moving body  10  in the first embodiment except that the moving body  11 B includes the information processing device  21 B instead of the information processing device  20 . 
     The information processing device  21 B includes a processing unit  40  and a storage unit  20 B. The information processing device  21 B includes the processing unit  40  instead of the processing unit  20 A. 
     The processing unit  40  includes the reception unit  20 C, the derivation unit  20 D, the object mapping unit  20 E, the non-observation mapping unit  20 F, the acquisition unit  20 G, a correction unit  40 H, the output control unit  20 I, and a first update unit  40 J. 
     The processing unit  40  is similar to the processing unit  20 A in the first embodiment except that the processing unit  40  includes the correction unit  40 H instead of the correction unit  20 H and further includes the first update unit  40 J. 
     The correction unit  40 H is similar to the correction unit  20 H in the first embodiment except that the correction unit  40 H uses a learned model with a parameter updated bye the first update unit  40 J to correct the correlation. 
     The first update unit  40 J updates the parameter of the learned model on the basis of a plurality of maps M derived from a plurality of pieces of observation information  30  at different timings. 
     The plurality of pieces of observation information  30  at different timings is the observation information  30  at different timings that is derived from a plurality of pieces of sensing information sensed at different timings by the derivation unit  20 D. 
     The plurality of maps M derived from the plurality of pieces of observation information  30  at different timings are a plurality of pairs of the object map M 1  and the non-observation map M 2 , each pair being generated by the object mapping unit  20 E and the non-observation mapping unit  20 F at each timing on the basis of the observation information  30  at each timing. 
     The first update unit  40 J acquires the maps M sequentially from the object mapping unit  205  and the non-observation mapping unit  20 F. That is to say, the first update unit  40 J sequentially acquires as the maps M, the object map M 1  generated by the object mapping unit  20 E and the non-observation map M 2  generated by the non-observation mapping unit  20 F. This enables the first update unit  40 J to obtain the plural maps M generated based on each of a plurality of pieces of sensing information sensed at different timings. 
     Then, the first update unit  40 J updates the parameter of the learned model by using the two maps M in which the sensing timings of the sensing information used in the generation are different. Here, in this description, the map M generated based on the sensing information sensed at a first timing is a map M at the first timing. A timing after the first timing is a second timing. The map M generated based on the sensing information sensed at the second timing is a map M at the second timing. 
     Then, among the grids G with which the non-observation information  30 B is correlated in the map M at the first timing, the first update unit  40 J specifies the grid G with which the object information  30 A is correlated in the map M at the second timing. 
     Note that the positions of the grids G between the map M at the first timing and the map M at the second timing may be correlated with each other as follows. For example, the first update unit  40 J estimates the movement of the moving body  10  between the two timings, and based on the map M at the first timing, calculates the corresponding position in the map M at the second timing. The first update unit  40 J performs this calculation for all the grids G in the map M. 
     Then, for each grid G in the map M at the first timing, the first update unit  40 J derives the parameter for correcting the correlation of the observation information  30  from the observation information  30  correlated with other peripheral grids G to the observation information  30  correlated with the grid G at the corresponding position in the map M at the second timing. 
     Then the first update unit  40 J updates the parameter of the learned model used in the correction unit  40 H to the derived parameter, and outputs the updated parameter to the correction unit  40 H. 
     If the parameter is updated by the first update unit  40 J, the correction unit  40 H corrects the correlation of the observation information  30  by using the learned model including the updated parameter, in a manner similar to the correction unit  20 H in the first embodiment. 
     Note that the first update unit  40 J may output the difference between the parameter before the update and the parameter after the update that is derived newly, to the correction unit  40 H. In this case, the correction unit  40 H may update the parameter of the learned model by using the received difference, and then use the updated parameter to correct the correlation of the observation information  30 . 
