Patent Publication Number: US-10759449-B2

Title: Recognition processing device, vehicle control device, recognition control method, and storage medium

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     Priority is claimed on Japanese Patent Application No. 2018-113053, filed Jun. 13, 2018, the content of which is incorporated herein by reference. 
     BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The present invention relates to a recognition processing device, a vehicle control device, a recognition control method, and a storage medium. 
     Description of Related Art 
     Conventionally, technology for recognizing an image or a moving image and converting the image or the like into a plain sentence (in other words, converting the image or the like into a sentence or a word string) has been disclosed (for example, Japanese Unexamined Patent Application, First Publication No. 2000-221991). According to the technology disclosed in Japanese Unexamined Patent Application, First Publication No. 2000-221991, an appropriate word string is estimated and provided by recognizing an image in which a person who is being shown uses sign language and outputting word string candidates representing natural language meanings represented by a sign language pattern of the person. 
     However, in the conventional technology, a natural language text sentence generation process capable of comprehensively representing an overall peripheral situation instead of a segmental word string representing a very small part of the peripheral situation has not been specifically considered. 
     An aspect of the present invention has been made in consideration of such circumstances and an objective of the present invention is to provide a recognition processing device, a vehicle control device, a recognition control method, and a storage medium capable of generating text for comprehensively predicting a change in a peripheral situation of a vehicle. 
     SUMMARY OF THE INVENTION 
     A recognition processing device, a vehicle control device, a recognition control method, and a storage medium according to the present invention adopt the following configurations. 
     (1): According to an aspect of the present invention, there is provided a recognition processing device including: a peripheral situation recognition unit configured to recognize a type of object around a vehicle and a positional relationship with the vehicle; an object recognition unit configured to select a word indicating the type of object recognized by the peripheral situation recognition unit and a word indicating a positional relationship between the vehicle and the object; and a text generation unit configured to generate text for describing a peripheral situation recognized by the peripheral situation recognition unit, wherein the text includes the word indicating the type of object selected by the object recognition unit and the word indicating the positional relationship between the vehicle and the object selected by the object recognition unit and is generated in accordance with a prescribed grammar. 
     (2): In the aspect (1), the text generation unit sets priority by identifying the object having a large influence on the vehicle among objects and preferentially includes the word for describing the peripheral situation including the object with high priority in the text. 
     (3): In the aspect (1), the text generation unit sets priority by identifying the object having a large influence on the vehicle among objects and causes the text for describing the peripheral situation including the object with high priority to be preferentially disposed in a higher-order paragraph among paragraphs constituting the text. 
     (4): In the aspect (1), the text generation unit sets priority by identifying a positional relationship between a plurality of objects having a large influence on the vehicle among objects and preferentially includes the word for describing the peripheral situation including a positional relationship between the objects having high priority in the text. 
     (5): According to an aspect of the present invention, there is provided a vehicle control device including: the recognition processing device according to any one of (1) to (4); a text analysis unit configured to predict a change in a peripheral situation on the basis of the text; and a driving control unit configured to execute driving control for controlling one or both of steering and acceleration/deceleration of the vehicle on the basis of the text generated by the text generation unit of the recognition processing device. 
     (6): In the aspect (5), the text analysis unit analyzes whether the text is text indicating the peripheral situation that is able to occur due to an attribute of the object, outputs the text generated by the text generation unit when it is analyzed that the text is text indicating the peripheral situation that is able to occur due to the attribute of the object, and discards the text generated by the text generation unit when it is analyzed that the text is not text indicating the peripheral situation that is able to occur due to the attribute of the object. 
     (7): According to an aspect of the present invention, there is provided a recognition control method including: recognizing, by a computer, a type of object around a vehicle and a positional relationship with the vehicle; selecting, by the computer, a word indicating the recognized type of object and a word indicating a positional relationship between the vehicle and the object; and generating, by the computer, text for describing a recognized peripheral situation, wherein the text includes the selected word indicating the type of object and the word indicating the positional relationship between the vehicle and the object and is generated in accordance with a prescribed grammar. 
     (8): According to an aspect of the present invention, there is provided a computer-readable non-transitory storage medium storing a program for causing a computer to: recognize a type of object around a vehicle and a positional relationship with the vehicle; select a word indicating the recognized type of object and a word indicating a positional relationship between the vehicle and the object; and generate text for describing a recognized peripheral situation, wherein the text includes the selected word indicating the type of object and the word indicating the positional relationship between the vehicle and the object and is generated in accordance with a prescribed grammar. 
     According to the aspects (1) to (8), it is possible to generate text for comprehensively predicting a change in a situation around a vehicle. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a configuration diagram of a vehicle control device according to an embodiment. 
         FIG. 2  is a functional configuration diagram of a first control unit and a second control unit. 
         FIG. 3  is a diagram for describing a process of a peripheral situation recognition unit. 
         FIG. 4  is a diagram for describing a process of an object recognition unit. 
         FIG. 5  is a diagram showing an example of an object recognized as a traffic participant or a road environment by a peripheral situation recognition unit. 
         FIG. 6  is a flowchart showing an example of a flow of a labeling process of the object recognition unit. 
         FIG. 7  is a diagram showing an example of a traffic scene selection rule of the traffic scene selection unit. 
         FIG. 8  is a flowchart showing an example of a flow of a traffic scene selection process of the traffic scene selection unit; 
         FIG. 9  is a diagram for describing a text generation process of a text generation unit. 
         FIG. 10  is a flowchart showing an example of a flow of the text generation process of the text generation unit. 
         FIG. 11  is a diagram for describing priority of the text generation process of the text generation unit. 
         FIG. 12  is a diagram for describing processes of the peripheral situation recognition unit and an object recognition unit  134 . 
         FIG. 13  is a diagram for describing the text generation process of the text generation unit. 
         FIG. 14  is another diagram for describing processes of the peripheral situation recognition unit and the object recognition unit  134 . 
         FIG. 15  is a diagram for describing the text generation process of the text generation unit. 
         FIG. 16  is a flowchart showing an example of a flow of a process in which the text generation unit reflects the priority in text generation. 
         FIG. 17  is a diagram showing an example of a hardware configuration of an automated driving control device according to an embodiment. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Hereinafter, embodiments of a recognition processing device, a vehicle control device, a recognition control method, and a storage medium of the present invention will be described with reference to the drawings. 
     [Overall Configuration] 
       FIG. 1  is a configuration diagram of a vehicle control device  1  according to the embodiment. A vehicle equipped with the vehicle control device  1  is, for example, a vehicle such as a two-wheeled vehicle, a three-wheeled vehicle, or a four-wheeled vehicle, and a driving source thereof is an internal combustion engine such as a diesel engine or a gasoline engine, an electric motor, or a combination thereof. The electric motor operates using electric power generated by a power generator connected to the internal combustion engine, or discharge power of a secondary battery or a fuel cell. 
