Industrial vehicle, speaker volume estimation system and volume estimation program

Abstract

To provide an industrial vehicle capable of preventing a person from falling due to being surprised by notification sound.SOLUTION: An industrial vehicle 1 includes a speaker that outputs notification sound to a person H, a learned model including a sound volume estimation unit, and a sound volume change unit. The sound volume estimation unit performs machine learning in advance using training data using a notification distance from the speaker to the person H and a sound volume of the notification sound as input data and a degree of surprise of the notified person as output data. When the sound volume estimation unit receives an input of the notification distance, the sound volume estimation unit estimates a sound volume with which the degree of surprise of the notified person becomes equal to or less than a predetermined degree of surprise. The sound volume change unit changes a sound volume of the speaker to equal to or less than the estimated sound volume.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a speaker volume estimation system for use in an industrial vehicle, an industrial vehicle equipped with the system, and a volume estimation program. [Background technology]

[0002] In recent years, the proportion of workers aged 60 or over among those killed or injured in work-related accidents has been increasing. One of the most common work-related accidents among the elderly is falling while working. Therefore, it is necessary to create a work environment that reduces the number of falls among the elderly.

[0003] Industrial vehicles such as forklifts are equipped with speakers that warn of their approach while in motion, and the warning sounds help to avoid contact with pedestrians (see, for example, Patent Documents 1 and 2). However, many elderly people have hearing difficulties and tend to have difficulty hearing the warning sounds. Therefore, it is conceivable to make the warning sounds louder, but if the warning sounds are too loud, there is a risk that they will startle pedestrians and cause them to fall over. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 6-149373 [Patent Document 2] Japanese Patent Application Publication No. 2022-139495 Summary of the Invention [Problem to be solved by the invention]

[0005] Therefore, an object of the present invention is to provide an industrial vehicle that can prevent people from being startled by the alarm sound and falling over. [Means for solving the problem]

[0006] In order to solve the above problems, the industrial vehicle according to the present invention comprises: a speaker that outputs an alarm sound to a person; a trained model having a volume estimation unit; a volume change unit, the volume estimation unit performs machine learning in advance using training data in which the notification distance from the speaker to a person and the volume of the notification sound are input data and the degree of surprise of the person who is notified is output data, and when the notification distance is input, the volume estimation unit estimates the volume at which the degree of surprise of the person who is notified is equal to or less than a predetermined level; The volume change unit changes the volume of the speaker to be equal to or lower than the estimated volume.

[0007] The industrial vehicle preferably has: Further comprising a camera that photographs a person and generates a person image; The trained model further includes a distance estimation unit, The distance estimation unit performs machine learning in advance using training data in which a person image is input data and the reported distance from the speaker to the person is output data, and when a person image is input, it estimates the reported distance from the speaker to the person.

[0008] The industrial vehicle preferably has: Further comprising a camera that photographs a person and generates a person image; The trained model further includes a posture estimation unit, The posture estimation unit performs machine learning in advance using training data in which a person's image is input data and the type of posture of the person is output data, and when a person's image is input, the unit estimates the type of posture of the person, When the type of posture of the person is input, the volume estimation unit estimates a volume that will be equal to or less than a predetermined surprise level based on the type of posture of the person.

[0009] The industrial vehicle preferably has: Further comprising a camera that photographs a person and generates a person image; The trained model further includes an orientation estimation unit, The orientation estimation unit performs machine learning in advance using training data in which a person's image is input data and the orientation of the person's face is output data, and when a person's image is input, it estimates the orientation of the person's face, When the direction of the person's face is input, the volume estimation unit estimates a volume that will be equal to or less than a predetermined surprise level based on the direction of the person's face.

[0010] The industrial vehicle preferably has: further comprising an environmental sound acquisition unit that acquires environmental sounds; When the environmental sound is input, the volume estimation unit estimates a volume that will be equal to or less than a predetermined surprise level based on the environmental sound.

[0011] The industrial vehicle preferably has: Further comprising a camera that photographs a person and generates a person image; The trained model further includes a surprise level estimation unit, the surprise level estimation unit performs machine learning in advance using training data in which the image of the notified person is input data and the surprise level is output data, and when the image of the notified person is input, the surprise level estimation unit estimates the surprise level; The volume estimation unit re-learns the volume that becomes equal to or less than a predetermined surprise level based on the image of the person before the notification, the notification distance, the volume of the notification sound when the notification is made, and the surprise level of the person who is notified.

