Patent Publication Number: US-2022237920-A1

Title: Outside environment recognition device

Description:
TECHNICAL FIELD 
     The technology disclosed herein belongs to a technical field of an external environment recognition device for use in an autonomous mobile object. 
     BACKGROUND ART 
     Autonomous mobile objects that move, travel, fly, etc. (simply hereinafter “move” may be used as a collective term) while recognizing the external environment use a sensing device, such as a plurality of cameras, to recognize the external environment, and determine a moving direction, a moving speed, etc., based on the recognition result. In the field of autonomous mobile objects, the following technologies have been known to recognize the external environment correctly: a plurality of cameras are mounted on an autonomous mobile object, in which synchronous processing is performed on the cameras, or image processing is performed on the images captured by the plurality of cameras to align timings. 
     For example, Patent Document 1 discloses a technique in which cameras output image signals to an image processor, using a synchronizing signal periodically output from the image processor as a trigger. 
     Patent Document 2 discloses a monitoring device that monitors the surroundings of a vehicle based on images captured by two imagers having imaging regions that overlap each other. To avoid using images captured at different timings in recognizing the external environment from the images captured by the two imagers, the monitoring device of Patent Document 2 includes an information generator that generates predetermined information for which the shift in the imaging timing has been corrected. 
     CITATION LIST 
     Patent Document 
     
         
         Patent Document 1: Japanese Unexamined Patent Publication No. 2005-117554 
         Patent Document 2: Japanese Unexamined Patent Publication No. 2008-211373 
       
    
     SUMMARY OF THE INVENTION 
     Technical Problem 
     However, according to the method as disclosed in Patent Document 1, the synchronous processing is performed by the cameras every time the synchronizing signal is periodically output from the image processor. Hence, the synchronous processing may be performed even when the synchronization is not necessary, which is waste of processing resources. 
     The monitoring device of Patent Document 2 requires correction processing by the image processing device, which may increase a processing load on the arithmetic unit or require complicated processing. The processing may become complicated or the processing load may be increased particularly when the amount of information to be dealt is increased by, for example, installing more cameras and increasing a frame rate to improve the accuracy in recognizing the external environment. 
     It is therefore an object of the technology disclosed herein to save processing resources for synchronous processing of an external environment recognition sensor that recognizes an external environment. 
     Solution to the Problem 
     To achieve the above object, an aspect of the technology disclosed herein is directed to an external environment recognition device that recognizes an external environment of an autonomous mobile object. The external environment recognition device includes: a plurality of external environment recognition sensors each having an information detection unit that detects information of an object outside the autonomous mobile object, the plurality of external environment recognition sensors being arranged such that a detection range of the information detection unit includes an overlapping region where at least part of the detection range of the information detection unit overlaps with at least part of the detection range of another one of the information detection units; and a synchronous processing unit that extracts identical objects in the overlapping region from results of detection made by the external environment recognition sensors, and performs synchronous processing to synchronize the plurality of external environment recognition sensors if there is a deviation in position between the identical objects in the overlapping region. 
     According to this configuration, synchronous processing for the plurality of external environment recognition sensors is performed if there is a deviation in position between the identical objects in the overlapping region. This configuration makes it possible to execute the synchronous processing when the positional deviation occurs. It is also possible not to execute the synchronous processing if there is no positional deviation. Thus, the processing resources for the synchronous processing can be saved, compared with the case like Patent Document 1 in which the synchronous processing is performed periodically. It is also possible to achieve synchronization at the stage of signal output from the external environment recognition sensors, that is, it is possible to synchronize signals at stages before various types of image processing or any other processing for recognizing an object outside the autonomous mobile object. The load on processing, such as image processing, can thus be reduced. 
     The external environment recognition device may have a configuration in which the plurality of external environment recognition sensors include a plurality of cameras arranged so as to have the overlapping region, and the synchronous processing unit extracts identical objects from images in the overlapping region of the plurality of cameras, identifies static objects from among the identical objects, and superimposes the static objects on each other to determine whether or not there is a deviation in position between the identical objects. 
