WO2023047961A1

WO2023047961A1 - Road surface condition detection system and road surface condition detection method

Info

Publication number: WO2023047961A1
Application number: PCT/JP2022/033607
Authority: WO
Inventors: 圭太金森; 仁吉澤
Original assignee: パナソニックＩｐマネジメント株式会社
Priority date: 2021-09-22
Filing date: 2022-09-07
Publication date: 2023-03-30

Abstract

A road surface condition detection system (20) comprises: a sensor (21) that acquires vehicle body travel information which is information related to travel of a vehicle body (10); an analysis unit (22) that acquires the vehicle body travel information from the sensor (21) and analyzes the acquired vehicle body travel information; and a control unit (24). The analysis unit (22) also estimates a condition of a traveling road surface related to the travel of the vehicle body (10) by analyzing the vehicle body travel information. The control unit (24) then controls the travel of the vehicle body (10) according to the estimated condition of the traveling road surface.

Description

ROAD CONDITION DETECTION SYSTEM AND ROAD CONDITION DETECTION METHOD

The present disclosure relates to a road surface condition detection system and a road surface condition detection method for estimating the condition of a road surface on which a vehicle body travels.

For example, Patent Literature 1 discloses a road surface condition estimation device that estimates the condition of a road surface by attaching an acceleration sensor to the tires of the vehicle and calculating the vibration level of the running vehicle from the output of the acceleration sensor. there is

JP 2007-55284 A

However, in the conventional road surface condition estimation device, it is necessary to attach acceleration sensors separately to the vehicle tires in order to estimate the condition of the road surface.

Therefore, an object of the present disclosure is to provide a road surface condition detection system and a road surface condition detection method capable of estimating the condition of the road surface on which the vehicle body travels using existing sensors.

In order to achieve the above object, a road surface condition detection system according to an aspect of the present disclosure includes a sensor that acquires vehicle body running information that is information related to running of a vehicle body; an analysis unit that analyzes vehicle body running information; and a control unit. and controlling the traveling of the vehicle body according to the estimated condition of the road surface.

Further, in a road surface state detection method according to an aspect of the present disclosure, a sensor acquires vehicle body running information, which is information related to running of a vehicle body, the vehicle body running information is acquired from the sensor, and the acquired vehicle body running information is analyzed. section analyzes the vehicle body running information, and the analysis section analyzes the vehicle body running information, thereby estimating the state of the road surface related to the running of the vehicle body, and according to the estimated state of the road surface, the running of the vehicle body is controlled by the control unit.

According to the road surface condition detection system and road surface condition detection method according to the present disclosure, it is possible to estimate the condition of the road surface on which the vehicle body travels using existing sensors.

FIG. 1 is a schematic diagram showing a road surface condition detection system according to an embodiment. FIG. 2 is a block diagram showing the road surface condition detection system according to the embodiment. FIG. 3 is a flow chart showing the processing operation of the road surface condition detection system according to the embodiment.

Below, embodiments of the present disclosure will be described in detail with reference to the drawings. It should be noted that each of the embodiments described below is a specific example of the present disclosure. Therefore, the numerical values, shapes, materials, components, arrangement and connection of components, steps, order of steps, etc. shown in the following embodiments are examples and are not intended to limit the present disclosure. Therefore, among the constituent elements in the following embodiments, constituent elements not described in independent claims will be described as optional constituent elements.

In addition, each figure is a schematic diagram and is not necessarily strictly illustrated. Therefore, for example, the scales and the like do not necessarily match in each drawing. Moreover, in each figure, the same code|symbol is attached|subjected about the substantially same structure, and the overlapping description is abbreviate|omitted or simplified.

A road surface condition detection system and a road surface condition detection method according to the present embodiment will be described below.

(Embodiment)
<Configuration>
Here, the configuration of the road surface state detection system 20 according to this embodiment will be described.

FIG. 1 is a schematic diagram showing a road surface condition detection system 20 according to an embodiment.

As shown in FIG. 1, the road surface condition detection system 20 can estimate the condition of the road surface on which the vehicle body 10 travels when the vehicle body 10 travels on the road. The road surface condition detection system 20 can store the estimated road surface condition in the storage device or transmit it to the external device 3 so that the storage device and/or the external device 3 store the estimated road surface condition. Here, the external device 3 is an operation unit having a display unit, a cycle computer, a personal computer, a smart phone, a tablet terminal, or the like.