     The vehicle control device  1  includes, for example, a camera  10 , a radar device  12 , a finder  14 , a physical object recognition device  16 , a communication device  20 , a human machine interface (HMI)  30 , a vehicle sensor  40 , a navigation device  50 , a map positioning unit (MPU)  60 , a driving operating element  80 , an automated driving control device  100 , a traveling driving force output device  200 , a brake device  210 , and a steering device  220 . These devices and apparatuses are connected to each other by a multiplex communication line such as a controller area network (CAN) communication line, a serial communication line, a wireless communication network, or the like. Also, the configuration illustrated in  FIG. 1  is merely an example, and a part of the configuration may be omitted or other components may be further added. 
     For example, the camera  10  is a digital camera using a solid-state imaging device such as a charge coupled device (CCD) or a complementary metal oxide semiconductor (CMOS). The camera  10  is attached to any position on the host vehicle M on which the vehicle control device  1  is mounted. When a view in front thereof is imaged, the camera  10  is attached to an upper portion of a front windshield, a rear surface of a rearview mirror, or the like. For example, the camera  10  periodically and iteratively images the vicinity of the host vehicle M. The camera  10  may be a stereo camera. 
     The radar device  12  radiates radio waves such as millimeter waves around the host vehicle M and detects at least a position (a distance to and a direction) of a physical object by detecting radio waves (reflected waves) reflected by the physical object. The radar device  12  is attached to any position on the host vehicle M. The radar device  12  may detect a position and a speed of the physical object in a frequency modulated continuous wave (FM-CW) scheme. 
     The finder  14  is a light detection and ranging (LIDAR) finder. The finder  14  radiates light in the vicinity of the host vehicle M and measures scattered light. The finder  14  detects a distance to an object on the basis of a time from light emission to light reception. The radiated light is, for example, pulsed laser light. The finder  14  is attached to any position on the host vehicle M. 
     The physical object recognition device  16  performs a sensor fusion process on detection results from some or all of the camera  10 , the radar device  12 , and the finder  14  to recognize a position, a type, a speed, and the like of a physical object. The physical object recognition device  16  outputs recognition results to the automated driving control device  100 . The physical object recognition device  16  may output detection results of the camera  10 , the radar device  12 , and the finder  14  to the automated driving control device  100  as they are. The physical object recognition device  16  may be omitted from the vehicle control device  1 . 
     The communication device  20  communicates with other vehicles present in the vicinity of the host vehicle M using, for example, a cellular network, a Wi-Fi network, Bluetooth (registered trademark), dedicated short range communication (DSRC), or the like or communicates with various types of server devices via a wireless base station. 
     The HMI  30  presents various types of information to an occupant within the host vehicle M and receives an operation input by the occupant. The HMI  30  includes various types of display devices, a speaker, a buzzer, a touch panel, a switch, keys, and the like. 
     The vehicle sensor  40  includes a vehicle speed sensor configured to detect the speed of the host vehicle M, an acceleration sensor configured to detect acceleration, a yaw rate sensor configured to detect an angular speed around a vertical axis, a direction sensor configured to detect a direction of the host vehicle M, or the like. 
     For example, the navigation device  50  includes a global navigation satellite system (GNSS) receiver  51 , a navigation HMI  52 , and a route determination unit  53 . The navigation device  50  stores first map information  54  in a storage device such as a hard disk drive (HDD) or a flash memory. The GNSS receiver  51  identifies a position of the host vehicle M on the basis of a signal received from a GNSS satellite. The position of the host vehicle M may be identified or corrected by an inertial navigation system (INS) using an output of the vehicle sensor  40 . The navigation HMI  52  includes a display device, a speaker, a touch panel, keys, and the like. The navigation HMI  52  may be partly or wholly shared with the above-described HMI  30 . For example, the route determination unit  53  determines a route (for example, a route on a map) from the position of the host vehicle M identified by the GNSS receiver  51  (or any input position) to a destination input by the occupant using the navigation HMI  52  with reference to the first map information  54 . The first map information  54  is, for example, information in which a road shape is expressed by a link indicating a road and nodes connected by a link. The first map information  54  may include a curvature of a road, point of interest (POI) information, and the like. The route on the map is output to the MPU  60 . The navigation device  50  may perform route guidance using the navigation HMI  52  on the basis of the route on the map. For example, the navigation device  50  may be implemented by a function of a terminal device such as a smartphone or a tablet terminal owned by the occupant. The navigation device  50  may transmit a current position and a destination to a navigation server via the communication device  20  and acquire a route equivalent to the route on the map from the navigation server. 
     For example, the MPU  60  includes a recommended lane determination unit  61  and stores second map information  62  in a storage device such as an HDD or a flash memory. The recommended lane determination unit  61  divides the route on the map provided from the navigation device  50  into a plurality of blocks (for example, divides the route every 100 [m] with respect to a traveling direction of the vehicle), and determines a recommended lane for each block with reference to the second map information  62 . The recommended lane determination unit  61  determines on what lane numbered from the left the vehicle will travel. The recommended lane determination unit  61  determines the recommended lane so that the host vehicle M can travel along a reasonable traveling route for traveling to a junction destination when there is a junction in a route on the map. 
     The second map information  62  is map information which has higher accuracy than the first map information  54 . For example, the second map information  62  includes information about a center of a lane or information about a boundary of a lane. Also, the second map information  62  may include road information, traffic regulations information, address information (an address/zip code), facility information, telephone number information, and the like. The second map information  62  may be updated as needed when the communication device  20  communicates with other devices. 
     For example, the driving operating element  80  includes an accelerator pedal, a brake pedal, a shift lever, a steering wheel, a variant steer, a joystick, and other operating elements. A sensor configured to detect an amount of operation or the presence or absence of an operation is attached to the driving operating element  80 , and a detection result thereof is output to the automated driving control device  100  or one or all of the traveling driving force output device  200 , the brake device  210 , and the steering device  220 . 
     For example, the automated driving control device  100  includes a first control unit  120  and a second control unit  160 . For example, each of the first control unit  120  and the second control unit  160  is implemented by a hardware processor such as a central processing unit (CPU) executing a program (software). Some or all of the functional elements thereof are implemented, for example, by hardware (a circuit unit including circuitry) such as large scale integration (LSI), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a graphics processing unit (GPU) or may be implemented by cooperation between software and hardware. The program may be pre-stored in a storage device such as an HDD or a flash memory of the automated driving control device  100  and may be stored in a removable storage medium such as a DVD or a CD-ROM and installed in the storage device such as the HDD or the flash memory of the automated driving control device  100  when the storage medium is mounted in a drive device. 
       FIG. 2  is a functional configuration diagram of the first control unit  120  and the second control unit  160 . The first control unit  120  includes, for example, a recognition unit  130  and an action plan generation unit  140 . For example, the first control unit  120  implements a function based on artificial intelligence (AI) and a function based on a previously given model in parallel. For example, an “intersection recognition” function may be implemented by executing intersection recognition based on deep learning or the like and recognition based on previously given conditions (signals capable of pattern matching, road signs, or the like) in parallel and performing comprehensive evaluation by assigning scores to both the recognitions. Thereby, the reliability of automated driving is secured. 