     According to this configuration, the static objects are superimposed on each other, and it is therefore possible to improve the accuracy in detecting the positional deviation. 
     The external environment recognition device may have a configuration in which the synchronous processing unit performs the synchronous processing when a moving speed of the autonomous mobile object is lower than a predetermined threshold value. 
     According to this configuration, it is possible to perform the synchronous processing based on the information detected in a situation that is not easily affected by vibration or the like caused by the movement of the autonomous mobile object. It is therefore possible to improve stability and accuracy of the synchronous processing. 
     The external environment recognition device may further include an abnormality notification device that provides notification of abnormality of any one of the external environment recognition sensors if the positional deviation between the identical objects is not eliminated after the synchronous processing unit executes the synchronous processing. 
     This configuration makes it possible to detect that the external environment recognition sensor is physically displaced, like a shift in the installation position of the external environment recognition sensor, and notify the users or the like of such a shift in position. 
     Advantages of the Invention 
     As can be seen from the foregoing description, the technology disclosed herein makes it possible to perform synchronous processing when a positional deviation occurs. It is therefore possible to perform the synchronous processing at a relatively early stage and save resources for the synchronous processing. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram showing a control system of a motor vehicle having an external environment recognition device on board. 
         FIG. 2  schematically shows a vehicle having an information display device for vehicle on board. 
         FIG. 3  is a block diagram showing a configuration of the external environment recognition device. 
         FIG. 4  illustrates an example of the arrangement of cameras and an example of an imaging area of the external environment recognition device. 
         FIG. 5  is a flowchart showing an example operation of the external environment recognition device. 
         FIG. 6A  illustrates operations of the external environment recognition device. 
         FIG. 6B  illustrates operations of the external environment recognition device. 
         FIG. 7  is a timing chart showing an example operation of the external environment recognition device. 
     
    
    
     DESCRIPTION OF EMBODIMENT 
     An exemplary embodiment will now be described in detail with reference to the drawings. In the following embodiment, a motor vehicle having autonomous driving functions will be described as an example of the autonomous mobile object. The external environment recognition device of the present disclosure is applicable not only to a motor vehicle, but also to autonomous mobile objects, such as autonomous mobile robots, vacuum cleaners, and drones. 
       FIG. 1  is a block diagram schematically showing a configuration of a control system of a vehicle  10  of the present embodiment. The vehicle  10  is configured to be capable of assisted driving and autonomous driving. 
     To achieve the assisted driving and autonomous driving, the vehicle  10  of the present embodiment includes an arithmetic unit  100  that calculates a route to be traveled by the vehicle  10  and determines motions of the vehicle  10  so that the vehicle  10  follows the route, based on outputs from a sensing device  30  or on information from a network outside the vehicle. The arithmetic unit  100  is a microprocessor comprised of one or more chips, and includes a CPU, a memory, and the like. Note that  FIG. 1  mainly shows a configuration to exert the route generating function of the present embodiment, and does not necessarily show all the functions the arithmetic unit  100  has. 
     The sensing device  30  that outputs information to the arithmetic unit  100  includes, for example: (1) a plurality of cameras  20  that are provided to the body or the like of the vehicle  10  and that take images of the vehicle&#39;s external environment (including an object outside the vehicle  10 ); (2) a plurality of radars  32  that are provided to the body or the like of the vehicle  10  and that detect a target or the like outside the vehicle  10 ; (3) a position sensor  33  that detects the position of the vehicle  10  (vehicle position information) by using a Global Positioning System (GPS); (4) a vehicle status sensor  34  that acquires a status of the vehicle  10  and that includes sensors detecting the behavior of the vehicle  10 , such as a vehicle speed sensor, an acceleration sensor, and a yaw rate sensor; (5) an occupant status sensor  35  that is comprised of an in-vehicle camera or the like and that acquires a status of an occupant on the vehicle  10 ; and (6) a driving operation information acquisition unit  36  for detecting the driving operation of the driver. In addition, the arithmetic unit  100  receives communication information from another vehicle (other vehicles) around the subject vehicle  10  or traffic information from a navigation system through an external communication unit  41  connected to a network outside the vehicle. 
