Control device, control method, and program

Abstract

To generate an appropriate route for a moving object based on what characteristics a pedestrian has regarding a movement in future.SOLUTION: A control device that controls, at least temporarily, a moving object that autonomously moves a region where a pedestrian walks includes: a recognizing unit that recognizes the position of the pedestrian in a time-series manner; a calculating unit that calculates, based on the recognition result by the recognizing unit, an index value which represents a cooperation level for each pedestrian; a setting unit which sets a risk region around the pedestrian and which makes the setting in such a way that the smaller the index value is, the larger the risk region becomes; and a generating unit that generates a route through which the moving object should move in future so as to avoid the risk region.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a control device, a control method, and a program. Regarding. [Background technology]

[0002] In recent years, research and practical application of mobile objects that can move in the same space as pedestrians has been progressing. This type of mobile object moves autonomously by generating a route that does not get too close to obstacles such as pedestrians. In this regard, an invention of a device that detects the reaction of pedestrians to the approach of the mobile object and expands or reduces a no-entry area (personal space) depending on the detected reaction of the pedestrian has been disclosed (Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2009-157735 Summary of the Invention [Problem to be solved by the invention]

[0004] In conventional technology, the personal space is expanded when a pedestrian makes a predetermined, quantified reaction, such as stopping, making a sharp movement of the upper body, etc. However, such processing sometimes fails to appropriately generate a path for the moving object based on the pedestrian's future movement characteristics.

[0005] The present invention has been made in consideration of these circumstances, and one of its objectives is to provide a control device, control method, and program that can appropriately generate a route for a moving body based on the future movement characteristics of pedestrians. [Means for solving the problem]

[0006] The control device, control method, and program according to the present invention employ the following configuration. (1): A control device according to one embodiment of the present invention is a control device that controls a mobile object that moves autonomously, at least temporarily, in an area where pedestrians walk, and includes a recognition unit that recognizes the positions of pedestrians in a time series, a calculation unit that calculates an index value indicating the degree of cooperation of each pedestrian based on the recognition results of the recognition unit, a setting unit that sets a risk area around the pedestrian, the smaller the index value, the larger the risk area is set, and a generation unit that generates a path that the mobile object should take in the future so as to avoid the risk area.

[0007] (2): In the above aspect (1), the calculation unit predicts, at a first time point, the position of the pedestrian at a second time point that is later than the first time point, and calculates the difference between the recognized position of the pedestrian and the predicted position of the pedestrian at the second time point, repeatedly in a chronological order, and the larger the aggregated value of the difference, the larger the index value becomes.

[0008] (3): A control device according to another aspect of the present invention is a control device that controls a moving object that moves autonomously, at least temporarily, in an area where pedestrians walk, and includes: a recognition unit that recognizes the positions of pedestrians in a time series; a calculation unit that calculates an index value for each pedestrian based on the recognition results of the recognition unit; a setting unit that sets a risk area around the pedestrian, the setting unit setting the risk area to be larger the lower the index value; and a generation unit that generates a path that the moving object should take in the future so as to avoid the risk area.The calculation unit predicts, at a first point in time, the position of the pedestrian at a second point in time that is later than the first point in time, and calculates the difference between the recognized position of the pedestrian and the predicted position of the pedestrian at the second point in time, repeatedly executing these steps in a time series; and the larger the aggregate value of the difference, the larger the calculated index value.

[0009] (4) In any of the above aspects (1) to (3), the calculation unit calculates the index value by narrowing down the pedestrians that are passing each other with respect to the moving body or another pedestrian.

[0010] (5): In the above aspect (4), the setting unit sets the risk area for pedestrians for whom the index value was not calculated using a value near the upper limit of the range of possible index values instead of the index value.

[0011] (6): Another aspect of the control method of the present invention is a control device that controls a mobile object that moves autonomously, at least temporarily, in an area where pedestrians walk, by recognizing the positions of the pedestrians in a time series, calculating an index value indicating the degree of cooperation of each pedestrian based on the results of the recognition, setting a large risk area around the pedestrian such that the smaller the index value, the larger the risk area, and generating a future route for the mobile object to travel so as to avoid the risk area.