     For example, the recognition unit  130  includes a peripheral situation recognition unit  132 , an object recognition unit  134 , a traffic scene selection unit  136 , and a text generation unit  138 . The peripheral situation recognition unit  132  recognizes states of a position, a speed, acceleration, and the like of a physical object present in the vicinity of the host vehicle M on the basis of information input from the camera  10 , the radar device  12 , and the finder  14  via the physical object recognition device  16 . For example, the position of the physical object is recognized as a position on an absolute coordinate with a representative point (a center of gravity, a driving shaft center, or the like) of the host vehicle M as the origin and is used for control. The position of the physical object may be represented by a representative point such as a center of gravity or a corner of the physical object or may be represented by a represented region. The “state” of a physical object may include acceleration or jerk of the physical object or an “action state” (for example, whether or not a lane change is being made or intended). 
     For example, the peripheral situation recognition unit  132  recognizes a lane (a traveling lane) in which the host vehicle M is traveling. For example, the peripheral situation recognition unit  132  recognizes the traveling lane by comparing a pattern of a road dividing line (for example, an arrangement of solid lines and broken lines) obtained from the second map information  62  with a pattern of a road dividing line in the vicinity of the host vehicle M recognized from an image captured by the camera  10 . The peripheral situation recognition unit  132  may recognize a host vehicle line or an adjacent lane by recognizing a traveling path boundary (a road boundary) including a road dividing line, a road shoulder, a curb stone, a median strip, a guardrail, or the like as well as a road dividing line. In this recognition, a position of the host vehicle M acquired from the navigation device  50  or a processing result of the INS may be added. The peripheral situation recognition unit  132  recognizes a stop line, an obstacle, red signal light, a toll gate, and other road events. 
     When the traveling lane is recognized, the peripheral situation recognition unit  132  recognizes a relative position or orientation of the host vehicle M with respect to the traveling lane. For example, the peripheral situation recognition unit  132  may recognize a deviation of a reference point of the host vehicle M from the center of the lane and an angle formed with respect to a line connecting the center of the lane in the traveling direction of the host vehicle M as a relative position and an orientation of the host vehicle M with respect to the traveling lane. Instead, the peripheral situation recognition unit  132  may recognize a position of the reference point of the host vehicle M relative to one side end portion (a road dividing line or a road boundary) of the traveling lane as a relative position of the host vehicle M with respect to the traveling lane. 
     The peripheral situation recognition unit  132  separately recognizes a traveling lane, a traveling road boundary, a roadside zone, a sidewalk (hereinafter referred to as a road environment) and traffic participants (other vehicles, bicycles, pedestrians, and the like) around the host vehicle M. The peripheral situation recognition unit  132  distinguishes, for example, whether the recognized object is a traffic participant or a road environment on the basis of a relative speed of the recognized object with respect to the host vehicle M in the same direction as the traveling direction of the host vehicle M. The peripheral situation recognition unit  132  recognizes types of road environment and traffic participant around the vehicle and a positional relationship between the recognized road environment or traffic participant and the host vehicle M. The peripheral situation recognition unit  132  may derive the positional relationship between the traffic participants on the basis of the positional relationship between the recognized road environment or traffic participant and the host vehicle M. 
     The peripheral situation recognition unit  132  may distinguish that the recognized object is a physical object when the relative speed of the recognized object is different from that of the host vehicle M or changed and may distinguish that the recognized object is a road environment when the relative speed of the recognized object is substantially same as that of the host vehicle M. Accordingly, the peripheral situation recognition unit  132  recognizes a stop line, a crosswalk, a road sign, a traffic sign, an obstacle, red signal light, a toll gate, and other road events as traffic participants instead of the road environment. For example, the peripheral situation recognition unit  132  identifies a type of traffic participant on the basis of an identifier stored in a peripheral situation storage unit  132 A. The identifier is software (a data structure) generated in advance according to machine learning such as deep learning using images of various types of traffic participants and the like as teacher data and outputs whether or not the recognized object is a traffic participant and a type thereof when an image, an image feature quantity, and data of another identification object are input. An object recognition unit  134 , a traffic scene selection unit  136  and a text generation unit  138  will be described below. 
     The action plan generation unit  140  generates a future target trajectory along which the host vehicle M automatically travels (independently of a driver&#39;s operation) so that the host vehicle M generally travels in the recommended lane determined by the recommended lane determination unit  61  and further copes with a peripheral situation of the host vehicle M. For example, the target trajectory includes a speed element. For example, the target trajectory is represented by sequentially arranging points (trajectory points) at which the host vehicle M is required to arrive. The trajectory point is a point where the host vehicle M is required to reach for each prescribed traveling distance (for example, about several meters [m]) along a road. In addition, a target speed and target acceleration for each prescribed sampling time (for example, about several tenths of a second [sec]) are generated as parts of the target trajectory. The trajectory point may be a position at which the host vehicle M is required to arrive at the sampling time for each prescribed sampling time. In this case, information about the target speed or the target acceleration is represented by an interval between the trajectory points. 
     The action plan generation unit  140  may set an automatic driving event when the target trajectory is generated. The automatic driving event includes a constant-speed traveling event, a low-speed following traveling event, a lane change event, a junction event, an interchange event, a takeover event and the like. The action plan generation unit  140  generates a target trajectory according to the activated event. 
     The action plan generation unit  140  includes, for example, a text analysis unit  142 . The text analysis unit  142  will be described below. 
     The second control unit  160  controls the traveling driving force output device  200 , the brake device  210 , and the steering device  220  so that the host vehicle M passes through the target trajectory generated by the action plan generation unit  140  at a scheduled time. 
     The second control unit  160  includes, for example, an acquisition unit  162 , a speed control unit  164 , and a steering control unit  166 . The acquisition unit  162  acquires information about a target trajectory (an orbit point) generated by the action plan generation unit  140 , and causes the information to be stored in a memory (not shown). The speed control unit  164  controls the traveling driving force output device  200  or the brake device  210  on the basis of the speed element associated with the target trajectory stored in the memory. The steering control unit  166  controls the steering device  220  in accordance with a degree of bending of the target trajectory stored in the memory. For example, processes of the speed control unit  164  and the steering control unit  166  are implemented by a combination of feed-forward control and feedback control. As one example, the steering control unit  166  combines and executes feed-forward control according to the curvature of the road in front of the host vehicle M and feedback control based on a deviation from the target trajectory. 
     Returning to  FIG. 1 , the traveling driving force output device  200  outputs a traveling driving force (a torque) to driving wheels so as to allow the vehicle to travel. For example, the traveling driving force output device  200  includes a combination of an internal combustion engine, an electric motor, a transmission, and the like, and an electric control unit (ECU) configured to control them. The ECU controls the above-described configuration in accordance with information input from the second control unit  160  or information input from the driving operating element  80 . 