     The cameras  20  are arranged to image the surroundings of the vehicle  10  at 360° in the horizontal direction. Specifically, as illustrated in  FIG. 3 , each of the cameras  20  includes an imaging unit  20   a  (corresponding to an information detection unit) that captures optical images showing the vehicle&#39;s external environment, using an imaging device, such as charged-coupled devices (CCDs) and complementary metal oxide semiconductors (CMOSs), to generate image data. As illustrated in  FIG. 4 , the cameras  20  are arranged such that the imaging area captured by the imaging unit  20   a  of each camera  20  includes an overlapping region RF where part of the imaging area captured by the camera  20  overlaps with part of the imaging area captured by an adjacent camera  20 .  FIG. 4  illustrates an example in which eight cameras  20  are installed in the vehicle  10 . For convenience of explanation in the following description, the cameras are denoted by  21 ,  22 , . . . , and  28  clockwise from the camera  20  installed on the left side of the front of the vehicle  10 . In  FIG. 4 , the imaging areas of the cameras  21 ,  22 , . . . , and  28  are schematically illustrated and denoted by R 21 , R 22 , . . . , and R 28 , respectively. Further, in  FIG. 4 , the overlapping regions are dotted and denoted by RF. The overlapping region RF by the cameras  21  and  22  capturing images ahead of the vehicle  10  is denoted by RF 1 . Similarly, the overlapping region RF by the cameras  21  and  28  is denoted by RF 2 , and the overlapping region RF by the cameras  22  and  23  is denoted by RF 3 . 
     Each camera  20  outputs the imaging data captured by the imaging unit  20   a  to the arithmetic unit  100 . The imaging data captured by each camera  20  is input not only to the arithmetic unit  100 , but also to a human machine interface (HMI) unit  61 . The HMI unit  61  displays information based on the image data acquired, on a display device or the like in the vehicle. Each camera  20  corresponds to an imaging device for capturing images of objects outside the vehicle  10 . 
     Similarly to the cameras  20 , the radars  32  are arranged so that the detection range covers 360° of the vehicle  10  in the horizontal direction. The type of the radars  32  is not particularly limited. For example, a millimeter wave radar may be adopted. Although not specifically shown in the drawings, each of the radars  32  may be an imaging radar or a laser radar capable of capturing images. Similarly to the cameras  20 , the radars  32  may output imaging data to the arithmetic unit  100 . In such a case, each radar  32  corresponds to an imaging device for capturing images of the external environment of the vehicle  10 . 
     The arithmetic unit  100  determines a target motion of the vehicle  10  based on outputs from the sensing device  30 , such as the cameras  20  and the radars  32 , and on information received from a network outside the vehicle via the external communication unit  41 , calculates a driving force, a braking force, and a steering amount for achieving the determined target motion, and outputs a calculation result to a control unit  50  that controls an engine, a brake, or the like. In the example configuration illustrated in  FIG. 2 , the arithmetic unit  100  includes a processor and a memory. The memory stores modules each of which is a software program executable by the processor. The function of each unit of the arithmetic unit  100  shown in  FIG. 1  is achieved, for example, by the processor executing the modules stored in the memory. In addition, the memory stores data of a model used by the arithmetic unit  100 . Note that a plurality of processors and a plurality of memories may be provided. Part of the function of each unit of the arithmetic unit  100  shown in  FIG. 1  may be achieved by a hardware circuit. 
