Information Processing Apparatus, Determination Method, Program, and Storage Medium

Abstract

A controller 13 of this information processing device 1 acquires predicted water level information D1 pertaining to predicted water levels at water level predicting locations where the water levels are predicted on one or more candidate routes that are candidates of an operation route of a ship. The controller 13 then calculates, on the basis of the predicted water level information D1, bridge predicted water levels that are predicted water levels at predicted passage times of the ship at respective bridge passage locations that are locations where the ship passes under a bridge existing in each candidate route. The controller 13 then determines the suitability of each candidate route as an operation route on the basis of the bridge predicted water levels.

Description

Technical Field

[0001] The present disclosure relates to the determination of a ship's operating route.

Background Art

[0002] Conventionally, there has been known a technique for estimating the self-position of a moving object by collating (matching) the shape data of surrounding objects measured using a measuring device such as a laser scanner with map information in which the shapes of the surrounding objects are stored in advance. For example, Patent Document 1 discloses an autonomous movement system that determines whether a detected object in a voxel obtained by dividing a space according to a predetermined rule is a stationary object or a moving object, and performs matching between map information and measurement data for voxels in which stationary objects exist. Further, Patent Document 2 discloses a scan matching method for estimating the self-position by collating voxel data including the average vector and covariance matrix of stationary objects for each voxel with point cloud data output by a lidar. Furthermore, Patent Document 3 describes a method for controlling the attitude of a ship so that light emitted from a lidar can be reflected by an object around the landing position and received by the lidar in an automatic landing device for automatically landing the ship.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0004] When determining the operating route, it is necessary to select a route that allows the ship to pass through the bridge safely. In selecting such a route, it is necessary to consider that the water level varies depending on the time and the location on the river, and accurately determine whether a candidate route is suitable as the ship's operating route.

[0005] The present disclosure has been made to solve the above problems, and one of the main objects is to provide an information processing apparatus that can accurately determine whether a route that is a candidate for the operating route is suitable as the operating route.

Means for Solving the Problems

[0006] The invention according to the claims is predicted water level information acquisition means for acquiring predicted water level information regarding the predicted water level at a predicted water level point where the water level is predicted on one or a plurality of candidate routes that are candidates for the ship's operating route; Passage scheduled time calculation means for calculating the passage scheduled time of the ship at each of the bridge passage points, which are the points where the ship passes through the bridges existing on each of the candidate routes, based on the scheduled departure time of the ship, the assumed speed of the ship, and the navigation distance from the departure place of the ship to each of the bridge passage points; bridge predicted water level calculation means for calculating a bridge predicted water level, which is the predicted water level at the scheduled passage time of the ship at each of the bridge passage points, based on the predicted water level information; determination means for determining whether each of the candidate routes has suitability as the operating route based on the bridge predicted water level; and an information processing apparatus having the above. Further, the invention according to the claim is Candidate route acquisition means for acquiring a plurality of candidate routes with different scheduled departure times, which are candidates for the navigation route of the ship; Predicted water level information acquisition means for acquiring predicted water level information regarding the predicted water level at the water level prediction points where the water level is predicted on each of the candidate routes; Bridge predicted water level calculation means for calculating the bridge predicted water level, which is the predicted water level at the passage scheduled time of the ship at each of the bridge passage points, which are the points where the ship passes through the bridges existing on each of the candidate routes, based on the predicted water level information; Judgment means for judging the suitability of each of the candidate routes as the navigation route based on the bridge predicted water level; and has The bridge predicted water level calculation means is an information processing device that calculates the passage scheduled time and the bridge predicted water level based on the predicted water level information and the scheduled departure time of each of the candidate routes. Further, the invention according to the claim is Predicted water level information acquisition means for acquiring predicted water level information regarding the predicted water level at the water level prediction points where the water level is predicted on one or more candidate routes, which are candidates for the navigation route of the ship; Bridge predicted water level calculation means for calculating the bridge predicted water level, which is the predicted water level at the passage scheduled time of the ship at each of the bridge passage points, which are the points where the ship passes through the bridges existing on each of the candidate routes, based on the predicted water level information; Judgment means for judging the suitability of each of the candidate routes as the navigation route based on the bridge predicted water level; and has The bridge predicted water level calculation means is an information processing device that calculates the bridge predicted water level corresponding to the bridge passage point based on the predicted water level at the water level prediction points that are closest to the bridge passage point in the upstream and downstream directions of the river.

[0007] Also, the invention according to the claims is a control method executed by a computer, acquiring prediction water level information regarding the predicted water level at a water level prediction point where the water level is predicted on one or more candidate routes that are candidates for the navigation route of the ship, Based on the scheduled departure time of the ship, the assumed speed of the ship, and the navigation distance from the departure place of the ship to each of the bridge passage points, which are the points where the ship passes through the bridges existing on each of the candidate routes, calculate the passage scheduled time of the ship at each of the bridge passage points, calculating, based on the prediction water level information, a bridge predicted water level that is the predicted water level at the scheduled passage time of the ship at each bridge passage point that is a point where the ship passes through a bridge existing on each of the candidate routes, determining, based on the bridge predicted water level, whether each of the candidate routes is suitable as the navigation route, which is a determination method.

[0008] Also, the invention according to the claims is acquiring prediction water level information regarding the predicted water level at a water level prediction point where the water level is predicted on one or more candidate routes that are candidates for the navigation route of the ship, Based on the scheduled departure time of the ship, the assumed speed of the ship, and the navigation distance from the departure place of the ship to each of the bridge crossing points where the ship passes through the bridges existing in each of the candidate routes, calculate the scheduled passing time of the ship at each of the bridge crossing points. calculating, based on the prediction water level information, a bridge predicted water level that is the predicted water level at the scheduled passage time of the ship at each bridge passage point that is a point where the ship passes through a bridge existing on each of the candidate routes, a program that causes a computer to execute a process of determining, based on the bridge predicted water level, whether each of the candidate routes is suitable as the navigation route.

Brief Description of the Drawings

[0009]

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Mode for Carrying Out the Invention

[0010] According to a preferred embodiment of the present invention, an information processing apparatus includes a predicted water level information acquisition means for acquiring predicted water level information regarding a predicted water level at a water level prediction point where the water level is predicted on one or a plurality of candidate routes that are candidates for a ship's navigation route, a bridge predicted water level calculation means for calculating, based on the predicted water level information, a bridge predicted water level that is the predicted water level at the scheduled passage time of the ship at each bridge passage point that is a point where the ship passes through a bridge existing on each of the candidate routes, and a determination means for determining, based on the bridge predicted water level, whether each of the candidate routes is suitable as the navigation route. According to this aspect, the information processing apparatus can accurately grasp the water level at the scheduled passage time of the ship for each bridge passage point existing on the candidate route and suitably determine whether each candidate route is suitable as the navigation route.

[0011] In one aspect of the above information processing apparatus, the determination means determines whether the ship can pass under the bridge based on the bridge predicted water level, and determines whether the suitability exists based on the determination result of whether the ship can pass. According to this aspect, the information processing apparatus can accurately determine whether each candidate route is suitable as the navigation route based on the determination result of whether the ship can pass under each bridge existing on the candidate route.

[0012] In another aspect of the above information processing apparatus, the determination means determines whether the ship can pass based on a bridge height representing the height of the bridge based on map data and a ship highest point height representing the height of the highest point of the ship calculated based on the bridge predicted water level. According to this aspect, the information processing apparatus can determine whether the ship can pass under the bridge considering the position of the highest point of the ship at the bridge passage point.

[0013] In another aspect of the above information processing apparatus, the information processing apparatus further includes a ship highest point height calculation means for calculating the ship highest point height based on the water surface measurement data which is the measurement data of the water surface measured by a measurement device provided on the ship, the highest point information regarding the height from the reference position of the ship to the highest point of the ship, and the predicted bridge water level. According to this aspect, the information processing apparatus can accurately calculate the ship highest point height which is the height of the highest point of the ship by using the predicted bridge water level.

[0014] In another aspect of the above information processing apparatus, the information processing apparatus further includes an operation route determination means for determining the operation route based on the determination result of the presence or absence of suitability. According to this aspect, the information processing apparatus can determine the operation route that the ship should pass through. In a preferred example, when there are a plurality of candidate routes determined to be suitable, the operation route determination means may display a screen for selecting the operation route from the candidate routes.

[0015] In another aspect of the above information processing apparatus, the information processing apparatus further includes a candidate route acquisition means for acquiring a plurality of candidate routes with different scheduled departure times, and the bridge predicted water level calculation means calculates the predicted passing time and the bridge predicted water level based on the predicted water level information and the scheduled departure time of each of the candidate routes. According to this aspect, the information processing apparatus can consider that the water level changes with time and determine the suitability of each of the candidate routes with different scheduled departure times as an operation route.

[0016] In another aspect of the above information processing apparatus, the bridge predicted water level calculation means calculates the bridge predicted water level corresponding to the bridge passing point based on the predicted water levels at the water level prediction points that are respectively the closest in the upstream and downstream directions of the bridge passing point. According to this aspect, the information processing apparatus can accurately calculate the bridge predicted water level at the bridge passing point from the predicted water levels at the water level prediction points.

[0017] According to another preferred embodiment of the present invention, there is provided a determination method executed by a computer, which includes: obtaining prediction water level information regarding a predicted water level at a water level prediction point where the water level is predicted on one or more candidate routes that are candidates for the operation route of a ship; calculating, based on the prediction water level information, a bridge predicted water level, which is the predicted water level at the scheduled passage time of the ship at each bridge passage point, which is the point where the ship passes through a bridge existing on each of the candidate routes; and determining, based on the bridge predicted water level, whether each of the candidate routes is suitable as the operation route. By executing this determination method, the computer can accurately determine whether a candidate route is suitable as an operation route.

[0018] According to still another preferred embodiment of the present invention, a program executed by a computer causes the computer to perform a process of: obtaining prediction water level information regarding a predicted water level at a water level prediction point where the water level is predicted on one or more candidate routes that are candidates for the operation route of a ship; calculating, based on the prediction water level information, a bridge predicted water level, which is the predicted water level at the scheduled passage time of the ship at each bridge passage point, which is the point where the ship passes through a bridge existing on each of the candidate routes; and determining, based on the bridge predicted water level, whether each of the candidate routes is suitable as the operation route. By executing this program, the computer can accurately determine whether a candidate route is suitable as an operation route. Preferably, the above program is stored in a storage medium.

Example

[0019] Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings.

[0020] <First Embodiment> (1-1) Overview of the Navigation Support System Figures 1 and 2 show the schematic configuration of the operation support system according to the first embodiment. Specifically, FIG. 1 shows a block configuration diagram of the operation support system, FIG. 2(A) is a top view exemplifying the visual field range (ranging possible range) 90 of the ship and the later-described Lidar 3 included in the operation support system, and FIG. 2(B) is a view showing the visual field range 90 of the ship and the Lidar 3 from the rear. The operation support system includes an information processing device 1 that moves together with a ship as a moving body, a sensor group 2 mounted on the ship, and a server device 7.

[0021] The information processing device 1 is electrically connected to the sensor group 2, and based on the outputs of various sensors included in the sensor group 2 and the later-described predicted water level information "D1" transmitted from the server device 7, performs operation support for the ship on which the information processing device 1 is provided. In this embodiment, the information processing device 1 determines the operation route of the ship by accurately determining in advance whether the ship can pass under the bridge. Note that operation support may include shore approach support such as automatic shore approach (landing). The information processing device 1 may be a navigation device provided on the ship or an electronic control device built into the ship.

[0022] The sensor group 2 includes various external sensors and internal sensors provided on the ship. In this embodiment, the sensor group 2 includes, for example, a Lidar (Light Detection and Ranging, or Laser Illuminated Detection And Ranging) 3 and a GPS (Global Positioning Satellite) receiver 5. Note that the sensor group 2 may have a receiver that generates a positioning result of GNSS other than GPS instead of the GPS receiver 5. The information processing device 1 acquires the position of the ship on the water surface required when referring to the later-described river map database from the GPS receiver 5 or the like.