     For example, the brake device  210  includes a brake caliper, a cylinder configured to transfer hydraulic pressure to the brake caliper, an electric motor configured to generate hydraulic pressure in the cylinder, and a brake ECU. The brake ECU controls the electric motor in accordance with information input from the second control unit  160  or information input from the driving operating element  80  so that a brake torque corresponding to a braking operation is output to each wheel. The brake device  210  may include a mechanism for transferring the hydraulic pressure generated by the operation of the brake pedal included in the driving operating element  80  to the cylinder via the master cylinder as a backup. The brake device  210  is not limited to the above-described configuration and may be an electronically controlled hydraulic brake device that controls an actuator in accordance with information input from the second control unit  160  and transfers the hydraulic pressure of the master cylinder to the cylinder. 
     For example, the steering device  220  includes a steering ECU and an electric motor. The electric motor, for example, changes a direction of the steering wheels by applying a force to a rack and pinion mechanism. The steering ECU drives the electric motor and causes the direction of the steering wheels to be changed in accordance with the information input from the second control unit  160  or the information input from the driving operating element  80 . 
     A combination of the peripheral situation recognition unit  132 , the peripheral situation storage unit  132 A, the object recognition unit  134 , the traffic scene selection unit  136 , and the text generation unit  138  among the components described above is an example of a recognition processing device. What a configuration in which the action plan generation unit  140  and the second control unit  160  are further added to the recognition processing device is an example of a vehicle control device. 
     [Configuration of First Control Unit] 
     The object recognition unit  134  will be described with reference to  FIGS. 3 and 4 .  FIG. 3  is a diagram for describing the process of the peripheral situation recognition unit  132  and is a schematic diagram showing an image in which a view in front of the host vehicle M in a traveling direction is imaged by the camera  10 . In the example of  FIG. 3 , the peripheral situation recognition unit  132  recognizes each of a median strip MS, a lane R 1  in which the host vehicle M is traveling, a lane R 0  adjacent to the lane R 1 , opposite lanes RR 0  and RR 1 , a crosswalk CR, a fence F between a sidewalk SW and the lane R 1 , and signal lights S 1  and S 2 . 
     For example, the peripheral situation recognition unit  132  recognizes a pedestrian P 1  on the sidewalk SW and recognizes another vehicle OV 1  on an opposite lane a distance therefrom. The peripheral situation recognition unit  132  analyzes an image shown in  FIG. 3  to recognize the appearance of the sky shown in the image. The peripheral situation recognition unit  132  outputs a recognition result of a sky part W to the object recognition unit  134 . 
       FIG. 4  is a diagram for describing a process of the object recognition unit  134 . The object recognition unit  134  identifies a traffic participant recognized by the peripheral situation recognition unit  132  and performs labeling for assigning information about a coordinate position of the traffic participant recognized by the peripheral situation recognition unit  132  and a label name (a label ID) to the traffic participant. The label name may include a type of traffic participant. The labeling may include setting a region occupied by the traffic participant. The object recognition unit  134  labels the traffic participant recognized by the peripheral situation recognition unit  132  to assign the label ID such as “Lb+(a code including a type of traffic participant)” indicating the traffic participant and sets a rectangular region including at least a part of the traffic participant recognized by the peripheral situation recognition unit  132  as a region. 
     For example, the object recognition unit  134  assigns the label ID “Lb(P 1 )” to the pedestrian P 1  recognized by the peripheral situation recognition unit  132  and sets a rectangular region including a part or all of the pedestrian P 1 . “P” is a code indicating a pedestrian within the label ID and “ 1 ” is a code indicating which pedestrian. The label ID “Lb(W)” is assigned to the sky part W recognized by the peripheral situation recognition unit  132  and a rectangular region including the sky part W is set. The object recognition unit  134  may assign only a label ID indicating a type to a traffic participant who cannot be defined in a closed region as in the lane R 1  without setting a region in the traffic participant. 
     Furthermore, the object recognition unit  134  groups the traffic participants on the basis of types of traffic participants that have been labeled and manages labeling processing results in units of groups. When the traffic participants subjected to the labeling process are another vehicle and a bus, the object recognition unit  134  may group the other vehicle and the bus into groups indicating “car” and “bus” or may group the other vehicle and the bus into a type name called “vehicles”. The object recognition unit  134  associates a group of labeling results with a word or a phrase indicating the group. The word or phrase indicating the group is selected from, for example, words stored in the text storage unit  152 . The present invention is not limited thereto. A type name indicating “car” is associated with a group indicating another vehicle and a type name indicating “bus” is associated with a group indicating a bus. The object recognition unit  134  may assign an identification number for individually identifying the traffic participant after the type name indicating the group as in “bus  1 ” and “bus  2 ” so that each traffic participant can be identified when a plurality of traffic participants are grouped into the same group. 
     For example, when road environments recognized by the peripheral situation recognition unit  132  are a traveling lane and a median strip, the object recognition unit  134  groups the traveling lane and the median strip into groups indicating “traveling lane” and “median strip”. 
     The object recognition unit  134  sets information about an operation and a situation of the recognized traffic participant and information about an operation predicted to be performed by the traffic participant and a situation in association with a label ID. For example, when it is recognized that another vehicle recognized by the peripheral situation recognition unit  132  is turning to the right, the object recognition unit  134  adds information about the “right turn” to the label ID. The object recognition unit  134  outputs the label ID set by the labeling and accompanying information to the traffic scene selection unit  136 . A result of labeling in the object recognition unit  134  shown in  FIG. 4  may be displayed on the display device of the HMI  30 . 
     The traffic scene selection unit  136  refers to the labeling result using the label ID output by the object recognition unit  134 , recognizes a situation in which the host vehicle M is traveling on the basis of the labeling result that has been referred to, and selects an estimated traffic scene by estimating a situation that the host vehicle M will approach soon. The traffic scene is a traffic scene classified as an optimum type from among preset types related to the situation in which the host vehicle M is traveling or a situation that the host vehicle M will approach soon. For example, when the object recognition unit  134  has recognized an intersection in front of the host vehicle M, the traffic scene selection unit  136  selects an optimum traffic scene from traffic scenes related to an intersection stored in the traffic scene storage unit  150 . 
     For example, when the labeling result from the object recognition unit  134  shown in  FIG. 4  has been output, the traffic scene selection unit  136  recognizes that there are a plurality of lanes and that the signal light S 1  and the crosswalk CR are labeled and selects a traffic scene “general road”. The traffic scene selection unit  136  outputs the selected traffic scene and the label ID set by the object recognition unit  134  to the text generation unit  138 . For example, when labeling results of a prescribed number of pedestrians or more have been recognized, the traffic scene selection unit  136  selects a traffic scene related to a commuting time period along with a time recognition result and map information from the communication device  20  and the like. 
     The text generation unit  138  generates text of at least one paragraph for describing a peripheral situation on the basis of the traffic scene and the label ID output by the traffic scene selection unit  136 . The text indicating the peripheral situation includes, for example, text including words indicating a traffic participant and a road environment recognized by the peripheral situation recognition unit  132  or words indicating type names of the traffic participant and the road environment set by the object recognition unit  134  and a word indicating the traffic scene selected by the traffic scene selection unit  136 . 