[0012] (7): In another aspect of the control method of the present invention, a control device that controls a mobile object that moves autonomously in an area where pedestrians walk, at least temporarily, performs the following steps: recognizes the positions of pedestrians in a time series; calculates an index value for each pedestrian based on the results of the recognition; sets a risk area around the pedestrian such that the lower the index value, the larger the risk area; and generates a future route for the mobile object to travel so as to avoid the risk area. The calculation includes: predicting, at a first point in time, the position of the pedestrian at a second point in time that is later than the first point in time; and calculating, at the second point in time, the difference between the recognized position of the pedestrian and the predicted position of the pedestrian; and calculating a larger index value as the aggregate value of the difference becomes larger.

[0013] (8): Another aspect of the present invention is a program that causes a processor of a control device that controls a mobile object that moves autonomously in an area where pedestrians walk, at least temporarily, to recognize the positions of pedestrians in a time series, calculate an index value indicating the degree of cooperation of each pedestrian based on the results of the recognition, set a large risk area around the pedestrian so that the smaller the index value, the larger the risk area, and generate a route that the mobile object should take in the future so as to avoid the risk area.

[0014] (9): Another aspect of the present invention provides a program for causing a processor of a control device that controls a mobile object that moves autonomously in an area where pedestrians walk, at least temporarily, to perform the following operations: recognize the positions of pedestrians in a time series; calculate an index value for each pedestrian based on the results of the recognition; set a risk area around the pedestrian such that the lower the index value, the larger the risk area; and generate a future route for the mobile object to travel so as to avoid the risk area. The calculation includes: predicting, at a first point in time, the position of the pedestrian at a second point in time that is later than the first point in time; and calculating, at the second point in time, the difference between the recognized position of the pedestrian and the predicted position of the pedestrian; and calculating a larger index value as the aggregate value of the difference becomes larger. [Effects of the Invention]

[0015] According to the above aspects (1) to (9), it is possible to appropriately generate a route for a moving object based on the future movement characteristics of a pedestrian. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a diagram showing the configuration of a moving body 1 equipped with a control device 100. FIG. [Figure 2] 1 is a diagram illustrating an example of the configuration of a control device 100. FIG. [Figure 3]FIG. 10 is a diagram showing an outline of the risk set by the setting unit 130. [Figure 4] FIG. 10 is a diagram showing an example of a distribution of risks set in consideration of deviation from an ideal route IP. [Figure 5] This figure compares the expected behavior of a highly cooperative pedestrian P1 when passing a moving object 1 with the expected behavior of a less cooperative pedestrian P2 when passing a moving object 1. [Figure 6] 10 is a diagram for explaining the content of processing performed by a calculation unit 120. FIG. [Figure 7] FIG. 6 is a diagram showing an example in which the transition of the risk area set according to the above principle is applied to pedestrians P1 and P2 shown in FIG. 5. [Figure 8] 1 is a diagram for explaining a pedestrian passing by the moving object 1. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0017] Hereinafter, with reference to the drawings, embodiments of a control device, a control method, and a program of the present invention will be described. The control device of the present invention controls the drive device of a mobile body to move the mobile body. In the present invention, a mobile body autonomously moves in an area where pedestrians walk. The area where pedestrians walk includes sidewalks, public open spaces, floors within buildings, etc., and may also include roadways. In the following description, it is assumed that no person rides on the mobile body, but it is also acceptable for a person to ride on the mobile body.

[0018] A mobile object may lead a leading subject, follow a following subject, or move independently toward a destination. The leading subject or following subject may be, for example, a pedestrian, but may also be a robot or an animal. When following a following subject, for example, a location around the following subject is treated as the destination. In the following description, it is assumed that the mobile object, including the following subject, moves toward the destination. Note that such an operation does not have to be performed constantly, but may be performed temporarily. For example, when the mobile object is placed in a predetermined state, the control device of the mobile object may execute the algorithm of the present invention to perform the operation temporarily.

[0019] 1 is a diagram showing the configuration of a mobile object 1 equipped with a control device 100. The mobile object 1 includes, for example, an HMI 10, a detection device 20, a position identification device 30, a body part 5 equipped with the control device 100, a movement mechanism 40 attached to the body part 5, and a sensor 50 attached to the movement mechanism 40 or the like.