     As shown in  FIG. 1 , to set the target motion of the vehicle  10 , the arithmetic unit  100  includes: a vehicle external environment recognition unit  110  that recognizes a vehicle&#39;s external environment based on the outputs from the sensing device  30 , such as cameras  20 , or the information input from the external communication unit  41 ; a candidate route generation unit  151  that calculates one or more candidate routes that can be traveled by the vehicle  10 , in accordance with the vehicle&#39;s external environment recognized by the vehicle external environment recognition unit  110 ; a vehicle behavior estimation unit  152  that estimates the behavior of the vehicle  10  based on an output from the vehicle status sensor  34 ; an occupant behavior estimation unit  153  that estimates the behavior of an occupant on the vehicle  10  based on an output from the occupant status sensor  35 ; a route decision unit  154  that decides a route to be traveled by the vehicle  10 ; and a vehicle motion decision unit  155  that decides the target motion of the vehicle  10  so that the vehicle  10  follows the route set by the route decision unit  154 . 
     &lt;Vehicle External Environment Recognition Unit&gt; 
     As illustrated in  FIG. 3 , the vehicle external environment recognition unit  110  recognizes the vehicle&#39;s external environment based on an output from each camera  20 . The vehicle external environment recognition unit  110  includes an object recognizer  111 , a map generator  112 , an identical object recognizer  113 , and a positional deviation detector  114 . 
     The object recognizer  111  recognizes what an object outside the vehicle is, based on the imaging data received from the cameras  20  or the peak list of reflected waves received from the radars  32 , for example. For example, the object recognizer  111  detects an object outside the vehicle based on the imaging data or the peak list, identifies the object outside the vehicle, using identification information or the like in a database or the like stored in the arithmetic unit  100 , and recognizes the object as “information of object outside vehicle.” The object recognizer  111  receives outputs from the radars  32  and may acquire “positioning information of target” including, e.g., the position and speed of the target present around the vehicle  1 , as the “information of object outside vehicle.” The object recognizer  111  classifies objects outside the vehicle based on “information of object outside vehicle” and “positioning information of target” and recognizes the location information of each object. The classification herein may be to classify the objects broadly according to whether the object is a “static object” or an “object that can move” or may be to classify the objects based on specific target objects, such as “persons”, “signs”, and “crosswalks.” The object recognizer  111  may identify or classify the objects outside the vehicle by a neural network or the like. Alternatively, the object recognizer  111  may obtain the position and speed of the vehicle  10  from the output information from the sensors comprising the sensing device  30 . 
     The map generator  112  compares three-dimensional information of the surroundings of the vehicle  10  with a vehicle external environment model, based on the information of object outside the vehicle which has been recognized in the object recognizer  111 , thereby recognizing the vehicle&#39;s external environment, including the road and obstacles, to create a map. The vehicle external environment model is, for example, a learned model generated by deep learning, and allows recognition of a road, an obstacle, and the like with respect to the three-dimensional information of the surroundings of the vehicle. The map generator  112  may generate three- or two-dimensional map of the surroundings, or both of such maps. 
     Specifically, for example, the map generator  112  identifies a free space, that is, an area without an object, based on the information of object outside the vehicle which has been recognized in the object recognizer  111 . In this processing, for example, a learned model generated by deep learning is used. The map generator  112  generates a two-dimensional map that represents the free space. The map generator  112  also generates a three-dimensional map that represents the surroundings of the vehicle  10 , using the positioning information of target. In this process, information of the installation positions and shooting directions of the cameras  20 , and information of the installation positions and the transmission direction of the radars  32  are used. The map generator  112  compares the generated three-dimensional map with the vehicle external environment model to recognize the vehicle&#39;s external environment including roads and obstacles and outputs the results of the recognition to the candidate route generation unit  151 . In the deep learning, a multilayer neural network, e.g., a deep neural network (DNN) is used. An example of the multilayer neural network is convolutional neural network (CNN). The candidate route generation unit  151  generates candidate routes that can be traveled by the vehicle  10 , based on the outputs from the vehicle external environment recognition unit  110 , the outputs from the position sensor  33 , the information transmitted from an external network via the external communication unit  41 , for example. 