Here, in the present embodiment, a case where the road surface condition detection system 20 is mounted on the vehicle body 10 is exemplified. Moreover, in this embodiment, the case where the vehicle body 10 is an electric bicycle is illustrated. The electric bicycle according to the present embodiment is a power-assisted bicycle in which a user's pedaling force is assisted by driving force from an electric motor. Also, the electric bicycle may be a bicycle in which the human-powered driving force for applying power to the wheels by pedaling force and the auxiliary driving force for applying power to the wheels by an electric motor are independent of each other, or may be a bicycle capable of autonomous travel only with the electric motor. In other words, the electric bicycle has an assist mode, a push-walking mode, and a self-propelled mode. The assist mode is a mode for assisting the forward movement of the electric bicycle based on the user's force on the pedals. The push-walking mode is a mode in which the forward movement of the electric bicycle is assisted based on the user's force pushing the electric bicycle forward when the user walks by pushing the electric bicycle. The self-propelled mode is a mode in which the forward movement of the electric bicycle is assisted when the user walks while supporting the electric bicycle.

It should be noted that the vehicle body 10 is not limited to an electric bicycle. The vehicle body 10 is a vehicle capable of traveling on a road surface by rotating its wheels, and may be, for example, an automobile, a motorcycle, a bicycle, or the like.

Here, when the vehicle body 10 is an electric bicycle, as shown in FIGS. , an operation unit, a manual switch, a battery 32, and the like. Here, FIG. 2 is a block diagram showing the road surface condition detection system 20 according to the embodiment.

In addition, the state of the running road surface includes a flat surface that is a flat road surface, an uneven surface that is a road surface with steps or depressions, a gravel road (a road surface with gravel such as sand and stones scattered on the road), and a frozen road surface. and a slope (sloping road surface). Here, slope is a general term for uphill and downhill.

In addition, the road surface condition detection system 20 can update the condition of the road surface on the map by reflecting flat surfaces, uneven surfaces, gravel roads, frozen road surfaces, and slopes on the map. can.

As shown in FIG. 2, the road surface condition detection system 20 includes a sensor 21, an analysis section 22, a notification section 23, and a control section 24.

[Sensor 21]
The sensor 21 acquires vehicle body running information. The sensor 21 outputs the acquired vehicle body running information to the analysis unit 22 . The vehicle body running information is information about running of the vehicle body 10 . Specifically, the vehicle body running information is information indicating the state of the vehicle body 10 when the vehicle body 10 is running. For example, the vehicle body traveling information is information indicating at least one of the acceleration of the vehicle body 10, vibration of the vehicle body 10, steering angle of the steering wheel, and human power driving force based on the force applied to the pedal.

Specifically, the sensor 21 includes, for example, at least one or more of an acceleration sensor, a vibration sensor, a steering angle sensor, an angular velocity sensor, a torque sensor, and the like. In the present embodiment, a case in which a plurality of sensors 21 are provided on vehicle body 10 is illustrated. The vehicle body 10 of the present embodiment is provided with two or more sensors 21 selected from an acceleration sensor, a vibration sensor, a steering angle sensor, an angular velocity sensor, a torque sensor, and the like. Therefore, the vehicle body 10 may be provided with all sensors 21 such as an acceleration sensor, a vibration sensor, a steering angle sensor, an angular velocity sensor, and a torque sensor.

When one of the sensors 21 includes an acceleration sensor, the acceleration sensor detects the acceleration of the vehicle body 10 while the vehicle is running, and acquires the detected acceleration as vehicle body running information. For example, the acceleration sensor detects acceleration based on vibrations generated in the vehicle body 10 due to road surface conditions while the vehicle body 10 is running. When the vehicle body 10 accelerates or decelerates, the acceleration sensor detects the acceleration, and outputs acceleration information indicating the detected acceleration to the analysis unit 22 as an example of vehicle travel information.