[0023] The lidar 3 is an external sensor that emits a pulsed laser with respect to a predetermined angular range in the horizontal direction (see Fig. 2(A)) and a predetermined angular range in the vertical direction (i.e., the direction of pitch angle) (see Fig. 2(B)), discretely measures the distance to an object existing in the external world, and generates three-dimensional point cloud data indicating the position of the object. In the examples of Fig. 2(A) and Fig. 2(B), as the lidar 3, a lidar directed toward the left side surface of the ship and a lidar directed toward the right side surface of the ship are respectively provided on the ship. Note that the number of lidars 3 installed on the ship is not limited to two, and may be one, or three or more. The lidar 3 includes an irradiation unit that irradiates laser light while changing the irradiation direction, a light receiving unit that receives the reflected light (scattered light) of the irradiated laser light, and an output unit that outputs scan data based on the light reception signal output by the light receiving unit. The data measured for each direction (scanning position) in which the laser light is irradiated is generated based on the irradiation direction corresponding to the laser light received by the light receiving unit and the response delay time of the laser light specified based on the above-described light reception signal. Note that the lidar 3 is not limited to the above-described scanning type lidar, and may be a flash type lidar that generates three-dimensional data by diffusely irradiating laser light over the field of view of a two-dimensional array sensor. The lidar 3 is an example of the "measurement device" in the present invention.

[0024] Also, in the present embodiment, the vertical range measured by the lidar 3 is at least a range including above the horizontal direction (i.e., the direction in which the elevation angle is positive) and below the horizontal direction (i.e., the direction in which the depression angle is positive). Thereby, the measurement range of the lidar 3 includes both the bridge when the ship passes under the bridge and the water surface on which the ship floats. When there are a plurality of lidars 3, it is sufficient that the measurement range of at least one lidar 3 includes above the horizontal direction and the measurement range of at least one lidar 3 includes below the horizontal direction.

[0025] The server device 7 transmits prediction water level information D1 representing the water level predicted as the water level at a plurality of points on the river at a predetermined time in the future (also referred to as "predicted water level") to the information processing device 1. Hereinafter, each point at which the predicted water level is calculated is also referred to as a "water level prediction point". For example, when the server device 7 receives request information including the current position of the information processing device 1 from the information processing device 1, the server device 7 transmits the prediction water level information D1 representing the predicted water level at the water level prediction points existing within a predetermined distance from the current position to the information processing device 1. In another example, when the server device 7 receives information specifying the river on which the information processing device 1 is operating or is scheduled to operate from the information processing device 1, the server device 7 transmits the prediction water level information D1 of the water level prediction points on the specified river to the information processing device 1. Note that the water level (water surface height) varies depending on the location of the river, and the phase of the temporal change in the water level also varies depending on the location of the river.

[0026] (1-2) Device Configuration FIG. 3(A) is a block diagram showing an example of the hardware configuration of the information processing device 1. The information processing device 1 mainly includes an interface 11, a memory 12, and a controller 13. These elements are interconnected via a bus line.

[0027] Interface 11 performs interface operations related to data transfer between the information processing device 1 and an external device. In this embodiment, interface 11 acquires output data from each sensor of the sensor group 2 such as the rider 3 and the GPS receiver 5, and supplies the acquired data to the controller 13. Further, interface 11 receives the predicted water level information D1 from the server device 7, and supplies the predicted water level information D1 to the controller 13. Also, interface 11 supplies, for example, a signal related to the control of the ship generated by the controller 13 to each component of the ship that controls the operation of the ship. For example, a ship includes a drive source such as an engine or an electric motor, a screw that generates a propulsive force in the traveling direction based on the driving force of the drive source, a thruster that generates a lateral propulsive force based on the driving force of the drive source, and a rudder or the like that is a mechanism for freely determining the traveling direction of the ship. During automatic operation such as automatic landing, interface 11 supplies the control signal generated by controller 13 to each of these components. When an electronic control device is provided on the ship, interface 11 supplies the control signal generated by controller 13 to the electronic control device. Interface 11 may be a wireless interface such as a network adapter for performing wireless communication, or may be a hardware interface for connecting to an external device via a cable or the like. Also, interface 11 may perform interface operations with various peripheral devices such as an input device, a display device, and a sound output device.

[0028] Memory 12 is composed of various volatile memories and non-volatile memories such as RAM (Random Access Memory), ROM (Read Only Memory), hard disk drive, and flash memory. Memory 12 stores a program for the controller 13 to execute a predetermined process. Note that the program executed by the controller 13 may be stored in a storage medium other than the memory 12.

[0029] Also, memory 12 stores the river map database (DB: DataBase) 10 and the highest point information IH.

[0030] The river map DB10 stores feature data which is data regarding features (landmarks) existing on or near a river. The above-described features include at least bridges provided on a river through which ships can pass. The feature data includes position information indicating the position where the feature is provided, and attribute information representing various attributes such as the type and size of the feature. Note that the attribute information of the feature data corresponding to a bridge includes at least information regarding the height (for example, elevation) of the underside of the bridge (in other words, the bottom surface of the bridge on the river).

[0031] Note that the river map DB10 may further include, in addition to the feature data, information regarding, for example, landing places (including shores and piers), information regarding waterways through which ships can move, and the like. Also, the river map DB10 may be stored in an external storage device of the information processing device 1 such as a hard disk connected to the information processing device 1 via the interface 11. The above-described storage device may be a server device that communicates with the information processing device 1. Also, the above-described storage device may be composed of a plurality of devices. Also, the river map DB10 may be updated periodically. In this case, for example, the controller 13 receives partial map information regarding the area to which its own position belongs from a server device that manages map information via the interface 11 and reflects it in the river map DB10.

[0032] The highest point information IH is information regarding the height of the part (highest point) of the ship that exists at the highest position in the ship coordinate system which is a coordinate system based on the ship. For example, the highest point information IH represents the height (distance in the height direction) from the reference position of the ship (also referred to as the "ship reference position") to the highest point. The ship reference position is, in other words, the origin in the coordinate system adopted in the point cloud data output by the lidar 3, and corresponds to, for example, the installation position of the lidar 3. The highest point information IH is generated based on a previous measurement result and is stored in advance in the memory 12.

[0033] In addition to the river map DB 10, the memory 12 stores information necessary for the processes executed by the information processing apparatus 1 in this embodiment. For example, the memory 12 stores information used to set the size of downsampling when performing downsampling on the point cloud data obtained when the lidar 3 performs scanning for one cycle. In another example, the memory 12 stores information regarding candidate routes of the navigation route that the ship should follow.

[0034] The controller 13 includes one or more processors such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and a TPU (Tensor Processing Unit), and controls the entire information processing apparatus 1. In this case, the controller 13 performs processes related to navigation support and the like by executing the programs stored in the memory 12 and the like.

[0035] Functionally, the controller 13 also includes a candidate route acquisition unit 15, a candidate route suitability determination unit 16, and a navigation route determination unit 17.

[0036] The candidate route acquisition unit 15 acquires one or more routes (also referred to as "candidate routes") that are candidates for the navigation route of the ship. In this case, the candidate route acquisition unit 15 may generate candidate routes based on route search processing, or may acquire information representing the determined candidate routes from the memory 12 or from another device via the interface 11. When generating candidate routes, for example, the candidate route acquisition unit 15 searches for a route on the river from the departure point (which may be the current position) to the destination based on the pair of the departure point and the destination specified by the input data supplied from the input device via the interface 11 as candidate routes. In this case, the candidate route acquisition unit 15 searches for candidate routes with reference to the river map DB 10 based on an arbitrary route search method. Note that in the candidate route generation process, no determination is made as to whether the ship can pass under the bridges existing on the route. That is, it is not guaranteed that the ship can pass under the bridges existing on each of the generated candidate routes.

[0037] Based on the predicted water level information D1 received from the server device 7, the point cloud data generated by the lidar 3, the river map DB10, and the highest point information IH, the candidate route suitability determination unit 16 determines whether a ship can pass under each bridge provided on the candidate route acquired by the candidate route acquisition unit 15 (also referred to as "bridge passageability determination"). Details of the bridge passageability determination will be described later.

[0038] The operation route determination unit 17 determines an operation route based on the result of the bridge passageability determination by the candidate route suitability determination unit 16. In this case, the operation route determination unit 17 recognizes a candidate route for which it is determined that a ship can pass through all the bridges on the route as a candidate route having suitability as an operation route (also referred to as a "suitable candidate route"), and determines an operation route for the ship to operate from the recognized suitable candidate route. Here, when there are a plurality of suitable candidate routes, the operation route determination unit 17 may select the suitable candidate route with the shortest required time as the operation route, or may display the suitable candidate routes on the display unit via the interface 11 so that they can be selected (that is, display a selection screen of the suitable candidate routes on the display unit), and determine the suitable candidate route selected by the input unit from the displayed suitable candidate routes as the operation route.

[0039] And the controller 13 in the first embodiment functions as a "predicted water level information acquisition means", "candidate route acquisition means", "bridge predicted water level calculation means", "ship highest point height calculation means", "determination means", "operation route determination means", and a computer that executes a program, etc.

[0040] FIG. 3(B) is a block diagram showing an example of the hardware configuration of the server device. The server device mainly includes an interface 71, a memory 72, and a controller 73. These elements are interconnected via a bus line.

[0041] The interface 71 performs interface operations related to the exchange of data between the server device and the external device. In this embodiment, the interface 71 performs a process of transmitting the predicted water level information D1 to the information processing device 1 based on the control of the controller 73. In this case, the interface 71 may be a wireless interface such as a network adapter for performing wireless communication, or may be a hardware interface for connecting to an external device via a cable or the like. Further, the interface 71 may perform interface operations with various peripheral devices such as an input device, a display device, and a sound output device.

[0042] The memory 72 is composed of various volatile memories and non-volatile memories such as RAM, ROM, hard disk drive, and flash memory. The memory 72 stores a program for the controller 73 to execute a predetermined process. Note that the program executed by the controller 73 may be stored in a storage medium other than the memory 72.

[0043] Further, the memory 72 stores the predicted water level DB70. The predicted water level DB70 is a database that records the predicted water levels at each water level prediction point on the river. In this case, for example, in the predicted water level DB70, for each water level prediction point, the position information of the water level prediction point and the predicted water level for each time (date and time) determined at a predetermined time interval are associated. Note that the predicted water level is determined by comprehensively considering the past measurement results of the water level gauge provided at the water level prediction point and the weather, atmospheric pressure, etc. up to the time when the water level is predicted. Note that the predicted water level DB70 may be stored in an external storage device of the server device 7 such as a hard disk connected to the server device 7 via the interface 71. The above storage device may be another server device that communicates with the server device 7. Further, the above storage device may be composed of a plurality of devices. Also, the predicted water level DB70 may be updated periodically.

[0044] The controller 73 includes one or more processors such as a CPU, a GPU, and a TPU, and controls the entire server device 7. In this case, the controller 73 performs processes related to the distribution of the predicted water level information D1 and the like by executing a program stored in the memory 72 or the like.

[0045] (1-3) Candidate Route Suitability Judgment Process Next, a process of determining an eligible candidate route (also referred to as "candidate route eligibility determination process") will be described. For each bridge existing on the candidate route, the candidate route eligibility determination unit 16 calculates a predicted water level under the bridge according to the scheduled passage time of the ship (also referred to as "bridge predicted water level"). Then, the candidate route eligibility determination unit 16 determines whether the ship can pass under each bridge based on the calculated bridge predicted water level, and determines whether the candidate route is an eligible candidate route based on the determination result.

[0046] FIG. 4(A) is a map showing the departure and destination of the ship and the bridges existing on the river. Further, FIG. 4(B) is a map further showing the candidate route 91 determined to be an eligible candidate route. In the examples of FIGS. 4(A) and 4(B), there are bridges on the shipping route from the departure place to the destination, and there are a plurality of candidate routes due to the branching and merging of the river, etc. On the other hand, the target ship cannot necessarily pass through all the bridges on the map. Therefore, the candidate route eligibility determination unit 16 calculates the predicted water level of the bridge according to the scheduled passage time of the ship for each bridge existing on the candidate route, and determines whether the ship can pass under each bridge based on the calculated bridge predicted water level. In the examples of FIGS. 4(A) and 4(B), the candidate route eligibility determination unit 16 determines that the ship can pass under the bridge based on the predicted water level of the bridge for each of the four bridges existing on the candidate route 91, and determines that the candidate route 91 is an eligible candidate route.

[0047] In addition, when there are a plurality of candidate routes, the candidate route suitability determination unit 16 determines whether a ship can pass under each bridge existing on each candidate route. Hereinafter, after explaining the method for calculating the predicted water level of the bridge, the method for determining whether a ship can pass through the bridge using the predicted water level of the bridge will be explained.