     Note that the first update unit  40 J may use one map M or a plurality of maps M as the map M at the second timing. The plurality of maps M at the second timing may be specifically the maps M which are sensed at the timings different from the first timing and generated based on a-plurality of pieces of sensing information sensed at timings different from each other. In the case of using the maps H at the second timing, the first update unit  40 J may perform the process after integrating the maps M at the second tithing into one map M. 
     The first update unit  40 J may derive the parameter of the learned model by using a plurality of pairs of the map M at the first timing and the map M at the second timing. 
     Next, one example of the procedure of the information processing performed by the processing unit  40  is described. 
     The processing unit  40  performs a process similar to the process performed by the processing unit  20 A in the first embodiment (see  FIG. 9 ). However, the processing unit  40  performs an interrupting process illustrated in  FIG. 11 . 
       FIG. 11  is a flowchart illustrating one example of the procedure of the interrupting process performed by the processing unit  40 . The processing unit  40  performs the interrupting process illustrated in  FIG. 11  while the information processing is performed according to the procedure in  FIG. 9 . 
     First, the first update unit  40 J determines whether to update the parameter of the learned model (step S 200 ). If it is determined the parameter is not updated at step S 200  (No at step S 200 ), the present routine ends. If it is determined the parameter is updated at step S 200  (Yes at step S 200 ), the process advances to step S 202 . 
     At step S 202 , the first update unit  40 J updates the parameter of the learned model by using the map M at the first timing and the map M at the second timing (step S 202 ). 
     Next, the first update unit  40 J outputs the learned model with the updated parameter to the correction unit  403  (step S 204 ). Having received the learned model with the updated parameter, the correction unit  40 H corrects the correlation of the observation information  30  for each of the grids G in the map M by using the received learned model. Then, the present routine ends. 
     As described above, in the information processing device  21 B according to the present embodiment, the first update unit  40 J updates the parameter of the learned model on the basis of the maps M derived from the pieces of observation information  30  at different timings. 
     Here, if the parameter of the learned model is not updated and the environment shown in the map M used to generate the learned model and the environment in the actual correction are largely different, the correction accuracy of the correlation may deteriorate. On the other hand, in the information processing device  21 B according to the present embodiment, the first update unit  40 J updates the parameter of the learned model on the basis of the maps M derived from the pieces of observation information  30  at different timings. 
     Therefore, the information processing device  21 B according to the present embodiment can correct the correlation that is suitable to the environment in the correction. 
     Therefore, in the information processing device  21 B according to the present embodiment, the object B at the non-observation position can be estimated more accurately in addition to the effect of the first embodiment. 
     Third Embodiment 
     The present embodiment will describe an example in which a plurality of correction units are provided. 
       FIG. 12  is a block diagram illustrating one example of a moving body  11 C including an information processing device  21 C. Note that the moving body  11 C is similar to the moving body  10  according to the first embodiment except that the moving body  11 C includes the information processing device  21 C instead of the information processing device  20 . 
     The information processing device  21 C includes a processing unit  42  and the storage unit  20 B. The information processing device  21 C includes the processing unit  42  instead of the processing unit  20 A. 
     The processing unit  42  includes the reception unit  20 C, the derivation unit  20 D, the object mapping unit  20 E, the non-observation mapping unit  20 F, the acquisition unit  20 G, a selection unit  42 K, a correction unit  42 H, and the output control unit  20 I. 
     The processing unit  42  is similar to the processing unit  20 A in the first embodiment except that the processing unit  42  includes the correction unit  42 E instead of the correction unit  20 H, and further includes the selection unit  42 K. 
     The correction unit  42 H includes a plurality of correction units  42 A. In the present embodiment, the correction unit  42 H includes three correction units  42 A (correction units  42 A 1  to  42 A 3 ). It is only necessary that the correction unit  42 H includes a plurality of correction units  42 A (correction units  42 A 1  to  42 A 3 ), and the correction unit  42 H is not limited to the mode of including the three correction units  42 A. 