[0020] The HMI 10 presents various information to the follower F and accepts input operations from the user. The HMI 10 includes various display devices, speakers, buzzers, touch panels, switches, keys, etc. For example, the HMI 10 accepts input of a destination (a predetermined location, oneself, etc.) by the user.

[0021] The detection device 20 is a device that generates data for recognizing objects and a follower F that exist around the moving body 1. The detection device 20 includes, for example, sensors such as a camera, a radar device, a LIDAR (Light Detection and Ranging), and an ultrasonic sensor that have a detection range around the moving body 1, and an object recognition device that identifies an object by performing sensor fusion processing based on the outputs of these sensors.

[0022] The position determining device 30 is a device that determines the position of the moving object 1. The position determining device 30 includes, for example, a GNSS (Global Navigation Satellite System) receiver that determines the position of the vehicle M based on signals received from GNSS satellites. The position determining device 30 may determine or complement the position of the moving object 1 by an INS (Inertial Navigation System) that uses the output of a sensor 50, which will be described later. The position determining device 30 may also have an electromagnetic wave receiving function and determine or complement the position of the moving object 1 based on the intensity of electromagnetic waves arriving from surrounding electromagnetic wave sources (whose positions are known).

[0023] The movement mechanism 40 is a mechanism for moving the moving body 1 including the body 5 in any direction. The movement mechanism 40 includes, for example, a plurality of wheels, a drive motor attached to one or more of the wheels, and a steering device attached to one or more of the wheels. There are no particular restrictions on the configuration of the movement mechanism 40, and the movement mechanism 40 may include pseudo feet for walking on two legs.

[0024] The sensor 50 is a sensor for detecting the behavior of the moving body 1. The sensor 50 includes, for example, a wheel speed sensor for detecting the wheel speed, an acceleration sensor for detecting the acceleration acting on the moving body 1, a yaw rate sensor attached near the center of gravity of the body part 5 in the horizontal direction, a steering angle sensor for detecting the steering angle of the steered wheels (steered wheels), and a direction sensor for detecting the orientation of the moving body 1 in the horizontal direction.

[0025] FIG. 2 is a diagram illustrating an example of the configuration of the control device 100. The control device 100 includes, for example, a recognition unit 110, a calculation unit 120, a setting unit 130, a generation unit 140, and a movement control unit 150. These components are realized by, for example, a hardware processor such as a CPU (Central Processing Unit) executing a program (software). Some or all of these components may be realized by hardware (including circuitry) such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a GPU (Graphics Processing Unit), or may be realized by a combination of software and hardware. The program may be stored in advance in a storage device (a storage device with a non-transitory storage medium) such as an HDD (Hard Disk Drive) or flash memory, or may be stored in a removable storage medium (a non-transitory storage medium) such as a DVD or CD-ROM, and installed in the storage device by inserting the storage medium into a drive device. The control device 100 may store map information including at least a local map of the location where the moving body 1 operates in a storage unit.

[0026] The processes of the recognition unit 110, calculation unit 120, setting unit 130, generation unit 140, and movement control unit 150, which will be described below, are repeatedly executed (executed in chronological order) for each control cycle that arrives at a predetermined interval. Therefore, the moving object 1 does not move along the route generated at a certain point until it reaches the destination, but rather the route is updated to a new one due to changes in the surrounding environment, etc., and the control content of the moving object 1 is updated based on the latest updated route.

[0027] The recognition unit 110 recognizes objects present around the moving body 1 based on information input from the detection device 20. Objects include pedestrians, including leading and following subjects if present, and static obstacles. The recognition unit 110 recognizes the state of the object, such as its position, speed, and acceleration. The position of the object is recognized, for example, as a relative position as seen from the moving body 1, and converted into a position on an imaginary plane S, which represents the space around the moving body 1 as a two-dimensional plane seen from above, and is used for subsequent processing. In the following description, a position refers to a single point.

[0028] The calculation unit 120 calculates an index value α for each pedestrian based on the recognition result of the recognition unit 110. The index value α indicates the degree of cooperation of the pedestrian, as will be described later.

[0029] The setting unit 130 sets a risk, which is an index value indicating the degree to which the follower F should not enter or approach, on the aforementioned imaginary plane S. The higher the risk value, the more likely it is that the follower F should not enter or approach, and the closer the value is to zero, the more favorable it is for the follower F to pass through. However, this relationship may be reversed. If the follower F is able to fly by boarding a flying object, the setting unit 130 may perform similar processing in three-dimensional space instead of on the imaginary plane S. An example of a "risk area" is a group of areas where the risk related to an object is not zero.

