System and method

Abstract

To provide a technique for detecting a target or the like present in a dead angle generated around a mobile object moving by driverless driving.SOLUTION: A system for moving a mobile object by driverless driving includes: at least one mobile object detector that detects mobile object information including at least any of a mobile object image and three-dimensional point group data on the mobile object; a position estimation section that estimates at least any of a position and direction of the mobile object using the detected mobile object information; a control section that generates a control command for causing the mobile object to travel by remote control using the position and direction of the estimated mobile object; and at least one dead angle detector that detects dead angle information including at least any of a dead angle image of the mobile object not detected by the mobile object detector, three-dimensional point group data on the dead angle, and presence / absence of a target in the dead angle.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a technology for moving a moving object by unmanned driving. [Background technology]

[0002] For example, Patent Document 1 discloses a vehicle driving method in a manufacturing system for manufacturing vehicles, in which a vehicle is driven by remote control from the end of an assembly line of the manufacturing system to a parking lot of the manufacturing system. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Special Publication No. 2017-538619 Summary of the Invention [Problem to be solved by the invention]

[0004] When a moving body such as a vehicle is moved by remote control, a process of estimating the position and orientation of the moving body may be executed. Data of the moving body detected by a detector such as a camera or a sensor may be used to estimate the position and orientation of the moving body. However, depending on the positional relationship between the moving body and the detector, a blind spot that cannot be detected by the detector may occur near the moving body. Therefore, a technology for detecting targets and the like present in the blind spot around the moving body is desired. [Means for solving the problem]

[0005] The present disclosure can be realized in the following forms.

[0006] (1) According to one embodiment of the present disclosure, a system for moving a moving object by unmanned driving is provided, the system includes at least one moving object detector that detects moving object information including at least one of an image of the moving object and three-dimensional point cloud data of the moving object, a position estimation unit that estimates at least one of a position and an orientation of the moving object using the detected moving object information, a control unit that generates a control command for driving the moving object using the estimated position and orientation of the moving object, and at least one blind spot detector that detects blind spot information including at least one of an image of a blind spot of the moving object not detected by the moving object detector, three-dimensional point cloud data of the blind spot, and the presence or absence of a target in the blind spot. According to the system of this embodiment, the blind spot detector that obtains blind spot information can detect targets and the like that exist in blind spots that occur around the moving body. (2) In the system of the above embodiment, the at least one blind spot detector may include a plurality of the blind spot detectors, and may further include a blind spot detector determination unit that determines, among the plurality of blind spot detectors, a blind spot detector that is capable of detecting the blind spot information corresponding to the blind spot for each position of the moving body relative to the moving body detector. According to the system of this embodiment, by appropriately switching the blind spot detector according to the position of the traveling mobile object, it is possible to detect blind spot information of blind spots that dynamically change in accordance with the movement of the mobile object. (3) The system of the above aspect may further include a blind spot estimation unit that estimates a position of the blind spot depending on a relative position of the moving object with respect to the moving object detector. According to the system of this embodiment, it is possible to detect blind spot information of blind spots that dynamically change in accordance with the movement of a mobile object. (4) In the system of the above form, the blind spot estimation unit may estimate the position of the blind spot by regarding a set of straight lines passing through the moving body from the moving body detector toward the moving body as the blind spot, the set being on the opposite side of the moving body from the moving body detector. This type of system makes it easy to estimate the location of blind spots. (5) In the system of the above aspect, the at least one moving object detector and the at least one blind spot detector may include detectors of different types. According to the system of this type, targets present in blind spots can be detected from multiple angles using different types of detectors. (6) In the system of the above aspect, the at least one moving object detector may be either a distance measuring device or a camera, and the at least one blind spot detector may be the other of the distance measuring device or the camera. According to this aspect, a distance measuring device and a camera can be used as different types of detectors. (7) The system of the above aspect may further include a composite map generating unit that generates a composite map by combining the detected moving body information and the detected blind spot information, and a surroundings monitoring unit that monitors the surroundings of the moving body. The generated composite map may be used for both surroundings monitoring by the surroundings monitoring unit and movement control of the moving body by the control unit. According to the system of this aspect, by using a common map for the movement control of the moving object and the monitoring of the surroundings of the moving object, it becomes easy to simultaneously execute the processes of the movement control and the monitoring of the surroundings. (8) According to another aspect of the present disclosure, there is provided a method for moving a moving body by unmanned driving, comprising: detecting, by at least one moving body detector, moving body information including at least one of an image of the moving body and three-dimensional point cloud data of the moving body, estimating at least one of a position and an orientation of the moving body using the detected moving body information, generating a control command for driving the moving body using the estimated position and orientation of the moving body, and detecting, by at least one blind spot detector, blind spot information including at least one of an image of a blind spot of the moving body not detected by the moving body detector, three-dimensional point cloud data of the blind spot, and the presence or absence of a target in the blind spot. According to the method of this aspect, a blind spot detector that acquires blind spot information can detect targets and the like that exist in blind spots that occur around the moving body. The present disclosure can also be realized in various forms other than a system or a method, for example, a control device, a moving method for a moving body, a manufacturing method for a moving body, a surrounding monitoring method for a moving body, a blind spot detection device, a blind spot detection method, a system control method, a control method for a control device, a computer program for realizing the control method, a non-transitory recording medium on which the computer program is recorded, etc. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is an explanatory diagram showing a schematic configuration of a system according to a first embodiment. [Figure 2A] 4 is a flowchart showing a processing routine for automatic vehicle driving and surroundings monitoring. [Figure 2B] 4 is a flowchart showing a processing procedure for vehicle travel control in the first embodiment. [Diagram 3] FIG. 2 is an explanatory diagram illustrating an example of a method for acquiring blind spot information while a vehicle is traveling. [Figure 4] FIG. 11 is an explanatory diagram showing the overall configuration of a system according to a second embodiment. [Diagram 5] FIG. 11 is an explanatory diagram showing the overall configuration of a system according to a third embodiment in a top view. [Figure 6] FIG. 2 is a block diagram showing the functional configuration of a control device. [Figure 7] 10 is a flowchart showing a processing procedure for vehicle travel control in a fourth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] A. First embodiment: FIG. 1 is an explanatory diagram showing a schematic configuration of a system 500 according to a first embodiment of the present disclosure. The system 500 includes one or more vehicle detectors 80 as moving body detectors, one or more blind spot detectors 90, and a control device 300. The control device 300 generates a control command for moving a vehicle 100 as a moving body by unmanned driving and transmits the control command to the vehicle 100. The system 500 is used, for example, in a factory that manufactures vehicles 100 that can move by unmanned driving. The vehicle detector 80 and the blind spot detector are external sensors arranged outside the vehicle 100.

