Estimation device, control system, learning model, learning model generation method, computer program, and storage medium

Abstract

To provide an estimation device, a control system, a learning model, a learning model creating method, a computer program, and a storage medium.SOLUTION: The center-of-gravity position of a human-powered vehicle on which a rider is riding is estimated on the basis of vehicle body information on a vehicle body of the human-powered vehicle and load information on load to the human-powered vehicle.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present invention relates to an estimation device, a control system, a learning model, a method for generating a learning model, a computer program, and a storage medium. [Background technology]

[0002] Non-Patent Document 1 discloses a method for calculating the position of the center of gravity of a bicycle. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] "Bicycle Practical Handbook," edited by the Bicycle Industry Promotion Association, 5th ed., Automobile Industry Promotion Association, 1993 Summary of the Invention [Problem to be solved by the invention]

[0004] In Non-Patent Document 1, the position of the center of gravity of the bicycle is calculated when no rider is riding on the bicycle, and it is not possible to estimate the position of the center of gravity of the bicycle when a rider is riding on the bicycle.

[0005] The present invention aims to provide an estimation device, a control system, a learning model, a method for generating a learning model, a computer program, and a storage medium that can estimate the center of gravity position of a human-powered vehicle with a rider on board. [Means for solving the problem]

[0006] An estimation device according to a first aspect of the present invention estimates the position of the center of gravity of a human-powered vehicle with a rider on board, based on vehicle body information related to the body of the human-powered vehicle and load information related to the load acting on the human-powered vehicle.

[0007] According to the estimation device according to the first aspect of the present invention, it is possible to estimate the position of the center of gravity of a human-powered vehicle with a rider on board, based on vehicle body information and load information of the human-powered vehicle.

[0008] The estimation device of a second aspect according to the first aspect of the present invention estimates the position of the center of gravity using a learning model configured to output an estimation result regarding the position of the center of gravity of the human-powered vehicle in response to input of the vehicle body information and the load information.

[0009] According to the estimation device according to the second aspect of the present invention, by inputting vehicle body information and load information of the human-powered vehicle into a learning model, it is possible to estimate the position of the center of gravity of the human-powered vehicle with a rider on board.

[0010] In the estimation device of the third aspect according to the first or second aspect of the present invention, the vehicle body information includes information on the vehicle body dimensions, vehicle body weight, and tilt angle of the human-powered vehicle.

[0011] According to the estimation device according to the third aspect of the present invention, the position of the center of gravity of a human-powered vehicle with a rider on board can be estimated based on information relating to the vehicle dimensions, vehicle weight, and tilt angle of the human-powered vehicle.

[0012] In the estimation device of a fourth aspect according to the first or second aspect of the present invention, the human-powered vehicle includes a front axle, a rear axle, a saddle, a handlebar, a crank, and pedals, and the load information includes information about a load acting on at least one of the front axle, the rear axle, the saddle, the handlebar, the crank, and the pedals.

[0013] With the estimation device according to the fourth aspect of the present invention, the position of the center of gravity of a human-powered vehicle with a rider on it can be estimated based on the load acting on at least one of the front axle, the rear axle, the saddle, the handlebars, the cranks, and the pedals.

[0014] In the estimation device of the fifth aspect according to the fourth aspect of the present invention, the load information includes at least one of the weight, height, posture, and sex of the rider.

[0015] According to the estimation device in accordance with the fifth aspect of the present invention, the position of the center of gravity of the human-powered vehicle with a rider riding on it can be estimated based on the weight, height, posture and sex of the rider.

[0016] A control system according to a sixth aspect of the present invention includes an estimation device according to any one of the first to fifth aspects of the present invention, and a control device that controls components of the human-powered vehicle based on a result of the estimation.

[0017] According to the control system according to the sixth aspect of the present invention, the components of the human-powered vehicle are controlled based on the results of estimating the center of gravity position, so that, for example, the components of the human-powered vehicle can be controlled in a manner that is comfortable for the rider.

[0018] In the control system of the seventh aspect according to the sixth aspect of the present invention, the components include an alarm device.

[0019] According to the control system according to the seventh aspect of the present invention, the controlled component includes an alarm device, so that the estimated result of the center of gravity position can be notified to the rider.

[0020] In the control system of an eighth aspect according to the seventh aspect of the present invention, the control device controls the notification device to notify the estimation result.

[0021] According to the control system according to the eighth aspect of the present invention, it is possible to notify the rider of the estimated center of gravity position.

[0022] In a control system according to a ninth aspect in accordance with the seventh aspect of the present invention, the control device controls the notification device to notify information relating to a recommended posture of the rider.

[0023] According to the control system in accordance with the ninth aspect of the present invention, it is possible to notify information relating to a recommended rider's posture so as to position the center of gravity at an appropriate position.

[0024] In the control system of a tenth aspect according to any one of the sixth to ninth aspects of the present invention, the components include at least one of a lighting device, a braking device, a gear shifting device, a suspension device, a drive assist device, and an adjustable seat post.

[0025] According to the control system according to the tenth aspect of the present invention, components of a human-powered vehicle including at least one of a lighting device, a braking device, a transmission, a suspension device, a drive assist device, and an adjustable seat post are controlled according to the results of estimating the center of gravity position, so that, for example, the components of the human-powered vehicle can be controlled in a manner that is comfortable for the rider.

[0026] A learning model according to an eleventh aspect of the present invention comprises an input layer to which vehicle body information related to the body of a human-powered vehicle and load information related to the load acting on the human-powered vehicle are input, an output layer to output an estimation result related to the center of gravity position of the human-powered vehicle with a rider on board, and an intermediate layer that uses the vehicle body information, the load information, and center of gravity information indicating the center of gravity position as teacher data to learn the relationship between the vehicle body information and the load information input to the input layer and the estimation result output from the output layer, and is configured so that a computer performs calculations in the intermediate layer to output the estimation result from the output layer in response to the input of the vehicle body information and the load information to the input layer.

[0027] According to the learning model according to the eleventh aspect of the present invention, an execution environment for estimating the position of the center of gravity of a human-powered vehicle with a rider on board can be realized by a computer.

[0028] A method for generating a learning model according to a twelfth aspect of the present invention uses body information about the body of a human-powered vehicle, load information about the load acting on the human-powered vehicle, and center of gravity information indicating the position of the center of gravity of the human-powered vehicle as teacher data, and uses a computer to generate a learning model that outputs an estimation result about the center of gravity position in response to input of the body information and the load information.

[0029] According to the method for generating a learning model in accordance with the twelfth aspect of the present invention, by collecting body information and load information of a human-powered vehicle, as well as center of gravity information indicating the center of gravity position of the human-powered vehicle, it is possible to generate a learning model for estimating the center of gravity position of a human-powered vehicle with a rider on board.