     The identical object recognizer  113  compares the results of image capturing of adjacent ones of the cameras  21 ,  22 , . . . , and  28 , based on the information of object outside the vehicle recognized in the object recognizer  111  and extracts objects identical with each other (hereinafter simply referred to as the “identical objects”) in the overlapping region RF. 
     Specifically, for example,  FIG. 4  illustrates an example in which the vehicle  10  stops before entering an intersection and in which a person P walks on a crosswalk ahead of the vehicle  10  in a direction that intersects with the moving direction of the vehicle  10 . The identical object recognizer  113  extracts, as the identical objects, objects included in the image data acquired by the cameras  21  and  22 , both of which are installed on the front side of the vehicle  10 , in the overlapping region RF 1  ahead of the vehicle  10 . For example, in the examples of  FIG. 4  and  FIGS. 6A and 6B , which will be described later, the person P, a crosswalk  71 , and a curb  72 , for example, are extracted as the identical objects. That is, the extraction processing by the identical object recognizer  113  is performed by comparing the results of capturing by both of the cameras  21  and  22  capturing the overlapping region RF. The identical objects are not limited to the above items and may be different objects such as other vehicles and signs. 
     The positional deviation detector  114  determines whether or not the positions of the identical objects in the overlapping region deviate from each other. If there is a positional deviation, the positional deviation detector  114  outputs a synchronization command signal for synchronizing the plurality of cameras  20 . For example, in the example of  FIG. 4 , the object extracted from the image obtained by capturing the overlapping region RF by the camera  21  and the object extracted from the image obtained by capturing the overlapping region RF by the camera  22  are compared with each other to extract identical objects and determine whether or not there is a deviation in position between the identical objects in the overlapping region RE. The method of determining the presence or absence of a positional deviation is not particularly limited. For example, a method may be employed in which among the identical objects, objects which have been recognized as static objects by the object recognizer  111  may be superimposed on each other to check whether or not there is a positional deviation between other identical objects. In the present embodiment, an example is shown in which the object recognizer  111 , the identical object recognizer  113 , and the positional deviation detector  114  implement a function corresponding to a synchronous processing unit. 
     (Operation of External Environment Recognition Device) 
     Next, the operation of the external environment recognition device will be described with reference to  FIGS. 5 to 7 . 
     In  FIG. 5 , Step S 11  to Step S 13  indicate a process from capturing of images by the cameras  20  to the extraction of identical objects by the object recognizer  111 . 
     First in Step S 11 , as illustrated in  FIG. 7 , the positional deviation detector  114  transmits a synchronous processing signal to the cameras  21 ,  22 , . . . , and  28  to perform synchronous processing. After the synchronous processing, the cameras  21 ,  22 , . . . , and  28  start capturing images. Each of the cameras  21 ,  22 , . . . , and  28  transmits the result of image capturing to the object recognizer  111  based on the clock signal of its own. The object recognizer  111  converts the imaging data transmitted from each of the cameras  21 ,  22 , . . . , and  28  to an image data and detects an object included in the image data. In  FIG. 7 , to facilitate understanding of the description, the number after the character “P” represents the identification number of a camera, and the number after the hyphen represents the timing of image: the same number represents the same timing. For example, in  FIG. 7 , P 1 - 1  indicates the first image captured by the camera  21 ; P 1 - 2  indicates the second image captured by the camera  21 ; and P 2 - 1  indicates the first image captured by the camera  22 . The characters w, x, y, and z are in a relationship of z=y+1=x+2=w+3. 
     In Step S 12 , the object recognizer  111  classifies objects and estimates the position of each object. A method of estimating the position of each object is not particularly limited. For example, the position of each object may be estimated by a method of reconstructing a three-dimensional space from the imaging data. Alternatively, for example, the position of each object may be estimated based on the position where each of the cameras  21 ,  22 , . . . , and  28  is installed, or a reference object may be chosen to estimate the position of each object based on a positional relationship with the reference object. 