[0009] The vehicle 100 may be, for example, a passenger car, a truck, a bus, or a construction vehicle. The vehicle 100 is preferably an electric vehicle (BEV: Battery Electric Vehicle). The vehicle 100 is not limited to an electric vehicle, and may be, for example, a gasoline-powered vehicle, a hybrid vehicle, or a fuel cell vehicle. The vehicle 100 includes a vehicle communication device 190, an actuator 140, and an ECU (Electronic Control Unit) 200. The vehicle communication device 190 performs wireless communication with devices outside the vehicle 100 connected to a network, such as a control device 300, via an access point in a factory or the like.

[0010] The vehicle 100 is configured to be capable of traveling by unmanned driving. "Unmanned driving" means driving without the driving operation of a passenger. Driving operation means at least one of the operations of "running", "turning" and "stopping" of the vehicle 100. Unmanned driving is realized by automatic or manual remote control using a device located outside the vehicle 100, or by autonomous control of the vehicle 100. The vehicle 100 traveling by unmanned driving may have a passenger who does not perform driving operation on board. The passenger who does not perform driving operation includes, for example, a person who simply sits in the seat of the vehicle 100, and a person who performs work other than driving operation, such as assembly, inspection, and operation of switches, while riding on the vehicle 100. Note that driving by a passenger who performs driving operation is sometimes called "manned driving".

[0011] In this specification, "remote control" includes "full remote control" in which all of the operations of the vehicle 100 are completely determined from outside the vehicle 100, and "partial remote control" in which some of the operations of the vehicle 100 are determined from outside the vehicle 100. In addition, "autonomous control" includes "full autonomous control" in which the vehicle 100 autonomously controls its own operations without receiving any information from devices external to the vehicle 100, and "partial autonomous control" in which the vehicle 100 autonomously controls its own operations using information received from devices external to the vehicle 100.

[0012] The ECU 200 is mounted on the vehicle 100 and executes various controls of the vehicle 100. The ECU 200 includes a storage device 220 such as an HDD (hard disk drive), an SSD (solid state drive), an optical recording medium, or a semiconductor memory, a CPU 210 as a central processing unit, and an interface circuit 230. The CPU 210, the storage device 220, and the interface circuit 230 are connected via an internal bus so as to be able to communicate bidirectionally. The actuator 140 and the vehicle communication device 190 are connected to the interface circuit 230.

[0013] Computer programs for implementing at least some of the functions provided in this embodiment are stored in a readable / writable area of ​​the storage device 220. The CPU 210 executes various computer programs stored in the memory to implement functions such as the operation control unit 212.

[0014] The driving control unit 212 executes driving control of the vehicle 100. The "driving control" refers to various controls for driving each actuator 140 that performs the functions of "running," "turning," and "stopping" of the vehicle 100, such as adjusting acceleration, speed, and steering angle. In this embodiment, the actuators 140 include an actuator of a driving device for accelerating the vehicle 100, an actuator of a steering device for changing the traveling direction of the vehicle 100, and an actuator of a braking device for decelerating the vehicle 100. The driving device includes a battery, a driving motor driven by the power of the battery, and driving wheels rotated by the driving motor. The actuator of the driving device includes a driving motor. The actuators 140 may further include an actuator for swinging the wipers of the vehicle 100, an actuator for opening and closing the power windows of the vehicle 100, and the like.

[0015] When a driver is on board the vehicle 100, the driving control unit 212 controls the actuator 140 in response to an operation by the driver, thereby allowing the vehicle 100 to travel. Regardless of whether a driver is on board the vehicle 100 or not, the driving control unit 212 can also control the actuator 140 in response to a control command transmitted from the control device 300, thereby allowing the vehicle 100 to travel.

[0016] The vehicle detector 80 is a device for detecting vehicle information as moving body information. The "vehicle information" refers to various information that can estimate at least one of the position of the vehicle 100 and the orientation of the vehicle 100. In this embodiment, the vehicle information is three-dimensional point cloud data of the vehicle 100. The three-dimensional point cloud data is data that indicates the three-dimensional position of a point cloud. The vehicle detector 80 uses LiDAR (Light Detection And Ranging), which is a distance measuring device that measures the three-dimensional point cloud data of the vehicle 100. By using LiDAR, highly accurate three-dimensional point cloud data can be acquired. Note that the orientation and running direction of the vehicle 100 may be estimated by acquiring only the position of the vehicle 100 by the vehicle detector 80 and acquiring changes over time of the vehicle 100.

[0017] The vehicle detector 80 is communicatively connected to the control device 300 via wireless or wired communication. The control device 300 can acquire the relative position and orientation of the vehicle 100 with respect to the target route RT in real time by acquiring three-dimensional point cloud data from the vehicle detector 80. In this embodiment, the position of the vehicle detector 80 is fixed near the travel route SR, and the relative relationship between the reference coordinate system Σr and the device coordinate system of the vehicle detector 80 is known. A coordinate transformation matrix for mutually transforming the coordinate values ​​of the reference coordinate system Σr and the coordinate values ​​of the vehicle detector 80 and the device coordinate system is stored in advance in the control device 300.

[0018] The blind spot detector 90 is a device for acquiring blind spot information. The "blind spot information" is various information related to the blind spot BS around the vehicle 100 that is not detected by the vehicle detector 80. The blind spot information is used to detect targets present in the blind spot BS. For example, as shown in FIG. 1, the "blind spot BS" can be defined as a set of parts of a straight line passing through the vehicle 100 from the vehicle detector 80 toward the vehicle 100, the parts being on the opposite side of the vehicle 100 from the vehicle detector 80. The blind spot BS is sufficient as long as it is at least an area used for monitoring the surroundings of the vehicle 100. In addition, when a single blind spot detector 90 cannot acquire all the blind spot information in the blind spot BS, another blind spot detector may be added, and one piece of blind spot information may be acquired by the multiple blind spot detectors 90. In this embodiment, the blind spot information is three-dimensional point cloud data including the blind spot BS. The blind spot detector 90 uses the same type of LiDAR as the vehicle detector 80. However, as described later, the blind spot detector 90 and the vehicle detector 80 may be different types of detectors. When the blind spot detector 90 and the vehicle detector 80 are the same type of detector, one detector may have the functions of the blind spot detector 90 and the functions of the vehicle detector 80, and these functions may be appropriately switched depending on the driving position of the vehicle 100, etc.