[0012] The industrial vehicle preferably has: Further provided is an irradiation unit that irradiates light with directionality, The illuminating unit emits light to warn of the approach of an industrial vehicle.

[0013] In order to solve the above problem, a speaker volume estimation system according to the present invention includes: A speaker volume estimation system for use in an industrial vehicle having a speaker that outputs an alarm sound to a person, a trained model having a volume estimation unit; a volume change unit, the volume estimation unit performs machine learning in advance using training data in which the notification distance from the speaker to a person and the volume of the notification sound are input data and the degree of surprise of the person who is notified is output data, and when the notification distance is input, the volume estimation unit estimates the volume at which the degree of surprise of the person who is notified is equal to or less than a predetermined level; The volume change unit changes the volume of the speaker to be equal to or lower than the estimated volume.

[0014] In order to solve the above problem, a volume estimation program according to the present invention includes: A volume estimation program for use in a speaker volume estimation system, The speaker volume estimation system is a system used in an industrial vehicle having a speaker that outputs an alarm sound to a person, the system including a computer, The volume estimation program is Machine learning is performed in advance using teacher data in which the notification distance from the speaker to a person and the volume of the notification sound are input data and the degree of surprise of the person who is notified is output data, and when the notification distance is input, the volume estimation unit is executed to estimate the volume at which the degree of surprise of the person who is notified is equal to or less than a predetermined level. [Effects of the Invention]

[0015] The industrial vehicle according to the present invention can prevent a person from falling over due to being startled by the alarm sound while still achieving the effect of the alarm sound. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a schematic diagram showing an industrial vehicle according to an embodiment of the present invention and its surroundings. [Figure 2] FIG. 2 is a block diagram of a control unit of the industrial vehicle. [Figure 3] FIG. 10 is a diagram illustrating the operation of each estimation unit of the trained model. [Figure 4] FIG. 1 is a diagram illustrating an example of a person's posture. [Figure 5] FIG. 10 is a diagram illustrating machine learning of a volume estimation unit. [Figure 6] FIG. 10 is a diagram illustrating the operation of a volume estimation unit. [Figure 7] FIG. 10 is a diagram showing the state where the irradiating unit irradiates light. [Figure 8] FIG. 10 is a flowchart showing an operation of an industrial vehicle to notify a person of its approach. DETAILED DESCRIPTION OF THE INVENTION

[0017] Hereinafter, an industrial vehicle, a speaker volume estimation system, and a volume estimation program according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0018] Fig. 1 is a schematic diagram showing an industrial vehicle 1 according to one embodiment of the present invention and its surroundings. As shown in Fig. 1, the industrial vehicle 1 is a forklift truck moving forward along an aisle R. In front of the industrial vehicle 1 is a pedestrian H1 (hereinafter sometimes referred to as "person H"), and next to the aisle R is a worker H2 working.

[0019] The industrial vehicle 1 is an autonomously traveling unmanned guided vehicle, but this is merely an example, and the industrial vehicle according to the present invention may be a manned industrial vehicle. Furthermore, the industrial vehicle 1 does not have to be a forklift, and may be, for example, a forkless transport vehicle, a forklift towing vehicle, a straddle carrier, or the like, and is not particularly limited.

[0020] As shown in Fig. 1, the industrial vehicle 1 includes a vehicle body 10 and a fork 11. The fork 11 is configured to be able to rise and fall, thereby lifting and lowering a load.

[0021] As shown in FIG. 2, the industrial vehicle 1 includes a speaker 12, a camera 13, an irradiation unit 14, and a control unit 18.

[0022] The speaker 12 outputs a directional alarm sound to the person H. The speaker 12 is made up of multiple parametric speakers and is configured so that the range of directivity can be expanded or contracted depending on the distance from the person H. This prevents the speaker 12 from outputting the alarm sound to anyone other than the target person H. Therefore, it is possible to prevent the alarm sound from being output to a worker H2 or the like who is not in the traveling direction of the industrial vehicle 1 and startling the worker H2. Since the industrial vehicle 1 moves forward and backward, the speaker 12 is configured so that it can output the alarm sound at least in the forward and backward directions of the industrial vehicle 1. Furthermore, the number of speakers 12 and the configuration of the directionality of the speakers 12 are not particularly limited.