[0048] (1-3-1) Calculation of the Predicted Water Level of the Bridge FIG. 5 is an overhead view showing the vicinity of bridges B1 and B2 existing on a certain candidate route. Here, the point "Pa1" is the water level prediction point closest to bridges B1 and B2 in the downstream direction of the river, and the point "Pa2" is the water level prediction point closest to bridges B1 and B2 in the upstream direction of the river. Further, the point "Pb1" is a point located directly below bridge B1 on the target candidate route, and the point "Pb2" is a point located directly below bridge B2 on the target candidate route. Here, the points Pb1 and Pb2 correspond to the passing points of the ship under the bridge (also referred to as "bridge passing points") when the ship passes through the illustrated river.

[0049] In this case, the candidate route suitability determination unit 16 specifies the predicted water levels in the time series at the water level prediction points Pa1 and Pa2, which are the closest to bridges B1 and B2 in the upstream and downstream directions respectively, based on the predicted water level information D1. FIG. 6(A) shows a graph "Ga1" representing the change in the predicted water level at the water level prediction point Pa1 and a graph "Ga2" representing the change in the predicted water level at the water level prediction point Pa2. As shown in FIG. 6(A), the water level varies depending on the location of the river, and the phase of the time change of the water level also varies depending on the location of the river.

[0050] Next, the candidate route suitability determination unit 16 calculates the predicted water levels of the bridge at the bridge passing points Pb1 corresponding to bridge B1 and the bridge passing points Pb2 corresponding to bridge B2 in the time series based on the predicted water levels in the time series at the water level prediction points Pa1 and Pa2.

[0051] In this case, for example, the candidate route suitability determination unit 16 obtains the predicted bridge water level at the bridge crossing point Pb1 by linear interpolation based on the distance between the water level prediction point Pa1 and the bridge crossing point Pb1, the distance between the water level prediction point Pa2 and the bridge crossing point Pb1, and the respective predicted water levels at the water level prediction points Pa1 and Pa2 for each prediction target time. Similarly, the candidate route suitability determination unit 16 obtains the predicted bridge water level at the bridge crossing point Pb2 by interpolation (for example, linear interpolation) based on the distance between the water level prediction point Pa1 and the bridge crossing point Pb2, the distance between the water level prediction point Pa2 and the bridge crossing point Pb2, and the respective predicted water levels at the water level prediction points Pa1 and Pa2 for each prediction target time. FIG. 6(B) shows the graph “Gb1” representing the transition of the predicted bridge water level at the bridge crossing point Pb1 and the graph “Gb2” representing the transition of the predicted bridge water level at the bridge crossing point Pb2 together with the graphs Ga1 and Ga2. As shown in FIG. 6(B), the graph Gb1 representing the predicted bridge water level at the bridge crossing point Pb1, which is closer to the water level prediction point Pa1 than the water level prediction point Pa2, is a graph with higher approximation to the graph Ga1 than the graph Ga2, and the graph Gb2 representing the predicted bridge water level at the bridge crossing point Pb2, which is closer to the water level prediction point Pa2 than the water level prediction point Pa1, is a graph with higher approximation to the graph Ga2 than the graph Ga1.

[0052] Note that, in addition to linear interpolation, the candidate route suitability determination unit 16 may calculate the predicted bridge water level at each bridge crossing point using any method (for example, spline interpolation or polynomial approximation). In this case, the candidate route suitability determination unit 16 may calculate the predicted water level at the target bridge crossing point based on the predicted water levels at three or more nearby water level prediction points for the target bridge crossing point.

[0053] Next, the candidate route suitability determination unit 16 determines the scheduled passage time, which is the time when the ship is scheduled to pass each bridge crossing point when the ship is operated according to the candidate route, and recognizes the predicted water level of the bridge at the scheduled passage time for each of the bridge crossing points. In this case, the candidate route suitability determination unit 16 predicts the time when the ship passes each bridge crossing point when the ship is operated according to the target candidate route based on the scheduled departure time. In this case, for example, the candidate route suitability determination unit 16 calculates the scheduled passage time at each bridge crossing point based on the scheduled departure time, the assumed predicted speed of the ship, and the required navigation distance from the departure point to each bridge crossing point. Note that the candidate route suitability determination unit 16 recognizes the current time as the scheduled departure time when the departure of the ship is scheduled immediately, for example, and recognizes the time specified by the input data supplied from the input device via the interface 11 as the scheduled departure time in other cases. Then, the candidate route suitability determination unit 16 reads the predicted water level of the bridge at the scheduled passage time from the predicted water level of the bridge in the time series calculated by interpolation for each bridge crossing point.

[0054] As described above, the candidate route suitability determination unit 16 can accurately calculate the predicted water level of each bridge existing on the candidate route based on the predicted water level information D1.

[0055] (1-3-2) Determination of Bridge Passageability Based on Predicted Bridge Water Level The candidate route suitability determination unit 16 predicts the height-direction interval (also referred to as the "predicted interval") between the ship and the bridge at the bridge crossing point based on the predicted water level of the bridge at the scheduled passage time, and determines the bridge passageability of each bridge based on this predicted interval. A specific example of this process will be described with reference to FIG. 7.

[0056] FIG. 7 is a diagram showing an outline of a method for calculating a prediction interval, and is a diagram showing a ship observed from behind assuming that the ship is present at a bridge passing point. In the example of FIG. 7, two lidars 3 are provided on the ship, and a position at the same height as the lidar 3 is defined as the ship reference position. Further, on the ship, there is a protrusion 33 that is the highest point of the ship. The bridge 30 has a lower girder part 32 that forms the bottom surface of the structural part located above the river, and forms a clearance 31 under the girder through which the ship can pass. The line L1 indicates the origin position of the height (for example, elevation) adopted at the predicted water level of the bridge, the line L2 indicates the water surface position, and the line L3 indicates the position at the same height as the ship reference position. The line L4 indicates the position at the same height as the highest point of the ship, and the line L5 indicates the position at the same height as the lower girder part 32 that forms the bottom surface of the bridge 30 on the river. Further, the measured points “m5” to “m8” indicate the measured points of the water surface measured by the lidar 3.

[0057] First, the candidate route suitability determination unit 16 extracts the feature data corresponding to the bridge 30 through which the ship is scheduled to pass from the river map DB10, and specifies the height of the bridge 30 (the height corresponding to the arrow A2, also referred to as the “bridge height”) by referring to the extracted feature data. The bridge height represents the height of the bottom surface of the bridge on the river (that is, the height of the under-girder part). The predicted water level of the bridge 30 is the height corresponding to the arrow A1.

[0058] Further, the candidate route suitability determination unit 16 calculates the height direction distance from the ship reference position to the water surface (the distance corresponding to arrow A3, also referred to as "water surface distance") based on the point cloud data of the lidar 3 that measures the water surface at an arbitrary timing before the operation route is determined (also referred to as "water surface measurement data"). In this case, the candidate route suitability determination unit 16 extracts the point cloud data below the horizontal plane (i.e., in the direction where the depression angle is positive) from the point cloud data output by the lidar 3 as the water surface measurement data. At this time, in order to exclude points where non-water surface locations such as bridge piers, quay walls, and other ships are detected, the minimum value of the z-coordinate value of the point cloud data is obtained, and the water surface measurement data can be obtained by extracting those with z-coordinate values of each measurement point close to the minimum value. In FIG. 7, the candidate route suitability determination unit 16 regards the data corresponding to the measured points "m5" to "m8" on the water surface as the water surface measurement data and extracts it from the point cloud data of the lidar 3. Then, the candidate route suitability determination unit 16 calculates a representative value such as the average value or the minimum value of the coordinate values in the height direction of the extracted water surface measurement data as the water surface distance. Note that the candidate route suitability determination unit 16 may execute the calculation of the water surface distance multiple times before the operation route is determined, and use the average value or the like of the calculation results of the water surface distance multiple times as the water surface distance in subsequent processing. In FIG. 7, only the measurement points "m5" to "m8" of the lidar 3 on one side (the right side surface side) are shown, but actually, the point cloud data of the lidar 3 on both sides (the right side surface side and the left side surface side) is used.

[0059] Next, the candidate route suitability determination unit 16 specifies the height direction width from the ship reference position to the highest point (refer to arrow A4) by referring to the highest point information IH from the memory 12. Then, the candidate route suitability determination unit 16 calculates the height of the highest point of the ship (the height corresponding to arrow A5, also referred to as "ship highest point height"), which corresponds to the height obtained by adding the water surface distance (refer to arrow A3) and the height direction width from the ship reference position to the highest point (refer to arrow A4) to the predicted water level of the bridge (refer to arrow A1).

[0060] Then, the candidate route suitability determination unit 16 calculates the width obtained by subtracting the ship highest point height from the bridge height as the predicted interval (refer to arrow A6).

[0061] After that, when the predicted interval calculated by the candidate route suitability determination unit 16 is equal to or greater than a threshold value (also referred to as the "predicted interval threshold Th"), it determines that the ship can pass through the bridge. When the predicted interval is less than the predicted interval threshold Th, it determines that there is a risk that the ship cannot pass through the bridge safely. The predicted interval threshold Th may be set as a fixed value stored in advance in the memory 12 or the like, or may be set as a variable value. In the latter case, the candidate route suitability determination unit 16 may determine the predicted interval threshold Th based on, for example, an index (e.g., standard deviation) representing the variation in the height direction values of each data of the water surface measurement data used for calculating the water surface distance. In this case, the candidate route suitability determination unit 16 considers, for example, that when the wave height is large, the vertical movement of the ship in the height direction also becomes large, and the variation in the water surface measurement data also becomes large. The larger the above-mentioned standard deviation, the larger the predicted interval threshold Th.

[0062] In this way, the candidate route suitability determination unit 16 can accurately execute the determination of whether the ship can pass through each bridge existing on the candidate route based on the predicted water level of the bridge, the point cloud data output by the lidar 3, and the map data related to the bridge.

[0063] Then, the candidate route suitability determination unit 16 preferably stores, in the memory 12 or the like, information associating the scheduled passing time, the predicted water level of the bridge, and the predicted interval for each bridge existing on the candidate route. FIG. 8 shows a table associating the scheduled passing times, the predicted water levels of the bridge, and the predicted intervals for each bridge (bridge B1, bridge B2, bridge B3,...) existing on the candidate route. As shown in FIG. 8, the candidate route suitability determination unit 16 calculates the above-mentioned scheduled passing time, the predicted water level of the bridge, and the predicted interval for all the bridges (bridge B1, bridge B2, bridge B3,...) existing on the candidate route, and stores the calculation results in the memory 12 or the like. In the above-mentioned table, each bridge may be represented by position information indicating the position where the bridge exists or other identifiable information, instead of or in addition to names such as bridge B1, bridge B2, bridge B3.

[0064] (1-4) Functional Block FIG. 9 is an example of the functional blocks of the controller 13 in the first embodiment. Functionally, the candidate route suitability determination unit 16 includes a bridge height acquisition unit 61, a predicted bridge water level calculation unit 62, a water surface distance calculation unit 63, a ship highest point height calculation unit 64, a prediction interval calculation unit 65, and a determination unit 66. In FIG. 9, the blocks that exchange data are connected by solid lines, but the combinations of blocks that exchange data are not limited to this. The same applies to the diagrams of other functional blocks described later.

[0065] First, the candidate route acquisition unit 15 acquires one or more candidate routes, and supplies information on the acquired candidate routes to the bridge height acquisition unit 61, the predicted bridge water level calculation unit 62, and the operation route determination unit 17, respectively. Note that the information on the candidate routes supplied by the candidate route acquisition unit 15 to the predicted bridge water level calculation unit 62 includes information (e.g., information on the departure time) necessary for calculating the scheduled passage time of each bridge.

[0066] The bridge height acquisition unit 61 extracts the feature data corresponding to the bridges existing on the candidate route from the river map DB 10, and acquires the bridge height of each bridge existing on the candidate route based on the extracted feature data.

[0067] Further, the predicted bridge water level calculation unit 62 calculates the scheduled passage time and the predicted bridge water level at the passage point corresponding to each bridge existing on the candidate route based on the predicted water level information D1 received from the server device 7 via the interface 11 and the information on the candidate route supplied from the candidate route acquisition unit 15. Also, the water surface distance calculation unit 63 calculates the water surface distance based on the water surface measurement data obtained by measuring the water surface (i.e., below the horizontal direction).