[0019] The control device 300 is a server that executes driving control of the vehicle 100 by remote control and monitoring of the surroundings of the vehicle 100. For example, the control device 300 transports the vehicle 100 in a manufacturing process in a factory by making the vehicle 100 travel automatically by remote control. Transporting the vehicle 100 using automatic travel by remote control is also called "self-propelled transport." By self-propelled transport, the vehicle 100 can be moved without using a transport device such as a crane or a conveyor.

[0020] The control device 300 includes a CPU 310 as a central processing unit, a storage device 340, an interface circuit 350, and a remote communication device 390. The CPU 310, the storage device 340, and the interface circuit 350 are connected to each other via an internal bus so as to be able to communicate in both directions. The remote communication device 390 is connected to the interface circuit 350. The remote communication device 390 communicates with the vehicle 100 via a network or the like.

[0021] The storage device 340 is, for example, a RAM, a ROM, a HDD, or an SSD. The storage device 340 stores vehicle point cloud data VP and a target route RT. The vehicle point cloud data VP is, for example, three-dimensional CAD data of the vehicle 100. The vehicle point cloud data VP includes information for identifying the orientation of the vehicle 100. The target route RT is a predetermined travel path of the vehicle 100 on the travel road SR.

[0022] When the computer program stored in the storage device 340 is executed by the CPU 310, the CPU 310 functions as a point cloud data acquisition unit 312, a position estimation unit 314, a blind spot estimation unit 316, a blind spot detector determination unit 318, a blind spot information acquisition unit 320, a composite map generation unit 322, a surroundings monitoring unit 324, and a control unit 326. However, some or all of these functions may be configured by hardware circuits. The point cloud data acquisition unit 312 acquires three-dimensional point cloud data measured by the vehicle detector 80.

[0023] The position estimation unit 314 estimates the position and orientation of the vehicle 100 in the driving environment including the driving road SR by using the acquired three-dimensional point cloud data. In this embodiment, the position estimation unit 314 estimates the position and orientation of the vehicle 100 by performing template matching using the vehicle point cloud data VP as a template stored in the storage device 340. For example, an ICP (Iterative Closest Point) algorithm or an NDT (Normal Distribution Transform) algorithm can be used for template matching. Note that the position estimation unit 314 may directly extract the position and orientation of the vehicle 100 from the acquired three-dimensional point cloud data instead of template matching.

[0024] The blind spot estimation unit 316 estimates the position of the blind spot BS around the vehicle 100. For example, the blind spot estimation unit 316 estimates the position of the blind spot BS of the vehicle 100 by using the relative position between the vehicle 100 and the vehicle detector 80 in the measured three-dimensional point cloud data. The "position of the blind spot BS" may be information on the three-dimensional coordinates of the area corresponding to the blind spot BS, or may be simple information such as the direction in which the blind spot BS exists relative to the vehicle 100. In addition, an area around the vehicle 100 where the three-dimensional point cloud data was not measured may be estimated as the blind spot BS. In addition, for example, the blind spot estimation unit 314 may estimate the position of the blind spot BS by regarding a set (area) on the opposite side of the vehicle 100 from the vehicle detector 80 among the straight lines passing through the vehicle 100 toward the vehicle 100 as the blind spot BS.

[0025] The blind spot detector determination unit 318 determines the blind spot detector 90 for detecting blind spot information from among the multiple blind spot detectors 90. The blind spot detector determination unit 318 determines the blind spot detector 90 corresponding to the position of the blind spot BS estimated by the blind spot estimation unit 316. In this embodiment, the blind spot detector determination unit 318 checks the detection range that each of the multiple blind spot detectors 90 can detect, and determines the blind spot detector 90 having a detection range that includes the blind spot BS as the blind spot detector 90 corresponding to the position of the blind spot BS. When the position of the blind spot BS is simple information such as the direction in which the blind spot BS exists with respect to the vehicle 100, the blind spot detector 90 located in that direction, or the blind spot detector 90 that can detect an area including that direction may be determined.

[0026] The blind spot information acquisition unit 320 acquires, as blind spot information, three-dimensional point cloud data of an area including the blind spot BS using the determined blind spot detector 90. The composite map generation unit 322 generates a composite map by combining the three-dimensional point cloud data acquired by the point cloud data acquisition unit 312 and the three-dimensional point cloud data of the blind spot BS acquired by the blind spot information acquisition unit 320. This makes it possible to generate a composite map in which the blind spot information is complemented. The surroundings monitoring unit 324 uses the generated composite map to monitor the surroundings of the vehicle 100, such as detecting obstacles around the vehicle 100.

[0027] The control unit 326 generates a control command (travel control signal) for remote control using the estimated position and orientation of the vehicle 100. The control unit 326 transmits the generated control command to the vehicle 100. This control command is a command to make the vehicle 100 travel according to the target route RT stored in the storage device 340. The control command can be generated as a command including a driving force or braking force and a steering angle. Alternatively, the control command may be generated as a command including at least one of the position and orientation of the vehicle 100 and a future travel route. When the vehicle 100 receives a request for remote control, the driving control is realized by the driving control unit 212 of the ECU 200, and as a result, the vehicle 100 travels automatically.

[0028] 2 is a flowchart showing a processing routine for automatic driving of the vehicle 100 by remote control and monitoring of the periphery of the vehicle 100. This flow is started when the vehicle 100 starts driving on the driving route SR. This flow can be repeatedly executed every time the point cloud data acquisition unit 312 acquires new three-dimensional point cloud data from the vehicle detector 80 in charge of measuring the vehicle 100 to be controlled. The point cloud data acquisition unit 312 may execute pre-processing to remove background point cloud data representing stationary objects from the newly acquired three-dimensional point cloud data.

[0029] In step S10, the point cloud data acquisition unit 312 acquires three-dimensional point cloud data as vehicle information from the vehicle detector 80. In step S20, the position estimation unit 314 estimates the position and orientation of the vehicle 100 using the acquired three-dimensional point cloud data. In step S30, the blind spot estimation unit 316 estimates the positions of the blind spots BS around the vehicle 100 using the relative position between the vehicle detector 80 that measured the vehicle 100 and the vehicle 100 in the measured three-dimensional point cloud data.

[0030] In step S40, the blind spot detector determination unit 318 determines a blind spot detector 90 capable of acquiring blind spot information of the estimated blind spot BS. In step S50, the determined blind spot detector 90 is used to acquire 3D point cloud data of an area including the blind spot BS as blind spot information.

[0031] In step S60, the composite map generating unit 322 generates a composite map by combining the three-dimensional point cloud data acquired by the point cloud data acquiring unit 312 and the three-dimensional point cloud data of the blind spot BS acquired by the blind spot information acquiring unit 320. In this embodiment, the generated composite map is used for both the surroundings monitoring by the surroundings monitoring unit 324 and the driving control (also referred to as "movement control") of the vehicle 100 by the control unit 326.