[0023] The specific sound of the notification sound is not particularly limited. For example, the notification sound may be a buzzer sound, a specific melody, or a human voice.

[0024] The speaker 12 may not output an alarm sound unless a person H is detected by the industrial vehicle 1. Alternatively, the speaker 12 may be configured to output an alarm sound at a predetermined volume only at an intersection of the passage R, even if a person H is not detected. In this embodiment, the speaker 12 is configured to output an alarm sound when a person H is detected by the camera 13 (i.e., when a photograph of the person H is taken).

[0025] The industrial vehicle 1 further includes a melody output unit (not shown) that outputs a melody around the vehicle while the vehicle is traveling. The volume of the melody is set to a volume that will not startle the person H.

[0026] The camera 13 is configured to be able to capture images of the area around the vehicle body 10, captures an image of a person H, and generates a person image HI. The camera 13 is made up of multiple cameras 13, and is configured to be able to capture images of at least the area in front of and behind the industrial vehicle 1.

[0027] The irradiating unit 14 is configured to be able to irradiate directional light L. The light L irradiated from the irradiating unit 14 may be of multiple colors, or may be configured to be able to switch between the multiple colors. The irradiating unit 14 may be configured to irradiate the light L by flashing it. The irradiating units 14 are disposed at the front and rear of the vehicle body 10, and are configured to be able to irradiate the light L at least in the forward and backward directions of the industrial vehicle 1.

[0028] The control unit 18 includes a computer having a storage device, a memory, and an arithmetic unit, and the storage device stores a volume estimation program. The volume estimation program causes the computer to execute a trained model 20 and a volume change unit 26, which will be described later.

[0029] <Configuration of trained model> 2, the control unit 18 has, as its functional configuration, a trained model 20 and a volume change unit 26. The trained model 20 has a surprise level estimation unit 21, a distance estimation unit 22, a posture estimation unit 23, a direction estimation unit 24, and a volume estimation unit 25.

[0030] The surprise level estimation unit 21 is, for example, a neural network, and has previously learned the correlation between the human image HI of the notified person H as input data and the surprise level Q as output data through machine learning using training data. The machine learning method may be, for example, a machine learning method that learns the correlation between the difference between the human image HI of the notified person H before the notification and the human image HI of the notified person H, and the surprise level Q. Alternatively, the surprise level estimation unit 21 may be configured to perform machine learning using facial images expressing emotions such as surprise or impatience as training data, and estimate the surprise level Q of the notified person H when the human image HI of the notified person H is input. Through such machine learning, the surprise level estimation unit 21 estimates the surprise level Q of the notified person H when the human image HI of the notified person H is input, as shown in FIG. 3A . The surprise level Q may be output as a numerical value between 0.01 and 1.0, for example.

[0031] In the present invention, the "predetermined surprise level RQ" refers to a surprise level Q that can prevent a person from being startled and falling over, dropping something they are holding, or making a mistake in their work. This predetermined surprise level RQ may be a numerical value for the surprise level Q, such as 0.7.

[0032] The distance estimation unit 22 is, for example, a neural network, and has previously learned the correlation between the human image HI and the notification distance DN from the speaker 12 to the person H by machine learning using training data having the human image HI as input data and the notification distance DN from the speaker 12 to the person H as output data. As a result, when the human image HI is input, the distance estimation unit 22 estimates the notification distance DN from the speaker 12 to the person H, as shown in FIG. 3B . The estimated notification distance DN is output to the volume estimation unit 25. Note that if the human image HI is not input, the distance estimation unit 22 does not output the notification distance DN to the volume estimation unit 25.

[0033] The posture estimation unit 23 is, for example, a neural network, and has previously learned the correlation between the human image HI and the posture type AP of the person H by machine learning using training data having the human image HI as input data and the posture type AP of the person H as output data. As a result, when the posture estimation unit 23 receives the human image HI as input, it estimates the posture type AP of the person H (hereinafter, may be simply referred to as the "posture AP of the person H") as shown in FIG. 3C . The estimated posture AP of the person H is output to the volume estimation unit 25.

[0034] Fig. 4 is a diagram showing examples of postures AP of person H. As shown in Fig. 4, types of postures AP of person H include, for example, a standing posture AP (see Fig. 4A), a posture AP carrying a load (see Fig. 4B), a walking posture AP (see Fig. 4C), and a working posture AP (see Fig. 4D).