     The correction units  42 A are similar to the correction unit  20 H according to the first embodiment. However, the correction units  42 A correct the correlation using learned models with different parameters. That is to say, in the correction units  42 A, the learned models with the different parameters are set in advance. 
     The selection unit  42 K selects the correction unit  42 A to correct the correlation of the observation information  30  on the basis of selection information. Specifically, the selection unit  42 K selects one correction unit  42 A that corrects the correlation of the observation information  30  among the correction units  42 A (correction units  42 A 1  to  42 A 3 ). 
     The selection unit  42 K receives the selection information. For example, the selection unit  42 K receives the selection information from at least one of the external device, the sensor  10 B, and the driving control unit  10 G through the input device  10 C and the communication unit  10 D. 
     Then, the selection unit  42 K selects one correction unit  42 A among the correction units  42 A (correction units  42 A 1  to  42 A 3 ) in accordance with the selection information. 
     The selection information is the information used to determine which one of the correction units  42 A is selected. Specifically, the selection information is the information representing the selection condition such as the ambient environment, the weather, the period of time, the travel state, or the user&#39;s preference. These selection conditions are represented by the information detected by the sensor  10 B, the driving state such as the vehicle speed or the acceleration of the moving body  11 C, and the use status of a device such as a wiper, a headlight, or an air conditioner. 
     For example, the selection information is input by the user&#39;s operation through the input device  10 C. In this case, the selection unit  42 K receives the selection information from the input device  10 C. The selection information is, for example, the identification information of the particular correction unit  42 A or the information representing that the particular object B is estimated with priority. 
     If the selection information is the information detected by the sensor  10 B, the selection unit  42 K receives the selection information from the sensor  10 B. For example, if the sensor  10 B is the sensor that measures the vibration, the selection information is a result of measuring the vibration. If the sensor in is a position detection sensor that detects a position of the moving body  11 C, the selection information is the information representing the position of the moving body  11 C. If the sensor  10 B is an internal sensor, the selection information is the information representing the driving state such as the vehicle speed or the acceleration of the moving body  11 C, or the use status of a device such as a wiper, a headlight, or an air conditioner. 
     Then, upon the reception of the selection information, the selection unit  42 K selects one correction unit  42 A in accordance with the received selection information. 
     The selection unit  42 K correlates the selection information and the identification information of the correction unit  42 A, and stores the correlated information in the storage unit  20 B in advance. For example, it is assumed that the correction unit  42 K includes two correction units  42 A (correction unit  42 A 1  and correction unit  42 A 2 ). Then, the correction unit  42 A 1  performs the correction by using a learned model in which a parameter to correct the correlation in accordance with the sunny weather is set. The correction unit  42 A 2  performs the correction by using a learned model in which a parameter to correct the correlation in accordance with the rainy weather is set. 
     In this case, the selection unit  42 K correlates the selection information representing the use status of the wiper for the sunny weather, and the identification information of the correction unit  42 A 1 , and stores the correlated information in the storage unit  23 B in advance. The processing unit  42  correlates the selection information representing the use status of the wiper for the rainy weather, and the identification information of the correction unit  42 A 2 , and stores the correlated information in the storage unit  20 K in advance. 
     Then, upon the reception of the selection information representing the use status of the wiper, the selection unit  42 K may select one correction unit  42 A by reading the correction unit  42 A for the selection information from the storage unit  20 B. 
     The selection of the correction unit  42 A by the selection unit  42 K is not limited to the above method. For example, the selection unit  42 K may calculate a predetermined variable from the received selection information, and select one correction unit  42 A corresponding to the calculated variable. 
     In the correction unit  42 H, one correction unit  42 A selected by the selection unit  42 K corrects the correlation in a manner similar to the correction unit  20 H in the first embodiment. 
     Next, one example of the procedure of the information processing performed by the processing unit  42  is described. 
     The processing unit  42  performs a process similar to the process performed by the processing unit  20 A in the first embodiment (see  FIG. 9 ). However, the processing unit  42  performs an interrupting process illustrated in  FIG. 13 . 
