Movement prediction method and program

Abstract

To provide a technique of improving prediction accuracy in regard to a movement prediction method of predicting movement of an object using map data.SOLUTION: According to a movement prediction method relating to the present disclosure in which map data is divided into a plurality of areas, and movement history of one or more moving bodies in each of the plurality of areas is stored, the movement history comprises history in movement directions of the one or more moving bodies classified by arrival directions. The movement prediction method relating to the present disclosure comprises the steps of: calculating existence possibility in which a target object is located inside in a predetermined time step in each of a plurality of adjacent areas near the target object; and calculating a movement prediction distribution of the target object during a predetermined period, based on a distribution in an area where the presence possibility is not zero among the plurality of adjacent areas.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present disclosure relates to a technique for predicting object movement using map data. [Background technology]

[0002] Patent Document 1 discloses a mobile robot system that includes historical map data in which the positions and speed information of objects that have moved within a predetermined period are recorded on map data showing the robot's movement space, reads speed information recorded in a position near a moving object appearance point where a moving object may appear from the historical map data, and determines the shape of a collision danger area according to the read speed information. Patent Document 1 also discloses setting a base ellipse that includes all speed vectors recorded in the historical map data at a position directly below the moving object appearance point as the collision danger area.

[0003] Other documents that demonstrate the technical level of this technical field include the following Patent Documents 2 and 3. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2015 / 072002 [Patent Document 2] Patent Publication No. 2021-098492 [Patent Document 3] JP 2016-052835 A Summary of the Invention [Problem to be solved by the invention]

[0005] In recent years, technologies that allow vehicles and robots to operate autonomously have been attracting attention. These technologies require accurate prediction of the movement of objects recognized by external sensors. Therefore, technologies that predict the movement of objects using map data have been devised.

[0006] By the way, how an object will move in the future is thought to be greatly influenced by how it has moved in the past. For example, a pedestrian who has been moving straight forward is likely to continue moving straight, but is unlikely to start moving backwards.

[0007] By applying the technology disclosed in Patent Document 1, it is possible to predict the range of movement of an object using a base ellipse centered on the position of the object. However, since the base ellipse is generated from speed information recorded in map data (historical map data) of the position directly below the object, it is not possible to take into account how the object has moved in the past. For this reason, there is a risk that the base ellipse will be excessively wide compared to the range in which the object actually moves. In addition, there is a risk that the prediction of the object's movement using the base ellipse cannot be ensured with sufficient accuracy when the object does not move linearly.

[0008] In view of the above-mentioned problems, one object of the present disclosure is to propose a technology for predicting the movement of an object using map data, with improved prediction accuracy. [Means for solving the problem]

[0009] A first aspect of the present disclosure relates to a movement prediction method for predicting the movement of a target object by using map data.

[0010] In the movement prediction method according to the first aspect, map data is divided into a plurality of regions, and a movement history of one or more moving objects is recorded for each of the plurality of regions, and the movement history includes a history of the movement direction of one or more moving objects classified by the arrival direction. The movement prediction method according to the first aspect includes: calculating a presence probability that the target object is located inside each of a plurality of neighboring regions in the vicinity of the target object among the plurality of regions at a predetermined time step; and calculating a movement prediction distribution of the target object during a predetermined period based on a distribution of the plurality of neighboring regions in which the presence probability is not zero; and calculating the presence probability includes: acquiring a transition probability of moving from the first region to the second region based on a relative direction of a second region among the plurality of neighboring regions to a first region among the plurality of neighboring regions and a history of the movement direction recorded in the first region, in each time step; and calculating a presence probability for the second region in the current time step based on the presence probability calculated for the first region in the previous time step and the transition probability acquired in the current time step.

[0011] A second aspect of the present disclosure relates to a movement prediction program that causes a computer to execute the movement prediction method according to the first aspect. Effect of the Invention

[0012] According to the present disclosure, the presence probability is calculated for each of a plurality of neighboring regions in the vicinity of the target object at a predetermined time step. Then, based on the distribution of regions with non-zero presence probability at each time step, a movement prediction distribution of the target object during a predetermined period is calculated. This makes it possible to obtain the time evolution of the distribution and to take into account how the target object has moved at each time step. As a result, the accuracy of the prediction of the target object can be improved. [Brief description of the drawings]

[0013] [Figure 1] FIG. 2 is a conceptual diagram for explaining map data according to the embodiment. [Diagram 2]FIG. 13 is a conceptual diagram showing some examples of the history of the moving direction. [Diagram 3] FIG. 13 is a conceptual diagram for explaining an example of calculation of the existence possibility. [Figure 4] FIG. 11 is a conceptual diagram for explaining a movement prediction distribution. [Diagram 5] 1 is a block diagram showing a configuration of a processing device according to an embodiment of the present invention; [Figure 6] 11 is a flowchart illustrating an example of a process executed by a processor. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Hereinafter, an embodiment will be described with reference to the drawings.