[0068] The ship highest point height calculation unit 64 calculates the ship highest point height based on the water surface distance calculated by the water surface distance calculation unit 63, the predicted bridge water level at the passage point corresponding to each bridge existing on the candidate route, and the height direction width from the ship reference position indicated by the highest point information IH to the highest point.

[0069] The prediction interval calculation unit 65 calculates a prediction interval based on the bridge height acquired by the bridge height acquisition unit 61 and the highest point height of the ship calculated by the ship highest point height calculation unit 64. Further, the determination unit 66 determines whether each bridge on the candidate route can be passed based on the prediction interval of each bridge on the candidate route and the prediction interval threshold Th. In this case, the determination unit 66 may adaptively set the prediction interval threshold Th based on the water surface reflection data acquired by the water surface distance calculation unit 63. Then, the determination unit 66 determines that a candidate route on which all the bridges existing on the candidate route can be passed by the ship is an eligible candidate route, and supplies information regarding the determined eligible candidate route to the operation route determination unit 17.

[0070] The operation route determination unit 17 determines an operation route based on the information regarding the eligible candidate route supplied from the determination unit 66. In this case, when only one eligible candidate route exists, the operation route determination unit 17 selects the eligible candidate route as the operation route. On the other hand, when a plurality of eligible candidate routes exist, the operation route determination unit 17 may select the eligible candidate route with the shortest required time as the operation route, or may select one operation route based on an index other than the required time (for example, the width of the river or the number of operating ships). In another example, the operation route determination unit 17 may display the eligible candidate routes on a display unit electrically connected via the interface 11 so that they can be selected, and select the eligible candidate route specified by the input data input via the interface 11 from an input device operated by the user as the operation route. In this case, the operation route determination unit 17 refers to the river map DB 10, generates display information of a screen for superimposing and displaying the eligible candidate routes on the map of the area including the departure point and the destination, and supplies the generated display information to the display device via the interface 11.

[0071] (1-5) Processing Flow FIG. 10 is an example of a flowchart of a process for determining an operation route. The controller 13 executes the process of the flowchart, for example, when a user input or the like for instructing the determination of the operation route is detected after the ship is started.

[0072] First, the candidate route acquisition unit 15 acquires candidate routes (step S11). In this case, for example, the candidate route acquisition unit 15 searches for candidate routes by performing a route search process based on the destination and the departure point (or the current position) input by the user through the input device operating via the interface 11. In another example, the candidate route acquisition unit 15 may acquire candidate routes by receiving a designation of candidate routes from the input device via the interface 11, or may acquire candidate routes by reading them from the memory 12 when the candidate routes are stored in the memory 12.

[0073] Next, the candidate route suitability determination unit 16 receives prediction water level information D1 indicating the predicted water level at the water level prediction points on each candidate route from the server device 7 (step S12). Then, the candidate route suitability determination unit 16 calculates the scheduled passing time and the predicted bridge water level at each bridge passing point on each candidate route based on the scheduled departure time and the prediction water level information D1 acquired in step S12 (step S13).

[0074] Then, the candidate route suitability determination unit 16 acquires the bridge height corresponding to each bridge on each candidate route from the river map DB10 (step S14). Further, the candidate route suitability determination unit 16 calculates the water surface distance based on the water surface measurement data, which is the point cloud data of the lidar 3 that measures downward (i.e., the direction in which the depression angle is positive) (step S15). Note that the candidate route suitability determination unit 16 may use a representative value such as the average value of the calculation results of a plurality of water surface distances obtained by executing step S15 a plurality of times as the value of the water surface distance in subsequent processing. Also, steps S11 to S15 may be executed in any order.

[0075] Then, for each bridge on each candidate route, the bridge passing point suitability determination unit 16 calculates the ship's highest point height at the bridge passing point based on the predicted bridge water level, the water surface distance, and the highest point information IH (step S16). Then, for each bridge on each candidate route, the bridge passing point suitability determination unit 16 calculates a predicted interval based on the bridge height and the ship's highest point height, and determines whether the ship can pass under each bridge on each candidate route based on the predicted interval (step S17). Then, based on the determination result by the bridge passing point suitability determination unit 16 in step S17, the operation route determination unit 17 selects a suitable candidate route, which is a candidate route capable of passing all the bridges existing on the route, as the operation route (step S18).

[0076] (1-6) Modification Example Hereinafter, modified examples suitable for the above-described embodiments will be described. The following modified examples may be applied to these embodiments in combination.

[0077] The candidate route acquisition unit 15 may generate a plurality of candidate routes of the same route with different departure times.

[0078] For example, in step S11 of the flowchart in FIG. 10, when there are N (N is an integer of 1 or more) routes from the departure point to the destination, the candidate route acquisition unit 15 generates M routes with different departure times at predetermined time intervals (for example, 30 minutes) from each of the N routes as candidate routes. In this case, N×M candidate routes are generated. In this case, in step S13, the bridge passing point suitability determination unit 16 calculates the scheduled passing time and the predicted bridge water level at each bridge passing point for each candidate route. Note that even for candidate routes with only different departure times, the scheduled passing time and the predicted bridge water level at each bridge passing point are different. Then, each step other than steps S11 and S13 described above is executed in the same manner as in the above-described embodiment.

[0079] According to this modified example, the information processing apparatus 1 can increase the candidate routes and determine a more suitable operation route.

[0080] As described above, the controller 13 of the information processing apparatus 1 according to the first embodiment acquires prediction water level information D1 regarding the predicted water level at a water level prediction point where the water level is predicted on one or a plurality of candidate routes that are candidates for the ship's operation route. Then, based on the prediction water level information D1, the controller 13 calculates a bridge predicted water level, which is the predicted water level at the scheduled passage time of the ship at each bridge passage point that is a point where the ship passes through the bridges existing on each of the candidate routes. Then, based on the bridge predicted water level, the controller 13 determines whether each of the candidate routes is suitable as an operation route. Thereby, the information processing apparatus 1 can accurately grasp the water level at the scheduled passage time of the ship for each bridge passage point existing on the candidate route, and can preferably determine whether each of the candidate routes is suitable as an operation route.

[0081] <Second Embodiment> After the ship starts operating according to the determined operation route, the information processing apparatus 1 according to the second embodiment measures the water level at the water level prediction point through which the ship passes, and generates correction information for the predicted water level provided by the server apparatus 7 based on the difference between the measured water level and the predicted water level. Thereby, the information processing apparatus 1 accurately executes the determination of whether the ship can pass under the unpassed bridges existing on the operation route (for example, the final confirmation of whether the ship can actually pass). Hereinafter, the same components as those in the first embodiment will be appropriately denoted by the same reference numerals, and the description thereof will be omitted.

[0082] (2-1) Block Configuration FIG. 11 is a block diagram of the information processing apparatus 1A according to the second embodiment. As shown in the figure, the information processing apparatus 1A includes an interface 11, a memory 12, and a controller 13.

[0083] The memory 12 has the river map DB10 and the highest point information IH described in the first embodiment. Further, the river map DB10 may include feature data regarding any feature (landmark) other than a bridge for which the height (e.g., elevation) is known. This feature data includes at least information regarding the position of the feature and the height (e.g., elevation) of the feature. Hereinafter, a feature (including a bridge) for which feature data is registered in the river map DB10 will also be referred to as a "registered feature".

[0084] Functionally, the controller 13 has a bridge passage determination unit 16A and a predicted water level correction unit 18A.

[0085] The bridge passage determination unit 16A determines whether passage under a bridge existing on the determined navigation route is possible. Further, when water level correction information is supplied from the predicted water level correction unit 18A described later, the bridge passage determination unit 16A corrects the predicted water level at each water level prediction point on the navigation route based on the water level correction information, and based on the corrected predicted water level, executes determination of whether a ship can pass each bridge on the navigation route.

[0086] The predicted water level correction unit 18A generates water level correction information for correcting the predicted water level at each water level prediction point indicated by the predicted water level information D1 received from the server device 7, and supplies the generated water level correction information to the bridge passage determination unit 16A.

[0087] Then, the controller 13 according to the second embodiment functions as a "ship reference height calculation means", "measured water level calculation means", "correction information generation means", "bridge water level calculation means", "bridge passage determination means", and a computer that executes a program, and the like.

[0088] Note that the information processing apparatus 1A may or may not execute the process related to the determination of the operation route described in the first embodiment. In the latter case, for example, in the information processing apparatus 1A, operation route information related to the operation route is stored in advance in the memory 12 or the like, and operation support for the ship is performed based on the operation route recognized by referring to the operation route information. In this case, the information processing apparatus 1A may determine the operation route based on the input data for specifying the operation route supplied from the input device operated by the user via the interface 11.

[0089] (2-2) Generation of Water Level Correction Information Next, the generation process of the water level correction information by the predicted water level correction unit 18A will be described. Roughly speaking, the predicted water level correction unit 18A calculates the height of the ship reference position (also referred to as the "ship reference height") based on the data obtained by measuring the registered features from the operation route by the lidar 3. Then, the predicted water level correction unit 18A generates the water level correction information based on the comparison result between the water level calculated based on the water surface distance and the ship reference height and the predicted water level based on the predicted water level information D1. The water level calculated based on the water surface distance and the ship reference height corresponds to the water level calculated based on the data obtained by measuring the registered features by the lidar 3, and hereinafter will also be referred to as the "measured water level".

[0090] FIG. 12 is an overhead view showing the vicinity of the bridges B3 and B4 existing on the operation route. Here, the point "Pa3" is the water level prediction point closest to the bridges B3 and B4 in the downstream direction, and the point "Pa4" is the water level prediction point closest to the bridges B3 and B4 in the upstream direction. Also, the points "Pb3" and "Pb4" are the points (also referred to as the "water level measurement points") for calculating the ship reference height and the measured water level. The dashed line 70 indicates the operation route.

[0091] First, the calculation of the ship reference height and the measured water level at the water level measurement point Pb3 will be described. When a ship exists at the point Pb3, the predicted water level correction unit 18A recognizes that a bridge B3, which is a registered feature, exists within the measurement range of the lidar 3 based on the position information of the ship based on the GPS receiver 5 or the like and the position information of the registered features included in the river map DB10. Then, the predicted water level correction unit 18A extracts data (also referred to as "feature measurement data") obtained by measuring the registered feature (here, the bridge B3) from the point cloud data generated by the lidar 3, and calculates the ship reference height based on the extracted feature measurement data.

[0092] FIG. 13 is a view of observing the ship from the rear when the bridge B3, which is a registered feature, exists within the measurement range of the lidar 3. The line L11 indicates the origin position of the height (for example, elevation) adopted in the predicted water level of the bridge and the like, the line L12 indicates the water surface position, and the line L13 indicates the position at the same height as the ship reference position. Further, the line L14 indicates the position at the same height as the height of the registered feature (here, the bridge B3) registered in the river map DB10. Furthermore, the measured points "m9" to "m12" indicate the measured points of the registered feature (here, the bridge B3) measured by the lidar 3.

[0093] In this case, when the distance between the current position of the ship and the position of the registered feature (here, the bridge B3) (specifically, the position of the registered feature registered in the river map DB10) is within the maximum measurement distance of the lidar 3, the predicted water level correction unit 18A extracts, from the point cloud data output by the lidar 3, the point cloud data above the horizontal plane (i.e., in the direction where the elevation angle is positive) as the feature measurement data. At this time, in order to exclude the points where locations other than the bridge, such as bridge piers, are detected, the maximum value of the z coordinate value of the point cloud data is obtained, and the bridge measurement data can be obtained by extracting the measurement points whose z coordinate values are close to the maximum value. In FIG. 13, the predicted water level correction unit 18A regards the data corresponding to the measured points m9 to m12 of the lower girder 32 as the feature measurement data and extracts it from the point cloud data generated by the lidar 3. Then, the predicted water level correction unit 18A calculates a representative value such as the average value or the minimum value of the coordinate values in the height direction of the extracted feature measurement data as the distance in the height direction between the registered feature (here, the bridge B3) and the ship reference position (the distance corresponding to the arrow A12, also referred to as the "feature distance"). Then, the predicted water level correction unit 18A specifies the height of the target registered feature registered in the river map DB10 (the height corresponding to the arrow A11, also referred to as the "feature height"), and calculates the ship reference height, which is the height corresponding to the arrow A13, by subtracting the feature distance from the specified feature height. When the registered feature is a bridge, the feature height represents the bridge height.