       FIG. 13  is a flowchart illustrating one example of the procedure of the interrupting process performed by the processing unit  42 . The processing unit  42  performs the interrupting process illustrated in  FIG. 13  while the information processing is performed according to the procedure in  FIG. 9 . Note that the processing unit  42  may perform the procedure in  FIG. 13  between steps S 108  and step S 110  in the flowchart in  FIG. 9 . 
     First, the selection unit  42 K determines whether the selection information is received (step S 300 ). If it is determined that the selection information is not received at step S 300  (No at step S 300 ), the present routine ends. If it is determined that the selection information is received at step S 300  (Yes at step S 300 ), the process advances to step S 302 . 
     At step S 302 , the selection unit  42 K selects the correction unit  42 A in accordance with the selection information received at step S 300  (step S 302 ). At step S 302 , the selection unit  42 K instructs the selected correction unit  42 A to correct the correlation of the observation information  30  in the map M acquired by the acquisition unit  20 G. The correction unit  42 A having received the instruction of the correction performs a process similar to that of the correction unit  20 H in the first embodiment by using the learned model used in the correction unit  42 A in regard to the map M received from the acquisition unit  20 G. Then, the present routine ends. 
     As described above, the information processing device  21 C according to the present embodiment includes the correction units  42 A using the learned models with different parameters. The selection unit  42 K selects the correction unit  42 A that corrects the correlation of the observation information  30 . 
     Here, in a case in which the correction unit  42 H is one and the learned model with the fixed parameter is used, in the environment different from the environment in which the parameter is set, the correction accuracy of the correlation of the observation information  30  may deteriorate. On the other hand, in the information processing device  21 C according to the present embodiment, the selection unit  42 K selects the correction unit  42 A that corrects the correlation of the observation information  30  on the basis of the selection information. 
     Therefore, the correction unit  42 A is selected in accordance with the selection condition represented by the selection information. That is to say, the information processing device  21 C according to the present embodiment can correct the correlation in accordance with the selection information such as the ambient environment, the weather, the period of time, the travel state, or the user&#39;s preference. 
     Thus, the information processing device  21 C according to the present embodiment can estimate the object B at the non-observation position more accurately in addition to the effect of the above embodiment. 
     Fourth Embodiment 
     The present embodiment will describe an example in which a condition to specify the object B from the sensing information is updated. 
       FIG. 14  is a block diagram illustrating one example of a moving body  11 D including an information processing device  21 D. Note that the moving body  11 D is similar to the moving body  10  according to the first embodiment except that the moving body  11 D includes the information processing device  21 D instead of the information processing device  20 . 
     The information processing device  21 D includes a processing unit  44  and the storage unit  20 B. The information processing device  21 D includes the processing unit  44  instead of the processing unit  20 A. 
     The processing unit  44  includes the reception unit  20 C, a derivation unit  44 D, the object mapping unit  20 E, the non-observation mapping unit  20 F, the acquisition unit  20 G, the correction unit  20 H, the output control unit  20 I, a specification unit  44 L, and a second update unit  44 M. The processing unit  44  is similar to the processing unit  20 A in the first embodiment except that the processing unit  44  includes the derivation unit  440  instead of the derivation unit  20 D, and further includes the specification unit  44 L and the second update unit  44 M. 
     The specification unit  44 L specifies the grid G whose correlation is corrected from the non-observation information  30 B to the object information  30 A by the correction of the correction unit  20 H in the map M after being corrected by the correction unit  20 H. 
     The second update unit  44 M updates the condition that is used to determine the object B when the observation information  30  is derived from the sensing information on the basis of the map M after being corrected by the correction unit  20 H. 
     As described in the first embodiment, the derivation unit  44 D specifies the point  32  representing the object B among the points  32  represented by the sensing information. Then, the derivation unit  44 D derives the object presence information  30 C representing the presence of the object B in regard to the position P of the specified point  32 . 