[0030] The setting unit 130 sets risks on the imaginary plane S not only for the present time, but also for future time points defined at regular time intervals, such as the current time t, after Δt (time t+Δt), after 2Δt (time t+2Δt), etc. The setting unit 130 predicts risks at future time points based on changes in the position of moving targets that are continuously recognized by the recognition unit 110.

[0031] FIG. 3 is a diagram illustrating an overview of the risks set by the setting unit 130. For objects other than the follower F and the moving object 1, the setting unit 130 sets risks on the imaginary plane S, with contour lines of ellipses or circles based on the direction of travel and speed, and sets a fixed value of risk for an unmovable area BD, such as a wall. In the figure, DF is the direction of travel of the follower F, and DM is the direction of travel of the moving object 1. R(OB1) is the risk of a stationary object (a person standing in place) OB1, R(OB2) is the risk of a moving object (pedestrian) OB2, R(OB3) is the risk of a moving object (pedestrian) OB3, and R(OB4) is the risk of a moving object (pedestrian) OB4. Since pedestrians continue to move, risks are set at different positions for each future point in time from the current time. R(OB2)_t is the risk of object OB2 in a given control cycle, R(OB2)_t + Δt is the risk of object OB2 in the next control cycle, and R(OB2)_t + Δ2t is the risk of object OB2 in the control cycle after that. R(BD) is the risk of the immovable area BD. In the figure, the density of the hatching indicates the risk value, and the darker the hatching, the greater the risk.

[0032] The setting unit 130 may set an ideal route connecting the moving object 1 to the moving object 1's destination, and may increase the risk as the distance from the ideal route increases. FIG. 4 is a diagram showing an example of a distribution of risks set in consideration of deviation from the ideal route IP. In the diagram, R(K) is the risk based on deviation from the ideal route IP. In the example of FIG. 4, the ideal route IP is set as a straight line, but depending on the structure of the location where the follower F travels, the ideal route IP may be set as a broken line or a curved line at a corner where the follower F turns, etc. In this case, the setting unit 130 (or another component) performs processing such as dividing the travel range into links and determining the detailed position for each link to generate the ideal route IP.

[0033] Based on the risk recognized by the recognition unit 110, the generation unit 140 generates a future route for the mobile object 1 so as to pass through areas with low risk (in other words, so as to avoid risk areas). The generation unit 140 generates a route, for example, by sequentially connecting positions at each future point in time where the risk corresponding to that point is not equal to or greater than a threshold. There may not be a single route that satisfies this condition, and multiple route candidates may be generated. The generation unit 140 may calculate a score for each of the multiple route candidates and select the route candidate with the highest score as the route. For example, the generation unit 140 calculates the score so that the smaller the degree of turning (evaluated, for example, by the angle between the vector from the past trajectory point to the target trajectory point and the vector from the target trajectory point to the future trajectory point) and the smaller the total risk at each passed point, the higher the score.

[0034] The movement control unit 150 controls the movement mechanism 40 so that the moving body 1 moves along the route. The movement control unit 150 controls the drive motor and steering device so that the position and behavior of the moving body 1 obtained from the output of the sensor 50 approach the route.

[0035] [Setting index values and risk areas based on them] The relationship between the index value α and the risk area will be described below. The calculation unit 120 predicts the pedestrian's position at a second time point, which is later than the first time point, and calculates the difference between the recognized pedestrian's position at the second time point and the predicted pedestrian's position at the first time point. This process is repeated in a chronological order, and the larger the aggregated difference, the larger the calculated index value α. The index value α represents the degree and frequency of a pedestrian's path change, and therefore represents the degree to which a pedestrian changes path after seeing the moving object 1 or other pedestrians, i.e., their cooperativeness. Figure 5 compares the expected behavior (path R1) of a pedestrian P1 with high cooperativeness (high index value α) when passing the moving object 1 with the expected behavior (path R2) of a pedestrian P2 with low cooperativeness (low index value α) when passing the moving object 1. As shown in the figure, a highly cooperative pedestrian is expected to change path early to avoid approaching the moving object 1, while a less cooperative pedestrian is expected to change path only after getting as close as possible to the moving object 1.