[0015] 1. Map data The movement prediction method according to this embodiment predicts the movement of an object (target object) using map data. The map data used in the movement prediction method according to this embodiment is distinctive.

[0016] The map data according to this embodiment is divided into a plurality of regions, and the movement history of one or a plurality of moving objects (hereinafter simply referred to as "moving objects") is recorded for each of the plurality of regions. The moving objects are, for example, pedestrians, bicycles, motorbikes, vehicles, etc.

[0017] FIG. 1A shows an example of map data D10. FIG. 1A shows a part of map data D10 divided by grid lines. That is, in the map data D10 shown in FIG. 1A, the movement history of a moving object is recorded in each of a plurality of areas separated by grid lines. However, the division of the map data D10 may be applied in other formats (for example, division by a triangular mesh). Furthermore, the size of each of the plurality of areas may be suitably determined according to the environment to which this embodiment is applied. In the following, in particular, map data D10 divided by grid lines will be considered. Furthermore, the areas divided by grid lines are called "cells."

[0018] In the map data D10 according to this embodiment, the movement history recorded in a cell includes at least the history of the movement direction of a moving object categorized by the direction of arrival.

[0019] For example, the map data D10 can be configured to manage the history of the moving direction classified by the direction of arrival for each of a plurality of cells in the form of a histogram. In this case, an example of the history of the moving direction will be described with reference to (B) and (C) of FIG. 1. In particular, (B) and (C) of FIG. 1 are examples in which the direction of arrival and the moving direction are managed in four directions: north (N), south (S), east (E), and west (W).

[0020] Now, assume that information is obtained that five mobile objects 1a, 1b, 1c, 1d, and 1e have moved through one cell C10 on the map data D10, as shown in FIG. 1B. Here, the mobile objects 1a, 1b, and 1c arrive at cell C10 from the south and move north from cell C10. The mobile objects 1d and 1e arrive at cell C10 from the west and move east from cell C10. Therefore, when the history of moving directions is managed using a histogram, the map data D10 adds 3 to the frequency of the arrival direction being south and the moving direction being north for cell C10, and adds 2 to the frequency of the arrival direction being west and the moving direction being east for cell C10.

[0021] FIG. 1C shows an example of a history of movement directions managed by a histogram.

[0022] By recording the history of the moving direction for each of the multiple cells in this way, the map data D10 can manage the empirical tendency of the moving direction of a moving object moving at each point on the map. In particular, in this embodiment, the history of the moving direction is categorized by the direction of arrival. This allows the map data D10 to further manage the tendency of the moving direction according to the direction of arrival.

[0023] For example, according to the movement direction history shown in Fig. 1(C), it can be seen that in this cell, moving objects coming from the south tend to move north, and moving objects coming from the west tend to move east. Furthermore, it can be seen that in this cell, there are almost no moving objects coming from the north or east.

[0024] In the map data D10 according to this embodiment, the history of the moving direction may be further classified by the category of the moving object. This allows the map data D10 to further manage the tendency of the moving direction according to the category of the moving object. FIG. 2A shows an example of the history of the moving direction further classified by the category of the moving object. FIG. 2A shows an example of the case where the history of the moving direction is classified into three categories of the moving object, namely, pedestrians, vehicles, and motorcycles. According to the history of the moving direction shown in FIG. 2A, it can be seen that in this cell, pedestrians tend to arrive from the east and move to the west, and vehicles or motorcycles tend to arrive from the south and move to the north or west.