     The second update unit  44 M updates the condition used in this determination of the object B on the basis of the map M after being corrected by the correction unit  20 H. 
     Specifically, the second update unit  44 M changes the condition used to determine the object B in regard to the position P in the actual space R for the grid G whose correlation is corrected by the correction unit  20 H from the non-observation information  30 B to the object information  30 A. For example, the second update unit  44 M changes the condition so that the derivation unit  44 D further receives the detection intensity of each point  32  from the reception unit  20 C without specifying all the observation points as the obstacle as the object B, and based on the detection intensity, the observation point more than or equal to a particular threshold is specified as the obstacle. 
     Specifically, the second update unit  44 M updates so as to decrease the threshold corresponding to one example of the condition used to determine the object B in regard to the position P in the actual space R for the grid G whose correlation is corrected from the non-observation information  30 B to the object presence information  30 C. That is to say, the second update unit  44 M updates the condition so that the object B is easily determined in regard to the position P. 
     On the other hand, the second update unit  44 M updates so as to increase the threshold corresponding to one example of the condition used to determine the object B in regard to the position P in the actual space R for the grid G whose correlation is corrected from the non-observation information  30 B to the object absence information  30 D. That is to say, the second update unit  44 M updates the condition so that the object B is less easily determined in regard to the position P. 
     If the sensing information is the camera image, the second update unit  44 M may change the condition to determine the object B used in the template matching of the camera image. The second update unit  44 B may change the method to a method of using another template. 
     Then, the derivation unit  44 D determines the object B using the changed condition in regard to the point  32  at the position P for the grid G whose correlation is corrected previously by the correction unit  20 H from the non-observation information  30 B to the object information  30 A among the points  32  represented by the sensing information. 
     For example, in some cases, the moving body  11 D or the object B around the moving body  11 D moves relative to the previous sensing timing of the sensor  10 B. In such cases, the non-observation position where the observation is not performed by the sensor  10 B at the previous sensing timing may be observed by the sensor  10 B at this sensing timing. 
     In the present embodiment, the second update unit  44 M updates the condition used to determine the object B by using the map M after being corrected by the correction unit  20 H. Therefore, the information processing device  21 D according to the present embodiment can estimate the object B stably and early by suppressing the non-detection or the over-detection of the object B when the reception unit  20 C receives the sensing information of a new detection timing from the sensor  10 B. 
     The method of updating the condition used to determine the object B is not limited to the above method. 
     For example, the second update unit  44 M may update so as to increase the threshold corresponding to one example of the condition used to determine the object B in regard to the position P in the actual space R for the grid G whose correlation is corrected from the non-observation information  30 B to the object presence information  30 C. That is to say, the second update unit  44 M may update the condition so that the object B is less easily determined in regard to that position. 
     The second update unit  44 M may update the condition so that the object B is not determined in regard to the position P in the actual space R for the grid G whose correlation is corrected from the non-observation information  30 B to the object information  30 A. 
     As described above, in the information processing device  21 D in the present embodiment, the specification unit  44 L specifies the grid G whose correlation is corrected by the correction unit  20 H from the non-observation information  30 B to the object information  30 A. The second update unit  44 M updates the condition used to determine the object B when the observation information  30  is derived from the sensing information on the basis of the map M after being corrected by the correction unit  20 H. 
     Here, when the condition used to determine the object B is fixed, the stable sensing of the object B may fail depending on the sensing environment. On the other hand, in the information processing device  21 D in the present embodiment, the condition used to determine the object B when the observation information  30  is derived from the sensing information is updated based on the map M after being corrected by the correction unit  20 H. 
     Therefore, in the information processing device  21 D according to the present embodiment, the more accurate observation information  30  can be derived and the correlation can be corrected more accurately than in the above embodiment. In the information processing device  21 D according to the present embodiment, the object B can be sensed early and stably even if the sensing environment varies. 