[0030] A computer program according to a thirteenth aspect of the present invention is a computer program for causing a computer to execute a process of estimating a center of gravity position of a human-powered vehicle with a rider on board, based on vehicle body information related to the body of the human-powered vehicle and load information related to the load acting on the human-powered vehicle.

[0031] According to the computer program according to the thirteenth aspect of the present invention, an execution environment for estimating the position of the center of gravity of a human-powered vehicle with a rider on board can be realized by a computer.

[0032] A storage medium according to the fourteenth aspect of the present invention stores a computer program according to the thirteenth aspect of the present invention.

[0033] According to the storage medium in accordance with the fourteenth aspect of the present invention, by installing the computer program stored in the storage medium into a computer, an execution environment for estimating the position of the center of gravity of a human-powered vehicle with a rider on board can be realized by the computer. Effect of the Invention

[0034] According to the present application, it is possible to estimate the position of the center of gravity of a human-powered vehicle with a rider on board. [Brief description of the drawings]

[0035] [Figure 1] 1 is a side view of a human-powered vehicle to which an estimation device of a first embodiment is applied. [Diagram 2] 1 is a block diagram showing an internal configuration of an estimation device according to a first embodiment. [Diagram 3] FIG. 1 is a schematic diagram showing an implementation example of a learning model. [Figure 4] FIG. 13 is a schematic diagram showing an example of setting a coordinate system. [Diagram 5] 11 is a flowchart illustrating a procedure for estimating a center of gravity position. [Figure 6] FIG. 11 is a block diagram illustrating a control system according to a second embodiment. [Figure 7] 10 is a flowchart showing a control procedure in a second embodiment. [Figure 8] FIG. 13 is a schematic diagram showing an example of a notification example. [Figure 9] FIG. 11 is a schematic diagram showing another example of the notification example. [Figure 10] FIG. 2 is a block diagram showing an internal configuration of a server device. [Figure 11] FIG. 13 is a conceptual diagram illustrating an example of teacher data. [Figure 12] 13 is a flowchart illustrating a procedure for generating a learning model by a server device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0036] Hereinafter, the present invention will be described in detail with reference to the drawings showing embodiments thereof. (First embodiment) FIG 1 is a side view of a human-powered vehicle 1 to which an estimation device 100 of a first embodiment is applied. The human-powered vehicle 1 is a vehicle that uses human power at least in part as a driving force for traveling. Vehicles that use only an internal combustion engine or an electric motor as a driving force are excluded from the human-powered vehicle 1 of this embodiment. The human-powered vehicle 1 is, for example, a bicycle, including a mountain bike, a road bike, a cross bike, a city cycle, etc.

[0037] The human-powered vehicle 1 comprises a vehicle body 10, a front wheel 12, a rear wheel 14, a handlebar 16, a saddle 18, and a drive mechanism 20. In the following description, terms expressing the front-rear, left-right, and up-down directions are used based on the directions when a rider is seated on the saddle 18 of the human-powered vehicle 1.

[0038] The vehicle body 10 includes a frame 10A and a front fork 10B. A front wheel 12 is rotatably supported at an end of the front fork 10B via a front wheel axle 12A. A rear wheel 14 is rotatably supported at a rear end of the frame 10A via a rear wheel axle 14A. A handlebar 16 is supported by the frame 10A so that the traveling direction of the front wheel 12 can be changed.

[0039] The drive mechanism 20 transmits the human-powered driving force to the rear wheel 14 by a chain drive, a belt drive, or a shaft drive. Fig. 1 illustrates a chain drive drive mechanism 20. The drive mechanism 20 includes a crank 22, a front sprocket 24A, a rear sprocket 24B, a chain 26, and a pair of pedals 28, 28. The drive mechanism 20 may further include a chain device or a chain tensioner for stably holding the chain 26.

[0040] The crank 22 includes a right crank 22A, a left crank 22B, and a crankshaft 22C. The crankshaft 22C is rotatably supported by the frame 10A. The right crank 22A and the left crank 22B are each connected to the crankshaft 22C. One of a pair of pedals 28, 28 is rotatably supported by the right crank 22A, and the other is rotatably supported by the left crank 22B.

[0041] The front sprocket 24A is connected to the crankshaft 22C and rotates integrally with the crankshaft 22C. In one example, the front sprocket 24A is a sprocket assembly made up of multiple sprockets with different outer diameters. When the front sprocket 24A includes multiple sprockets, the outer diameters of these sprockets increase, for example, as they move away from the vehicle body 10.

[0042] The rear sprocket 24B is rotatably supported by the hub of the rear wheel 14. In one example, the rear sprocket 24B is a sprocket assembly made up of multiple sprockets with different outer diameters. When the rear sprocket 24B includes multiple sprockets, the outer diameters of these sprockets become smaller, for example, as they move away from the vehicle body 10 toward the outside.

[0043] The chain 26 is wound around the front sprocket 24A and the rear sprocket 24B. When the crank 22 rotates forward due to human driving force applied to the pedals 28, 28, the front sprocket 24A rotates forward together with the crank 22. The rotation of the front sprocket 24A is transmitted to the rear sprocket 24B via the chain 26, causing the rear wheel 14 to rotate.

[0044] The human-powered vehicle 1 includes components including at least one of an alarm device 30, a lighting device 32, a braking device 34, a gear shift device 36, a suspension device 38, a driving assistance device 40, and an adjustable seat post 42. The human-powered vehicle 1 further includes an operating device 44 that switches the control state of at least one of the components, and a battery unit 46 that supplies power to the components.

[0045] The notification device 30 is a device that notifies the rider of various information. The notification device 30 is provided, for example, on the handlebar 16. In one example, the notification device 30 is a display device equipped with a liquid crystal display panel or the like, and displays information such as characters, figures, and symbols based on a control signal input from the outside. In another example, the notification device 30 is an audio output device equipped with a speaker or the like, and outputs audio based on a control signal input from the outside. The notification device 30 is not limited to audio, and may be configured to output a sound such as a warning sound or generate vibrations. The notification device 30 does not need to be an independent device, and may be a device mounted on the estimation device 100.

[0046] The lighting device 32 is a device that illuminates the traveling direction of the human-powered vehicle 1. The lighting device 32 is provided, for example, on the front fork 10B. The lighting device 32 is configured to turn on, blink, or turn off in accordance with a control signal input from the outside.

[0047] In one example, the brake device 34 is a device that applies a braking force to the wheel of the front wheel 12. The brake device 34 includes an electric drive unit 34A, a caliper 34B, a brake operation device 34C, and the like. The electric drive unit 34A includes an electric motor that operates the caliper 34B, and a drive circuit that drives the electric motor. The caliper 34B includes a brake pad that can contact the wheel of the front wheel 12. The brake operation device 34C is provided, for example, on the handlebar 16, and receives an operation of a rider who applies the brakes. The electric drive unit 34A of the brake device 34 drives the electric motor in response to an operation of the brake operation device 34C, and operates the caliper 34B to bring the brake pad into contact with the wheel of the front wheel 12, thereby attenuating the rotational force of the front wheel 12. The brake device 34 may be configured to operate in a plurality of control states with different braking forces.