Note that the acceleration sensor may be a vibration sensor. In this case, sensor 21 may include a vibration sensor. The vibration sensor may detect the amount of vibration that occurs while the vehicle body 10 is running. In other words, the vibration sensor may detect the vibration generated in the vehicle body 10 while the vehicle body 10 is running, and the vibration sensor may detect the acceleration based on the detected vibration generated in the vehicle body 10 . Further, the vibration sensor may output vibration information, which is information indicating the detected vibration amount, to the analysis unit 22 as an example of the vehicle body running information.

Also, if one of the sensors 21 includes a steering angle sensor, the steering angle sensor detects the steering angle of the steering wheel of the vehicle body 10 and acquires the detected steering angle as vehicle body travel information. The steering angle sensor detects the angle (rudder angle) of the vehicle body 10 with respect to the traveling direction when the user operates the steering wheel, and the analysis unit 22 uses the steering angle information, which is information indicating the detected steering angle, as an example of the vehicle body traveling information. output to

Also, when one of the plurality of sensors 21 includes an angular velocity sensor, the angular velocity sensor detects the amount of change in the tilt of the vehicle body 10 (angular velocity) and acquires the detected angular velocity as vehicle body travel information. The angular velocity sensor detects an angular velocity of the vehicle body 10 generated while the vehicle body 10 is traveling, and outputs angular velocity information indicating the detected angular velocity to the analysis unit 22 as an example of vehicle traveling information.

Further, when one of the plurality of sensors 21 includes a torque sensor, the torque sensor detects the human power driving force based on the force applied to the pedal of the vehicle body 10 given by the state of the running road surface. The driving force is acquired as vehicle body running information. Specifically, the torque sensor detects torque, which is human power driving force generated by the rotation of the crankshaft. When the user applies a force to the pedal, the torque sensor detects torque based on the force applied to the pedal, and outputs torque information, which is information indicating the detected torque, to the analysis unit 22 as an example of vehicle body running information. .

Further, the sensor 21 is provided on the vehicle body 10, and is provided, for example, on at least one or more of the frame, saddle, steering wheel, crank, transmission, electric motor 31, etc. of the vehicle body 10.

[Analysis part 22]
The analysis unit 22 acquires vehicle body running information from the sensor 21 . That is, the analysis unit 22 acquires vehicle body running information, which is at least one of acceleration information, vibration information, steering angle information, angular velocity information, torque information, and the like, from the sensor 21 .

Also, the analysis unit 22 analyzes the acquired vehicle body running information. For example, the analysis unit 22 analyzes the acceleration indicated by the acceleration information, the vibration amount indicated by the vibration information, the amount of change in the steering angle indicated by the steering angle information, the angular velocity indicated by the angular velocity information, the torque indicated by the torque information, and the like. Analyze at least one information of

Also, the analysis unit 22 estimates the state of the road surface on which the vehicle body 10 travels by analyzing the vehicle body travel information. That is, by analyzing the vehicle body running information, the analysis unit 22 determines, based on the vehicle body running information, the state of the road surface on which the vehicle body 10 is running, such as a flat surface, an uneven surface, a gravel road, a frozen road surface, an uphill road, and a Estimate any of the downhills.

Here, in the present embodiment, an example of means for estimating the state of the traveling road surface related to traveling of the vehicle body 10 by analyzing the vehicle traveling information by the analysis unit 22 is shown.

Specifically, the analysis unit 22 estimates that the road surface is uneven based on the acceleration information. For example, when the vehicle body 10 runs on an uneven surface, the acceleration of the vehicle body 10 is considered to be lower than when the vehicle body 10 runs on a flat surface. Therefore, the analysis unit 22 estimates that the road surface is uneven when the acceleration indicated by the acceleration information is less than a predetermined threshold.

The analysis unit 22 can also estimate that the road surface is uneven based on the vibration information. For example, when the vehicle body 10 runs on an uneven surface, it is considered that the amount of vibration of the vehicle body 10 increases compared to when the vehicle body 10 runs on a flat surface. Therefore, if the amount of vibration indicated by the vibration information is equal to or greater than a predetermined threshold value, the analysis unit 22 estimates that uneven surfaces such as steps and depressions are formed on the road surface.