[0094] Next, the predicted water level correction unit 18A calculates the measured water level at the water level measurement point Pb3 based on the above-mentioned ship reference height based on the feature measurement data obtained at the water level measurement point Pb3 and the water surface measurement data obtained at the water level measurement point Pb3. In this case, the predicted water level correction unit 18A calculates the water surface distance, which is the distance corresponding to the arrow A14, based on the water surface measurement data, and calculates the measured water level, which is the height corresponding to the arrow A15, by subtracting the water surface distance from the ship reference height.

[0095] Then, the predicted water level correction unit 18A also calculates the ship reference height and the measured water level at the water level measurement point Pb4 by the same processing procedure as that at the water level measurement point Pb3.

[0096] Next, the predicted water level correction unit 18A performs arbitrary interpolation processing using the measured water levels at the water level measurement points (i.e., the water level measurement points where the measured water levels have been calculated) through which the ship including the water level measurement points Pb3 and Pb4 has passed, thereby calculating continuous measured water levels (also referred to as "interpolated measured water levels") along the navigation route through which the ship has passed. In this case, the predicted water level correction unit 18A may calculate continuous interpolated measured water levels on the navigation route through which the ship has passed based on any interpolation method such as linear interpolation, spline interpolation, or polynomial approximation, in addition to linear interpolation. Further, in this case, when model information indicating a model representing the water level along the navigation route is stored in advance in the memory 12 or the like, the predicted water level correction unit 18A may determine the parameters of the model by fitting the measured water level values at the water level measurement points to the model. In this case, the predicted water level correction unit 18A recognizes the interpolated measured water level at any point on the navigation route based on the model to which the determined parameters are applied. Note that the above-described model information may be generated in advance for each river, or may be generated in advance for each section divided by the river. Further, the model information may be a part of the river map DB 10.

[0097] Then, each time the predicted water level correction unit 18A passes through a water level prediction point, it compares the predicted water level at the passed water level prediction point with the interpolated measured water level, and calculates a difference value between the predicted water level and the interpolated measured water level (also referred to as the "water level difference value"). Hereinafter, for the sake of convenience, it is assumed that the water level difference value is a value obtained by subtracting the predicted water level from the measured water level (i.e., "water level difference value = interpolated measured water level - predicted water level"). Then, the predicted water level correction unit 18A calculates a representative value such as an average value of the water level difference values at a plurality of passed water level prediction points, and calculates the calculated average value or the like as a correction amount for the predicted water level of the bridge.

[0098] Figure 14(A) shows the time variations of the predicted water levels and the measured water levels at the locations where the ship exists. Here, the dashed circles 60a to 60k represent the measured water levels at each water level measurement location through which the ship has passed, and the solid circles 68a to 68e represent the predicted water levels at each water level prediction location through which the ship has passed. Also, the graph 69 is a graph representing the interpolated measured water levels generated by interpolating the measured water levels indicated by the dashed circles 60a to 60k. Also, the arrows corresponding to the solid circles 68a to 68e respectively indicate the widths corresponding to the water level difference values.

[0099] As shown in Figure 14(A), the predicted water level correction unit 18A calculates continuous interpolated measured water levels on the passed operation route by performing interpolation, and calculates a water level difference value corresponding to the difference between the interpolated measured water level and the predicted water level at the passed water level prediction location. In the example of Figure 14(A), the predicted water level correction unit 18A calculates the water level difference values at five locations corresponding to the solid circles 68a to 68e.

[0100] Figure 14(B) is an example of a distribution showing the frequency (i.e., the number of times) of a plurality of water level difference values calculated within the immediately preceding predetermined period. The predicted water level correction unit 18A totals the water level difference values calculated for each passed water level prediction location, and calculates statistical quantities such as the average and standard deviation of the water level difference values. Then, the predicted water level correction unit 18A supplies the calculated average and standard deviation of the water level difference values, etc. to the bridge passage determination unit 16A as water level correction information necessary for correcting the predicted water level of the bridge.

[0101] After that, based on the water level correction information received from the predicted water level correction unit 18A, the bridge passage determination unit 16A corrects the predicted water levels at each predicted water level point on the navigation route, and based on the corrected predicted water levels (i.e., the sum of the predicted water levels before correction and the average water level difference value), similar to the bridge predicted water level calculation unit 62 in the first embodiment, calculates the scheduled passage times and bridge predicted water levels at each bridge passage point on the navigation route where the ship has not passed. Hereinafter, the bridge predicted water level calculated based on the corrected predicted water level is also referred to as the "bridge corrected water level". Then, the bridge passage determination unit 16A calculates the highest point height of the ship based on the water surface distance, the bridge corrected water level, and the highest point information IH by the same process as the ship highest point height calculation unit 64, and further calculates the prediction interval from the highest point height of the ship by the same process as the prediction interval calculation unit 65.

[0102] Preferably, for each bridge existing on the navigation route where the ship has not passed, the bridge passage determination unit 16A stores information associating the calculated scheduled passage time, the bridge corrected water level, and the prediction interval in the memory 12 or the like. FIG. 15 shows a table associating the scheduled passage times, the bridge corrected water levels, and the prediction intervals of each bridge (bridge B5, bridge B6, bridge B7,...) existing on the navigation route where the ship has not passed. As shown in FIG. 15, the candidate route suitability determination unit 16 calculates the scheduled passage times, the bridge corrected water levels, and the prediction intervals described above for all the bridges (bridge B5, bridge B6, bridge B7,...) existing on the navigation route where the ship has not passed, and stores the calculation results in the memory 12 or the like.

[0103] (2-3) Functional Block FIG. 16 is an example of a functional block of the predicted water level correction unit 18A related to the generation of water level correction information in the second embodiment. Functionally, the predicted water level correction unit 18A includes a ground object height acquisition unit 81, a ground object distance calculation unit 82, a ship reference height calculation unit 83, a water surface distance calculation unit 84, a measured water level calculation unit 85, and a water level correction information generation unit 86.

[0104] The ground object height acquisition unit 81 acquires the ground object information corresponding to the registered ground objects existing within a predetermined distance from the ship from the river map DB 10, and acquires the ground object height included in the ground object information. In this case, the ground object height acquisition unit 81 identifies the above-mentioned registered ground objects based on, for example, the current position information based on the GPS receiver 5 or the like and the position information included in the ground object information.

[0105] The ground object distance calculation unit 82 extracts the ground object measurement data corresponding to the above-mentioned registered ground objects from the point cloud data output by the lidar 3, and calculates the ground object distance based on the extracted ground object measurement data. The point where the ground object measurement data is generated corresponds to the water level measurement point.

[0106] The ship reference height calculation unit 83 calculates the ship reference height based on the ground object height acquired by the ground object height acquisition unit 81 and the ground object distance calculated by the ground object distance calculation unit 82.

[0107] The water surface distance calculation unit 84 extracts the water surface measurement data obtained by measuring the water surface from the point cloud data output by the lidar 3, and calculates the water surface distance based on the extracted water surface measurement data. The measured water level calculation unit 85 calculates the measured water level at the water level measurement point based on the ship reference height calculated by the ship reference height calculation unit 83 and the water surface distance calculated by the water surface distance calculation unit 84. Further, the measured water level calculation unit 85 calculates the continuous interpolated measured water level from the measured water level at the water level measurement point calculated in the past from the departure time of the operation route to the present (specifically, up to the time point of the immediately preceding measured water level calculation).

[0108] The water level correction information generation unit 86 compares the predicted water level and the interpolated measured water level at the water level prediction point where the ship has passed based on the continuous interpolated measured water level calculated by the measured water level calculation unit 85 and the predicted water level information D1, and calculates the water level difference value. Then, the water level correction information generation unit 86 generates water level correction information including the average value and other statistical quantities of the water level difference values at the water level prediction points where the ship has passed, and supplies the generated water level correction information to the bridge passage determination unit 16A.

[0109] (2-4) Processing Flow FIG. 17 is an example of a flowchart showing the procedure of the water level correction information generation process executed by the predicted water level correction unit 18A in the second embodiment. The predicted water level correction unit 18A repeatedly executes the processes of the flowchart.

[0110] First, when the registered feature exists within the measurement range of LiDAR 3, the predicted water level correction unit 18A calculates the feature distance based on the feature measurement data extracted from the point cloud data generated by LiDAR 3 (step S21). Then, the predicted water level correction unit 18A calculates the ship reference height based on the feature distance calculated in step S21 and the feature height based on the river map DB 10 (step S22).

[0111] Then, the predicted water level correction unit 18A calculates the measured water level based on the ship reference height and the water surface distance calculated from the water surface measurement data extracted from the point cloud data generated by LiDAR 3. In this case, the predicted water level correction unit 18A calculates at least the interpolated measured water level at the water level prediction point by interpolation processing or the like.

[0112] Then, the predicted water level correction unit 18A generates water level correction information based on the comparison result between the predicted water level at the water level prediction point where the ship has passed through the water level prediction point and the interpolated measured water level at the water level prediction point (step S24).

[0113] As described above, the controller 13 of the information processing apparatus 1A according to the second embodiment calculates the ship reference height, which is the height of the reference position of the ship, based on the measurement data of the ground objects measured by the lidar 3 provided on the ship. Then, the controller 13 calculates the measured water level representing the height of the water surface based on the water surface measurement data, which is the measurement data of the water surface measured by the lidar 3, and the ship reference height. Then, the controller 13 generates water level correction information regarding the predicted water level information based on the predicted water level represented by the predicted water level information indicating the predicted water level for each predicted water level point in the river and the measured water level calculated at the point closest to the water level prediction point corresponding to the predicted water level. Therefore, by using the generated water level correction value, it is possible to accurately estimate the water level at the bridge location on the candidate route. As a result, the information processing apparatus 1A generates water level correction information for accurately correcting the predicted water level in a mode where it does not perform highly accurate self-position estimation in the height direction, and can accurately execute the re-determination of whether the ship can pass under the unpassed bridges existing on the water level prediction operation route.

[0114] <Third Embodiment> The information processing apparatus 1 according to the third embodiment is different from the second embodiment in that it acquires the ship reference height by executing a highly accurate self-position estimation process including the height direction. Hereinafter, the same reference numerals will be appropriately assigned to the same components as those in the second embodiment, and the description thereof will be omitted.

[0115] (3-1) Block Configuration FIG. 18 is a block diagram of the information processing apparatus 1B according to the third embodiment. As shown in the figure, the information processing apparatus 1B includes an interface 11, a memory 12, and a controller 13.

[0116] The interface 11 acquires output data from each sensor of the sensor group 2 and supplies it to the controller 13. The sensor group 2 includes a lidar 3, a speed sensor 4 for detecting the speed of the ship, a GPS receiver 5, and an inertial measurement unit (IMU) 6 for measuring the acceleration and angular velocity of the target moving ship in three axial directions. The speed sensor 4 may be, for example, a speedometer using Doppler or a speedometer using GNSS.

[0117] The memory 12 has the river map DB 10 and the highest point information IH described in the first embodiment. And the river map DB 10 contains voxel data VD.

[0118] The voxel data VD is data that records the position information of stationary structures for each voxel indicating a cube (regular lattice) that is the minimum unit of three-dimensional space. The voxel data VD includes data representing the measured point cloud data of stationary structures in each voxel by a normal distribution and is used for scan matching using NDT (Normal Distributions Transform) as described later. The information processing device 1B estimates, for example, the position on the plane of the ship, the height position, the yaw angle, the pitch angle, and the roll angle by NDT scan matching. Unless otherwise specified, the self-position shall also include attitude angles such as the yaw angle of the ship. Note that the voxel data VD may be a part of the river map DB 10.

[0119] Functionally, the controller 13 includes a bridge passage determination unit 16B, a predicted water level correction unit 18B, and a self-position estimation unit 19B.

[0120] The bridge passageability determination unit 16B determines whether the ship can pass under the bridges existing on the determined navigation route. The process executed by the bridge passageability determination unit 16B is the same as that of the bridge passageability determination unit 16A in the second embodiment. The predicted water level correction unit 18B generates water level correction information for correcting the predicted water levels at the respective water level prediction points indicated by the predicted water level information D1 received from the server device 7, and supplies the generated water level correction information to the bridge passageability determination unit 16B.