[0035] The orientation estimation unit 24 is, for example, a neural network that has undergone machine learning in advance using training data in which the human image HI is input data and the facial orientation DF of the person H is output data. As a result, when the orientation estimation unit 24 receives the human image HI as input, it estimates the facial orientation DF of the person H, as shown in FIG. 3D . The facial orientation DF of the person H is, for example, the direction of the industrial vehicle 1, the direction opposite to the industrial vehicle 1, the direction of a load that the person H is carrying, or the direction of a work tool when working. The estimated facial orientation DF of the person H is output to the volume estimation unit 25 and the irradiation unit 14.

[0036] The volume estimation unit 25 is, for example, a neural network, and as shown in Fig. 5, receives as input data the notification distance DN, the posture AP of the person H, the facial direction DF of the person H, and the volume of the notification sound (hereinafter referred to as "alert volume") db, and has previously machine-learned the correlation therebetween using training data that receives as output data the surprise level Q. As a result, when the notification distance DN, the posture AP of the person H, and the facial direction DF estimated by the distance estimation unit 22, the direction estimation unit 24, and the posture estimation unit 23 are input, the volume estimation unit 25 estimates the notification volume db that is equal to or less than a predetermined surprise level RQ, as shown in Fig. 6. The estimated notification volume db is output to the volume change unit 26.

[0037] In this embodiment, the volume estimation unit 25 does not output the notification volume db to the volume change unit 26 unless the notification distance DN is input (i.e., unless the person H is detected). Note that the volume estimation unit 25 may be configured to estimate the notification volume db to be equal to or less than a predetermined surprise level RQ as zero when the notification distance is equal to or less than a predetermined notification distance DN. This makes it possible to prevent the notification sound from being output when the distance between the industrial vehicle 1 and the person H is too close.

[0038] The volume change unit 26 changes the notification volume db of the speaker 12 to be equal to or lower than the notification volume db estimated by the volume estimation unit 25. By making the speaker 12 output the notification sound at a volume equal to or lower than the estimated notification volume db, the volume change unit 26 can prevent the person H from being startled by the notification sound and falling over.

[0039] Furthermore, when an alarm sound is actually output, the volume estimation unit 25 uses as training data the alarm distance DN, the posture AP of the person H, the facial orientation DF of the person H, the actually output alarm volume db, and the surprise level Q estimated by the surprise level estimation unit 21 at that time, and re-learns the correlations among them. That is, the volume estimation unit 25 in this embodiment uses as input data the alarm distance DN, the posture AP of the person H, the facial orientation DF, and the actually output alarm volume db estimated by the distance estimation unit 22, the orientation estimation unit 24, and the posture estimation unit 23, and re-learns the correlations among them, thereby enabling more accurate estimation from the next time.

[0040] The illumination unit 14 illuminates light L in the direction the person H's face is facing based on the facial direction DF estimated by the direction estimation unit 24, thereby alerting the person H to the approach of the industrial vehicle 1. FIG. 7 shows the light L being illuminated toward the load the person H is carrying when the person H is facing the load. In this way, if a loud alarm sound is output when the person H is facing the load he or she is carrying, the person H may be startled. Therefore, by reducing the volume of the alarm sound and illuminating the load with light L, the approach of the industrial vehicle 1 can be alerted without startling the person H. The direction in which the illumination unit 14 illuminates light L may be, for example, the road surface.

[0041] A system including the above-described camera 13, trained model 20, and volume change unit 26 corresponds to the speaker volume estimation system of the present invention. In this embodiment, the speaker volume estimation system further includes an irradiation unit 14.

[0042] <Industrial vehicle operation> Next, the operation of the industrial vehicle 1 will be described again with reference to FIG.

[0043] (1) While the industrial vehicle 1 is traveling, the melody output unit outputs a melody (see S1 (step 1) in FIG. 8). The industrial vehicle 1 sets the notification volume db to zero until the person H is photographed by the camera 13 (see S2 in FIG. 8).

[0044] (2) When the industrial vehicle 1 photographs a person H using the camera 13 (i.e., detects a person H) (Yes in S3 of Figure 8), the learned model 20 estimates the alarm distance DN, the posture AP of the person H, and the facial direction DF based on the person image HI (see S4 of Figure 8).