[0048] The brake device 34 may be a disc brake. In the disc brake, for example, an electric motor controls fluid to move brake pads and press the brake pads against a rotor, thereby attenuating the rotational force of the front wheel 12 or the rear wheel 14. The disc brake may also be configured to directly move brake pads using an electric motor and attenuate the rotational force of the front wheel 12 or the rear wheel 14 by pressing the brake pads against a rotor.

[0049] The brake device 34 may be provided for the rear wheel 14. The front and rear brake devices 34 may share the brake operation device 34C. The front and rear brake devices 34 may each be provided with a brake operation device 34C. The configuration of the brake device 34 provided for the rear wheel 14 is similar to that of the brake device 34 for the front wheel 12, and therefore description thereof will be omitted.

[0050] The transmission 36 is a device that changes the gear ratio of the human-powered vehicle 1. The transmission 36 includes an electric drive unit 36A whose operation is controlled based on a control signal output in response to the operation of the operating device 44. The electric drive unit 36A includes a drive circuit and an electric motor. The transmission 36 drives the electric motor included in the electric drive unit 36A to change the sprocket around which the chain 26 is wound, thereby switching the gear ratio of the human-powered vehicle 1.

[0051] An example of the transmission 36 is an external gear shifter. More specifically, the transmission 36 is a rear derailleur. In this case, the rear sprocket 24B includes a plurality of sprockets with different outer diameters. When the drive circuit of the electric drive unit 36A receives a control signal from the operation device 44 instructing an upshift, the drive circuit of the electric drive unit 36A drives the electric motor, for example, to change the sprocket around which the chain 26 is wound from the top side to the low side. When the drive circuit of the electric drive unit 36A receives a control signal from the operation device 44 instructing a downshift, the drive circuit of the electric drive unit 36A drives the electric motor, for example, to change the sprocket around which the chain 26 is wound from the low side to the top side.

[0052] The transmission 36 may be a front derailleur. In this case, the front sprocket 24A includes a plurality of sprockets with different outer diameters. The transmission 36 may include both a front derailleur and a rear derailleur. The transmission 36 may be an internal transmission. When the transmission 36 is an internal transmission, the transmission 36 is provided, for example, in the hub of the rear wheel 14, and transmits the rotation input to the rear sprocket 24B to the rear wheel 14 after changing the speed. The transmission 36 is not limited to an external transmission or an internal transmission, but may also be a continuously variable transmission.

[0053] In one example, the suspension device 38 is a front suspension provided on the front fork 10B and damps shocks applied to the front wheel 12. In another example, the suspension device 38 is a rear suspension that damps shocks applied to the rear wheel 14. The suspension device 38 includes an electric drive unit 38A whose operation is controlled based on a control signal output in response to the operation of the operating device 44. The electric drive unit 38A includes a drive circuit and an electric motor. The suspension device 38 is controlled by setting a damping rate, a stroke amount, and a lockout state as operation parameters. The suspension device 38 may be configured to operate in a plurality of control states having different cushioning properties.

[0054] The drive assist device 40 is a device for assisting the manual drive, and is provided for the drive mechanism 20. The drive assist device 40 includes an electric drive unit 40A whose operation is controlled based on a control signal input from the outside. The electric drive unit 40A includes a drive circuit and an electric motor, and the drive circuit drives the electric motor to assist the manual drive. The electric drive unit 40A may be controlled based on a control signal output in response to the operation of the operation device 44. The drive assist device 40 can operate in a plurality of control states with different assist forces that assist the manual drive. The control state in the drive assist device 40 is also called an assist level. The drive assist device 40 changes the assist level according to, for example, the manual drive force acting on the crank 22. When four assist levels are provided, for example, they include a strong assist level, a medium assist level, a weak assist level, and a zero assist level. At the zero assist level, the drive assist device 40 does not perform assist.

[0055] The adjustable seat post 42 is attached to the frame 10A. The adjustable seat post 42 is equipped with an electric drive unit 42A whose operation is controlled based on a control signal output in response to the operation of an operating device 44. The electric drive unit 42A includes an electric actuator that raises and lowers the saddle 18 relative to the frame 10A, and a drive circuit that drives the electric actuator. The adjustable seat post 42 is controlled by setting the support position of the saddle 18 relative to the frame 10A.

[0056] The operating device 44 is provided, for example, on the handlebar 16. The operating device 44 includes operating switches 44A, 44B that are operated by the rider's fingers. The operating switches 44A, 44B are switches for switching the control state of at least one of the alarm device 30, the lighting device 32, the braking device 34, the gear shift device 36, the suspension device 38, the drive assist device 40, and the adjustable seat post 42.

[0057] The number of operation devices 44 mounted on the human-powered vehicle 1 does not need to be one, and may be multiple. In the example shown in Fig. 1, the operation device 44 is configured to include two operation switches 44A, 44B, but it may also be configured to include one or three or more operation switches. The operation device 44 is not limited to a configuration including a switch, and may also be configured to include an operation lever, an operation dial, or the like.

[0058] The operation device 44 is connected to each component so as to transmit a control signal to the component to be controlled in response to the operation of the operation switch 44A or the operation switch 44B. In one example, the operation device 44 is connected to the component to be controlled by a communication line or an electric wire capable of PLC (Power Line Communication). In another example, the operation device 44 is connected to the component to be controlled by a wireless communication unit capable of wireless communication.

[0059] The human-powered vehicle 1 may include a battery unit 46 that supplies power to at least one of the aforementioned components. The battery unit 46 includes a battery 46A and a battery holder 46B. The battery 46A is a storage battery including one or more battery cells. The battery holder 46B is fixed to the frame 10A of the human-powered vehicle 1. The battery 46A is detachable from the battery holder 46B. The battery 46A is electrically connected to the notification device 30, the lighting device 32, the gear shift device 36, the brake device 34, the suspension device 38, the driving assistance device 40, the adjustable seat post 42, and the like.

[0060] The human-powered vehicle 1 may further include at least one of an inclination sensor S1, a load sensor S2, and an attitude sensor S3. In the following description, when there is no need to describe each of the inclination sensor S1, the load sensor S2, and the attitude sensor S3 individually, they will also be referred to as sensors S1 to S3.