[0121] The self-position estimation unit 19B estimates the self-position by performing scan matching based on Normal Distributions Transform (NDT) (NDT scan matching) based on the point cloud data based on the output of the lidar 3 and the voxel data VD corresponding to the voxel to which the point cloud data belongs. Here, the point cloud data to be processed by the self-position estimation unit 19B may be the point cloud data generated by the lidar 3 or the point cloud data after the downsampling process of the point cloud data. Then, the controller 13 according to the third embodiment functions as a "ship reference height calculation means", "measured water level calculation means", "correction information generation means", "bridge water level calculation means", "bridge passageability determination means", and a computer that executes a program, and the like.

[0122] (3-2) NDT Scan Matching Next, an explanation regarding the position estimation based on the NDT scan matching executed by the self-position estimation unit 19B will be given.

[0123] FIG. 19 is a diagram showing the position of a ship in three-dimensional orthogonal coordinates. As shown in the figure, the self-position on a plane defined on the three-dimensional orthogonal coordinates of xyz is represented by the coordinates "(x, y, z)", the roll angle "φ" of the ship, the pitch angle "θ", and the yaw angle (azimuth) "ψ". Here, the roll angle φ is defined as the rotation angle about the traveling direction of the ship, the pitch angle θ is the elevation angle of the traveling direction of the ship with respect to the xy plane, and the yaw angle ψ is the angle formed between the traveling direction of the ship and the x-axis. The coordinates (x, y, z) are, for example, absolute positions corresponding to a combination of latitude, longitude, and altitude, or world coordinates indicating a position with a predetermined point as the origin. And the self-position estimation unit 19B performs self-position estimation using these x, y, z, φ, θ, and ψ as estimation parameters.

[0124] Next, the voxel data VD used for NDT scan matching will be described. The voxel data VD includes data representing the measured point cloud data of static structures in each voxel by a normal distribution.

[0125] FIG. 20 shows an example of a schematic data structure of the voxel data VD. The voxel data VD includes information on parameters when representing the point cloud in a voxel by a normal distribution. In this embodiment, for each voxel, it includes a "voxel ID", "voxel coordinates", "attribute information", "average vector", and "covariance matrix".

[0126] The "voxel ID" indicates the identification information of each voxel. The "voxel coordinates" indicate the absolute three-dimensional coordinates of a reference position such as the center position of each voxel. Since each voxel is a cube obtained by dividing the space into a grid pattern and its shape and size are determined in advance, it is possible to specify the space of each voxel by the voxel coordinates. The voxel coordinates may be used as the voxel ID.

[0127] "Attribute information" indicates information regarding the attributes of a target voxel. For example, in this embodiment, the "attribute information" of the voxel corresponding to a bridge includes information indicating that it is a bridge section. Note that the "attribute information" of the voxel corresponding to the lower part of the bridge girder (i.e., the bottom surface of the structure above the river) may further include information indicating that it is the lower part of the girder.

[0128] "Average vector" and "covariance matrix" indicate the average vector and covariance matrix corresponding to the parameters when expressing the point cloud within the target voxel by a normal distribution. Note that the coordinates of an arbitrary point "i" within an arbitrary voxel "n" are X n (i)=[x n (i), y n (i), z n (i)] T defined as such, and assuming the number of points in voxel n is "N n ", the average vector "μ n " and covariance matrix "V n " of voxel n are respectively represented by the following equations (1) and (2).

[0129]

Number

[0130]

Number

[0131] Next, an overview of NDT scan matching using voxel data VD will be described.

[0132] Scan matching by NDT assuming a ship is an estimation parameter P = [t x , t y , t z , t φ , t θ , t ψ ​T will be estimated. Here, "t x " represents the movement amount in the x direction, "t y " represents the movement amount in the y direction, "t z " represents the movement amount in the z direction, "t φ " represents the roll angle, "t θ " represents the pitch angle, "t ψ " represents the yaw angle.

[0133] Also, the coordinates of the point cloud data output by the lidar 3 are X L (j)=[x n (j), y n (j), z n (j)] T If we set it as such, the average value "L´ L " of X n "(j) is expressed by the following formula (3).

[0134]

Equation

[0135] Then, the self-position estimation unit 19B searches for voxel data VD associated with the point cloud data transformed into the world coordinate system. Here, the world coordinate system is the absolute coordinate system adopted in the map DB10 (including the voxel data VD). At this time, the self-position estimation unit 19B may exclude the voxel data VD of the voxels located below the water surface (in the height direction) from the search target. Thereby, when the information processing apparatus 1B performs the association between the point cloud data and the voxels, unnecessary processing can be omitted, and a decrease in the position estimation accuracy due to an error in the association can be suppressed.

[0136] Then, the self-position estimation unit 19B uses the average vector μ n contained in the searched voxel data VD and the covariance matrix V nUsing these, the evaluation function value regarding the matching of voxel n (also referred to as the "individual evaluation function value") "E" n is calculated.

[0137] In this case, the self-position estimation unit 19B calculates the individual evaluation function value E of voxel n based on the following formula (4). n is calculated.

[0138] [Equation]

[0139] Then, the self-position estimation unit 19B calculates the overall evaluation function value (also referred to as the "score value") "E(k)" for all voxels to be matched, which is represented by the following formula (5). The score value E is an index indicating the degree of matching.

[0140] [Equation] After that, the self-position estimation unit 19B calculates the estimated parameter P at which the score value E(k) becomes maximum by an arbitrary root-finding algorithm such as the Newton method. Then, the self-position estimation unit 19B applies the estimated parameter P to the position ("DR position" also) "X" DR (k) calculated by dead reckoning at time k, and calculates the self-position based on NDT scan matching ("NDT position" also) "X" NDT (k). Then, the self-position estimation unit 19B regards the NDT position X NDT (k) as the final estimated result of the self-position at the current processing time k ("estimated self-position" also) "X^(k)". Here, the DR position X DR (k) corresponds to the provisional self-position before the calculation of the estimated self-position X^(k), and is also denoted as the predicted self-position "X" - (k). In this case, the NDT position X NDT (k) is represented by the following formula (6).

[0141]

Number

[0142] Figure 21 is an example of a functional block diagram of the self - position estimation unit 19B. As shown in the figure, the self - position estimation unit 19B includes a dead reckoning unit 91, a coordinate conversion unit 92, a water - surface reflection data removal unit 93, and an NDT position calculation unit 94.

[0143] The dead reckoning unit 91 calculates the DR position based on the signals output by the sensor group 2. Specifically, the dead reckoning unit 91 uses the moving speed and angular velocity of the ship based on the outputs of the speed sensor 4 and the IMU 6, etc., to obtain the moving distance and azimuth change from the previous time. Then, the dead reckoning unit 91 adds the moving distance and azimuth change from the previous time to the estimated self - position X ^ (k - 1) at the previous processing time immediately before the current processing time k to calculate the DR position X DR (k) at time k. This DR position X DR (k) is the self - position obtained at time k based on dead reckoning and corresponds to the predicted self - position X - (k). Note that when the estimated self - position X ^ (k - 1) at time k - 1 does not exist immediately after the start of self - position estimation, etc., the dead reckoning unit 91 determines the DR position X DR (k) based on, for example, the signal output by the GPS receiver 5.

[0144] The coordinate conversion unit 92 converts the point - cloud data based on the output of the lidar 3 into the world coordinate system, which is the same coordinate system as the map DB10. In this case, the coordinate conversion unit 92 performs coordinate conversion of the point - cloud data at time k based on, for example, the predicted self - position output by the dead reckoning unit 91 at time k. Note that the process of converting the point - cloud data in the coordinate system based on the lidar installed on the moving body (ship in this embodiment) into the coordinate system of the moving body, and the process of converting from the coordinate system of the moving body to the world coordinate system, etc., are disclosed in, for example, International Publication WO2019 / 188745, etc.

[0145] The water surface reflection data removal unit 93 removes data (also referred to as "water surface reflection data") that is erroneously generated when the lidar 3 receives light reflected from the water surface from the point cloud data supplied from the coordinate conversion unit 92. In this case, the water surface reflection data removal unit 93 removes data representing a position below the water surface position (including the same height, the same hereinafter), that is, a position where the z coordinate value is the same or lower, from the point cloud data as water surface reflection data. Note that the water surface reflection data removal unit 93 may estimate the water surface position based on, for example, the z coordinate value after coordinate conversion processing of the point cloud data output by the lidar 3 when the ship is at a position more than a predetermined distance away from the shore. Then, the water surface reflection data removal unit 93 supplies the point cloud data obtained by removing data below the water surface position from the point cloud data supplied from the coordinate conversion unit 92 to the NDT position calculation unit 94.

[0146] The NDT position calculation unit 94 calculates the NDT position based on the point cloud data supplied from the water surface reflection data removal unit 93. In this case, the NDT position calculation unit 94 collates the point cloud data in the world coordinate system supplied from the water surface reflection data removal unit 93 with the voxel data VD represented in the same world coordinate system to perform association between the point cloud data and the voxels. Then, the NDT position calculation unit 94 calculates an individual evaluation function value based on Equation (4) for each voxel associated with the point cloud data, and calculates an estimated parameter P at which the score value E(k) based on Equation (5) becomes maximum. Then, the NDT position calculation unit 94, based on Equation (6), applies the estimated parameter P obtained at time k to the DR position X DR (k) output by the dead reckoning unit 91 to obtain the NDT position X NDT (k) at time k determined thereby. The NDT position calculation unit 94 outputs the NDT position X NDT (k) as the estimated own position X^(k) at time k.

[0147] (3-3) Generation of Water Level Correction Information The predicted water level correction unit 18B calculates the measured water level based on the ship reference height obtained by self-position estimation and the water surface distance based on the water surface measurement data, and generates water level correction information based on the comparison result between the measured water level and the predicted water level.

[0148] FIG. 22 is a view of observing the ship from behind. Line L21 indicates the origin position of the height (e.g., elevation) adopted at the predicted water level of the bridge, etc., line L22 indicates the water surface position, and line L23 indicates the position at the same height as the ship reference position. Further, the measured points “m13” to “m16” indicate the measured points of the water surface measured by the lidar 3. In this case, the predicted water level correction unit 18B calculates the water surface distance, which is the distance corresponding to arrow A22, by performing the same processing as the candidate route suitability determination unit 16 of the first embodiment based on the water surface measurement data corresponding to the measured points “m13” to “m16” of the water surface. Then, the predicted water level correction unit 18B calculates the height obtained by subtracting the water surface distance of arrow A22 from the ship reference height, which is the height corresponding to arrow 21 obtained by self-position estimation (i.e., the height indicated by arrow A23), as the measured water level.

[0149] FIG. 23(A) shows the time change of the predicted water level and the measured water level at the point where the ship exists. Here, graph 69B represents the measured water level calculated from the self-position estimation result in the height direction (z coordinate) and the water surface distance based on the water surface measurement data. As described above, every time the lidar 3 detects point cloud data, it is possible to calculate the measured water level from the self-position estimation and the water surface distance. Therefore, although the measured water level is discrete, since the period of the lidar 3 is short (e.g., 100 [ms]), graph 69B is close to a continuous line. The solid circles 68a to 68e represent the predicted water levels at the water level prediction points where the ship has passed. Also, the arrows corresponding to the solid circles 68a to 68e respectively indicate the widths corresponding to the water level difference values.

[0150] As shown in the figure, the predicted water level correction unit 18B calculates the measured water level on the operation route based on the self-position estimation result continuously obtained by the self-position estimation unit 19B, and calculates the water level difference value corresponding to the difference between the measured water level and the predicted water level at each water level prediction point where the ship has passed. In the example of FIG. 23(A), the predicted water level correction unit 18B calculates the water level difference values at five points corresponding to the solid circles 68a to 68e.

[0151] FIG. 23(B) is an example of a distribution showing the frequency (i.e., the number of occurrences) of a plurality of water level difference values calculated within the immediately preceding predetermined period. The predicted water level correction unit 18B aggregates the water level difference values calculated for each passed water level prediction point, and calculates statistical quantities such as the average and standard deviation of the water level difference values. Then, the predicted water level correction unit 18B supplies the calculated average and standard deviation of the water level difference values, etc. to the bridge passage determination unit 16B as water level correction information necessary for correcting the bridge predicted water level. Thereafter, the bridge passage determination unit 16B corrects the predicted water level at each water level prediction point on the operation route based on the water level correction information received from the predicted water level correction unit 18B, and based on the corrected predicted water level (i.e., the sum of the predicted water level before correction and the average water level difference value), calculates the scheduled passage time and the bridge corrected water level at each bridge passage point on the operation route where the ship has not passed. Then, the bridge passage determination unit 16B calculates the ship highest point height based on the water surface distance, the bridge corrected water level, and the highest point information IH, and calculates the prediction interval from the ship highest point height. Note that the bridge passage determination unit 16B preferably stores information (see FIG. 15) associating the calculated scheduled passage time, the bridge corrected water level, and the prediction interval for each bridge existing on the operation route where the ship has not passed in the memory 12 or the like.