[0032] In step S70, the surroundings monitoring unit 324 performs surroundings monitoring of the vehicle 100 using the generated composite map. Specifically, the surroundings monitoring unit 324 monitors the composite map to detect obstacles around the vehicle 100 including the blind spot BS. In step S80, the surroundings monitoring unit 324 checks whether there is an obstacle around the vehicle 100 that hinders the vehicle 100 from traveling. If there is no obstacle around the vehicle 100 (S80: NO), the surroundings monitoring unit 324 shifts the process to step S82 and outputs an instruction to cause the vehicle 100 to travel along the target route RT. In step S90, the control unit 326 uses the estimated position and orientation of the vehicle 100 to generate a control command to cause the vehicle 100 to travel along the target route RT, and transmits the control command to the vehicle 100.

[0033] When an obstacle is detected around the vehicle 100 (S80: YES), the surroundings monitoring unit 324 transitions to step S84 and outputs a command to stop the vehicle 100. In this case, in step S90, the control unit 326 generates a control command to stop the vehicle 100 and transmits the control command to the vehicle 100.

[0034] FIG. 2B is a flowchart showing a processing procedure of the driving control of the vehicle 100 in the first embodiment. In step S1, the server 300 (synonymous with the above-mentioned "control device 300") acquires vehicle position information using a detection result output from an external sensor that is a sensor located outside the vehicle 100. The vehicle position information is position information that is the basis for generating a driving control signal. In this embodiment, the vehicle position information includes the position and orientation of the vehicle 100 in the reference coordinate system Σr of the factory. In this embodiment, the reference coordinate system Σr of the factory is a global coordinate system, and any position in the factory can be expressed by X, Y, and Z coordinates in the global coordinate system. In this embodiment, the external sensor includes at least the vehicle detector 80 installed in the factory. That is, in step S1, the server 300 acquires vehicle position information using data acquired from the vehicle detector 30 that is an external sensor.

[0035] In detail, in step S1, the server 300 acquires vehicle position information of the vehicle 100 by template matching using, for example, three-dimensional point cloud data acquired from a distance measuring device, which is the vehicle detector 30, and the above-mentioned vehicle point cloud data VP, which is reference point cloud data prepared in advance. When the vehicle detector 30 is a camera, the server 300 detects the outer shape of the vehicle 100, calculates the coordinates of the positioning point of the vehicle 100 in the coordinate system of the captured image, i.e., in the local coordinate system, and converts the calculated coordinates into coordinates in the global coordinate system to acquire the position of the vehicle 100. The outer shape of the vehicle 100 included in the captured image can be detected, for example, by inputting the captured image into a detection model that utilizes artificial intelligence. The detection model is prepared, for example, inside or outside the system 50, and is stored in advance in the storage device 340, which is the memory of the server 300. As the detection model, for example, a trained machine learning model that has been trained to realize either semantic segmentation or instance segmentation can be given. As the machine learning model, for example, a convolutional neural network (hereinafter, CNN) trained by supervised learning using a training data set can be used. The training data set has, for example, a plurality of training images including the vehicle 100, and labels indicating whether each area in the training image is an area indicating the vehicle 100 or an area indicating something other than the vehicle 100. During training of the CNN, it is preferable to update the parameters of the CNN by backpropagation (error backpropagation method) so as to reduce the error between the output result of the detection model and the label. In addition, the server 300 can acquire the orientation of the vehicle 100 by estimating based on the orientation of the movement vector of the vehicle 100 calculated from the position change of the feature points of the vehicle 100 between frames of the captured image using, for example, an optical flow method.

[0036] In step S2, the server 300 determines a target position to which the vehicle 100 should next head. In this embodiment, the target position is expressed by X, Y, and Z coordinates in a global coordinate system. A reference route (target route RT), which is a route to be traveled by the vehicle 100, is stored in advance in the storage device 340, which is the memory of the server 300. The route is represented by nodes indicating the departure point, nodes indicating passing points, nodes indicating the destination, and links connecting the nodes. The server 300 uses the vehicle position information and the reference route to determine a target position to which the vehicle 100 should next head. The server 300 determines a target position on the reference route that is ahead of the current location of the vehicle 100.

[0037] In step S3, the server 300 generates a driving control signal (control command value) for driving the vehicle 100 toward the determined target position. In this embodiment, the driving control signal includes the acceleration and steering angle of the vehicle 100 as parameters. The server 300 calculates the driving speed of the vehicle 100 from the transition of the position of the vehicle 100, and compares the calculated driving speed with the target speed. When the driving speed is lower than the target speed as a whole, the server 300 determines the acceleration so that the vehicle 100 accelerates, and when the driving speed is higher than the target speed, the server 300 determines the acceleration so that the vehicle 100 decelerates. In addition, when the vehicle 100 is located on the reference route, the server 300 determines the steering angle and acceleration so that the vehicle 100 does not deviate from the reference route, and when the vehicle 100 is not located on the reference route, in other words, when the vehicle 100 deviates from the reference route, the server 300 determines the steering angle and acceleration so that the vehicle 100 returns to the reference route. In other embodiments, the cruise control signal may include the speed of the vehicle 100 as a parameter instead of or in addition to the acceleration of the vehicle 100.

[0038] In step S4, server 300 transmits the generated driving control signal to vehicle 100. Server 300 repeats, at a predetermined cycle, acquisition of vehicle position information, determination of a target position, generation of a driving control signal, and transmission of the driving control signal.

[0039] In step S5, the vehicle 100 receives a traveling control signal transmitted from the server 300. In step S6, the vehicle 100 uses the received traveling control signal to control the actuators of the vehicle 100, thereby causing the vehicle 100 to travel at the acceleration and steering angle represented in the traveling control signal. The vehicle 100 repeats receiving the traveling control signal and controlling the actuators of the vehicle 100 at a predetermined cycle. According to the system 50 in this embodiment, the vehicle 100 can be caused to travel by remote control, and the vehicle 100 can be moved without using transportation equipment such as a crane or a conveyor.

[0040] FIG. 3 is an explanatory diagram that shows a schematic example of a method for acquiring blind spot information while the vehicle 100 is traveling. FIG. 3 shows a state in which the vehicle 100 is transported autonomously in a factory FC where the vehicle 100 is manufactured. The factory FC includes a first process 50 and a second process 60. The first process 50 is, for example, a place where the vehicle 100 is assembled, and the second process 60 is, for example, a place where the vehicle 100 is inspected. The first process 50 and the second process 60 are connected by a travel path SR along which the vehicle 100 can travel. Any position in the factory FC is expressed by xyz coordinate values ​​of a reference coordinate system Σr.