[0045] (3) Next, the industrial vehicle 1 uses the trained model 20 to estimate the notification volume db that will be less than or equal to a predetermined surprise level RQ based on the estimated notification distance DN, the posture AP of the person H, and the facial direction DF (see S5 in Figure 8).

[0046] (4) Next, the industrial vehicle 1 changes the notification volume db of the speaker 12 by the volume change unit 26 so that the notification volume db is equal to or less than the estimated notification volume db (see S6 in FIG. 8).

[0047] (5) Next, the industrial vehicle 1 outputs an alarm sound from the speaker 12 and irradiates light L from the irradiating unit 14 (see S7 in FIG. 8).

[0048] While the industrial vehicle 1 is capturing an image of the person H with the camera 13 (i.e., while the person H is continuously being detected), it repeats steps S4 to S7 in Fig. 8, and automatically changes the notification volume db as the notification distance DN, the posture AP of the person H, and the facial direction DF change. Then, when the camera 13 no longer captures an image of the person H (i.e., when the person H is no longer detected) (No in S3 in Fig. 8), the notification volume db is changed to zero again (see S2 in Fig. 8).

[0049] The industrial vehicle 1, having the above configuration, can prevent the person H from being startled by the alarm sound and falling over. Furthermore, the industrial vehicle 1 uses the trained model 20 to learn not only the correlation between the alarm distance DN, the alarm volume db, and the surprise level Q, but also the correlation between the posture AP and facial direction DF of the person H, so it is possible to prevent the alarm volume db from being output too loudly to a person H who is concentrating on work or looking in the opposite direction from the industrial vehicle 1, startling the person H and causing them to fall over. Moreover, because the industrial vehicle 1 estimates the alarm distance DN, the posture AP, and facial direction DF of the person H based on the human image HI, there is no need to add any special sensors.

[0050] Although one embodiment of the industrial vehicle, speaker volume estimation system, and volume estimation program of the present invention has been described above, the present invention is not limited to the above embodiment. For example, the industrial vehicle, speaker volume estimation system, and volume estimation program of the present invention may be implemented by appropriately combining the following modifications.

[0051] <Modification> The industrial vehicle 1 may be equipped with a known sensor capable of detecting the direction of the person H and the notification distance DN. In this case, the volume estimation unit 25 acquires the notification distance DN from the sensor, and the trained model 20 does not need to include the distance estimation unit 22.

[0052] The human image HI used in machine learning of each component of the trained model 20 and the human image HI input when making each estimation may be a plurality of human images HI. In other words, the human image HI according to the present invention is a concept that includes moving images.

[0053] The irradiating unit 14 may irradiate the light L not in the direction of the face DF of the person H, but, for example, toward the ground in the direction in which the person H is facing.

[0054] The input data of the teacher data used in machine learning by the volume estimation unit 25 only needs to include at least the notification distance DN and the notification volume db. In this case, when the notification distance DN is input, the volume estimation unit 25 estimates the notification volume db that is equal to or less than a predetermined surprise level RQ.

[0055] The industrial vehicle 1 may further include an environmental sound acquisition unit that acquires environmental sound. In this case, the volume estimation unit 25 further receives the environmental sound, the notification distance DN, the posture AP of the person H, the facial orientation DF of the person H, and the notification volume db as input data, and performs machine learning in advance to learn correlations among them using training data having the surprise level Q as output data, and estimates the notification volume db that will be equal to or less than a predetermined surprise level RQ when the environmental sound, the notification distance DN, the posture AP, and the facial orientation DF of the person H are input. In this case, the volume estimation unit 25 may also receive the environmental sound, the notification distance DN, and the notification volume db as input data, and perform machine learning in advance to learn correlations among them using training data having the surprise level Q as output data, and estimate the notification volume db that will be equal to or less than a predetermined surprise level RQ when the environmental sound and the notification distance DN are input.