[0036] Furthermore, the index value α can be calculated not only when a pedestrian changes course after seeing the moving object 1, but also when a pedestrian changes course after seeing another pedestrian. Even when the pedestrian and the moving object 1 are far apart, the index value α may be calculated based on the degree to which the pedestrian changes course after seeing another pedestrian. This allows the index value α to be calculated for a wider range of pedestrians.

[0037] FIG. 6 is a diagram for explaining the processing performed by the calculation unit 120. In the diagram, t, t-1, t-2, ... represent control timings (hereinafter referred to as times) that arrive at predetermined intervals, Lp(t) represents the position of the pedestrian at time t, and #Lp(t) represents the position of the pedestrian at time t predicted at time t-1. At the control timings that arrive at predetermined intervals, the calculation unit 120 predicts the position of the pedestrian at the control timing one time ahead (or a predetermined time ahead). For example, the calculation unit 120 assumes a constant speed or constant acceleration and predicts the position of the pedestrian by extending the movement vector between the previous time and the current time. Then, the calculation unit 120 calculates an index value α, for example, based on equation (1). In the equation, q represents the number of retroactive steps when aggregating the difference.

[0038] α=Σ k=t-q t {Lp(k)-#LP(k)} …(1)

[0039] The setting unit 130 sets the risk area by reflecting the index value α. In the following, it is assumed that the setting unit 130 sets the risk area to a circle, but the risk area is set based on the same principle even when it is set to an ellipse or other shape. For example, the setting unit 130 determines the radius r of the risk area based on equation (2). In the equation, D is the distance between the moving object and the pedestrian, V is the speed of the pedestrian, and α is the index value. The function f is a function that returns a smaller value as D increases, returns a larger value as V increases, and returns a smaller value as α increases.

[0040] r = f(D, V, α) …(2)

[0041] FIG. 7 shows an example in which the transition of the risk area set according to the above principle is applied to pedestrians P1 and P2 shown in FIG. 5. In this figure, it is assumed that pedestrians P1 and P2 have the same speed. In the figure, Path1(1) and Path1(2) are the paths of the moving object 1 generated in each case. As shown in the figure, both the risk area Rp1 corresponding to pedestrian P1 and the risk area Rp2 corresponding to pedestrian P2 increase as the moving object 1 approaches the moving object 1. However, the risk area Rp2 is set larger than the risk area Rp1 at an earlier stage and continues to be set larger thereafter. Due to the processing characteristics of the generation unit 140 described above, the path of the moving object 1 is set to significantly avoid pedestrian P2. This allows the moving object 1 to take an avoidance trajectory early on for a pedestrian with a relatively high probability of contact and a small index value α.

[0042] In this way, according to the embodiment, it is possible to appropriately generate a route for a moving object based on the future movement characteristics of a pedestrian.

[0043] The calculation unit 120 may perform the above-described process of calculating the index value α only for pedestrians who are passing the moving body 1 or other pedestrians. FIG. 8 is a diagram for explaining pedestrians who are passing the moving body 1. As shown in the figure, the calculation unit 120 may set a monitoring area WA centered on the moving direction DM of the moving body 1, and calculate the index value α by narrowing down the pedestrians who are within the monitoring area and whose movement vector P→ of the pedestrian is within a predetermined range with respect to a line connecting the pedestrian and the moving body 1. The calculation unit 120 may not calculate the index value α for other pedestrians. In this case, since the pedestrians excluded from the narrowing down process are pedestrians with low importance in path generation for the moving body 1, the radius r of the risk area may be determined using a value near the upper limit of the possible range of the index value α instead of the index value α. Pedestrians who are passing other pedestrians can also be extracted using a similar method.

[0044] The above-described embodiment can be expressed as follows. A control device that controls a moving object that moves autonomously in an area where pedestrians walk, at least temporarily, one or more storage media storing computer-readable instructions; a processor coupled to the one or more storage media; The processor executes the computer-readable instructions to: Recognizes pedestrians' positions in time series, calculating an index value indicating a degree of cooperation for each of the pedestrians based on the result of the recognition; A risk area is set around the pedestrian so that the risk area increases as the index value decreases; generating a route to be taken by the mobile unit in the future so as to avoid the risk area; Control device.