[0041] A plurality of vehicle detectors 80 including vehicle detectors 80A, 80B, and 80C, and a blind spot detector 90 including blind spot detectors 90A, 90B, and 90C for detecting three-dimensional point cloud data of blind spots BSa, BSb, and BSc not acquired by the vehicle detectors 80A, 80B, and 80C are installed around the travel path SR. A control unit 326 of the control device 300 causes the ECU 200 to execute driving control of the vehicle 100 while analyzing the three-dimensional point cloud data of the travel path SR and the vehicle 100 acquired by the vehicle detectors 80 at predetermined time intervals.

[0042] In the example of FIG. 3, the target route RT set on the travel road SR includes target routes RT1, RT2, and RT3 that are continuous with each other. The vehicle 100A that departs from the first step 50 travels along the target route RT1 by remote control using three-dimensional point cloud data acquired by the vehicle detector 80A. At this time, the position of the blind spot BSa is estimated from the relative position between the vehicle detector 80A and the vehicle 100A, and the blind spot detector 90A for detecting blind spot information of the estimated blind spot BSa is determined. A composite map is generated by combining the three-dimensional point cloud data acquired by the vehicle detector 80A and the three-dimensional point cloud data of the blind spot BSa acquired by the determined blind spot detector 90A. The vehicle 100A automatically travels along the target route RT1 by remote control using the composite map.

[0043] When the vehicle 100A reaches the target route RT2, the vehicle detector 80A is switched to the vehicle detector 80B suitable for detecting the vehicle 100B. The position of the blind spot BSb is estimated from the relative positions of the vehicle detector 80B and the vehicle 100B, and the blind spot detector 90B corresponding to the blind spot BSb is determined. The vehicle 100B travels along the target route RT2 by remote control using a composite map that combines the three-dimensional point cloud data acquired by the vehicle detector 80B and the three-dimensional point cloud data of the blind spot BSb acquired by the blind spot detector 90B.

[0044] When the vehicle 100B reaches a predetermined position on the target route RT2, the vehicle detector 80B is switched to the vehicle detector 80C suitable for detecting the vehicle 100C. The position of the blind spot BSc is estimated from the relative positions of the vehicle detector 80C and the vehicle 100C, and the blind spot detector 90C corresponding to the blind spot BSc is determined. The vehicle travels to the target route RT3 by remote control using a composite map that combines the three-dimensional point cloud data acquired by the vehicle detector 80C and the three-dimensional point cloud data of the blind spot BSc acquired by the blind spot detector 90C, and the same processing is executed thereafter.

[0045] In this way, the vehicle detector 80 is appropriately switched in accordance with the traveling position of the vehicle 100. The position of the blind spot BS is estimated from the switched vehicle detector 80 and the relative position of the traveling vehicle 100, and the blind spot detector 90 corresponding to the estimated blind spot BS is determined. Surroundings monitoring and driving control of the vehicle 100 are performed using a composite map generated using the determined blind spot detector 90 and the switched vehicle detector 80.

[0046] As described above, the system 500 of this embodiment includes a plurality of vehicle detectors 80 that detect vehicle information including three-dimensional point cloud data of the vehicle 100, a position estimation unit 314 that estimates the position and orientation of the vehicle 100 using the detected vehicle information, a control unit 326 that generates a control command for automatically driving the vehicle 100 by remote control using the estimated position and orientation of the vehicle 100 and transmits the control command to the vehicle 100, and a plurality of blind spot detectors 90 that detect blind spot information including three-dimensional point cloud data of the blind spots BS of the vehicle 100. The blind spot detector 90 that acquires the three-dimensional point cloud data of the blind spots BS can detect targets and the like that exist in the blind spots BS that occur around the vehicle 100.

[0047] The system 500 of the present embodiment further includes a blind spot detector determination unit 318 that determines, from the multiple blind spot detectors 90, a blind spot detector 90 capable of detecting blind spot information corresponding to a blind spot BS for each position of the vehicle 100 relative to the vehicle detector 80. By appropriately switching the blind spot detector 90 for each position of the traveling vehicle 100, it is possible to detect blind spot information of the blind spot BS that dynamically changes in accordance with the traveling of the vehicle 100.

[0048] The system 500 of the present embodiment further includes a blind spot estimation unit 316 that estimates the position of the blind spot BS according to the relative position of the vehicle 100 with respect to the vehicle detector 80. The position of the blind spot BS that changes as the vehicle 100 travels can be dynamically detected.

[0049] According to the system 500 of this embodiment, the composite map generated by the composite map generation unit 322 is used for both the surroundings monitoring by the surroundings monitoring unit 324 and the movement of the vehicle 100 by the control unit 326. By using a common map using three-dimensional point cloud data, it becomes easy to simultaneously execute the processes of traveling the vehicle 100 and monitoring the surroundings of the vehicle 100. In addition, the process can be simplified compared to the case where the driving control of the vehicle 100 and the monitoring of the surroundings of the vehicle 100 are executed separately.

[0050] B. Second embodiment: 4 is an explanatory diagram showing the overall configuration of a system 500 according to a second embodiment of the present disclosure. The system 500 of the second embodiment is different from the system 500 of the first embodiment in that a control device 300b is provided instead of the control device 300, and a blind spot detector 90b using a detector other than LiDAR is provided instead of the blind spot detector 90 using LiDAR, but the other configurations are the same.

[0051] The control device 300b differs from the control device 300 shown in the first embodiment in that it includes a CPU 310b that omits the functions of the blind spot estimation unit 316 and the composite map generation unit 322 instead of the CPU 310, and further includes a storage device 340b in which a correspondence table TB is stored, but otherwise has the same configuration. In this embodiment, the blind spot detector 90 is a detector different from the vehicle detector 80, so a composite map is not generated.

[0052] In this embodiment, the blind spot detector 90b is a detector different from LiDAR, for example, a camera that captures an image including the blind spot BS. The blind spot detector 90b may be a detector other than a camera or LiDAR, and may be various detectors capable of detecting the presence or absence of a target in the blind spot BS, such as an infrared sensor, an ultrasonic sensor, or a millimeter wave radar.

[0053] In the above-described first embodiment, an example was shown in which the blind spot detector 90 is a LiDAR and the same detector as the vehicle detector 80 is used. In contrast, as in the present embodiment, the blind spot detector 90 and the vehicle detector 80 may be different types of detectors. According to the system 500 configured in this manner, targets in the blind spots BS can be detected from multiple angles using different types of detectors. In addition, the process of generating a composite map can be omitted, and the processing speed of the system 500 can be improved.