[0025] In the map data D10 according to the present embodiment, the history of the moving direction may be further classified by the speed zone (passing speed zone) at which the moving object passes through the cell. This allows the map data D10 to further manage the tendency of the moving direction according to the speed of the moving object. FIG. 2B shows an example of the history of the moving direction classified by the passing speed zone of the moving object. FIG. 2B shows an example in which the history of the moving direction is classified into three passing speed zones of 0-10 km / h, 10-20 km / h, and 20-3 km / h. According to the history of the moving direction shown in FIG. 2B, it can be seen that in this cell, moving objects with a speed of 0-10 km / h or 10-20 km / h tend to arrive from the north and move to the south, and moving objects with a speed of 20-30 km / h tend to arrive from the south and move to the north.

[0026] The map data D10 according to the present embodiment is configured as described above. However, the arrival direction and the movement direction may be in other formats. For example, the map data D10 may be configured to manage the arrival direction and the movement direction by azimuth angles of 0-360 degrees. For example, the map data D10 may be configured to manage the arrival direction and the movement direction by identifying the cell before arrival and the cell of the movement destination, respectively. For example, the map data D10 may be configured to manage the movement direction by the movement range of the moving object during a certain period after arriving at the cell. For example, the map data D10 can manage the history of the movement direction by a two-dimensional histogram (heat map) as shown in (C) of FIG. 2. The two-dimensional histogram shown in (C) of FIG. 2 is the history of the movement direction recorded for the cell at the center position (star mark), and shows the frequency of the cells through which a moving object arriving from the south passed during a certain period.

[0027] 2. Movement prediction method The movement prediction method according to this embodiment will be described below.

[0028] In the movement prediction method according to this embodiment, first, for each of a number of cells on the map data D10 that are in the vicinity of the target object (hereinafter simply referred to as "nearby cells"), the presence probability that the target object is located inside is calculated at a predetermined time step.

[0029] An example of calculating the presence possibility will be described with reference to FIG.

[0030] FIG. 3A is a conceptual diagram showing a neighborhood cell C20 of the target object 2. FIG. 3A shows the existence probability p[(i,j),t] when the time step is t for each cell (i,j) of the neighborhood cell C20. The range of the neighborhood cell C20 may be, for example, a range that is predefined based on the position of the target object 2. In this case, the defined range may be given differently depending on the category of the target object 2. Alternatively, the range of the neighborhood cell C20 may be a range that is determined depending on the state (e.g., speed) of the target object 2.

[0031] At each time step, first, for each cell (i, j) of the neighboring cells C20, the possibility of moving to another cell from the previous time step to the current time step (hereinafter, simply referred to as "transition possibility") is obtained. When one of the neighboring cells C20 is the first cell and the other cell is the second cell, the acquisition of the transition possibility from the first cell to the second cell is performed based on the relative direction of the second cell with respect to the first cell and the history of the moving direction recorded in the first cell.

[0032] For example, consider the case where a transition possibility for cell (2,3) is obtained at time step t. In particular, assume that the map data D10 manages the history of movement directions as described in FIG. 1. In this case, as shown in FIG. 3B, it is possible to obtain a transition possibility f for cell (2,3) and its adjacent cells (2,2), (1,3), (2,4), and (3,4). In this case, when focusing on the movement from cell (2,3) to cell (2,2), the relative direction of cell (2,2) with respect to cell (2,3) is west. Therefore, the transition possibility f[(2,3)→(2,2),t] from cell (2,3) to cell (2,2) is given by: It can be expressed as the frequency of moving west from cell (2,3). The frequency of moving west from cell (2,3) can be obtained from the history of the moving direction recorded in cell (2,3) as shown in Figure 1(C). The transition probability f to each of cells (1,3), (2,4), and (3,4) can also be obtained in the same way.

[0033] Here, the relative direction may be specified according to the format of the direction of arrival and the direction of movement in the map data D10. For example, when the map data D10 manages the direction of arrival and the direction of movement with an azimuth angle of 0-360 degrees, the relative direction may be specified with an azimuth angle of 0-360 degrees. The transition possibility may be acquired by expanding it to a movement to a non-adjacent cell. Furthermore, the transition possibility may be acquired for a movement to the same cell (not to another cell). However, it is desirable that the transition possibility f acquired for a certain cell (i, j) satisfies the following formula. Here, x is a variable that moves through all the cells (which may include cell (i, j)) that are expected to be the destination of cell (i, j) from the previous time step t-1 to the current time step t.

[0034]

number

[0035] Then, in each time step, for each of the neighboring cells C20, the presence probability in the current time step is calculated based on the presence probability calculated in the previous time step and the transition probability acquired in the current time step.