     For example, when the host vehicle M reaches an intersection and stops after a red light signal is seen, the text generation unit  138  generates text including a paragraph having a plain sentence “There is an intersection” or “The signal lights red in front of the road”. The text generation unit  138  outputs the generated text to the text storage unit  152 . 
     [Object Labeling] 
     Hereinafter, labeling of traffic participants and road environments by the object recognition unit  134  will be described with reference to  FIG. 5 .  FIG. 5  is a diagram showing an example of an object stored by the peripheral situation storage unit  132 A and recognized as a traffic participant or a road environment by the peripheral situation recognition unit  132 . 
     For example, as shown in  FIG. 5 , the object recognition unit  134  recognizes information about the surrounding environment, information about the road environment, information about the traffic participant, and information about a position of the traffic participant, and labels recognition results. The information about the position of the traffic participant may be information about a position of the traffic participant when the information is based on the host vehicle M, may be information indicating a positional relationship between the traffic participants, or may be information indicating an operation of the traffic participant. 
     Information about the environment recognized by the object recognition unit  134  includes, for example, a time, a season, a temperature, and the weather. The information about the environment recognized by the object recognition unit  134  may be derived from a result of recognizing the information about the environment in the peripheral situation recognition unit  132  when the physical object recognition device  16  can recognize the environment such as the weather of the peripheral situation. Alternatively, information acquired by the communication device  20  communicating with another device may be used. 
     The object recognition unit  134  selects and labels a name indicating a result of recognizing a traffic participant or a road environment around the host vehicle M recognized by the peripheral situation recognition unit  132  from information about a traffic participant and information about the road environment stored by the peripheral situation recognition unit  132 A. Furthermore, the object recognition unit  134  selects and labels a name indicating a result of recognizing an operation or a situation of a traffic participant around the host vehicle M recognized by the peripheral situation recognition unit  132  from information about the operation or the situation of the traffic participant stored by the peripheral situation storage unit  132 A. 
       FIG. 6  is a flowchart showing an example of a flow of a labeling process of the object recognition unit  134 . 
     First, the object recognition unit  134  detects and labels information about a peripheral environment (step S 100 ). Next, the object recognition unit  134  detects and labels information about a road environment (step S 102 ). Next, the object recognition unit  134  detects and labels information about a traffic participant (step S 104 ). Next, the object recognition unit  134  detects and labels information about a position of the traffic participant (step S 106 ). Thus, the process of the present flowchart ends. 
     [Traffic Scene Selection] 
     Hereinafter, the selection of a traffic scene in the traffic scene selection unit  136  will be described using  FIG. 7 .  FIG. 7  shows an example of a traffic scene and a traffic participant and a road environment related to the traffic scene stored by the peripheral situation storage unit  132 A. 
       FIG. 7( a )  is a diagram showing an example of a traffic scene selection rule in the traffic scene selection unit  136 . The traffic scene selection unit  136  selects a traffic scene indicating a peripheral situation of the host vehicle M on the basis of the presence or absence of a traffic participant and a road environment labeled by the object recognition unit  134 . 
     A double-circle mark shown in  FIG. 7( a )  indicates that an associated traffic scene can be derived when a traffic participant or the road environment has been recognized by the object recognition unit  134 . For example, when the peripheral situation recognition unit  132  has recognized a crossing lane and the object recognition unit  134  has labeled the crossing lane, the traffic scene selection unit  136  selects that an associated traffic scene is an “intersection”. 
     A circle mark shown in  FIG. 7( a )  indicates that the associated traffic participant or road environment is likely to be labeled by the object recognition unit  134  when a traffic scene has been derived. For example, when a traffic scene of an “intersection” has been selected, the traffic scene selection unit  136  estimates that another traffic participant (for example, a “signal light” or a “crosswalk”) associated with the selected traffic scene is likely to be labeled by the object recognition unit  134  and outputs an estimation result to the object recognition unit  134 . When a traffic participant corresponding to the traffic scene estimated to be likely to be labeled by the traffic scene selection unit  136  has been labeled, the object recognition unit  134  improves the accuracy of text generated by the text generation unit  138  indicating a peripheral situation by collecting labeling results thereof and outputting the collected labeling results to the text generation unit  138 . When the traffic participant corresponding to the traffic scene estimated to be likely to be labeled by the traffic scene selection unit  136  has not been labeled, the object recognition unit  134  can cope with a labeling error in the object recognition unit  134  at an early stage by outputting information about the traffic participant estimated to be likely to be labeled by the object recognition unit  134  or a group to which the traffic participant belongs. 
     Square marks shown in  FIG. 7( a )  indicate a traffic participant and a road environment that are not associated when the traffic scene has been derived. For example, the traffic scene selection unit  136  selects that an associated traffic scene is a “main road” when a pedestrian, a signal light, and a crosswalk are not labeled by the object recognition unit  134  or when a labeling proportion is less than or equal to a prescribed proportion and when the number of times a traveling lane or a traffic sign is labeled is less than or equal to a prescribed proportion. 
     As described above, the traffic scene selection unit  136  selects the traffic scene indicating the peripheral situation of the host vehicle M on the basis of the presence or absence of the traffic participant and the road environment labeled by the object recognition unit  134 . 
       FIG. 7( b )  is a diagram showing a traffic scene, a traffic participant associated with the traffic scene, and an operation or a selection rule of the traffic participant in a tabular form. For example, when the traffic scene selection unit  136  has already selected the “intersection” of the traffic scene and when the “crosswalk” of the traffic participant has been labeled by the object recognition unit  134 , the traffic scene selection unit  136  estimates that the operation or the situation of the traffic participant associated with the “crosswalk” is likely to be labeled by the object recognition unit  134  on the basis of the table shown in  FIG. 7( b )  and outputs an estimation result to the object recognition unit  134 . When traffic participants corresponding to the traffic scene estimated to be likely to be labeled by the traffic scene selection unit  136  have been labeled, the object recognition unit  134  improves the accuracy of text generated by the text generation unit  138  indicating a peripheral situation by collecting labeling results thereof and outputting the collected labeling results to the text generation unit  138 . 
     When an operation or a situation of the traffic participant corresponding to a traffic scene estimated to be likely to be labeled by the traffic scene selection unit  136  has not been labeled, the object recognition unit  134  can detect a labeling error of the object recognition unit  134  at an early stage and take a countermeasure by outputting information about the operation or the situation of the traffic participant corresponding to the traffic scene estimated to be likely to be labeled and a group to which the operation or the situation of the traffic participant belongs to the object recognition unit  134 . 
     In the relationship table shown in  FIG. 7( b ) , for example, a relationship between a traffic participant for which a manufacturer of the host vehicle M or the like causes the text generation unit  138  to preferentially generate text and an operation or a situation of the traffic participant is set. The traffic scene selection unit  136  can improve the accuracy of text generated by the text generation unit  138  indicating a peripheral situation by selecting a traffic scene on the basis of the relationship shown in  FIG. 7( b )  and outputting the selected traffic scene to the text generation unit  138 . 