[0056] The speaker volume estimation system may further include a server computer capable of communicating with the industrial vehicle 1, and the trained model 20 may be configured by the server computer. In this case, the industrial vehicle 1 communicates with the server computer to receive an alert volume db that is equal to or less than a predetermined surprise level RQ, and outputs an alert sound at this alert volume db. [Explanation of symbols]

[0057] 1. Industrial vehicles (forklifts) 10. Body 11. Fork 12 speakers 13 Camera 14 Irradiation unit 18 Control Unit 20 Pre-trained models 21 Surprise Level Estimation Unit 22 Distance estimation unit 23 Posture estimation section 24 Orientation estimation unit 25 Volume estimation unit 26 Volume change section Types of AP posture DF Face Direction DN broadcast distance H people (pedestrians, workers) HI People Images L light Q Surprise level R aisle RQ Predetermined surprise level

Claims

1. a speaker that outputs an alarm sound to a person; a trained model having a volume estimation unit; a volume change unit, the volume estimation unit performs machine learning in advance using teacher data in which the notification distance from the speaker to the person and the volume of the notification sound are input data and the degree of surprise of the person who has been notified is output data, and when the notification distance is input, estimates a volume at which the degree of surprise of the person who has been notified will be equal to or less than a predetermined level; The volume change unit of the industrial vehicle changes the volume of the speaker to a volume equal to or lower than the estimated volume.

2. a camera for capturing an image of the person and generating a person image; The trained model further includes a distance estimation unit, the distance estimation unit performs machine learning in advance using teacher data in which the human image is input data and the notification distance from the speaker to the person is output data, and when the human image is input, estimates the notification distance from the speaker to the person; The estimated notification distance is output to the volume estimation unit, The industrial vehicle according to claim 1 , wherein the volume estimation unit estimates a volume that will be equal to or less than the predetermined surprise level using the estimated notification distance.

3. Further comprising a camera that photographs a person and generates a person image; The trained model further includes a posture estimation unit, the posture estimation unit performs machine learning in advance using training data in which the human image is input data and a type of human posture is output data, and when the human image is input, estimates the type of human posture; The industrial vehicle according to claim 1 , wherein the volume estimation unit, when receiving input of the type of posture of the person, estimates a volume that will be equal to or less than the predetermined surprise level based on the type of posture of the person.

4. Further comprising a camera that photographs a person and generates a person image; The trained model further includes an orientation estimation unit, the orientation estimation unit performs machine learning in advance using training data in which the human image is input data and the orientation of the face of the person is output data, and when the human image is input, estimates the orientation of the face of the person; The industrial vehicle according to claim 1 , wherein the volume estimation unit, upon receiving the direction of the person's face, estimates a volume that will be equal to or less than the predetermined surprise level based on the direction of the person's face.

5. further comprising an environmental sound acquisition unit that acquires environmental sounds; The industrial vehicle according to any one of claims 1 to 4, wherein when the environmental sound is input, the volume estimation unit estimates a volume that will be equal to or less than the predetermined surprise level based on the environmental sound.

6. a camera for capturing an image of the person and generating a person image; The trained model further includes a surprise level estimation unit, the surprise level estimation unit performs machine learning in advance using teacher data in which the human image of the notified person is input data and a surprise level is output data, and when the human image of the person notified by the alarm sound is input, the surprise level estimation unit estimates the surprise level; The industrial vehicle according to claim 1, wherein the volume estimation unit re-learns the volume that will be below the predetermined surprise level based on the human image of the person before the alert, the alert distance, the volume of the alert sound when the alert is made, and the surprise level of the person who is alerted.

7. Further provided is an irradiation unit that irradiates light with directionality, The industrial vehicle according to claim 1 , wherein the illuminating unit irradiates the light to warn of the approach of the industrial vehicle.

8. A speaker volume estimation system for use in an industrial vehicle having a speaker that outputs an alarm sound to a person, a trained model having a volume estimation unit; a volume change unit, the volume estimation unit performs machine learning in advance using teacher data in which the notification distance from the speaker to the person and the volume of the notification sound are input data and the degree of surprise of the person who has been notified is output data, and when the notification distance is input, estimates a volume at which the degree of surprise of the person who has been notified will be equal to or less than a predetermined level; The volume change unit changes the volume of the speaker to a volume equal to or lower than the estimated volume.

9. A volume estimation program for use in a speaker volume estimation system, The speaker volume estimation system is a system used in an industrial vehicle having a speaker that outputs an alarm sound to a person, the system including a computer, The volume estimation program is configured to: A volume estimation program that performs machine learning in advance using teacher data in which the notification distance from the speaker to the person and the volume of the notification sound are input data and the level of surprise of the person when notified is output data, and that, when the notification distance is input, estimates the volume at which the level of surprise of the person when notified is below a predetermined level.

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