     In Step S 13 , the identical object recognizer  113  extracts identical objects in the overlapping region RF. For example, in each of  FIGS. 6A and 6B , the upper left illustration shows the result of image capturing by the camera  21 , and the upper right illustration shows the result of image capturing by the camera  22 . A region in each result of image capturing surrounded by the virtual line is the overlapping region RF 1 . In the examples of  FIGS. 6A and 6B , the identical object recognizer  113  recognizes the crosswalk  71 , the person P walking on the crosswalk  71 , the L-shaped curb  72  visible at the back of the image, and the stop line  73  as identical objects in both of the upper right image data and the upper left image data in each of  FIGS. 6A and 6B . At the recognition, the identical object recognizer  113  may identify, from among the identical objects, the crosswalk  71 , the curb  72 , and the stop line  73  as static objects and the person P as a moving object with reference to images preceding and succeeding in time, a template stored in advance, or the like. 
     In the subsequent Step S 14 , the positional deviation detector  114  detects a positional deviation between the image in the overlapping region RF captured by the camera  21  and the image in the overlapping region RF captured by the camera  22 .  FIG. 6A  illustrates example images in the case where there is no positional deviation.  FIG. 6B  illustrates example images in the case where there is a positional deviation. For example, the positional deviation detector  114  identifies the near side corners  72   a  of the curbs  72 , which are identical objects and are also static objects, from the respective images captured by the cameras  21  and  22 , and superimposes the corners  72   a  on each other to align the images and see whether or not the other objects, except the corners  72   a , are superimposed, thereby detecting the positional deviation of the images. The detection is performed in this manner, and in the case of  FIG. 6A , for example, the positional deviation detector  114  detects that there is no positional deviation in all the identical objects. 
     Turning back to  FIG. 5 , if the positional deviation is not detected in Step S 14 , the answer will be NO in the subsequent Step S 15 , and the process returns to the aforementioned Step S 11 . 
     On the other hand, in the case of  FIG. 6B , the positional deviation detector  114  detects that blur has occurred in the image of the person P and that there is a positional deviation. If the positional deviation detector  114  detects a positional deviation, the positional deviation detector  114  sets a flag indicating the occurrence of the positional deviation, as illustrated in  FIG. 7 , for example. In the example of  FIG. 7 , although the image P 1 - w  of the camera  21  and the image P 8 - w  of the camera  28  are the images captured at the same timing, a positional deviation occurs, and hence a flag indicating the detection of the positional deviation is set, because the image P 2 - x  of the camera  22  is an image captured at different timing from the timing at which the images of the cameras  21  and  28  are captured. 
     Turning back to  FIG. 5 , if the positional deviation is detected in Step S 14 , the answer will be YES in the subsequent Step S 15 , and the positional deviation detector  114  checks whether or not the synchronous processing of the camera  20  has been executed (Step  16 ). That is, the positional deviation detector  114  checks whether or not the synchronous processing in Step S 17 , which will be described later, has been executed. 
     In Step S 16 , if the synchronous processing of the camera  20  has not been executed (NO in Step S 16 ), the positional deviation detector  114  performs synchronous processing to synchronize the camera  20  where the positional deviation occurs (Step S 17 ). For example, the positional deviation detector  114  transmits a synchronous processing signal to the camera  20  to be synchronized, so that the camera  20  is synchronized. As illustrated in  FIG. 7 , the synchronous processing may be performed on all the cameras, or may be performed on the cameras  20  (cameras  21  and  22  in the example of the  6 B) that capture the overlapping region RF in which the positional deviation is detected. Various known methods can be employed as the method of performing synchronizing processing on the camera  20 . Thus, detailed description thereof will be omitted herein. When the synchronous processing in Step S 17  ends, the process returns to Step S 11 . 