       FIG. 8  is a flowchart showing an example of a flow of a traffic scene selection process of the traffic scene selection unit  136 . Step S 200  is the process of the flowchart shown in  FIG. 6 . 
     First, the object recognition unit  134  labels a traffic participant and a road environment recognized by the peripheral situation recognition unit  132  and outputs labeling results to the traffic scene selection unit  136  (step S 200 ). Next, the traffic scene selection unit  136  selects a traffic scene on the basis of the traffic participant and the road environment labeled by the object recognition unit  134  (step S 202 ). Next, the traffic scene selection unit  136  outputs results of labeling the traffic participant, the road environment, and an operation or a situation of the traffic participant associated with the selected traffic scene to the text generation unit  138  (step S 204 ). Thus, the process of the present flowchart ends. 
     [Text Generation] 
       FIG. 9  is a diagram for describing a text generation process of the text generation unit  138 . The text generation unit  138  selects a word or a phrase serving as an output target on the basis of a traffic scene selected by the traffic scene selection unit  136  and a traffic participant, a road environment, and an operation or a situation of the traffic participant labeled by the object recognition unit  134  and causes the selected word or phrase to be reflected in text to be generated. 
     The text generation unit  138  generates, for example, text of seven paragraphs as shown in  FIG. 9  from the labeling results of the object recognition unit  134  shown in  FIG. 4 . The text generation unit  138  may be configured to be able to identify a word or a phrase (for example, a word or a phrase indicating a traffic participant) needing a high degree of attention and a word or a phrase (for example, an article or a conjunction) needing a low degree of attention by color or size. For example, as shown in  FIG. 9 , the text generation unit  138  underlines a word or a phrase needing a high degree of attention indicating the traffic participant. 
     The text generation unit  138  causes a word or a phrase indicating a result of selecting the term “open road” selected by the traffic scene selection unit  136  to be reflected in a first paragraph (A sunny day on an open road. There are one man and one car.). Among sentences constituting the first paragraph, a term (for example, the term “There are”) other than a word or a phrase indicating the selection result of the traffic scene selection unit  136  is an example of a template provided according to a prescribed grammar which is a rule when the text generation unit  138  generates text. 
     The text generation unit  138  reflects words or phrases containing a grouping result such as a sky part W (sunny day), a pedestrian P 1  (one man), and another vehicle OV 1  (one car) on the basis of the labeling results of the object recognition unit  134  in the first paragraph. 
     As shown in a second paragraph (There is a median strip on an open road.), the text generation unit  138  generates text in which a phrase indicating a result of selecting the term “general road” (open road) selected by the traffic scene selection unit  136  is reflected and a phrase indicating a median strip MS (median strip) which is a labeling result of the object recognition unit  134  is reflected. As shown in a third paragraph (The man is on the left of lane R 1 .), the text generation unit  138  generates text in which a word or a phrase indicating the term “left” indicating a position between the pedestrian P 1  (man) and the lane R 1  (lane R 1 ) which are labeling results of the object recognition unit  134  is reflected. 
     The text generation unit  138  does not reflect a recognition result that may be noise among recognition results of the peripheral situation recognition unit  132  in the text indicating the peripheral situation to be generated. The recognition result that may be noise is, for example, a detailed recognition result such as an estimated gender or age of the pedestrian P 1  recognized by the peripheral situation recognition unit  132  and is a recognition result that is not useful in driving control of the host vehicle M. The text generation unit  138  generates text that accurately indicates a possible event by excluding a recognition result that is likely not to be useful in driving control of the host vehicle M from text generation details. Therefore, the text analysis unit  142  to be described below can further improve robust stability by generating a target trajectory on the basis of text generated by the text generation unit  138  in comparison with a method of directly generating a target trajectory from a recognition result recognized by the peripheral situation recognition unit  132 . 
       FIG. 10  is a flowchart showing an example of a flow of a text generation process of the text generation unit  138 . The order of execution of the steps shown in  FIG. 10  is not particularly limited and may be performed in another order. 
     First, the text generation unit  138  outputs a word or phrase related to a traffic scene (step S 300 ). Next, the text generation unit  138  outputs a word indicating information about a surrounding environment (step S 302 ). Next, the text generation unit  138  outputs a word indicating information about a road environment (step S 304 ). Next, the text generation unit  138  outputs a word indicating information about a traffic participant (step S 306 ). Next, the text generation unit  138  outputs a word indicating information about a position of the traffic participant (step S 308 ). Thus, the process of the present flowchart ends. 
     Furthermore, for example, the text generation unit  138  generates text by increasing priority of text indicating a positional relationship or an action when a positional relationship between a traffic participant and a road environment having an influence on driving control of the host vehicle M or a change in the action of the traffic participant is indicated. The text generation unit  138  outputs the generated text to the action plan generation unit  140 . 
       FIG. 11  is a diagram for describing the priority of the text generation process of the text generation unit  138 . In  FIG. 11 , classification  1  indicates a positional relationship between a traffic participant labeled by the object recognition unit  134  and the host vehicle M and classification  2  indicates a change in an action of the traffic participant labeled by the object recognition unit  134 . For example, when the traffic participant labeled by the object recognition unit  134  is located within the same travel lane as that of the host vehicle M, the priority is set to high priority. When the traffic participant labeled by the object recognition unit  134  is not located within the travel lane of the host vehicle M and when the traffic participant labeled by the object recognition unit  134  is not approaching the traveling lane of the host vehicle M, the priority is set to low priority. 
     The text generation unit  138  preferentially generates text including a word indicating a traffic participant whose priority is set to high priority. In the text shown in  FIG. 9 , a sentence constituting a first paragraph or a second paragraph includes a word indicating a traffic participant whose priority is set to high priority by the text generation unit  138 . Although text related to the pedestrian P 1  is output to a fourth paragraph in  FIG. 9 , this is a case in which the traffic participant labeled by the object recognition unit  134  is not located within the traveling lane of the host vehicle M and a case in which the traffic participant labeled by the object recognition unit  134  is not approaching the travel lane of the host vehicle M and this is because it is indicated that the priority is set to low priority. If the object recognition unit  134  labels that the pedestrian P 1  is approaching the traveling lane R 1  of the host vehicle M, the text generation unit  138  sets high priority for the pedestrian P 1  and generates text indicating “The man is moving towards the lane R 1 .” in a higher-order paragraph (for example, a first or second paragraph) for the text related to the pedestrian P 1 . 
     Although an example in which the text generation unit  138  sets the priority of a traffic participant located on the traveling lane R 1  of the host vehicle M or a traffic participant labeled as approaching the traveling lane R 1  to high priority is shown in  FIG. 11 , the priority may be set to high priority when a relative speed of the other vehicle OV 1  traveling in the opposite lane RR 0  is greater than or equal to a prescribed speed. 
     The priority setting by the text generation unit  138  will be described below with reference to  FIGS. 12 to 15 .  FIG. 12  is a diagram for describing processes of the peripheral situation recognition unit  132  and the object recognition unit  134  and shows a peripheral situation recognized by the peripheral situation recognition unit  132  when the host vehicle M stops at an intersection while the host vehicle M is traveling on a general road in a city area. 