[0061] The inclination sensor S1 is a sensor that outputs a signal according to the inclination angle of the human-powered vehicle 1. The inclination angle detected by the inclination sensor S1 is, for example, a rotation angle around a pitch axis along the left-right direction of the human-powered vehicle 1. As an example, the inclination sensor S1 includes a sensor that detects the angular velocity of the pitch angle, and calculates the value obtained by integrating the angular velocity around the pitch axis as the pitch angle. The inclination sensor S1 may be configured to measure both the rotation angle around a roll axis along the front-rear direction of the human-powered vehicle 1 and the rotation speed around a yaw axis along the up-down direction of the human-powered vehicle 1.

[0062] The load sensor S2 is a sensor that outputs a signal corresponding to the load acting on the human-powered vehicle 1. An example of the load sensor S2 is a strain gauge type load cell. The load sensor S2 is provided on at least one of the front wheel axle 12A, the rear wheel axle 14A, the handlebar 16, the saddle 18, and the pedal 28.

[0063] The posture sensor S3 is a sensor that outputs a signal according to the rider's posture. One example of the posture sensor S3 is a piezoelectric sensor, which is provided at multiple locations on the human-powered vehicle 1 where the rider's weight is applied. For example, the posture sensor S3 is provided at one or multiple locations such as the grip of the handlebar 16, the surface of the saddle 18, the pedals 28, etc. The posture sensor S3 may be a motion sensor provided in a wearable terminal attached to the rider. The posture sensor S3 may be shared with at least one of the load sensors S2.

[0064] The estimation device 100 estimates the center of gravity position of the human-powered vehicle 1 with a rider riding on it, based on vehicle body information related to the vehicle body of the human-powered vehicle 1 and load information related to the load acting on the human-powered vehicle 1. In one example, the estimation device 100 is a dedicated terminal such as a cycle computer provided on the human-powered vehicle 1. In another example, the estimation device 100 is a general-purpose terminal such as a smartphone, tablet terminal, or wearable terminal carried by the rider of the human-powered vehicle 1.

[0065] The vehicle body information used to estimate the center of gravity position includes information on the vehicle body dimensions, vehicle body weight, and tilt angle of the human-powered vehicle 1. For the vehicle body dimensions and vehicle body weight of the human-powered vehicle 1, design values ​​or values ​​measured in advance may be stored in advance in the estimation device 100. On the other hand, the tilt angle of the human-powered vehicle 1 is information obtained based on the output of the tilt sensor S1.

[0066] The load information used to estimate the center of gravity position includes information about the load acting on at least one of the front axle 12A, the rear axle 14A, the handlebar 16, the saddle 18, the crank 22, and the pedals 28. This information is obtained based on the output of the load sensor S2.

[0067] The load information used to estimate the center of gravity position may include at least one of the rider's weight, height, posture, and gender. The rider's weight, height, and gender are information input by the rider using, for example, the operation device 44. These pieces of information are input using, for example, the operation device 44 and stored in the estimation device 100. The rider's posture is information obtained based on the output of the posture sensor S3.

[0068] The configuration of the estimation device 100 will be described below. 2 is a block diagram showing the internal configuration of the estimation device 100 according to the first embodiment. The estimation device 100 includes an input unit 102, a calculation processing unit 104, a storage unit 106, and an output unit .

[0069] The input unit 102 includes an interface for connecting the sensors S1 to S3. The interface included in the input unit 102 is, for example, a wired interface, and connects the sensors S1 to S3 via a communication cable. Information based on a signal input through the input unit 102 is temporarily stored in the storage unit 106. The interface included in the input unit 102 may be a wireless interface. As the wireless interface, a communication interface conforming to a communication standard including Bluetooth (registered trademark), WiFi (registered trademark), ZigBee (registered trademark), LTE (Long Term Evolution), and other wireless LANs (Local Area Networks) can be used.

[0070] The arithmetic processing unit 104 includes a central processing unit (CPU), a read only memory (ROM), a random access memory (RAM), etc. The ROM included in the arithmetic processing unit 104 stores computer programs and the like for controlling the operation of each hardware unit included in the estimation device 100. The CPU in the arithmetic processing unit 104 executes computer programs stored in the ROM or the storage unit 106, and controls the operation of each hardware unit, thereby controlling the operation of the entire device. The RAM included in the arithmetic processing unit 104 temporarily stores data used during the execution of calculations.

[0071] The arithmetic processing unit 104 is configured to include a CPU, a ROM, and a RAM, but may also be one or more arithmetic circuits including a GPU (Graphics Processing Unit), an FPGA (Field Programmable Gate Array), a DSP (Digital Signal Processor), a quantum processor, a volatile or non-volatile memory, etc.

[0072] The storage unit 106 includes memories such as an EEPROM (Electronically Erasable Programmable Read Only Memory) and an SRAM (Static Random Access Memory). The storage unit 106 stores various computer programs including an estimation processing program 110 executed by the arithmetic processing unit 104, a learning model 120 (described later), various data used when estimating the center of gravity position of the human-powered vehicle 1, and the like.

[0073] The estimation processing program 110 is a computer program for causing the estimation processing device 100 to execute processing for estimating the position of the center of gravity of the human-powered vehicle 1 with a rider on board, based on vehicle body information related to the vehicle body of the human-powered vehicle 1 and load information related to the load acting on the human-powered vehicle 1. By executing the estimation processing program 110, the calculation processing unit 104 estimates the position of the center of gravity of the human-powered vehicle 1 with a rider on board, using the learning model 120, based on the vehicle body information and load information of the human-powered vehicle 1.

[0074] In one example, various computer programs including the estimation processing program 110 may be provided by a storage medium M in which the computer programs are stored. The storage medium M is, for example, a portable memory such as a CD-ROM, a USB memory, a Secure Digital (SD) card, a micro SD card, or a Compact Flash (registered trademark). In this embodiment, the storage medium M is a non-transitory storage medium that readably stores various computer programs including the estimation processing program 110. The calculation processing unit 104 reads the various computer programs from the storage medium M using a reading device not shown in the figure, and installs the various computer programs that have been read into the storage unit 106.

[0075] The learning model 120 stored in the storage unit 106 is described by its definition information. The definition information of the learning model 120 includes structural information of the learning model 120, various parameters such as weights and biases between nodes used in the learning model 120, and the like. In this embodiment, the learning model 120 learned by a predetermined learning model is stored in the storage unit 106 using the body information and load information of the human-powered vehicle 1, and center of gravity information indicating the center of gravity position of the human-powered vehicle 1 as teacher data. As an example, a supervised learning algorithm using a neural network can be used as the learning algorithm.

[0076] The output unit 108 includes an output interface that outputs the estimation result by the arithmetic processing unit 104. The output format of the estimation result is arbitrary. In one example, the output unit 108 includes a display device such as a liquid crystal display or an organic EL (Electro-Luminescence) display. In this case, the output unit 108 may display the estimation result of the center of gravity position on the display device as character information or image information. In another example, the output unit 108 includes an audio output device such as a speaker. In this case, the output unit 108 may output the estimation result of the center of gravity position as audio from the audio output device. In yet another example, the output unit 108 includes a communication interface. In this case, the output unit 108 may output the estimation result of the center of gravity position to an external device connected to the communication interface.