Also, based on the torque information, the analysis unit 22 estimates that the road surface is a frozen road surface when the vehicle body 10 travels on a slope. For example, when the vehicle body 10 travels on a frozen road surface, the wheels of the vehicle body 10 slip on the frozen road surface, so that the grip force of the wheels decreases compared to the case of traveling on an uneven surface, and it is considered that acceleration is difficult to increase. . That is, when the vehicle body 10 travels on the frozen road surface, it is considered that the torque indicated by the torque information becomes less than the threshold. On the other hand, when the vehicle body 10 runs on an uneven surface, it is considered that the torque indicated by the torque information becomes equal to or greater than the threshold. Therefore, the analysis unit 22 can estimate whether the road surface is a frozen road surface or an uneven surface from the torque indicated by the torque information.

Also, the analysis unit 22 may estimate that the road surface is a frozen road surface based on the steering angle information. For example, if the surface of a road is icy, the user is expected to bypass only the icy surface. Therefore, the analysis unit 22 estimates that the user has unexpectedly detoured the travel route from the amount of change in the steering angle indicated by the steering angle information, and estimates that the road surface is a frozen road surface. good too.

Also, the analysis unit 22 estimates that the road is a slope based on the angular velocity information. For example, when the vehicle body 10 travels on a slope, the angular velocity of the vehicle body 10 is considered to be greater than when the vehicle body 10 travels on a flat surface. That is, the analysis unit 22 estimates that the road is a slope when the integral value of the angular velocities indicated by the angular velocity information is equal to or greater than a predetermined threshold. When the integrated value is equal to or greater than the predetermined threshold, the analysis unit 22 determines that the slope is steeper as the integrated value is greater than the predetermined threshold, and the slope is gentler as the integrated value is closer to the predetermined threshold. It may be assumed that

Also, the analysis unit 22 estimates that the vehicle is traveling uphill or downhill as the condition of the road surface. For example, the analysis unit 22 can estimate whether the vehicle body 10 is traveling uphill or downhill based on the angular velocity information and the acceleration information. For example, when the vehicle body 10 climbs a slope, it is conceivable that the integral value of the angular velocity indicated by the angular velocity information becomes equal to or greater than the threshold and the acceleration indicated by the acceleration information becomes less than the threshold. Also, when the vehicle body 10 descends a slope, it is considered that the integral value of the angular velocity indicated by the angular velocity information becomes equal to or greater than the threshold and the acceleration indicated by the acceleration information becomes equal to or greater than the threshold. Therefore, the analysis unit 22 estimates whether the slope on which the vehicle body 10 is traveling is uphill or downhill from the integral value of the angular velocity indicated by the angular velocity information and the acceleration indicated by the acceleration information.

The analysis unit 22 can also estimate whether the vehicle body 10 is traveling uphill or downhill based on the torque information and the acceleration information. For example, when the vehicle body 10 climbs a slope, it is conceivable that the torque (pedal force) indicated by the torque information becomes equal to or greater than the threshold and the acceleration indicated by the acceleration information becomes less than the threshold. Also, when the vehicle body 10 descends a slope, it is considered that the torque indicated by the torque information is less than the threshold and the acceleration indicated by the acceleration information is equal to or greater than the threshold. Therefore, the analysis unit 22 can also estimate whether the slope is uphill or downhill from the torque indicated by the torque information and the acceleration indicated by the acceleration information.

Also, the analysis unit 22 estimates that the road is a gravel road based on the torque information and the vibration information. For example, when the vehicle body 10 travels on a gravel road, the wheels of the vehicle body 10 slip due to the gravel, so it is considered that the grip force of the wheels decreases compared to when the vehicle travels on an uneven surface. That is, when the vehicle body 10 travels on a gravel road, it is considered that the torque indicated by the torque information becomes equal to or greater than the threshold, and the amount of vibration indicated by the vibration information becomes equal to or greater than the threshold. On the other hand, when the vehicle body 10 runs on an uneven surface, it is considered that the torque indicated by the torque information becomes equal to or greater than the threshold, and the amount of vibration indicated by the vibration information becomes equal to or greater than the threshold. Therefore, the analysis unit 22 can estimate whether the road is a gravel road or an uneven surface from the torque indicated by the torque information and the vibration indicated by the vibration information.