     Therefore, the information processing device  21 D according to the present invention, the object B at the non-observation position can be estimated more accurately in addition to the effect of the above embodiment. 
     Fifth Embodiment 
     In the above embodiment, the corrected map M is output as the output information. However, in addition to that, various pieces of information on the corrected map M may be output as the output information. 
       FIG. 15  is a block diagram illustrating one example of a moving body  11 E including an information processing device  21 E. Note that the moving body  11 E is similar to the moving body  10  according to the first embodiment except that the moving body  11 E includes the information processing device  21 E instead of the information processing device  20 . 
     The information processing device  21 E includes a processing unit  46  and the storage unit  20 B. The information processing device  21 E includes the processing unit  46  instead of the processing unit  20 A. 
     The processing unit  46  includes the reception unit  20 C, the derivation unit  20 D, the object mapping unit  20 E, the non-observation mapping unit  20 F, the acquisition unit  20 G, the correction unit  20 H, and an output control unit  46 I. The processing unit  46  is similar to the processing unit  20 A in the first embodiment except that the processing unit  46  includes the output control unit  46 I instead of the output control unit  20 I. 
     The output control unit  46 I outputs the output information to at least one of the output unit  10 A and the driving control unit  10 G in a manner similar to the output control unit  20 I in the first embodiment. 
     The first embodiment has described the case in which the output information is the map M after being corrected by the correction unit  20 H. However, it is only necessary that the output information is the information representing the map M after being corrected by the correction unit  20 H and the output information is not limited to the corrected map M. 
     For example, the output information may be the information representing the grid G whose correlation is corrected by the correction unit  20 H in the map M after being corrected by the correction unit  20 H. 
     The information representing the corrected grid G is, for example, the information representing at least one of the position of the grid G and the observation information  30  correlated by the correction of the grid G. 
     Specifically, the output control unit  46 I acquires the map M before being corrected by the correction unit  20 H from the acquisition unit  20 G. The output control unit  46 I acquires the map M corrected by the correction unit  20 H. Then, the output control unit  46 I specifies the grid G with which the non-observation information  30 B is correlated in the map M before the correction, among the grids G with which the object information  30 A is correlated in the corrected map M. Then, the output control unit  46 I outputs the information representing the grid G whose correlation is corrected from, the non-observation information  30 B to the object information  30 A in the corrected map M, as the output information. 
     Therefore, in the present embodiment, the output control unit  46 I can handle as the output information, only the grid G whose correlation is corrected from the non-observation information  30 B to the object information  30 A in the corrected map M. The output control unit  46 I does not output the grid G with which the non-observation information  30 B is correlated in the corrected map M and the grid G with which the object information  30 A is correlated in the map M before the correction. 
     Note that the output control unit  46 I may control at least one of the output unit  10 A (communication unit  10 D, display  10 E, and speaker  10 F) and the driving control unit  10 G so that the information representing the grid G whose correlation is corrected from the non-observation information  30 B to the object information  30 A in the corrected map M is output as the output information. 
     For example, in the case in which the output control unit  46 I controls the display  10 E so as to output an image representing the output information, the output information may be an overlapped image. The overlapped image is an overlapped image of the camera image of the actual space F corresponding to the map M and the information (for example, a reference symbol) representing the grid G whose correlation is corrected in the corrected map M. 
     In this case, the output control unit  46 I controls the display  10 E so that the overlapped image of the camera image of the actual space R corresponding to the map M and the reference symbol representing the grid G whose correlation is corrected in the corrected map M is displayed. 
     The output control unit  46 I may acquire the camera image of the actual space F from the sensor  10 B. The reference symbol representing the grid G with the corrected correlation is, for example, an icon, text information, or the like representing that the correlation is corrected. 
     The output control unit  46 I specifies the pixel position for each grid G in the corrected map M in the camera image, Then, the output control unit  46 I generates the overlapped image in which the reference symbol representing the corrected position is overlapped on the camera image corresponding to the grid G with the corrected correlation in the corrected map M. Then, the output control unit  46 I causes the display  10 E to display the overlapped image. 