[0077] 3 is a schematic diagram showing an implementation example of the learning model 120. The learning model 120 is, for example, a machine learning learning model including deep learning, and is configured by a neural network. The learning model 120 includes an input layer 122, intermediate layers 124A and 124B, and an output layer 126. In the example of FIG. 3, two intermediate layers 124A and 124B are described, but the number of intermediate layers is not limited to two, and may be three or more.

[0078] The input layer 122, the intermediate layers 124A and 124B, and the output layer 126 each have one or more nodes, and the nodes of each layer are connected in one direction to the nodes in the previous and next layers with desired weights and biases. Vector data having the same number of components as the number of nodes in the input layer 122 is provided as input data to the learning model 120. For example, data provided to each node of the input layer 122 is body information and load information of the human-powered vehicle 1. The body information includes information on the body dimensions, body weight, and tilt angle of the human-powered vehicle 1. Information stored in the storage unit 106 of the estimation device 100 is used for the body dimensions and body weight. Information obtained based on the output signal of the tilt sensor S1 is used for the tilt angle. The load information includes information on the load acting on at least one of the front wheel axle 12A, the rear wheel axle 14A, the handlebar 16, the saddle 18, the crank 22, and the pedals 28. Information obtained based on the output signal of the load sensor S2 is used for this load information. The load information may further include at least one of the rider's weight, height, posture, and gender. For the rider's weight, height, and gender, information stored in the storage unit 106 of the estimation device 100 is used. For the information regarding the rider's posture, information obtained based on the output signal of the posture sensor S3 is used.

[0079] Data given to each node of the input layer 122 is given to the first hidden layer 124A. In the hidden layer 124A, an output is calculated using an activation function including weights and biases, and the calculated value is given to the next hidden layer 124B, and so on until the output of the output layer 126 is obtained. Various parameters such as weights and biases connecting the nodes are learned by a predetermined learning algorithm. For example, a deep learning learning algorithm is used as the learning algorithm for learning the various parameters. In this embodiment, various parameters can be learned by a predetermined learning algorithm using the vehicle body information and load information of the human-powered vehicle 1, and center of gravity information indicating the center of gravity position of the human-powered vehicle 1 as teacher data. The learning method will be described in detail later.

[0080] The output layer 126 outputs an estimation result regarding the center of gravity position of the human-powered vehicle 1 when the rider is riding on the vehicle. The output form of the estimation result by the output layer 126 is arbitrary. For example, the output layer 126 may set a coordinate system based on the human-powered vehicle 1, and output the probability that each of the positions set in this coordinate system is the center of gravity position. FIG. 4 is a schematic diagram showing an example of setting the coordinate system. The example of FIG. 4 shows an orthogonal coordinate system in which the front-rear direction of the human-powered vehicle 1 is the X-axis direction and the up-down direction is the Z-axis direction. In this coordinate system, when m points are taken in the X-axis direction and n points are taken in the Z-axis direction, the position coordinates of each point can be expressed as (X1, Z1), (X2, Z1), ..., (Xi, Zj), ..., (Xm, Zn). Here, i is an integer from 1 to m, and j is an integer from 1 to n. m and n are integers equal to or greater than 2. The output layer 126 of the learning model 120 may output the probability that each position indicated by these position coordinates is the center of gravity position. In this case, the output layer 126 is provided with m×n nodes, and each node outputs the probability that each position is the center of gravity position.

[0081] As described above, in the learning model 120, the estimation device 100 is configured to perform calculation processing in the intermediate layers 124A and 124B in order to output an estimation result of the center of gravity position from the output layer 126 in response to input of vehicle body information and load information to the input layer 122.

[0082] In the example of Figure 4, for simplicity, the position coordinates are set in a two-dimensional Cartesian coordinate system that includes the forward / rearward and upward directions of the human-powered vehicle 1. However, it is of course also possible to set the position coordinates in a three-dimensional Cartesian coordinate system that includes the left / right direction as the Y-axis direction.

[0083] Next, the procedure for estimating the center of gravity position will be described. 5 is a flow chart for explaining the procedure for estimating the center of gravity position. In step S101, the calculation processing unit 104 of the estimation device 100 acquires vehicle body information and load information of the human-powered vehicle 1. The calculation processing unit 104 may acquire the vehicle body information by reading the vehicle body dimensions and vehicle body weight from the storage unit 106, or may acquire the inclination angle obtained from the output signal of the inclination sensor S1 as the vehicle body information. The calculation processing unit 104 may acquire the load information based on the output signal of the load sensor S2, and further, the calculation processing unit 104 may acquire the load information by reading the weight, height, and sex of the rider from the storage unit 106, or may acquire the load information based on the output signal of the attitude sensor S3.

[0084] In step S102, the calculation processing unit 104 inputs the vehicle body information and load information of the human-powered vehicle 1 to the learning model 120. At this time, the calculation processing unit 104 may provide the vehicle body information and load information acquired in step S101 to the input layer 122 of the learning model 120, or may provide the vehicle body information and load information that have been subjected to calculation processing so as to match the input form of each node to the input layer 122 of the learning model 120.

[0085] By providing the vehicle body information and load information to the input layer 122, the learning model 120 executes a calculation. The information provided to each node of the input layer 122 is output to the adjacent node of the intermediate layer 124A. In the intermediate layer 124A, a calculation is performed using an activation function including weights and biases between the nodes, and the calculation result is output to the subsequent intermediate layer 124B. In the intermediate layer 124B, a calculation is further performed using an activation function including weights and biases between the nodes, and the calculation result is output to each node of the output layer 126. In this way, the learning model 120 outputs an estimation result regarding the center of gravity position of the human-powered vehicle 1. In other words, the learning model 120 is configured such that the estimation device 100 executes calculation processing in the intermediate layers 124A and 124B in order to output an estimation result of the center of gravity position from the output layer 126 in response to the input of the vehicle body information and load information to the input layer 122.

[0086] In step S103, the calculation processing unit 104 acquires the calculation result by the learning model 120 from the output layer 126. For example, when position coordinates are set in an orthogonal coordinate system as shown in Fig. 4, the calculation processing unit 104 acquires the probability that each position indicated by the position coordinates (X1, Z1), (X2, Z1), ..., (Xi, Zj), ..., (Xm, Zn) is the center of gravity position.

[0087] In step S104, the calculation processing unit 104 estimates the center of gravity position of the human-powered vehicle 1 with the rider on board, based on the calculation result acquired from the output layer 126 of the learning model 120. In one example, the calculation processing unit 104 compares the magnitude of the probability output from each node of the output layer 126, and estimates the position with the highest probability as the center of gravity position. In another example, the calculation processing unit 104 may calculate a weighted average using the probability at each position coordinate as a weight, and estimate the calculation result as the center of gravity position.