Although the means by which the analysis unit 22 estimates the state of the road surface on which the vehicle is traveling using the vehicle body traveling information has been illustrated, this is merely an example and is not limited to the present embodiment.

Also, the analysis unit 22 can use at least one of a rule base and machine learning to analyze vehicle travel information and estimate the state of the travel road surface. Also, the rule base and the learning model constructed by machine learning are stored in a storage unit or the like mounted on the vehicle body 10 .

In addition, the analysis unit 22 outputs to the notification unit 23 the condition of the road surface, which is the result of the estimation as described above.

[Notification unit 23]
The notification unit 23 notifies the external device 3 of traveling road surface condition information, which is information indicating the condition of the traveling road surface estimated by the analysis unit 22 . The notification unit 23 is a communication module capable of wireless communication or wired communication with the external device 3 . In addition, the notification unit 23 may notify the external device 3 of the vehicle body running information detected by the sensor 21 .

[Control unit 24]
The control unit 24 controls traveling of the vehicle body 10 according to the estimated condition of the road surface. Specifically, the control unit 24 controls the electric motor 31 according to the state of the road surface estimated by the analysis unit 22 . More specifically, when the vehicle body 10 is an electric bicycle, the controller 24 controls the electric motor 31 to allow the vehicle body 10 to travel in any of the assist mode, push-walking mode, and self-propelled mode. to control the driving force that assists the

The electric motor 31 is controlled by the control unit 24 to provide the chain of the vehicle body 10 with driving force (auxiliary driving force) that assists the running of the vehicle body 10 . Further, the electric motor 31 is driven by power supplied from a battery 32 mounted on the vehicle body 10 under the control of the control unit 24 .

For example, when it is estimated that the road surface is flat, the control unit 24 controls the electric motor 31 so that the driving force that assists the traveling of the vehicle body 10 becomes normal. In this case, the control unit 24 controls the driving force with which the electric motor 31 assists the running of the vehicle body 10 according to the user's pedaling force. This allows the user to drive the vehicle body 10 comfortably.

Further, when the state of the road surface is estimated to be a downhill or a frozen road surface, the control unit 24 does not apply the driving force to assist the traveling of the vehicle body 10, or when the vehicle body 10 travels on a flat surface. The electric motor 31 is controlled so as to reduce the driving force that assists the running. As a result, overturning or rapid acceleration of the vehicle body 10 can be suppressed, so that the safety of the user can be ensured.

Further, when the state of the road surface is estimated to be a gravel road, the control unit 24 controls the electric motor 31 so as to reduce the driving force that assists the traveling compared to when the vehicle body 10 travels on a flat surface. do. As a result, overturning or rapid acceleration of the vehicle body 10 can be suppressed, so that the safety of the user can be ensured.

Further, when the state of the road surface is estimated to be an uphill or an uneven surface, the control unit 24 increases the driving force for assisting the traveling of the vehicle body 10 compared to when the vehicle body 10 travels on a flat surface. to control the electric motor 31. As a result, the user can comfortably drive the vehicle body 10 by suppressing the vehicle body 10 from decelerating.

<Processing operation>
Here, the processing operations of the road surface condition detection system 20 and the road surface condition detection method according to the present embodiment will be described using FIG. 3 and the like.

FIG. 3 is a flow chart showing the processing operation of the road surface condition detection system 20 according to the embodiment.

First, as shown in FIGS. 2 and 3, when the vehicle body 10 travels on the road surface, the plurality of sensors 21 acquire vehicle body travel information from the vehicle body 10 (S11). The multiple sensors 21 include, for example, acceleration sensors, vibration sensors, steering angle sensors, angular velocity sensors, torque sensors, and the like. In this embodiment, the vehicle body 10 is provided with a plurality of sensors 21 . Therefore, in the present embodiment, the plurality of sensors 21 can acquire vehicle body running information including at least two pieces of information among acceleration information, vibration information, steering angle information, angular velocity information, torque information, and the like. The multiple sensors 21 output the acquired vehicle body running information to the analysis unit 22 .

Next, the analysis unit 22 analyzes the vehicle body travel information acquired from the sensor 21 (S12).

Next, the analysis unit 22 estimates the state of the road surface on which the vehicle body 10 travels based on the analysis result of the vehicle body travel information (S13).