[0054] The correspondence table TB shows the correspondence between the traveling position of the vehicle 100, the vehicle detector 80 capable of detecting the vehicle 100 at the traveling position, and the blind spot detector 90 capable of detecting the blind spot information of the blind spot BS occurring around the vehicle 100 at the traveling position. Since the vehicle 100 travels on the travel path SR along the target route RT, it may be possible to set in advance the correspondence between the vehicle detector 80 capable of detecting the vehicle 100 at each traveling position of the vehicle 100 and the blind spot detector 90 capable of detecting the blind spot information of the blind spot BS occurring around the vehicle 100 at each traveling position. In the example of FIG. 3, the correspondence table TB specifies that when the traveling position of the vehicle 100 is the target route RT1, a combination of the vehicle detector 80A and the blind spot detector 90A is used, and when the traveling position of the vehicle 100 is the target route RT2, a combination of the vehicle detector 80B and the blind spot detector 90B and a combination of the vehicle detector 80C and the blind spot detector 90C are used. In this embodiment, the correspondence table TB utilizing this feature is prepared in advance.

[0055] According to the system 500 of this embodiment, by using the correspondence table TB utilizing the traveling position of the vehicle 100, it is possible to determine the corresponding vehicle detector 80 and blind spot detector 90 by a simple method of acquiring the traveling position of the vehicle 100 by the position estimation unit 314 or the like. Therefore, it is possible to simplify the process for determining the vehicle detector 80 and blind spot detector 90 corresponding to the traveling position of the vehicle 100, for example by omitting the functions of the blind spot estimation unit 316 and the composite map generation unit 322.

[0056] C. Third embodiment: FIG. 5 is an explanatory diagram showing an example of the overall configuration of a system 500c according to the third embodiment in a top view. The system 500c according to this embodiment differs from the system 500 according to the first embodiment in that the system 500c includes a control device 300c instead of the control device 300. The control device 300c is an unmanned transport vehicle also called a guide mobility. The control device 300c has a function of automatically driving the vehicle 100 by remote control as in the first embodiment, and also has a function of automatically driving the control device 300c on the travel path SR. That is, the control device 300c automatically drives the following vehicle 100 by remote control while automatically driving the travel path SR.

[0057] The control device 300c includes a self-propelled driving wheel 142, a self-propelled detector 70, and a vehicle detector 80. The function of the vehicle detector 80 is the same as that of the vehicle detector 80 shown in the first embodiment, and therefore the description will be omitted. The self-propelled detector 70 is a distance measuring device such as a camera or LiDAR. Data acquired by the self-propelled detector 70 is used for simultaneous execution of self-position estimation and environmental map creation (SLAM: Simultaneous Localization and Mapping) for the control device 300c to travel automatically. As the camera, a stereo camera, a monocular camera, an RGB-D camera (depth camera), or other camera may be used. Instead of the camera or LiDAR, a ToF sensor or the like may be used.

[0058] 6 is a block diagram showing the functional configuration of a control device 300c included in a system 500c according to the third embodiment. The control device 300c differs from the control device 300 of the first embodiment in that, instead of the CPU 310, it further includes a CPU 310c that functions as a SLAM unit 328 and an automatic driving control unit 330, instead of the storage device 340, it further includes a storage device 340c in which a guide vehicle route GR is stored, and it further includes an actuator 360. The other functions are the same. The guide vehicle route GR is a predetermined target route along which the control device 300c should travel.

[0059] The actuator 360 includes actuators for the drive device, steering device, and braking device for automatic driving of the control device 300c. The drive device includes a battery, a driving motor driven by the battery power, and drive wheels 142 rotated by the driving motor.

[0060] The SLAM unit 328 executes SLAM using data detected by the self-driving detector 70, and executes the generation of a map for automatic driving of the control device 300c, etc. The automatic driving control unit 330 controls the actuator 360 to cause the control device 300c to automatically drive. The automatic driving control unit 330 uses the generated map to cause the control device 300c to automatically drive according to the guide vehicle route GR stored in the storage device 340c.

[0061] As shown in Fig. 5, a plurality of blind spot detectors 90 including blind spot detectors 90P, 90Q, and 90R are installed around the road SR. In the example of Fig. 5, the vehicle detector 80 is only mounted on the control device 300c, but it may be further installed around the road SR like the blind spot detector 90.

[0062] As shown in the left side of FIG. 5, when the control device 300c travels along the linear guide vehicle route GR1, the vehicle 100P is guided by remote control of the control device 300c and travels along the same travel route as the control device 300c. In this case, the blind spot BSp not detected by the vehicle detector 80 may occur, for example, on the opposite side of the vehicle 100P from the control device 300c, that is, on the rear side of the vehicle 100P. The blind spot estimation unit 316 estimates the position of the blind spot BSp, and the blind spot detector determination unit 318 determines the blind spot detector 90P capable of detecting the blind spot BSp. When the vehicle 100P travels further, the blind spot BSp leaves the detection range of the blind spot detector 90A. In this case, the blind spot detector determination unit 318 switches to, for example, the blind spot detector 90Q.

[0063] 5, when the control device 300c travels along the curved guide vehicle route GR2, for example, a blind spot BSr occurs over the rear and side of the vehicle 100Q that cannot be detected by the vehicle detector 80. In this case, the blind spot detector determination unit 318 determines, for example, the blind spot detector 90R that can detect the blind spot information of the blind spot BSr.

[0064] As described above, according to the system 500c of the present embodiment, the control device 300c equipped with the vehicle detector 80 for detecting the vehicle 100 is configured to perform automatic driving. According to the system 500c of the present embodiment, even if the blind spot BS around the vehicle 100 changes dynamically due to the automatic driving of the control device 300c and the automatic driving of the vehicle 100 by remote control, it is possible to acquire blind spot information by dynamically switching the blind spot detector 90.

[0065] D. Fourth embodiment: FIG. 7 is a flowchart showing a processing procedure of vehicle travel control in the fourth embodiment. Since the device configuration of the vehicle in this embodiment is similar to that of the first embodiment, for convenience, the vehicle in this embodiment is written as the vehicle 100. In addition, in the fourth embodiment, the control devices 300, 300b, and 300c in the first to third embodiments are mounted on the vehicle 100. That is, in the fourth embodiment, the functions of the CPUs 310, 310b, and 310c executed by the first to third embodiments and the data stored in the storage devices 340, 340b, and 340c are possessed by the vehicle 100. In step S901, the vehicle 100 acquires vehicle position information using the detection result output from the vehicle detector 80, which is an external sensor. In step S902, the vehicle 100 determines a target position to which the vehicle 100 should next head. In step S903, the vehicle 100 generates a travel control signal for driving the vehicle 100 toward the determined target position. In step S904, the vehicle 100 uses the generated driving control signal to control the actuators of the vehicle 100, thereby causing the vehicle 100 to drive according to the parameters represented in the driving control signal. The vehicle 100 repeats the acquisition of vehicle position information, the determination of a target position, the generation of a driving control signal, and the control of the actuators at a predetermined cycle. According to the driving control in this embodiment, the vehicle 100 can be driven by the autonomous control of the vehicle 100, without remotely controlling the vehicle 100 by the control devices 300, 300b, and 300c, which are servers.