     In the example of  FIG. 12 , the peripheral situation recognition unit  132  recognizes each of a lane R 1  in which the host vehicle M is traveling, a lane R 0  adjacent to the lane R 1 , a stop line SL, a crosswalk CR 1  adjacent to the stop line SL, a road shoulder RS adjacent to the lane R 1 , a crosswalk CR 2  located in front of a left side of the host vehicle M in a traveling direction thereof, and signal lights S 1  to S 8 . The peripheral situation recognition unit  132  recognizes another vehicle OV 1  located on the same lane as the traveling lane R 1  of the host vehicle M and stopping in front of the host vehicle M in a traveling direction thereof, a motorcycle MB located on the lane R 0  adjacent to the traveling lane of the host vehicle M and stopping in front of the host vehicle M in a traveling direction thereof, pedestrians P 1  to P 6 , and a bus B 1  and other vehicles OV 2  to OV 4  on a crossing lane. The peripheral situation recognition unit  132  analyzes the image shown in  FIG. 12  to recognize the appearance of the sky shown in the image. The peripheral situation recognition unit  132  outputs a result of recognizing the sky part W to the object recognition unit  134 . 
     The object recognition unit  134  identifies the traffic participant recognized by the peripheral situation recognition unit  132  and performs a labeling process as in  FIG. 4 . For example, the object recognition unit  134  labels the pedestrians P 1  to P 6  recognized by the peripheral situation recognition unit  132 , adds label IDs of Lb(P 1 ) to Lb(P 6 ), and sets a rectangular region including some or all of the pedestrians P 1  to P 6 . The object recognition unit  134  outputs the label IDs set through labeling to the traffic scene selection unit  136 . The traffic scene selection unit  136  refers to labeling results shown in  FIG. 12  using the label IDs output by the object recognition unit  134  and selects a traffic scene related to an intersection. 
     The text generation unit  138  generates text indicating a peripheral situation of the host vehicle M shown in  FIG. 12  on the basis of the traffic scene selected by the traffic scene selection unit  136 , the traffic participant and the road environment labeled by the object recognition unit  134 , and the operation or the situation of the traffic participants. 
       FIG. 13  is a diagram for describing the text generation process of the text generation unit  138 . The text generation unit  138  generates text of six paragraphs as shown in  FIG. 13  from labeling results of the object recognition unit  134  shown in  FIG. 12 . As shown in a first sentence (One person is crossing the left crosswalk.) of a third paragraph of the text shown in  FIG. 13 , the text generation unit  138  generates text in which a word or a phrase indicating a result of recognizing the pedestrian P 1  as a labeling result of the object recognition unit  134  is reflected. As shown in a fourth sentence (No person is on the front crosswalk.) of the third paragraph of the text shown in  FIG. 13 , the text generation unit  138  generates text indicating that no pedestrian (No person) has been recognized in the vicinity of a crosswalk CR 1  (front crosswalk) by the peripheral situation recognition unit  132 . 
     On the other hand, the text generation unit  138  does not reflect words or phrases indicating the road shoulder RS or the other vehicles OV 2  to OV 4  on the intersection in the text shown in  FIG. 13 . This indicates that higher priority is set with respect to the other recognition results of the other vehicle OV 1 , the motorcycle MB, and the like among the recognition results of the object recognition unit  134  and the other recognition results with the higher priority are preferentially included in the text and therefore words indicating recognition results of the other vehicle OV 2  to OV 4  having lower priority than the other recognition results are not reflected in the text by the text generation unit  138 . 
     As shown in a first sentence (A car is waiting on the turn left line before the stop line.) of a fourth paragraph of the text shown in  FIG. 13 , the text generation unit  138  generates text indicating a positional relationship between traffic participants such as a state in which the other vehicle OV 1  has stopped in front of the stop line SL. In a sentence constituting a first sentence of the fourth paragraph, the term (for example, the term “is waiting on the” or “before the”) other than words or phrases indicating the selection result from the traffic scene selection unit  136  is another example of a template when the text generation unit  138  generates text. 
       FIG. 14  is another diagram for describing processes of the peripheral situation recognition unit  132  and the object recognition unit  134  and shows a peripheral situation recognized by the peripheral situation recognition unit  132  while the host vehicle M is traveling on a main road at night. 
     In the example of  FIG. 14 , the peripheral situation recognition unit  132  recognizes each of a lane R 1  in which the host vehicle M is traveling, lanes R 0  and R 2  adjacent to the lane R 1 , a median strip MS adjacent to the lane R 0 , traffic signs D 1  to D 3 , and street lights L 1  to L 4 . The peripheral situation recognition unit  132  recognizes other vehicles OV 1  to OV 3  located on the same lane as the traveling lane R 1  of the host vehicle M and traveling in front of the host vehicle M in the traveling direction thereof and trucks T 1  and T 2 . The peripheral situation recognition unit  132  analyzes the image shown in  FIG. 14  to recognize the appearance of the sky reflected in the image, and outputs a recognition result of a sky part W to the object recognition unit  134 . 
     The object recognition unit  134  identifies a traffic participant recognized by the peripheral situation recognition unit  132  and performs a labeling process as in  FIGS. 4 and 12 . The traffic scene selection unit  136  refers to the labeling result shown in  FIG. 14  using a label ID output by the object recognition unit  134  and selects a traffic scene related to a main road at night. The text generation unit  138  generates text indicating the peripheral situation of the host vehicle M shown in  FIG. 15  on the basis of a traffic scene selected by the traffic scene selection unit  136 , a traffic participant and a road environment labeled by the object recognition unit  134 , and an operation or a situation of the traffic participant. 
       FIG. 15  is a diagram for describing a text generation process of the text generation unit  138 . The text generation unit  138  generates text of seven paragraphs as shown in  FIG. 15  from the labeling results of the object recognition unit  134  shown in  FIG. 14 . 
     As shown in  FIG. 15 , second and third paragraphs of text contain words (lane and line) for lanes R 0  to R 2  and a fourth paragraph contains a word (truck) for trucks T 1  and T 2 . This indicates that a sentence including a word indicating a result of recognizing the lanes R 0  to R 2  among recognition results of the object recognition unit  134  is disposed in a higher-order paragraph by the text generation unit  138  as a result of setting the priority of the result of recognizing the lanes R 0  to R 2  shown in a third paragraph to be higher than the priority of the result of recognizing the trucks T 1  and T 2  shown in the fourth paragraph. As shown in  FIG. 13 , a sixth paragraph of the text includes a word (traffic sign) related to traffic signs D 1  to D 3 . This indicates that a sentence including a word indicating a result of recognizing the trucks T 1  and T 2  shown in the fourth paragraph among recognition results of the object recognition unit  134  is disposed in a paragraph lower than that of a sentence including a word indicating a result of recognizing the lanes R 0  to R 2  and disposed in a paragraph higher than that of a sentence including a word indicating a result of recognizing the traffic signs D 1  to D 3  by the text generation unit  138  as a result of setting the priority of the result of recognizing the trucks T 1  and T 2  shown in the fourth paragraph to be lower than the priority of the result of recognizing the lanes R 0  to R 2  shown in the third paragraph and higher than priority of the result of recognizing the traffic signs D 1  to D 3 . 