     By displaying the overlapped image in the display  10 E, the area corresponding to the grid G with the corrected correlation can be emphasized on the camera image in the actual space R. 
     Note that the output information may be the information representing the region satisfying the predetermined condition in the map M after being corrected by the correction unit  20 H. The predetermined condition may be determined in advance. 
     For example, the region in which the predetermined condition is satisfied is the grid G with which the object absence information  30 D is correlated in the corrected map M. In addition, the region in which the predetermined condition is satisfied in a case in which the presence probability is correlated with the grid G in the corrected map M is the grid G with which the observation information  30  representing a presence probability of a predetermined value or more is correlated. 
     As described above, in the present embodiment, the output control unit  46 I outputs as the output information, the information representing the grid G whose correlation is corrected by the correction unit  20 H in the map M after being corrected by the correction unit  20 H. 
     Therefore, in the present embodiment, the output information with the change before and after the correction of the map M emphasized can be output in addition to the effect of the above embodiment. 
     Hardware Structure 
     Next, one example of the hardware structure of the information processing device  20 , the information processing device  21 B, the information processing device  21 C, the information processing device  21 D, and the information processing device  21 E in the above embodiments will be described.  FIG. 16  is a diagram illustrating one example of the hardware structure of the information processing device  20 , the information processing device  21 B, the information processing device  210 , the information processing device  21 D, and the information processing device  21 E in the above embodiments. 
     The information processing device  20 , the information processing device  21 B, the information processing device  21 C, the information processing device  21 D, and the information processing device  21 E in the above embodiments each include a control device such as a central processing unit (CPU)  86 , a storage devices such as a read only memory (ROM)  88 , a random access memory (RAM)  90 , or a hard disk drive (HDD)  92 , an I/F unit  82  corresponding to the interface with various devices, an output unit  80  that outputs various information such as the output information, an input unit  94  that receives the user&#39;s operation, and a bus  96  that connects those units, and include a hardware structure using a general computer. 
     In the information processing device  20 , the information processing device  21 B, the information processing device  21 C, the information processing device  21 D, and the information processing device  21 E in the above embodiments, the CPU  86  loads computer programs from the ROM  88  to the RAM  90  and executes the computer programs, so that each function is achieved on the computer. 
     Note that the computer programs for executing each process performed in the information processing device  20 , the information processing device  21 B, the information processing device  21 C, the information processing device  21 D, and the information processing device  21 E in the above embodiments may be stored in the HDD  92 . Alternatively, the computer programs for executing each process performed in the information processing device  20 , the information processing device  21 E, the information processing device  21 C, the information processing device  21 D, and the information processing device  21 E in the above embodiments may be provided by being incorporated in the ROM  88 . 
     The computer programs for executing each process performed in the information processing device  20 , the information processing device  21 B, the information processing device  210 , the information processing device  21 D, and the information processing device  21 E in the above embodiments may be stored in a computer-readable storage medium such as a CD-ROM, a CD-R, a memory card, a digital versatile disc (DVD), or a flexible disk (FD) in an installable or executable format and provided as a computer program product. Furthermore, the computer programed for executing each process performed in the information processing device  20 , the information processing device  21 B, the information processing device  21 C, the information processing device  21 D, and the information processing device  21 E in the above embodiments may be stored on a computer connected to a network such as the Internet and provided by being downloaded through the network. Moreover, the computer programs for executing each process performed in the information processing device  20 , the information processing device  21 B, the information processing device  21 C, the information processing device  21 D, and the information processing device  21 E in the above embodiments may be provided or distributed through the network such as the Internet. 
     While certain embodiments have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the inventions. Indeed, the novel embodiments described herein may be embodied in a variety of other forms; furthermore, various omissions, substitutions and changes in the form of the embodiments described herein may be made without departing from the spirit of the inventions. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the scope and spirit of the inventions.