[0066] E. Other embodiments: (E1) In the above embodiment, an example was given in which the vehicle detector 80 was a LiDAR. In contrast, a camera that captures an image of the vehicle 100 as vehicle information may be used as the vehicle detector 80. The image captured by the camera is used to capture various information used for remote control, such as the position and orientation of the vehicle 100, by image analysis by the control device 300, such as object recognition using a machine learning model.

[0067] (E2) In the above embodiments, the system 500, 500c includes a plurality of blind spot detectors 90, but may include a single blind spot detector 90. When there is a single blind spot detector 90, instead of determining which blind spot detector 90 to use, only a determination as to whether or not to use the blind spot detector 90 may be executed. In this case, the function of the blind spot detector determination unit 318 may be omitted, and the blind spot detector determination unit 318 may determine whether or not to use the blind spot detector 90. In addition, although the system 500, 500c includes a plurality of vehicle detectors 80, but may include a single vehicle detector 80.

[0068] (E3) In the above first embodiment, an example was shown in which the generated composite map is used for both the surroundings monitoring by the surroundings monitoring unit 324 and the driving control of the vehicle 100 by the control unit 326. In contrast, the composite map may be used only for the surroundings monitoring. Furthermore, surroundings monitoring may be performed using only the blind spot information without generating a composite map, or surroundings monitoring may be performed using the blind spot information and the vehicle information separately. When the composite map is not generated, the automatic driving of the vehicle 100 by remote control can be realized by using the vehicle information acquired by the vehicle detector 80.

[0069] (E4) In the above embodiments, examples have been given in which the vehicle 100 is a passenger car, a truck, a bus, a construction vehicle, and the like. However, the vehicle 100 is not limited to these, and may include various automobiles such as two-wheeled vehicles and four-wheeled vehicles, trains, and the like. In addition, the vehicle 100 may be various moving bodies other than the vehicle 100. "Moving body" means an object that can move. Moving bodies include, for example, vehicles, electric vertical take-off and landing aircraft (so-called flying cars), ships, aircraft, robots, linear motor cars, and the like. In this case, the expressions "vehicle" and "car" in this disclosure can be appropriately replaced with "moving body", and the expression "running" can be appropriately replaced with "moving".

[0070] (E5) The vehicle 100 may have a configuration capable of moving by unmanned driving. For example, the vehicle 100 may be in the form of a platform having the configuration described below. Specifically, the vehicle 100 may have a configuration for performing the functions of "running," "turning," and "stopping" by unmanned driving. That is, the vehicle 100 may have at least a control device that controls the running of the vehicle 100, and actuators such as a drive device, a steering device, and a braking device. When the vehicle 100 acquires information from the outside for unmanned driving, the vehicle 100 may further have a communication device. That is, the vehicle 100 capable of moving by unmanned driving may not be equipped with at least a part of interior parts such as a driver's seat and a dashboard, may not be equipped with at least a part of exterior parts such as a bumper and a fender, and may not be equipped with a body shell. In this case, the remaining parts such as the body shell may be attached to the vehicle 100 before the vehicle 100 is shipped from the factory, or the remaining parts such as the body shell may be attached to the vehicle 100 after the vehicle 100 is shipped from the factory without the remaining parts such as the body shell being attached to the vehicle 100. Each part may be attached from any direction such as the upper side, lower side, front side, rear side, right side, or left side of the vehicle 100, and may be attached from the same direction or from different directions. Note that the position of the platform shape may be determined in the same manner as for the vehicle 100 in each of the above embodiments.

[0071] (E6) In the above first to third embodiments, the servers 300, 300b, and 300c execute the processes from obtaining vehicle position information to generating the driving control signal. In contrast, at least a part of the processes from obtaining vehicle position information to generating the driving control signal may be executed by the vehicle 100. For example, the following forms (1) to (3) may be used.

[0072] (1) The servers 300, 300b, and 300c may acquire vehicle position information, determine a target position to which the vehicle 100 should next head, and generate a route from the current location of the vehicle 100 represented in the acquired vehicle position information to the target position. The servers 300, 300b, and 300c may generate a route to a target position between the current location and the destination, or may generate a route to the destination. The servers 300, 300b, and 300c may transmit the generated route to the vehicle 100. The vehicle 100 may generate a driving control signal so that the vehicle 100 drives on the route received from the servers 300, 300b, and 300c, and control the actuators of the vehicle 100 using the generated driving control signal.

[0073] (2) Servers 300, 300b, 300c may acquire vehicle position information and transmit the acquired vehicle position information to vehicle 100. Vehicle 100 may determine a target position to which vehicle 100 should next head, generate a route from the current location of vehicle 100 represented in the received vehicle position information to the target position, generate a driving control signal so that vehicle 100 travels on the generated route, and control an actuator of vehicle 100 using the generated driving control signal.

[0074] (3) In the above-mentioned (1) and (2) modes, the vehicle 100 may be equipped with an internal sensor, and a detection result output from the internal sensor may be used for at least one of generating a route and generating a driving control signal. The internal sensor is a sensor equipped in the vehicle 100. Specifically, the internal sensor may include, for example, a camera, a LiDAR, a millimeter wave radar, an ultrasonic sensor, a GPS sensor, an acceleration sensor, a gyro sensor, and the like. For example, in the above-mentioned (1) mode, the server 300 may acquire the detection result of the internal sensor, and may reflect the detection result of the internal sensor in the route when generating a route. In the above-mentioned (1) mode, the vehicle 100 may acquire the detection result of the internal sensor, and may reflect the detection result of the internal sensor in the driving control signal when generating a driving control signal. In the above-mentioned (2) mode, the vehicle 100 may acquire the detection result of the internal sensor, and may reflect the detection result of the internal sensor in the route when generating a route ... driving control signal when generating a driving control signal.