[0088] As described above, the estimation device 100 in the first embodiment can estimate the center of gravity position of the human-powered vehicle 1 with a rider on board by inputting the vehicle body information and load information of the human-powered vehicle 1 to the learning model 120.

[0089] The estimation device 100 in the first embodiment is configured to estimate the center of gravity position using the learning model 120, but may be configured to estimate the center of gravity position without using the learning model 120. For example, the estimation device 100 may be configured to prepare a function that outputs the center of gravity position of the human-powered vehicle 1 with a rider on board in response to input of vehicle body information and load information of the human-powered vehicle 1, and to estimate the center of gravity position using this function.

[0090] Second embodiment In the second embodiment, a control system that controls components of a human-powered vehicle 1 based on the estimation result by the estimation device 100 will be described.

[0091] Fig. 6 is a block diagram illustrating a control system according to the second embodiment. The control system according to the second embodiment includes an estimation device 100 and a control device 200 that controls components of the human-powered vehicle 1 based on the estimation result by the estimation device 100. The control device 200 may be provided as an independent device, or may be provided within the component to be controlled. In the example shown in Fig. 6, the estimation device 100 and the control device 200 are described as separate entities, but the estimation device 100 and the control device 200 may also be configured as an integrated unit.

[0092] The control device 200 includes a control unit 202, a storage unit 204, an input unit 206, and an output unit 208. The control unit 202 includes, for example, a CPU, a ROM, and a RAM. The ROM included in the control unit 202 stores a control program for controlling the operation of each hardware unit included in the control device 200. The CPU in the control unit 202 executes the control program stored in the ROM and various programs stored in the storage unit 204, and controls the operation of each hardware unit, thereby implementing the control device of the present application. Specifically, the control unit 202 generates a control signal for controlling a component of the human-powered vehicle 1 based on the estimation result from the estimation device 100 input through the input unit 206, and outputs the generated control signal from the output unit 208 to the component to be controlled.

[0093] The control unit 202 is not limited to the above configuration. The control unit 202 may be one or more control circuits including a single-core CPU, a multi-core CPU, an FPGA, a volatile or non-volatile memory, etc. The control unit 202 may also have functions such as a clock that outputs date and time information, a timer that measures the elapsed time from when a measurement start instruction is given to when a measurement end instruction is given, and a counter that counts numbers.

[0094] The storage unit 204 includes memories such as an EEPROM, an SRAM, etc. The storage unit 204 stores computer programs executed by the control unit 202, data used by these computer programs, and the like.

[0095] The input unit 206 includes an interface that connects the estimation device 100 via a cable. The estimation result output from the estimation device 100 is input to the input unit 206. The estimation result input to the input unit 206 indicates the center of gravity position of the human-powered vehicle 1 with a rider on board. The input unit 206 outputs the input estimation result to the control unit 202. In this embodiment, the estimation device 100 is connected via a cable, but the estimation device 100 and the control device 200 may be configured to transmit and receive data between them via a wireless communication interface. For wireless communication, a wireless communication method conforming to a communication standard such as Bluetooth, WiFi, ZigBee, LTE, or other wireless LAN can be used.

[0096] The output unit 208 includes an interface that connects the components to be controlled via a cable. The components connected to the control device 200 include the notification device 30, the lighting device 32, the brake device 34, the gear shift device 36, the suspension device 38, the drive assist device 40, and the adjustable seat post 42. The output unit 208 outputs a control signal output from the control unit 202 to the components to be controlled. In this embodiment, the components of the human-powered vehicle 1 are connected via cables, but the control device 200 and the components of the human-powered vehicle 1 may be configured to transmit and receive data between them via a wireless communication interface. For wireless communication, a wireless communication method conforming to the various communication standards described above can be used.

[0097] Hereinafter, a control procedure in which the control device 200 controls the components of the human-powered vehicle 1 based on the estimation result of the estimation device 100 will be described.

[0098] 7 is a flowchart showing a control procedure in the second embodiment. In step S201, the control unit 202 of the control device 200 acquires the estimation result output from the estimation device 100 through the input unit 206. In step S202, the control unit 202 generates a control signal for controlling a component to be controlled, based on the estimation result acquired from the estimation device 100.

[0099] In step S203, the control unit 202 outputs the control signal generated in step S202 from the output unit 208 to the component to be controlled, thereby controlling the component to be controlled.

[0100] For example, when the controlled component is the notification device 30, the control unit 202 generates a control signal to cause the notification device 30 to notify the estimation result, and transmits the control signal to the notification device 30 via the output unit 208. The notification device 30 notifies the estimation result by the estimation device 100, based on the control signal output from the control device 200.

[0101] Fig. 8 is a schematic diagram showing an example of a notification example. Fig. 8 shows an example in which the estimation result of the estimation device 100 is displayed as an image. In this notification example, the position of the center of gravity of the estimation result is indicated by a hatched rectangular area.

[0102] FIG. 9 is a schematic diagram showing another example of the notification example. FIG. 9 shows an example in which the estimated center of gravity position and the recommended center of gravity position are displayed. In this notification example, the estimated center of gravity position is indicated by a hatched area, and the recommended center of gravity position is indicated by a cross. In one example, the recommended center of gravity position is calculated by the control device 200 using a function that derives the recommended center of gravity position based on the body dimensions, body weight, tilt angle, rider's weight, height, etc. of the human-powered vehicle 1. In another example, the recommended center of gravity position is read by the control device 200 using a table that indicates the relationship between the data of the body dimensions, body weight, tilt angle, rider's weight, height, etc. of the human-powered vehicle 1 and the recommended center of gravity position. The control device 200 notifies the rider of the recommended posture by notifying the rider of the recommended center of gravity position from the notification device 30.

[0103] As described above, the control device 200 of the second embodiment controls the components of the human-powered vehicle 1 based on the estimation result of the estimation device 100, and can therefore notify the rider of information including the center of gravity position of the estimation result, for example.

[0104] In the second embodiment, the component to be controlled is an alarm device 30, but the components to be controlled may include at least one of a lighting device 32, a braking device 34, a transmission device 36, a suspension device 38, a drive assist device 40, and an adjustable seat post 42.

[0105] When the component to be controlled is the lighting device 32, the control unit 202 generates a control signal for controlling the operation of the lighting device 32 based on the estimation result of the estimation device 100. The control unit 202 outputs the generated control signal from the output unit 208 to the lighting device 32. For example, when the center of gravity position estimated by the estimation device 100 is not present in a preset area, the control unit 202 may warn the rider by controlling the lighting device 32 to blink.