     On the other hand, if the synchronous processing of the camera  20  has already been executed in Step S 16  (YES in Step S 16 ), the arithmetic unit  100  provides notification that the positional deviation is not eliminated by the synchronous processing (Step S 18 ). If the positional deviation is not eliminated by the synchronous processing on the camera  20 , it may be because the camera  20  is physically displaced due to impact from the outside and is thus in a state in which “the installation position of the camera  20  itself is shifted.” Thus, the notification from the arithmetic unit  100  can urge the driver or other passengers to check the circumstances. The notification method in Step S 18  is not particularly limited. Although not illustrated, the notification may be a warning sound from a speaker, a beeper, or any other equipment, or a warning lamp, or may be given via a display screen of a car navigation system or the like, for example. The speaker, the beeper, the warning lamp, and the car navigation system are examples of the abnormality notification device. 
     Turning back to  FIG. 1 , the blocks subsequent to the block of the candidate route generation unit  151  will be briefly described below. 
     The vehicle behavior estimation unit  152  estimates a status of the vehicle from the outputs of the sensors which detect the behavior of the vehicle, such as a vehicle speed sensor, an acceleration sensor, and a yaw rate sensor. The vehicle behavior estimation unit  152  generates a six-degrees-of-freedom (6DoF) model of the vehicle indicating the behavior of the vehicle. 
     The occupant behavior estimation unit  153  particularly estimates the driver&#39;s health condition and emotion from the results of the detection of the occupant status sensor  35 . The health conditions include, for example, good health condition, slightly fatigue, poor health condition, decreased consciousness, and the like. The emotions include, for example, fun, normal, bored, annoyed, uncomfortable, and the like. 
     The route decision unit  154  decides the route to be traveled by the vehicle  10  based on the outputs from the occupant behavior estimation unit  153 . If the number of routes generated by the candidate route generation unit  151  is one, the route decision unit  154  decides this route to be the route to be traveled by the vehicle  10 . If the candidate route generation unit  151  generates a plurality of routes, a route that an occupant (in particular, the driver) feels most comfortable with, that is, a route that the driver does not perceive as a redundant route, such as a route too cautiously avoiding an obstacle, is selected out of the plurality of candidate routes, in consideration of an output from the occupant behavior estimation unit  153 . 
     The vehicle motion decision unit  155  decides a target motion for the travel route decided by the route decision unit  154 . The target motion means steering and acceleration/deceleration for following the travel route. In addition, with reference to the 6DoF model of the vehicle, the vehicle motion decision unit  155  calculates the motion of the vehicle body for the travel route selected by the route decision unit  154 . 
     A physical amount calculation unit calculates a driving force, a braking force, and a steering amount for achieving the target motion, and includes a driving force calculation unit  161 , a braking force calculation unit  162 , and a steering amount calculation unit  163 . To achieve the target motion, the driving force calculation unit  161  calculates a target driving force to be generated by powertrain devices (the engine and the transmission). To achieve the target motion, the braking force calculation unit  162  calculates a target braking force to be generated by a brake device. To achieve the target motion, the steering amount calculation unit  163  calculates a target steering amount to be generated by a steering device. 
     A peripheral device operation setting unit  170  sets operations of body-related devices of the vehicle  10 , such as lamps and doors, based on outputs from the vehicle motion decision unit  155 . The devices include, for example, actuators and sensors to be controlled while the motor vehicle is traveling or while the motor vehicle is being stopped or parked. 
     &lt;Output Destination of Arithmetic Unit&gt; 
     An arithmetic result of the arithmetic unit  100  is output to a control unit  50  and a body-related microcomputer  60 . The control unit  50  includes a powertrain microcomputer  51 , a brake microcomputer  52 , and a steering microcomputer  53 . Specifically, information related to the target driving force calculated by the driving force calculation unit  161  is input to the powertrain microcomputer  51 . Information related to the target braking force calculated by the braking force calculation unit  162  is input to the brake microcomputer  52 . Information related to the target steering amount calculated by the steering amount calculation unit  163  is input to the steering microcomputer  53 . Information related to the operations of the body-related devices set by the peripheral device operation setting unit  170  is input to the body-related microcomputer  60 . The steering microcomputer  53  includes a microcomputer for electric power assisted steering (EPAS). 