[0152] (3-4) Functional Block FIG. 24 is an example of a functional block diagram of the predicted water level correction unit 18B related to the generation of water level correction information in the third embodiment. Functionally, the predicted water level correction unit 18B includes a ship reference height acquisition unit 83B, a water surface distance calculation unit 84B, a measured water level calculation unit 85B, and a water level correction information generation unit 86B.

[0153] The ship reference height acquisition unit 83B acquires the z coordinate value included in the self-position estimation result supplied from the self-position estimation unit 19B as the ship reference height. The water surface distance calculation unit 84B extracts the water surface measurement data obtained by measuring the water surface from the point cloud data output by the lidar 3, and calculates the water surface distance based on the extracted water surface measurement data. Note that the calculation of the water surface distance may be performed at a frequency lower than the frequency of the self-position estimation by the self-position estimation unit 19B.

[0154] The measured water level calculation unit 85B calculates the measured water level based on the ship reference height acquired by the ship reference height acquisition unit 83B and the water surface distance calculated by the water surface distance calculation unit 84B. The measured water level calculation unit 85B may calculate the measured water level each time a self-position estimation result is obtained, or may calculate the measured water level at a frequency lower than the frequency at which the self-position estimation result is obtained.

[0155] The water level correction information generation unit 86B compares the predicted water level and the measured water level at the water level prediction point where the ship has passed based on the measured water level calculated by the measured water level calculation unit 85B and the predicted water level information D1, and calculates a water level difference value. Then, the water level correction information generation unit 86B generates water level correction information including the average value and other statistical quantities of the water level difference values at the water level prediction points where the ship has passed, and supplies the generated water level correction information to the bridge passage determination unit 16B.

[0156] (3-5) Processing Flow FIG. 25 is an example of a flowchart showing the procedure of the water level correction information generation process executed by the predicted water level correction unit 18B in the third embodiment.

[0157] First, the predicted water level correction unit 18B acquires the ship reference height based on the self-position estimation result by the self-position estimation unit 19B (step S31). Then, the predicted water level correction unit 18B calculates the measured water level based on the ship reference height and the water surface distance calculated from the water surface measurement data extracted from the point cloud data generated by the lidar 3 (step S32). The predicted water level correction unit 18B repeatedly executes the processes of step S31 and step S32 until the ship passes through the water level prediction point.

[0158] Then, when the ship passes through the water level prediction point, the predicted water level correction unit 18B generates water level correction information based on the comparison result between the predicted water level and the measured water level at the water level prediction point (step S33). In this case, the predicted water level correction unit 18B generates water level correction information based on a plurality of water level difference values (that is, the water level difference value at the water level prediction point immediately after passing, and the water level difference value at the water level prediction point passed before the water level prediction point).

[0159] (3-6) Modification Example As shown in FIG. 20, the voxel data VD is not limited to a data structure including an average vector and a covariance matrix. For example, the voxel data VD may directly include the point cloud data used when calculating the average vector and the covariance matrix. Further, the self-position estimation method using the voxel data VD is not limited to NDT scan matching. For example, the information processing device 1B may perform self-position estimation by matching (matching) the voxel data VD and the point cloud data of the lidar 3 based on ICP (Iterative Closest Point).

[0160] As described above, the controller 13 of the information processing device 1B according to the third embodiment calculates the ship reference height, which is the height of the reference position of the ship, based on the self-position estimation result using the measurement data of the ground objects measured by the lidar 3 provided on the ship. Then, the controller 13 calculates a measured water level representing the height of the water surface based on the water surface measurement data, which is the measurement data of the water surface measured by the lidar 3, and the ship reference height. Then, the controller 13 generates water level correction information regarding the predicted water level information based on the predicted water level represented by the predicted water level information for each predicted water level point in the river and the measured water level calculated at the water level prediction point corresponding to the predicted water level or the point closest to the water level prediction point. Therefore, by using the generated water level correction value, it is possible to accurately estimate the water level at the bridge location on the candidate route. As a result, in the mode where the information processing device 1B performs highly accurate self-position estimation in the height direction, the information processing device 1B generates water level correction information for accurately correcting the predicted water level using the self-position estimation result, and can accurately execute the determination of whether the ship can pass under the unpassed bridge existing on the water level prediction operation route.

[0161] <Fourth Embodiment> In the fourth embodiment, the server device 7 collects the water level difference values calculated based on the second or third embodiment, and updates the predicted water level DB70 based on the collected water level difference values. Thereby, the server device 7 preferably distributes to the information processing device 1 the predicted water level information D1 indicating the highly accurate water level corrected based on the measured water level.

[0162] (4-1) Configuration FIG. 26 shows a schematic configuration of the operation support system according to the fourth embodiment. In the operation support system according to the fourth embodiment, there are a plurality of information processing devices 1C (1Ca, 1Cb,...) that execute the processing according to either the second or third embodiment. And the server device 7C according to the fourth embodiment receives the measured water level information "D2" regarding the correction of the predicted water level based on the measured water level from each of the information processing devices 1C, and updates the predicted water level DB based on the received measured water level information D2. Hereinafter, the same reference numerals are appropriately given to the same components as in the first, second, or third embodiment, and the description thereof is omitted.

[0163] The information processing devices 1C according to the fourth embodiment each have the same hardware configuration as the information processing device 1A according to the second embodiment shown in FIG. 11 or the information processing device 1B according to the third embodiment shown in FIG. 18. And the information processing devices 1C are each moving together with the ships existing on the river, and based on the outputs of various sensors included in the sensor group 2 (not shown) and the predicted water level information D1 transmitted from the server device 7C, perform operation support for the ship on which the information processing device 1 is provided. And when the information processing device 1C does not perform highly accurate self-position estimation including the height direction, it calculates the water level difference value based on the second embodiment, and when performing highly accurate self-position estimation including the height direction (for example, self-position estimation based on NDT scan matching), it calculates the water level difference value based on the third embodiment. Then, the information processing device 1C transmits to the server device 7C the measured water level information D2 including the calculated water level difference value, the position information indicating the location where the water level difference value was calculated (i.e., the water level prediction point), and the time (date and time) when the water level difference value was calculated.

[0164] The server device 7C according to the fourth embodiment updates the predicted water level DB70 based on the measured water level information D2 received from the information processing device 1C. FIG. 27 is a block diagram showing an example of the hardware configuration of the server device 7C. The server device 7C mainly includes an interface 71, a memory 72, and a controller 73. These elements are interconnected via a bus line.

[0165] The interface 71 performs an interface operation related to the exchange of data between the server device 7C and an external device. In this embodiment, the interface 71 performs a process of transmitting the predicted water level information D1 to the information processing device 1C and a process of receiving the measured water level information D2 from the information processing device 1C based on the control of the controller 73. The memory 72 is composed of various volatile memories and non-volatile memories such as a RAM, a ROM, a hard disk drive, and a flash memory. The memory 72 stores a program for the controller 73 to execute a predetermined process. Further, the memory 72 stores the predicted water level DB70 described in the first embodiment and the measured water level information DB75 which is a database storing the measured water level information D2. The predicted water level DB70 is updated based on the measured water level information DB75 as will be described later.

[0166] (4-2) Data Structure FIG. 28(A) is an example of the data structure of the predicted water level DB70. The predicted water level DB70 mainly has items of "water level prediction location", "update time", and "predicted water level".

[0167] The "water level prediction location" is information that identifies the target water level prediction location in each record of the predicted water level DB70. Here, as an example, the two-dimensional coordinate values (for example, a pair of latitude and longitude) of the water level prediction location are recorded as the "water level prediction location". The "update date and time" indicates the date and time when the predicted water level was updated. When the predicted water level has not been updated, the "update date and time" indicates the prediction date and time of the predicted water level. The "update date and time" may be provided for each predicted water level at each time. The "predicted water level" indicates the predicted water level at each time when the water level is predicted. Here, the value of each predicted water level of the "predicted water level" is updated based on the measured water level information DB75.

[0168] Figure 28(B) is an example of the data structure of the measured water level information D2. The measured water level information D2 mainly has each item of "location information", "date and time", "water level difference value", "standard deviation of water level difference", "predicted water level", and "measured water level".

[0169] The "location information" is location information representing the place where the water level difference value was calculated (that is, the water level prediction location or a place close to the water level prediction location). Here, the "location information" is represented by two-dimensional coordinate values representing the above-mentioned place on the horizontal plane. The "date and time" represents the date and time (time) when the water level difference value (or measured water level) was calculated. The "water level difference value" represents the average value of the water level difference. The "standard deviation of water level difference" represents the standard deviation of the water level difference. The "predicted water level" represents the predicted water level used for calculating the corresponding water level difference value. Note that the "predicted water level" may include information regarding the date and time (update date and time) when the prediction or update of the predicted water level was performed, in addition to or instead of the predicted water level. When the information regarding this time, for example, when the information on the update date and time shown in Figure 28(A) is included in the predicted water level information D1 received by the information processing device 1C, it represents the said update date and time. The "measured water level" represents the measured water level used for calculating the corresponding water level difference value.

[0170] Note that the data structure of the measured water level information D2 is not limited to the illustrated data structure. For example, the measured water level information D2 may not include information regarding the "predicted water level" and the "measured water level". In other examples, instead of including a plurality of records, the measured water level information D2 may include only one record. In this case, each time the information processing device 1C calculates a water level difference value, for example, it transmits the measured water level information D2 including the record regarding the calculated water level difference value to the server device 7C.

[0171] (4-3) Predicted Water Level Update Process Next, the update process of the predicted water level recorded in the predicted water level DB70 (predicted water level update process) will be specifically described.

[0172] FIG. 29 is an example of a functional block of the controller 73 of the server device 7C according to the fourth embodiment. As shown in the figure, the controller 73 of the server device 7C according to the fourth embodiment functionally includes a receiving unit 76, an updating unit 77, and a distributing unit 78.

[0173] The receiving unit 76 receives the measured water level information D2 from each information processing device 1C via the interface 71. Then, the receiving unit 76 stores the received measured water level information D2 in the measured water level information DB75.

[0174] The updating unit 77 updates the predicted water level DB70 based on the measured water level information DB75. In this case, for example, the updating unit 77 first identifies, for each record recorded in the measured water level information DB75 (i.e., the record of the measured water level information D2 shown in FIG. 28), the water level prediction location indicated by the position information (i.e., the water level prediction location closest to the location indicated by the position information). Then, the updating unit 77 aggregates the water level difference values included in the records of the measured water level information DB75 for each water level prediction location, and corrects the predicted water level of the corresponding water level prediction location recorded in the predicted water level DB70 based on a representative value such as the average value of the water level difference values for each water level prediction location. In this case, for example, the updating unit 77 performs statistical processing on the water level difference values received from a plurality of information processing devices for each water level prediction location, and adds a representative value such as the average value to the predicted water level at each time for the corresponding water level prediction location.

[0175] In addition, when performing statistical processing on the water level difference values received from the plurality of information processing devices described above, the update unit 77 may perform weighted averaging processing using the standard deviation of the water level difference. For example, for the water level difference values μ i and the water level difference standard deviation σ i received from N information processing devices 1C (i = 1, 2, ···, N), the update unit 77 calculates a correction value "Cw" for the predicted water level according to the following formula (7).

[0176] [Number] In formula (7), since the reciprocal of the variance value obtained by squaring the standard deviation is used as the weight, for the results where the variation in the water level difference value is small and calculated stably, the weight value becomes large, and for the results where the variation in the water level difference value is large and the stability is low, the weight value becomes small. Therefore, the update unit 77 can calculate a highly reliable correction value Cw through processing that emphasizes highly stable results.