       FIG. 16  is a flowchart showing an example of a flow of a process in which the text generation unit  138  reflects the priority in text generation. The execution order of steps S 400  to S 404  and steps S 406  to S 410  shown in  FIG. 16  is not particularly limited. For example, steps S 400  to S 404  may be executed after steps S 406  to S 410  are executed. 
     First, the text generation unit  138  derives a positional relationship between a traffic participant and a road environment (step S 400 ). Next, the text generation unit  138  determines whether or not there is a high possibility that a positional relationship between the traffic participant and the road environment will affect the driving control of the host vehicle M (step S 402 ). When it is determined that there is a high possibility that a positional relationship between the traffic participant and the road environment will affect the driving control of the host vehicle M, the text generation unit  138  increases the output priority of the positional relationship between the traffic participant and the road environment (step S 404 ). When it is determined that there is no high possibility that a positional relationship between the traffic participant and the road environment will affect the driving control of the host vehicle M, the text generation unit  138  moves the process to step S 406 . 
     The text generation unit  138  derives a positional relationship between traffic participants (step S 406 ). Next, the text generation unit  138  determines whether or not there is a high possibility that the positional relationship between the traffic participants will affect the driving control of the host vehicle M (step S 408 ). When it is determined that there is a high possibility that the positional relationship between the traffic participants will affect the driving control of the host vehicle M, the text generation unit  138  increases the output priority of the positional relationship between the traffic participants (step S 410 ) and formats the entire text in accordance with a prescribed grammar using a template held by the text generation unit  138  (step S 412 ). When it is determined that there is no high possibility that the positional relationship between the traffic participants will affect the driving control of the host vehicle M, the text generation unit  138  moves the process to step S 412 . Thus, the process of the present flowchart ends. 
     [Text Analysis] 
     The text analysis unit  142  generates a target trajectory on the basis of text output by the text generation unit  138 . The text analysis unit  142  analyzes, for example, whether or not text shown in  FIGS. 9, 13 and 15  accurately represents a peripheral situation, i.e., whether or not the situation shown in the text can actually occur. When the text analysis unit  142  analyzes that an event indicated by the text can actually occur, the text analysis unit  142  generates a target trajectory in consideration of the event indicated by the text. When the text analysis unit  142  analyzes that the event indicated by the text cannot actually occur, the text analysis unit  142  discards the text and generates a target trajectory from only text for which it is analyzed that the event can occur. 
     For example, the text generation unit  138  generates text including a sentence in which the corresponding event cannot occur due to attributes of the truck T 2  and the median strip MS such as a sentence (There is a truck T 2  on the median strip MS.) on the basis of the peripheral situation shown in  FIG. 14  and outputs the generated text to the text analysis unit  142 . When it is analyzed that the event indicated by the text output by the text generation unit  138  cannot actually occur, the text analysis unit  142  may discard the corresponding sentence “There is a truck T 2  on the median strip MS.” to generate a target trajectory from text other than the discarded sentence or may discard the entire text to cause the text generation unit  138  to generate text again. 
     In the text output by the text generation unit  138 , it can be said that a result of recognizing an object related to driving control of the host vehicle M has been selected in comparison with a recognition result recognized by the peripheral situation recognition unit  132 . Therefore, it is easy to perform standard unification when the text analysis unit  142  generates the target trajectory from the text generated by the text generation unit  138  and it is possible to reduce a processing cost required for generating the target trajectory. 
     Even when there are minor differences in the recognition results recognized by the peripheral situation recognition unit  132 , similar events may be indicated and grouped. The text generation unit  138  generates text indicating the recognition result recognized by the peripheral situation recognition unit  132 , so that it is possible to improve the accuracy of target trajectory prediction to be performed in the text analysis unit  142  by excluding information of a minor difference that may be ignored or decreasing the priority of the information. 
     A part of text unnecessary for target trajectory generation performed by the text analysis unit  142  may be deleted. For example, the text analysis unit  142  analyzes that a part or all of text may be discarded and deletes the text from the text storage unit  152  because text related to the street lights L 1  to L 4  shown in a seventh paragraph of the text shown in  FIG. 15  has low priority for use when the target trajectory is generated. 
     According to the present embodiment as described above, the vehicle control device  1  includes the peripheral situation recognition unit  132  configured to recognize a peripheral situation of the host vehicle M; the object recognition unit  134  configured to select types of traffic participant and road environment recognized by the peripheral situation recognition unit or a word or a phrase indicating a positional relationship between the host vehicle M and the traffic participant and the road environment; and the text generation unit  138  configured to generate text indicating the peripheral situation recognized by the peripheral situation recognition unit  132 , so that it is possible to further improve the accuracy of prediction by predicting a change in the peripheral situation on the basis of text indicating a result of recognizing the peripheral situation of the host vehicle. 
     [Hardware Configuration] 
       FIG. 17  is a diagram showing an example of a hardware configuration of the automated driving control device  100  of an embodiment. As shown, the automated driving control device  100  has a configuration in which a communication controller  100 - 1 , a CPU  100 - 2 , a RAM  100 - 3  used as a working memory, a ROM  100 - 4  storing a boot program and the like, a storage device  100 - 5  such as a flash memory or an HDD, a drive device  100 - 6 , and the like are mutually connected by an internal bus or a dedicated communication line. The communication controller  100 - 1  communicates with components other than the automated driving control device  100 . A program  100 - 5   a  executed by the CPU  100 - 2  is stored in the storage device  100 - 5 . This program is loaded to the RAM  100 - 3  by a direct memory access (DMA) controller (not shown) or the like and executed by the CPU  100 - 2 . Thereby, the recognition unit  130  and the action plan generation unit  140  are implemented. 
     The above-described embodiment can be represented as follows. 
     A vehicle control device including: 
     a storage device configured to store a program; and 
     a hardware processor, 
     wherein the hardware processor executes the program stored in the storage device to: 
     recognize a peripheral situation of a vehicle, and 
     execute driving control for controlling one or both of steering and acceleration/deceleration of the vehicle on the basis of the recognized peripheral situation, 
     wherein the hardware processor includes 
     an object recognition unit configured to select a word indicating the type of object recognized by the peripheral situation recognition unit and a word indicating a positional relationship between the vehicle and the object; and 
     a text generation unit configured to generate text for describing a peripheral situation, wherein the text includes the word indicating the type of object selected by the object recognition unit and the word indicating the positional relationship between the vehicle and the object selected by the object recognition unit, wherein the driving control unit executes the driving control on the basis of the text generated by the text generation unit. 
     Although modes for carrying out the present invention have been described above using embodiments, the present invention is not limited to these embodiments at all and various modifications and substitution can be made without departing from the spirit and scope of the present invention.