     In summary, the external environment recognition device of the present embodiment includes: a plurality of cameras  20  each having an imaging unit  20   a  that captures an external environment of a vehicle and arranged such that an imaging area captured by the imaging unit  20   a  includes an overlapping region RF where at least part of the imaging area captured by the imaging unit  20   a  overlaps with at least part of an imaging area captured by another imaging unit  20   a ; and a synchronous processing unit that extracts identical objects (e.g., a person P) in the overlapping region RF from results of image capturing by the cameras  20 , and performs synchronous processing to synchronize the plurality of cameras  20  if there is a deviation in position between the identical objects in the overlapping region RF. 
     The external environment recognition device of the present embodiment performs synchronous processing to synchronize the plurality of cameras  20  if there is a deviation in position between the identical objects in the overlapping region RF. This configuration allows the cameras  20  to be synchronized when the positional deviation occurs. It is also possible not to execute synchronous processing if there is no positional deviation in the results of image capturing by the cameras  20 , that is, while the cameras  20  are maintained in the synchronized state with each other. Thus, the processing resources for the synchronous processing can be saved, compared with the case like Patent Document 1 in which the synchronous processing is performed periodically. Further, since the synchronous processing is performed on the cameras  20 , it is possible to obtain output signals after the synchronous processing from the cameras  20 . This means that it is possible to synchronize signals at stages before execution of various types of image processing or any other processing for recognizing the external environment of the vehicle. The load on processing, such as image processing, can thus be reduced. 
     OTHER EMBODIMENTS 
     In the above embodiment, an example in which cameras  20  are used as an external environment recognition sensor has been described. However, the external environment recognition sensor is not limited to the cameras  20 , and may be a different sensing device  30 . For example, radars  32  may be used as the external environment recognition sensor in addition to or instead of the cameras  20 . 
     In the above embodiment, an example has been described in which the static objects (e.g., the curb  72 ) are extracted from the overlapping region RF and the static objects (e.g., the corner  72   a  of the curb  72 ) are superimposed on each other to detect a deviation in position between identical objects (e.g., a person P). However, the method of determining whether or not there is a deviation in position between the identical objects is not limited thereto. For example, since the cameras  20  are installed at fixed positions, the positional relationship between the regions overlapping each other may be such that the overlapping regions are directly superimposed on each other based on the installation positions of the cameras  20 , or may be determined based on the physical distance between objects. However, images are more accurately aligned by identifying static objects and superimposing the static objects on each other. 
     In the above embodiment, an example has been described in which the external environment recognition device performs the synchronous processing while the vehicle  10  stops at the intersection. However, a similar process as in  FIG. 5  (detection of a positional deviation and synchronous processing) may be performed while the vehicle  10  is traveling. 
     The camera  20  that causes a positional deviation may be identified using the images of a plurality of overlapping regions. Specifically, the positional deviation detection processing similar to that in the above embodiment may be performed on the overlapping region RF 2  of the cameras  21  and  28  and the overlapping region RF 3  of the cameras  22  and  23 , in addition to the overlapping region RF 1  of the cameras  21  and  22 . In a case in which a positional deviation such as the one in  FIG. 6B  occurs in the overlapping region RF 1 , and a similar positional deviation occurs in the overlapping region RF 2 , as well, it can be determined that the camera  20  causes the positional deviation. Identifying the camera that causes the positional deviation in this manner makes it possible to perform the synchronous processing on that camera alone, or give notification to the user or the like. 
     INDUSTRIAL APPLICABILITY 
     The technology disclosed herein is useful as an external environment recognition device that recognizes an external environment of an autonomous mobile object. 
     DESCRIPTION OF REFERENCE CHARACTERS 
     
         
           10  Vehicle (Autonomous Mobile Object) 
           20  Camera (External Environment Recognition Sensor) 
           20   a  Imaging Unit (Information Detection Unit) 
         RF Overlapping Region