[0075] (E7) In the above embodiment in which the vehicle 100 can travel by autonomous control, an internal sensor may be mounted on the vehicle 100, and a detection result output from the internal sensor may be used for at least one of generating a route and generating a travel control signal. For example, the vehicle 100 may obtain the detection result of the internal sensor, and when generating a route, may reflect the detection result of the internal sensor in the route. The vehicle 100 may obtain the detection result of the internal sensor, and when generating a travel control signal, may reflect the detection result of the internal sensor in the travel control signal.

[0076] (E8) In each of the above embodiments, the servers 300, 300b, 300c automatically generate a driving control signal to be transmitted to the vehicle 100. In contrast, the server 300 may generate a driving control signal to be transmitted to the vehicle 100 in accordance with the operation of an external operator located outside the vehicle 100. For example, the external operator may operate a control device including a display for displaying an image output from an external sensor, a steering wheel for remotely controlling the vehicle 100, an accelerator pedal, a brake pedal, and a communication device for communicating with the server 300 by wired communication or wireless communication, and the servers 300, 300b, 300c may generate a driving control signal in accordance with the operation applied to the control device.

[0077] (E9) The vehicle 100 may be manufactured by combining a plurality of modules. The module means a unit composed of one or more parts that are grouped together according to the configuration and function of the vehicle 100. For example, the platform of the vehicle 100 may be manufactured by combining a front module that configures the front part of the platform, a central module that configures the central part of the platform, and a rear module that configures the rear part of the platform. The number of modules that configure the platform is not limited to three, and may be two or less, or four or more. In addition to or instead of the platform, a part of the vehicle 100 that is different from the platform may be modularized. The various modules may include any exterior part such as a bumper or a grille, or any interior part such as a seat or a console. In addition, any mobile body, not limited to the vehicle 100, may be manufactured by combining a plurality of modules. Such a module may be manufactured, for example, by joining a plurality of parts by welding or a fastener, or may be manufactured by integrally molding at least a part of the module as a single part by casting. The molding method of integrally molding at least a part of the module as a single part is also called gigacast or megacast. By using Gigacast, each part of a moving body that was previously formed by joining multiple parts can be formed as a single part. For example, the front module, center module, and rear module described above may be manufactured using Gigacast.

[0078] (E10) Transporting the vehicle 100 by using the driving of the vehicle 100 through unmanned driving is also called "self-propelled transport." Also, a configuration for realizing self-propelled transport is also called a "vehicle remote-controlled autonomous driving transport system." Also, a production method for producing the vehicle 100 using self-propelled transport is also called "self-propelled production." In self-propelled production, for example, at a factory where the vehicle 100 is manufactured, at least a part of the transportation of the vehicle 100 is realized by self-propelled transport.

[0079] The present disclosure is not limited to the above-mentioned embodiment, and can be realized in various configurations without departing from the spirit of the present disclosure. For example, the technical features in the embodiments corresponding to the technical features in each aspect described in the Summary of the Invention column can be appropriately replaced or combined to solve some or all of the above-mentioned problems or to achieve some or all of the above-mentioned effects. Furthermore, if the technical feature is not described as essential in this specification, it can be appropriately deleted. [Explanation of symbols]

[0080] 50...first process, 60...second process, 70...self-driving detector, 80, 80A, 80B, 80C...vehicle detector, 90, 90A, 90B, 90C, 90P, 90Q, 90R, 90b...blind spot detector, 100, 100A, 100B, 100C, 100P, 100Q...vehicle, 140, 360...actuator, 142...drive wheel, 190...vehicle communication device, 200...ECU, 210...CPU, 212...driving control unit, 220...storage device, 230, 350...interface circuit, 300, 300b, 300c...control device, 310, 310b, 310c...CPU, 312...point cloud data data acquisition unit, 314...position estimation unit, 316...blind spot estimation unit, 318...blind spot detector determination unit, 320...blind spot information acquisition unit, 322...synthetic map generation unit, 324...surroundings monitoring unit, 326...control unit, 328...SLAM unit, 330...autonomous driving control unit, 340, 340b, 340c...storage device, 390...remote communication device, 500, 500c...system, BS, BSa, BSb, BSc, BSp, BSr...blind spot, FC...factory, GR, GR1, GR2...guide vehicle route, RT, RT1, RT2, RT3...target route, SR...driving path, TB...correspondence table, VP...vehicle point cloud data

Claims

1. A system for moving a moving object by unmanned driving, at least one moving object detector that detects moving object information including at least one of an image of the moving object and three-dimensional point cloud data of the moving object; a position estimation unit that estimates at least one of a position and an orientation of the moving object using the detected moving object information; a control unit that generates a control command for causing the moving object to travel using the estimated position and orientation of the moving object; A system comprising at least one blind spot detector that detects blind spot information including at least one of an image of a blind spot of the moving object not detected by the moving object detector, three-dimensional point cloud data of the blind spot, and the presence or absence of a target in the blind spot.

2. 2. The system of claim 1, The at least one blind spot detector includes a plurality of the blind spot detectors, The system further includes a blind spot detector determination unit that determines, from among the plurality of blind spot detectors, a blind spot detector that is capable of detecting the blind spot information corresponding to the blind spot for each position of the moving body relative to the moving body detector.

3. 3. The system according to claim 1 or 2, The system further comprises a blind spot estimation unit that estimates a position of the blind spot depending on a relative position of the moving object with respect to the moving object detector.

4. 4. The system of claim 3, The blind spot estimation unit estimates the position of the blind spot by considering a set of straight lines passing through the moving body from the moving body detector toward the moving body as the blind spot, the set being on the opposite side of the moving body from the moving body detector.

5. 2. The system of claim 1, The at least one moving object detector and the at least one blind spot detector include different types of detectors.

6. 6. The system of claim 5, the at least one moving object detector is one of a distance measuring device and a camera; The at least one blind spot detector is the other of the range finder and the camera.

7. 2. The system of claim 1, a composite map generating unit that generates a composite map by combining the detected moving object information and the detected blind spot information; a surroundings monitoring unit that monitors the surroundings of the moving object, A system in which the generated composite map is used both for surroundings monitoring by the surroundings monitoring unit and for movement control of the moving object by the control unit.

8. A method for moving a moving object by unmanned driving, comprising: detecting, by at least one moving object detector, moving object information including at least one of an image of the moving object and three-dimensional point cloud data of the moving object; estimating at least one of a position and an orientation of the moving object using the detected moving object information; generating a control command for causing the moving object to travel using the estimated position and orientation of the moving object; and detecting, by at least one blind spot detector, blind spot information including at least one of an image of a blind spot of the moving object not detected by the moving object detector, three-dimensional point cloud data of the blind spot, and the presence or absence of a target in the blind spot.

Images