[0177] In this case, the update unit 77 preferably corrects the predicted water level based on the water level difference value calculated after the update time corresponding to the predicted water level recorded in the predicted water level DB70. For example, when the update unit 77 updates the first record in Fig. 28(A), the "position information" indicates a position within a predetermined distance (the threshold for determining whether it is the same point) from the water level prediction point "(va, wa)", and the "date and time" is after "17:00 on September 12th", the update unit extracts records from the measured water level information DB75, and corrects the predicted water level based on the water level difference values indicated by the extracted records. In addition, when the information indicating the update date and time of the predicted water level used for calculating the water level difference value is included in the measured water level information DB75, the update unit 77 may correct the predicted water level using only the water level difference value that matches the update date and time recorded in the predicted water level DB70. This preferably suppresses the correction of the predicted water level using the water level difference value calculated based on the old predicted water level.

[0178] The distribution unit 78 transmits prediction water level information D1 including information on the predicted water level extracted from the predicted water level DB 70 to the information processing device 1C via the interface 71. In this case, when the distribution unit 78 receives predetermined request information from the information processing device 1C, it may transmit the prediction water level information D1 regarding the predicted water level at the water level prediction point in the area or river specified by the request information to the information processing device 1C, or may transmit the prediction water level information D1 regarding the updated predicted water level each time the predicted water level DB 70 is updated to the information processing device 1C. Thus, the distribution unit 78 performs information distribution regarding the predicted water level to the information processing device 1C based on either a push-type information distribution or a pull-type information distribution method.

[0179] FIG. 30(A) is an example of a flowchart executed by the information processing device 1C according to the fourth embodiment. The information processing device 1C repeatedly executes the processing of this flowchart during the operation of the ship.

[0180] The information processing device 1C receives the prediction water level information D1 from the server device 7C and stores the received prediction water level information D1 (step S41). Next, the information processing device 1C calculates the measured water level and the water level difference value at each water level prediction point where the ship has passed according to the second or third embodiment (step S42). In this case, the information processing device 1C obtains the measured water level and the water level difference value at each water level prediction point by executing the flowchart of the water level correction information generation process shown in FIG. 17 or FIG. 25. Then, the information processing device 1C determines whether it is the transmission timing of the measured water level information D2 (step S43). Note that the information processing device 1C may, for example, summarize the records corresponding to the calculated water level difference values as the measured water level information D2 and transmit them to the server device 7C each time a predetermined number of water level calculation values are calculated, or may summarize the records corresponding to all the calculated water level difference values as the measured water level information D2 and transmit them to the server device 7C after the operation of the operation route is completed.

[0181] When it is the transmission timing of the measured water level information D2 (step S43; Yes), the information processing apparatus 1C transmits the measured water level information D2 including the set of the water level difference value calculated in step S42, the corresponding date and time information, and the position information to the server apparatus 7C (step S44). On the other hand, when the information processing apparatus 1C determines that it is not the transmission timing of the measured water level information D2 (step S43; No), if there is a water level prediction point where the ship has passed, the information processing apparatus 1C continues to calculate the measured water level and the water level difference value at the water level prediction point in step S42.

[0182] FIG. 30(B) is an example of a flowchart executed by the server apparatus 7C according to the third embodiment. The server apparatus 7C repeatedly executes the processing of this flowchart.

[0183] First, the server apparatus 7C receives the measured water level information D2 transmitted from the information processing apparatus 1C and stores the received measured water level information D2 in the measured water level information DB75 (step S51). Then, the server apparatus 7C determines whether it is the update timing of the predicted water level DB70 (step S52). For example, when there is a water level prediction point where a predetermined number or more of water level difference values have been collected, the server apparatus 7C may determine that it is the update timing for the predicted water level at the water level prediction point. In another example, the server apparatus 7C may determine that it is the update timing of the predicted water level DB70 at predetermined time intervals and update the predicted water level at the water level prediction point where the water level difference value has been collected. When the server apparatus 7C determines that it is not the update timing of the predicted water level DB70 (step S52; No), it continues to receive and store the measured water level information D2 in step S51.

[0184] On the other hand, when the server device 7C determines that it is the update timing of the predicted water level DB70 (step S52; Yes), it calculates an updated value of the predicted water level based on the water level difference value registered in the measured water level information DB75 (step S53). In this case, the server device 7C calculates the updated value of the predicted water level based on, for example, the average value of the water level difference values and the predicted water level before the update for each water level prediction location. Then, the server device 7C updates the predicted water level DB70 based on the calculated updated value of the predicted water level (step S54). After that, the server device 7C distributes the predicted water level information D1 based on the updated predicted water level DB70 to each information processing device 1C. Thereby, the server device 7C can distribute the predicted water level information D1 indicating an accurate predicted water level that accurately reflects the water levels measured at each water level prediction location to the information processing device 1C.

[0185] (4-4) Modification Example Instead of calculating the updated value of the predicted water level based on the water level difference value included in the measured water level information D2, the server device 7C may calculate the updated value of the predicted water level based on the measured water level included in the measured water level information D2.

[0186] In this modification example, the measured water level information D2 includes the measured water level at the water level prediction location and information indicating the date and time and location where the measured water level was calculated. Then, the server device 7C calculates the water level difference value by referring to the predicted water level DB70 and the measured water level information DB75 reflecting the measured water level information D2, and calculates the updated value of the predicted water level according to the description of the fourth embodiment above based on the calculated water level difference value. In this case, the server device 7C refers to the measured water level information DB75 and calculates a representative value such as the average value of the water level measurement values for each water level prediction location and time zone (the time zones corresponding to "time a" and "time b" of the predicted water level DB70 shown in FIG. 28(A)). Then, the server device 7C calculates, for each water level prediction location and time, the difference value between the predicted water level registered in the predicted water level DB70 and the representative value such as the average value of the corresponding water level measurement values as the water level difference value. Then, the server device 7C calculates a representative value such as the average value of the calculated water level difference values for each water level prediction location, and calculates the updated value of the predicted water level at each water level prediction location based on the calculated water level difference value.

[0187] Also in this modified example, the server device 7C can update each predicted water level recorded in the predicted water level DB70 to an accurate value that accurately reflects the water level measured at each water level prediction point, and distribute prediction water level information D1 indicating the accurate predicted water level to the information processing device 1C.

[0188] As described above, the controller 73 of the server device 7C according to the fourth embodiment stores a predicted water level DB70 representing the predicted water level for each water level prediction point in the river. Then, the controller 73 receives measurement water level information D2 regarding the measurement water level measured by the plurality of ships from the plurality of ships. Then, the controller 73 updates the predicted water level DB70 based on the measurement water level information D2. Then, the controller 73 distributes prediction water level information D1 based on the predicted water level DB70. With this mode, the server device 7C can distribute prediction water level information D1 indicating an accurate predicted water level that accurately reflects the water level measured at each water level prediction point to the information processing device 1C.

[0189] Note that in the above-described embodiment, the program can be stored using various types of non-transitory computer readable media and supplied to a controller or the like that is a computer. The non-transitory computer readable media include various types of tangible storage media. Examples of the non-transitory computer readable media include magnetic storage media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical storage media (e.g., magneto-optical disks), CD-ROM (Read Only Memory), CD-R, CD-R / W, semiconductor memories (e.g., mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, RAM (Random Access Memory)).

[0190] The present invention has been described with reference to the embodiments above, but the present invention is not limited to the above embodiments. Various changes that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. That is, the present invention naturally includes various modifications and corrections that those skilled in the art could make in accordance with the entire disclosure including the claims and the technical idea. Also, each disclosure of the above-cited patent documents and the like is incorporated herein by reference.

Explanation of Reference Numerals

[0191] 1, 1A, 1B, 1C Information Processing Apparatus 2 Sensor Group 3 LiDAR 5 GPS Receiver 7, 7C Server Apparatus 10 River Map DB

Claims

1. Predicted water level information acquisition means for acquiring predicted water level information regarding the predicted water level at a predicted water level point where the water level is predicted on one or more candidate routes that are candidates for the ship's operating route; Based on the scheduled departure time of the ship, the assumed speed of the ship, and the sailing distance from the departure point of the ship to each of the bridge passing points that are the points where the ship passes through the bridges existing on each of the candidate routes, passage scheduled time calculation means for calculating the scheduled passage time of the ship at each of the bridge passing points; Bridge predicted water level calculation means for calculating a bridge predicted water level, which is the predicted water level at the scheduled passage time of the ship at each of the bridge passing points, based on the predicted water level information; Judgment means for judging the suitability of each of the candidate routes as the operating route based on the bridge predicted water level; An information processing apparatus having the above.

2. The judgment means judges the passability of the ship under the bridge based on the bridge predicted water level, and judges the suitability based on the judgment result of the passability. The information processing apparatus according to Claim 1.

3. The judgment means judges the passability based on the bridge height representing the height of the bridge based on the map data and the height of the highest point of the ship calculated based on the bridge predicted water level. The information processing apparatus according to Claim 2.

4. The information processing apparatus according to Claim 3, further comprising ship highest point height calculation means for calculating the ship highest point height based on the water surface measurement data which is the measurement data of the water surface measured by a measuring device provided on the ship, the highest point information regarding the height from the reference position of the ship to the highest point of the ship, and the bridge predicted water level.

5. The information processing apparatus according to any one of Claims 1 to 4, further comprising an operating route determination means for determining the operating route based on the determination result of the suitability.

6. When there are a plurality of candidate routes determined to be suitable, the operating route determination means displays a screen for selecting the operating route from the candidate routes. The information processing apparatus according to Claim 5.

7. Candidate route acquisition means for acquiring a plurality of candidate routes with different scheduled departure times that are candidates for the ship's operating route; Predicted water level information acquisition means for acquiring predicted water level information regarding the predicted water level at a predicted water level point where the water level is predicted on the candidate route; Based on the predicted water level information, a bridge predicted water level calculating means for calculating a bridge predicted water level, which is the predicted water level at the time of the scheduled passage of the ship at each of the bridge passage points, which are the points where the ship passes through the bridges existing on each of the candidate routes; A determination means for determining the suitability of each of the candidate routes as the operation route based on the bridge predicted water level; It has; The bridge predicted water level calculating means calculates the scheduled passage time and the bridge predicted water level based on the predicted water level information and the scheduled departure time of each of the candidate routes. An information processing device.

8. A predicted water level information acquisition means for acquiring predicted water level information regarding the predicted water level at a water level prediction point where the water level is predicted on one or more candidate routes that are candidates for the operation route of the ship; Based on the predicted water level information, a bridge predicted water level calculating means for calculating a bridge predicted water level, which is the predicted water level at the time of the scheduled passage of the ship at each of the bridge passage points, which are the points where the ship passes through the bridges existing on each of the candidate routes; A determination means for determining the suitability of each of the candidate routes as the operation route based on the bridge predicted water level; It has; The bridge predicted water level calculating means calculates the bridge predicted water level corresponding to the bridge passage point based on the predicted water levels at the water level prediction points that are respectively closest to the bridge passage point in the upstream and downstream directions of the river. An information processing device.

9. A determination method executed by a computer, Acquire predicted water level information regarding the predicted water level at a water level prediction point where the water level is predicted on one or more candidate routes that are candidates for the operation route of the ship, Based on the scheduled departure time of the ship, the assumed speed of the ship, and the operation distance from the departure place of the ship to each of the bridge passage points, which are the points where the ship passes through the bridges existing on each of the candidate routes, calculate the scheduled passage time of the ship at each of the bridge passage points, Based on the predicted water level information, calculate a bridge predicted water level, which is the predicted water level at the time of the scheduled passage of the ship at each of the bridge passage points, which are the points where the ship passes through the bridges existing on each of the candidate routes, Based on the bridge predicted water level, determine the suitability of each of the candidate routes as the operation route, Determination method.

10. Acquire predicted water level information regarding the predicted water level at a water level prediction point where the water level is predicted on one or more candidate routes that are candidates for the operation route of the ship, Based on the scheduled departure time of the ship, the assumed speed of the ship, and the sailing distance from the departure place of the ship to each of the bridge passing points which are the points where the ship passes through the bridges existing in each of the candidate routes, calculate the scheduled passing time of the ship at each of the bridge passing points. Based on the predicted water level information, calculate the predicted water level of the bridge, which is the predicted water level at the scheduled passing time of the ship at each of the bridge passing points which are the points where the ship passes through the bridges existing in each of the candidate routes. A program for causing a computer to execute a process of determining whether each of the candidate routes is suitable as the sailing route based on the predicted water level of the bridge.

11. A storage medium storing the program according to Claim 10.

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