[0036] For example, consider the case where the existence probability of cell (2,2) is calculated at time step t. Here, for each cell (i,j) of the neighboring cell C20, the transition probability f to the adjacent cell is assumed to be acquired as described in FIG. 3B. In this case, the existence probability p[(2,2),t] of cell (2,2) at the current time step t is calculated based on the existence probability p calculated at the previous time step t-1 for each of the adjacent cells (2,1), (1,2), (2,3), and (3,2) and the transition probability f to cell (2,2) acquired at the current time step t, as shown in FIG. 3C. For example, for each of the adjacent cells (2,1), (1,2), (2,3), and (3,2), the product of the existence probability p calculated at the previous time step t-1 and the transition probability f to cell (2,2) acquired at the current time step t is calculated, and the sum of the products calculated for each can be set as the existence probability p[(2,2),t].

[0037] In general, for each cell (i, j) of the neighboring cell C20, the probability of existence p[(i, j), t] when the time step is t can be calculated by the following formula: Here, y is a variable that moves through all cells (which may include cell (i, j)) that are expected to arrive at cell (i, j) from the previous time step t-1 to the current time step t.

[0038]

number

[0039] Incidentally, the product p[y→(i,j),t] of f[y→(i,j),t] and p[y,t-1] can be considered as the possibility of arrival from y to cell (i,j) from the previous time step t-1 to the current time step t (hereinafter referred to as "arrival possibility"). In other words, in the above formula, the existence possibility p[(i,j),t] is the sum of multiple arrival possibilities p[y→(i,j),t]. Furthermore, when calculating the existence possibility p[(i,j),t] in this way, the existence possibility p[(i,j),t] can be classified into multiple arrival possibilities p[y→(i,j),t].

[0040] When categorizing the existence probability p[(i,j),t] into multiple arrival probabilities p[y→(i,j),t], the magnitude of each of the multiple arrival probabilities p[y→(i,j),t] is an index showing the direction from which the target object 2 is likely to have arrived when the target object 2 is located inside the cell (i,j). In other words, it is considered to include information about how the target object 2 has moved.

[0041] Therefore, the transition probability from the first cell to the second cell may be obtained based on the magnitude of each of the plurality of arrival probabilities calculated for the first cell in the previous time step.

[0042] For example, consider the case where a transition possibility is obtained for cell (2,3) at time step t. In this case, as described in FIG. 3B, the transition possibility f[(2,3)→(2,2),t] from cell (2,3) to cell (2,2) can be expressed as the frequency of moving westward from cell (2,3). Here, since the history of the moving direction recorded in cell (2,3) is classified by the arrival direction, the frequency of moving westward from cell (2,3) can be obtained for each arrival direction to cell (2,3). Thus, the transition possibility f[(2,3)→(2,2),t] can be expressed as a weighted average of the frequencies obtained for each arrival direction, with the magnitude of each of the multiple arrival possibilities calculated for cell (2,3) at the previous time step t-1 as the weight. Alternatively, the transition possibility f[(2,3)→(2,2),t] may include multiple transition possibilities, each of which is represented by the frequency obtained for each arrival direction, corresponding to each of the multiple arrival possibilities.

[0043] In the map data D10, when the history of the moving direction is further classified by the category of the moving object, the possibility of transition from the first cell to the second cell may be acquired based on the category of the target object 2. For example, when the category of the target object 2 is pedestrian, the possibility of transition is acquired by referring to the history of the moving direction in the category of pedestrian. In the map data D10, when the history of the moving direction is further classified by the passing speed zone of the moving object, the possibility of transition from the first cell to the second cell may be acquired based on the speed of the target object 2. For example, when the speed of the target object 2 is 25 km / h, the possibility of transition is acquired by referring to the history of the moving direction in the passing speed zone of 20-30 km / h.

[0044] By acquiring the transition possibility from the first cell to the second cell in this manner, it is possible to acquire an appropriate transition possibility according to the state of the target object 2. In turn, it is possible to improve the accuracy of prediction of the movement of the target object 2.

[0045] As described above, in the movement prediction method according to this embodiment, the presence probability is calculated for each cell (i, j) of the neighboring cell C20 at a predetermined time step. Fig. 4(A) is a conceptual diagram showing an example of the presence probability calculated at each time step. In Fig. 4(A), t=0 is the current time when the target object 2 is detected. As shown in Fig. 4(A), the movement prediction method according to this embodiment makes it possible to obtain the time evolution of the distribution of cells related to the presence probability.