[0106] When the component to be controlled is the braking device 34, the control unit 202 generates a control signal for controlling the operation of the braking device 34 based on the estimation result of the estimation device 100. The control unit 202 outputs the generated control signal from the output unit 208 to the braking device 34. For example, the control unit 202 performs control to change the braking force of the braking device 34 according to the position of the center of gravity in the front-rear direction estimated by the estimation device 100.

[0107] When the component to be controlled is the transmission 36, the control unit 202 generates a control signal for controlling the operation of the transmission 36 based on the estimation result of the estimating device 100. The control unit 202 outputs the generated control signal from the output unit 208 to the transmission 36. For example, the control unit 202 performs control to change the gear ratio of the transmission 36 according to the position of the center of gravity in the front-rear direction estimated by the estimating device 100.

[0108] When the component to be controlled is the suspension device 38, the control unit 202 generates a control signal for controlling the operation of the suspension device 38 based on the estimation result of the estimating device 100. The control unit 202 outputs the generated control signal from the output unit 208 to the suspension device 38. For example, the control unit 202 performs control to change the cushioning properties of the suspension device 38 according to the vertical position of the center of gravity estimated by the estimating device 100.

[0109] When the component to be controlled is the driving assist device 40, the control unit 202 generates a control signal for controlling the operation of the driving assist device 40 based on the estimation result of the estimation device 100. The control unit 202 outputs the generated control signal from the output unit 208 to the driving assist device 40. For example, the control unit 202 may perform control to change the assist force by the driving assist device 40 according to the position of the center of gravity in the front-rear direction estimated by the estimation device 100.

[0110] When the component to be controlled is the adjustable seatpost 42, the control unit 202 generates a control signal for controlling the operation of the adjustable seatpost 42 based on the estimation result of the estimating device 100. The control unit 202 outputs the generated control signal from the output unit 208 to the adjustable seatpost 42. For example, the control unit 202 may control the operation of the adjustable seatpost 42 to change the height position of the saddle 18 in accordance with the vertical position of the center of gravity estimated by the estimating device 100.

[0111] Third embodiment In the third embodiment, a method for generating a learning model 120 will be described.

[0112] The learning model 120 used in the estimation device 100 of the human-powered vehicle 1 is generated, for example, in a server device 300. Fig. 10 is a block diagram showing the internal configuration of the server device 300. The server device 300 includes a control unit 302, a storage unit 304, an input unit 306, and a communication unit 308.

[0113] The control unit 302 includes, for example, a CPU, a ROM, and a RAM. The ROM included in the control unit 302 stores a control program for controlling the operation of each hardware unit included in the server device 300. The CPU in the control unit 302 executes the control program stored in the ROM and various programs stored in the storage unit 304, and controls the operation of each hardware unit.

[0114] The control unit 302 is not limited to the above-mentioned configuration. The control unit 302 is not limited to a configuration including a CPU, a ROM, and a RAM. The control unit 302 may be, for example, one or more control circuits or arithmetic circuits including a GPU, an FPGA, a DSP, a quantum processor, a volatile or non-volatile memory, etc. The control unit 302 may also have functions such as a clock that outputs date and time information, a timer that measures the elapsed time from when a measurement start instruction is given to when a measurement end instruction is given, and a counter that counts numbers.

[0115] The storage unit 304 includes a storage device such as a flash memory, a hard disk drive, etc. The storage unit 304 stores various computer programs executed by the control unit 302, teacher data used to generate the learning model, the learning model generated by the server device 300, etc.

[0116] The input unit 306 includes an input interface for acquiring data and programs from a storage medium storing various data or programs. The various data and programs input through the input unit 306 are stored in the storage unit 304.

[0117] The communication unit 308 includes a communication interface and receives information addressed to the server device 300 from an external device and transmits information to be sent to the external device.

[0118] In this embodiment, for the sake of simplicity, the server device 300 is described as a single device, but it may be configured with multiple server devices, or may be configured with one or multiple virtual machines.

[0119] FIG. 11 is a conceptual diagram showing an example of teacher data. The teacher data includes vehicle body information related to the vehicle body of the human-powered vehicle 1, load information related to the load acting on the human-powered vehicle 1, and center of gravity information indicating the center of gravity position of the human-powered vehicle 1. The vehicle body information includes information related to the vehicle body dimensions, vehicle body weight, and tilt angle of the human-powered vehicle 1. Design values ​​or values ​​measured in advance are used for the vehicle body dimensions and vehicle body weight of the human-powered vehicle 1. A value measured in advance using the tilt sensor S1 is used for the tilt angle of the human-powered vehicle 1.

[0120] The load information used to estimate the center of gravity position includes information about the load acting on at least one of the front wheel axle 12A, the rear wheel axle 14A, the handlebar 16, the saddle 18, the crank 22, and the pedal 28. Values ​​measured in advance using the load sensor S2 are used as this information.

[0121] The load information used to estimate the center of gravity position may include at least one of the rider's weight, height, posture, and gender. The rider's weight, height, and gender are set by an administrator or the like. The rider's posture is measured in advance using the posture sensor S3.

[0122] A value measured in advance is used for the center of gravity information indicating the center of gravity position. A known method is used to measure the center of gravity position. As an example, a method of measuring the center of gravity position of the human-powered vehicle 1 with a rider on board by image analysis will be described. When measuring the center of gravity position in the X-axis and Z-axis directions as described in the first embodiment, a captured image taken from the Y-axis direction is used. The position coordinates of each pixel in the captured image are defined as P ij Let the position coordinate P ij The mass of the infinitesimal part in m ij Then, the center of gravity is (m ij P ij ) / M, where M is the sum of the body weight of the human-powered vehicle 1 and the weight of the rider. ij The mass of the infinitesimal part in mij may be given by a table, or may be given according to which part of the human-powered vehicle 1 or the rider the fraction belongs to.

[0123] In this embodiment, center of gravity information is measured when the vehicle body information and load information of the human-powered vehicle 1 are variously changed. The server device 300 acquires the measured center of gravity information and the vehicle body information and load information used when measuring the center of gravity information, associates these with each other, stores them in the storage unit 304, and uses them as training data.

[0124] FIG. 12 is a flowchart for explaining the procedure for generating a learning model by the server device 300. In step S301, the control unit 302 of the server device 300 acquires training data for training the learning model from the storage unit 304. In the initial stage of generating the learning model, the training data may be set by, for example, an administrator of the server device 300. As the learning progresses, the training data may be set using the estimation result by the learning model. In the latter case, the input to the learning model and the label data obtained as the estimation result can be set as the training data.

[0125] In step S302, the control unit 302 inputs the vehicle body information and load information of the human-powered vehicle 1 to the learning model to be learned. Before learning begins, initial settings are given to the definition information describing the learning model. The learning model includes an input layer, an intermediate layer, and an output layer, similar to the learning model 120 described in the first embodiment. The vehicle body information and load information are given to the input layer of the learning model. The information given to the input layer is output to the adjacent intermediate layer nodes. In the intermediate layer, calculations are performed using activation functions including weights and biases between nodes, and the calculation results by the intermediate layer are output to each node in the output layer.