[0045] The above-described embodiment can also be expressed as follows. A control device that controls a moving object that moves autonomously in an area where pedestrians walk, at least temporarily, one or more storage media storing computer-readable instructions; a processor coupled to the one or more storage media; The processor executes the computer-readable instructions to: Recognizes pedestrians' positions in time series, calculating an index value for each pedestrian based on the result of the recognition; A risk area is set around the pedestrian so that the risk area increases as the index value decreases, and generating a route to be taken by the mobile unit in the future so as to avoid the risk area; The calculating step includes: at a first time point, predicting a position of the pedestrian at a second time point that is later than the first time point, and calculating a difference between the recognized position of the pedestrian and the predicted position of the pedestrian at the second time point, repeatedly executing the calculation in a time series, and calculating a larger index value as the total value of the difference increases. Control device.

[0046] The above describes the form for carrying out the present invention using an embodiment, but the present invention is not limited to such an embodiment, and various modifications and substitutions can be made within the scope that does not deviate from the gist of the present invention. [Explanation of symbols]

[0047] 1. Mobile 10 HMI 20. Detection Devices 30 Locating device 40 Moving mechanism 50 sensors 100 control device 110 Recognition part 120 Calculation Unit 130 Setting section 140 Generation part 150 Movement control unit

Claims

1. A control device that controls a moving object that moves autonomously in an area where pedestrians walk, at least temporarily, a recognition unit that recognizes the position of a pedestrian in time series; a calculation unit that calculates an index value indicating a degree of cooperation for each pedestrian based on the recognition result of the recognition unit, the index value indicating a degree and frequency of the pedestrian changing its course; a setting unit that sets a risk area around the pedestrian, the setting unit setting the risk area to be larger as the index value is smaller; a generation unit that generates a route that the moving object should take in the future so as to avoid the risk area; Equipped with The calculation unit At a first time point, a position of the pedestrian at a second time point that is later than the first time point is predicted, and at the second time point, a difference between the recognized position of the pedestrian and the predicted position of the pedestrian is calculated. The larger the aggregated value of the difference, the larger the index value is set. the setting unit sets the size of the risk area based on the index value, the distance between the moving object and the pedestrian, and the speed of the pedestrian. Control device.

2. the calculation unit calculates the index value by narrowing down the number of pedestrians to those who are passing each other with respect to the moving object or another pedestrian. The control device according to claim 1 .

3. the setting unit sets the risk area for a pedestrian for whom the index value has not been calculated, using a value near an upper limit of a range in which the index value can be taken, instead of the index value. The control device according to claim 2.

4. A control device that controls a moving object that moves autonomously in an area where pedestrians walk, at least temporarily, Recognizing the position of pedestrians in time series; calculating an index value indicating a degree of cooperation for each pedestrian based on the recognition result, the index value indicating the degree and frequency of the pedestrian changing its course; a setting unit that sets a risk area around the pedestrian, the smaller the index value, the larger the risk area that is set; generating a route to be taken by the moving body in the future so as to avoid the risk area; Run The calculating step includes: At a first time point, a position of the pedestrian at a second time point that is later than the first time point is predicted, and at the second time point, a difference between the recognized position of the pedestrian and the predicted position of the pedestrian is calculated. The larger the aggregated value of the difference, the larger the index value is set. setting a size of the risk area based on the index value, a distance between the moving object and the pedestrian, and a speed of the pedestrian. Control method.

5. A processor of a control device that controls a mobile object that moves autonomously in an area where pedestrians walk at least temporarily, Recognizing the position of pedestrians in time series; calculating an index value indicating a degree of cooperation for each pedestrian based on the recognition result, the index value indicating the degree and frequency of the pedestrian changing its course; a setting unit that sets a risk area around the pedestrian, the smaller the index value, the larger the risk area that is set; generating a route to be taken by the moving body in the future so as to avoid the risk area; A program for executing The calculating step includes: At a first time point, a position of the pedestrian at a second time point that is later than the first time point is predicted, and at the second time point, a difference between the recognized position of the pedestrian and the predicted position of the pedestrian is calculated. The larger the aggregated value of the difference, the larger the index value is set. setting a size of the risk area based on the index value, a distance between the moving object and the pedestrian, and a speed of the pedestrian. program.

Images