[0046] In the movement prediction method according to the present embodiment, a movement prediction distribution of the target object 2 during a predetermined period is calculated based on a distribution of cells with a non-zero probability of existence in each time step. In particular, the distribution of cells with a non-zero probability of existence may be calculated as the movement prediction distribution of the target object 2 using each time step as time information.

[0047] According to the movement prediction distribution calculated in this way, it is possible to obtain the time evolution of the distribution of cells related to the existence possibility, so that it is possible to take into consideration how the target object 2 has moved at each time step, which in turn makes it possible to improve the accuracy of prediction of the target object 2.

[0048] FIG. 4B and FIG. 4C are conceptual diagrams showing an example of the movement prediction distribution 5. FIG. 4B and FIG. 4C show a case where an autonomous vehicle 3 traveling on a road on which a guardrail 4 is arranged predicts the movement of a recognized target object 2 (pedestrian). In FIG. 4B and FIG. 4C, the movement prediction distribution 5 shows a simplified range of cells in which the probability of existence is not zero during a predetermined period. According to the movement prediction distribution 5 shown in FIG. 4B, it can be seen that there is no possibility that the target object 2 will climb over the guardrail 4 and move to the road side. On the other hand, according to the movement prediction distribution 5 shown in FIG. 4C, it can be seen that the target object 2 may move to the road side from the point where the guardrail 4 ends. This is, for example, a case where there are many records in the history of the movement direction that a pedestrian has moved to the road side from the point where the guardrail 4 ends. In this way, according to this embodiment, it is also possible to predict realistic and complex movements of the target object 2.

[0049] 3. Configuration The movement prediction method according to this embodiment can be realized by processing executed by a computer.

[0050] 5 is a block diagram showing a configuration of a processing device 100 that realizes the movement prediction method according to this embodiment. The processing device 100 is connected to a data server 200 and an external sensor 300 so as to be able to communicate with each other.

[0051] The data server 200 acquires a movement history by communicating with the mobile object, and manages the acquired movement history in a movement history database D20.

[0052] The external sensor 300 is provided in an autonomous vehicle or a robot and detects the surrounding environment of the autonomous vehicle or the robot. In particular, the external sensor 300 recognizes surrounding objects. Examples of the external sensor 300 include a camera, a radar, and a LiDAR.

[0053] The processing device 100 is a computer including a processor 110 and a storage device 120. The processing device 100 may be, for example, a device that executes processing related to the control of an autonomous vehicle or a robot.

[0054] The processor 110 executes various processes. The processor 110 can be configured, for example, by a CPU (Central Processing Unit) including an arithmetic unit, a register, and the like. The storage device 120 is coupled to the processor 110 and stores various information required for the processor 110 to execute the processes. The storage device 120 can be configured, for example, by a recording medium such as a ROM (Read Only Memory), a RAM (Random Access Memory), a HDD (Hard Disk Drive), or a SSD (Solid State Drive).

[0055] The storage device 120 stores a computer program 121, map data D10, and detection information D11.

[0056] The computer program 121 is composed of a plurality of instructions 122 and is stored in a computer-readable recording medium. The processor 110 operates in accordance with the plurality of instructions 122, thereby realizing the execution of various processes by the processor 110.

[0057] The map data D10 may be updated by processing executed by the processor 110. For example, the processor 110 may be configured to update the map data D10 based on the travel history database D20 by periodically accessing the travel history database D20.

[0058] The detection information D11 is information detected by the external sensor 300. The detection information D11 is acquired from the external sensor 300. The detection information D11 may include information such as the position, speed, and category of the target object 2 on the map.

[0059] 4. Processing Fig. 6 is a flowchart showing an example of processing executed by the processor 110. The flowchart shown in Fig. 6 starts, for example, when the external sensor 300 recognizes an object (target object 2).

[0060] In step S100, the processor 110 acquires various information necessary for executing the process. For example, the processor 110 acquires the detection information D11 and the history of the moving direction of the target object 2 related to the neighboring cell C20.

[0061] In step S200, the processor 110 initializes a time step (t=0). Then, at t=0, the processor 110 sets the existence possibility of the cell in which the target object 2 is located among the neighboring cells C20 to 1 (step S300).