[0126] In step S303, the control unit 302 acquires the computation results of the learning model from each node in the output layer.

[0127] In step S304, the control unit 302 evaluates the calculation result obtained from the output layer of the learning model. In step S305, the control unit 302 judges whether the learning of the learning model is completed. Specifically, the control unit 302 can evaluate the calculation result using an error function based on the calculation result obtained in step S303 and the teacher data. The error function is also called an objective function, a loss function, or a cost function. In the process of optimizing the error function by a gradient descent method such as the steepest descent method, when the error function becomes equal to or smaller than a threshold value or equal to or larger than a threshold value, the control unit 302 judges that the learning of the learning model is completed. In order to avoid the problem of overfitting, the control unit 302 may adopt a method such as cross-validation or early termination to terminate the learning at an appropriate timing.

[0128] If it is determined in step S305 that the learning is not complete, the control unit 302 updates the weights and biases between nodes used in the intermediate layer in step S306, and returns the process to step S301. The control unit 302 can update the weights and biases between each node using an error backpropagation method that sequentially updates the weights and biases between nodes from the output layer to the input layer.

[0129] If it is determined in step S305 that the learning is completed, the control unit 302 stores the learning model in the storage unit 304 as a learned learning model in step S307, and ends the process according to this flowchart.

[0130] As described above, in the third embodiment, the server device 300 can be used to generate a learning model that uses the body information and load information of the human-powered vehicle 1, as well as center of gravity information indicating the center of gravity position of the human-powered vehicle 1, as teacher data, and outputs an estimation result regarding the center of gravity position in response to the input of the body information and load information.

[0131] The server device 300 may provide the trained learning model to the estimation device 100 as necessary. For example, when the estimation device 100 has a communication interface for communicating with the server device 300, the server device 300 may provide the trained learning model to the estimation device 100 by communication. The server device 300 may write the trained learning model to a storage medium M by a writing device (not shown) and provide the trained model written to the storage medium M to the estimation device 100. The trained learning model provided by communication or the storage medium M is stored in the storage unit 106 of the estimation device 100. The learning model stored in the storage unit 106 of the estimation device 100 functions as the learning model 120 described in the first embodiment and is configured to output an estimation result regarding the center of gravity position of the human-powered vehicle 1 in response to input of vehicle body information and load information of the human-powered vehicle 1.

[0132] The embodiments disclosed herein are illustrative in all respects and should not be considered as limiting. The scope of the present invention is defined by the claims, not by the above meaning, and is intended to include all modifications within the scope and meaning equivalent to the claims. [Explanation of symbols]

[0133] 1...human-powered vehicle, 30...alarm device, 32...lighting device, 34...brake device, 36...gearbox, 38...suspension device, 40...driving assistance device, 42...adjustable seat post, 100...estimation device, 102...input unit, 104...arithmetic processing unit, 106...storage unit, 108...output unit, 110...estimation processing program, 120...learning model, 200...control device, 202...control unit, 204...storage unit, 206...input unit, 208...output unit, 300...server device, 302...control unit, 304...storage unit, 306...input unit, 308...communication unit, S1...tilt sensor, S2...load sensor, S3...attitude sensor

Claims

1. estimating a center of gravity position of the human-powered vehicle with a rider on the basis of vehicle body information relating to a vehicle body of the human-powered vehicle and load information including a load acting on the human-powered vehicle; the vehicle body information includes information on at least one of a vehicle body dimension, a vehicle body weight, and an inclination angle of the human-powered vehicle; The load is a measurement value measured by a load sensor mounted on the human-powered vehicle.

2. An estimation device that estimates a center of gravity position using a learning model configured to output an estimation result regarding the center of gravity position of the human-powered vehicle with a rider on board, based on vehicle body information regarding the body of a human-powered vehicle and load information regarding a load acting on the human-powered vehicle, in response to input of the vehicle body information and the load information.

3. The estimation device according to claim 2 , wherein the vehicle body information includes information regarding a vehicle body dimension, a vehicle body weight, and an inclination angle of the human-powered vehicle.

4. The human-powered vehicle includes a front axle, a rear axle, a saddle, a handlebar, a crank, and pedals; The estimation device according to claim 1 or 2, wherein the load information includes information regarding a load acting on at least one of the front wheel axle, the rear wheel axle, the saddle, the handlebars, the cranks, and the pedals.

5. estimating a center of gravity position of the human-powered vehicle with a rider on the basis of vehicle body information relating to a vehicle body of the human-powered vehicle and load information including a load acting on the human-powered vehicle; the load is a measurement value measured by a load sensor mounted on the human-powered vehicle, The load information further includes at least one of the rider's weight, height, posture, and gender.

6. The estimation device according to any one of claims 1 to 5, a control device that controls components of the human-powered vehicle based on the center of gravity position; A control system comprising:

7. The control system of claim 6 , wherein the component includes an annunciator.

8. The control system according to claim 7 , wherein the control device controls the notification device to notify the position of the center of gravity.

9. The control system according to claim 7 , wherein the control device controls the notification device to notify information related to a recommended posture of the rider.

10. 10. The control system of claim 6, wherein the components include at least one of a lighting device, a braking device, a transmission device, a suspension device, a drive assist device, and an adjustable seat post.

11. an input layer to which vehicle body information relating to a vehicle body of a human-powered vehicle and load information relating to a load acting on the human-powered vehicle are input; an output layer that outputs an estimation result regarding the center of gravity position of the human-powered vehicle with a rider on board; an intermediate layer that uses the vehicle body information, the load information, and center of gravity information indicating the center of gravity position as teacher data to learn a relationship between the vehicle body information and the load information input to the input layer and the estimation result output from the output layer; A learning model, wherein a computer is configured to perform calculation processing in the intermediate layer to output the estimation result from the output layer in response to input of the vehicle body information and the load information to the input layer.

12. A method for generating a learning model using, as teacher data, vehicle body information regarding the body of a human-powered vehicle, load information regarding the load acting on the human-powered vehicle, and center of gravity information indicating the center of gravity position of the human-powered vehicle with a rider on board, and using a computer to generate a learning model that outputs an estimation result regarding the center of gravity position in response to input of the vehicle body information and the load information.

13. A computer program for causing a computer to execute a process of estimating the position of the center of gravity of a human-powered vehicle with a rider on board, based on vehicle body information including information regarding at least one of vehicle body dimensions, vehicle weight, and tilt angle for the body of the human-powered vehicle, and load information including measurement values ​​obtained by measuring the load acting on the human-powered vehicle using a load sensor mounted on the human-powered vehicle.

14. A storage medium on which the computer program according to claim 13 is stored.

Images