[0062] In step S400, the processor 110 increments the time step (t=t+1).

[0063] In step S500, the processor 110 obtains a transition possibility for each of the neighboring cells C20 based on the history of the moving direction.

[0064] In step S600, the processor 110 calculates the presence probability in the current time step for each of the neighboring cells C20 based on the presence probability calculated in the previous time step t-1 and the transition probability acquired in the current time step t.

[0065] In step S700, it is determined whether the current time step t is smaller than a predetermined value T. The predetermined value T corresponds to a period for predicting the movement of the target object 2, and may be suitably given.

[0066] If the current time step t is smaller than the predetermined value T (step S700; Yes), the processor 110 increments the time step (step S400) and repeats steps S500 and S600 again.

[0067] If the current time step t exceeds the predetermined value T (step S700; No), the processor 110 extracts cells whose existence probability at each time step is not zero.

[0068] In step S900, the processor 110 calculates a movement prediction distribution of the target object 2 based on the cells extracted in step S800.

[0069] After step S900, the process ends.

[0070] As described above, the movement prediction method according to the present embodiment is realized by the processing executed by the processor 110. Moreover, by configuring the computer program 121 that causes the processor 110 to execute the processing in this manner, the movement prediction program according to the present embodiment is realized. [Explanation of symbols]

[0071] 2 target object, 5 movement prediction distribution, 100 processing device, 110 processor, 120 storage device, 121 computer program, D10 map data

Claims

1. A movement prediction method for predicting movement of a target object using map data, comprising: The map data is divided into a plurality of areas, and a movement history of one or a plurality of moving objects is recorded for each of the plurality of areas; The movement history includes a history of a movement direction of the one or more moving objects categorized by a direction of arrival, The movement prediction method includes: Calculating a presence probability that the target object is located inside each of a plurality of neighboring regions among the plurality of regions, the plurality of neighboring regions being located near the target object, at a predetermined time step; calculating a predicted distribution of movement of the target object during a predetermined period based on a distribution of the areas among the plurality of neighboring areas where the possibility of the object being present is not zero; Including, Calculating the presence probability At each time step, acquiring a transition possibility of moving from the first region to the second region based on a relative direction of a second region among the plurality of neighboring regions with respect to a first region among the plurality of neighboring regions and a history of the moving direction recorded in the first region; Calculating the presence probability for the second region in a current time step based on the presence probability calculated for the first region in a previous time step and the transition probability acquired in a current time step; Includes A movement prediction method comprising:

2. The movement prediction method according to claim 1 , The presence probability is a sum of a plurality of arrival probabilities classified by the arrival direction, Obtaining the transition probability further includes obtaining the transition probability based on a magnitude of each of the plurality of arrival probabilities calculated for the first region in a previous time step. A movement prediction method comprising:

3. The movement prediction method according to claim 1 or 2, The history of the moving direction is further categorized by the category of the one or more moving objects; Obtaining the transition probability further includes obtaining the transition probability based on a category of the target object. A movement prediction method comprising:

4. The movement prediction method according to claim 1 or 2, The history of the moving direction is further classified by a passing speed band of the one or more moving objects, Obtaining the transition probability further includes obtaining the transition probability based on a velocity of the target object. A movement prediction method comprising:

5. A movement prediction program for causing a computer to execute a process of predicting movement of a target object using map data, The map data is divided into a plurality of areas, and a movement history of one or a plurality of moving objects is recorded for each of the plurality of areas; The movement history includes a history of a movement direction of the one or more moving objects categorized by a direction of arrival, The movement prediction program includes: A process of calculating a presence possibility that the target object is located inside each of a plurality of neighboring regions in the vicinity of the target object among the plurality of regions at a predetermined time step; A process of calculating a movement prediction distribution of the target object during a predetermined period based on a distribution of the areas among the plurality of neighboring areas where the existence probability is not zero; The method is configured to cause the computer to execute Calculating the presence probability At each time step, acquiring a transition possibility of moving from the first region to the second region based on a relative direction of a second region among the plurality of neighboring regions with respect to a first region among the plurality of neighboring regions and a history of the moving direction recorded in the first region; Calculating the presence probability for the second region in a current time step based on the presence probability calculated for the first region in a previous time step and the transition probability acquired in a current time step; Includes A movement prediction program.

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