Specifically, based on the analysis result of the acceleration information, the analysis unit 22 estimates that the road surface is uneven when the acceleration indicated by the acceleration information is less than a predetermined threshold. Further, by analyzing the steering angle information, the analysis unit 22 estimates that the road surface is a frozen road surface from the amount of change in the steering angle indicated by the steering angle information. Further, the analysis unit 22 analyzes the angular velocity information, and estimates that the road is a slope when the integral value of the angular velocity indicated by the angular velocity information is equal to or greater than a predetermined threshold. Further, the analysis unit 22 analyzes the angular velocity information and the acceleration information, and when the integrated value is equal to or greater than the threshold and the acceleration is less than the threshold, it is estimated that the vehicle body 10 is climbing a slope. Further, the analysis unit 22 analyzes the angular velocity information and the acceleration information, and estimates that the vehicle body 10 is descending a slope when the integrated value is equal to or greater than the threshold and the acceleration is equal to or greater than the threshold. Further, the analysis unit 22 analyzes the torque information and the acceleration information, and when the torque (pedal force) indicated by the torque information is equal to or greater than the threshold and the acceleration is less than the threshold, the vehicle body 10 is running up the slope. We assume that Further, by analyzing the torque information and the acceleration information, the analysis unit 22 estimates that the vehicle body 10 is descending a slope when the torque is less than the threshold and the acceleration is greater than or equal to the threshold. Moreover, the analysis unit 22 analyzes the torque information and the vibration information, and estimates that the road is a gravel road when the torque is equal to or greater than the threshold and the amount of vibration is equal to or greater than the threshold.

Also, the analysis unit 22 can use at least one of a rule base and machine learning to analyze vehicle travel information and estimate the state of the travel road surface.

For example, the analysis unit 22 can estimate the state of the vehicle body 10 by analyzing vehicle travel information based on preset conditions by using a rule base. Here, the preset condition is a set condition such as a threshold value. When using a rule base, a rule base indicating the state of the road surface derived from at least one or more information (vehicle running information) among acceleration information, vibration information, steering angle information, angular velocity information, torque information, etc. must be created in advance. By using this rule base, the analysis unit 22 determines whether the road surface is flat or uneven, based on at least one of acceleration information, vibration information, steering angle information, angular velocity information, torque information, and the like. It can be estimated that the vehicle is in at least one of a surface, gravel road, frozen road, uphill, and downhill.

For example, the analysis unit 22 can analyze the vehicle travel information and estimate the state of the vehicle body 10 by using machine learning and deep learning included in the machine learning. When machine learning is used, the condition of the road surface is estimated from at least one information (vehicle traveling information) among acceleration information, vibration information, steering angle information, angular velocity information, torque information, etc. using teacher data. It is necessary to create in advance a learning model that has learned the Here, the training data includes acceleration information, vibration information, steering angle information, angular velocity information and This is vehicle body running information including torque information and the like. By using this machine learning, the analysis unit 22 uses at least one or more information among acceleration information, vibration information, rudder angle information, angular velocity information, torque information, etc. to determine whether the state of the traveling road surface is a flat surface, an uneven surface, an uneven surface, It can be estimated that one or more of gravel road, icy road, uphill and downhill conditions.

Thus, when estimating the state of the road surface on the basis of the rule base and the learning model of machine learning, even if the sensor 21 for actually measuring the state of the road surface is not mounted on the vehicle body 10, the analysis unit 22 , it is possible to estimate from the vehicle travel information that the state of the travel road surface is one or more of a flat surface, an uneven surface, a gravel road, a frozen road surface, an uphill slope, and a downhill slope.

It should be noted that the external device 3 may update the rule base and machine-learned learning model by acquiring and learning the vehicle body traveling information from other vehicle bodies 10 . In this case, the external device 3 may update the rule base and machine-learned learning model of the vehicle body 10 by transmitting the updated rule base and machine-learned learning model to the vehicle body 10 .

Then, the analysis unit 22 outputs the state of the road surface, which is the estimated result, to the notification unit 23 .

Next, the notification unit 23 notifies the external device 3 of the state of the road surface estimated by the analysis unit 22 (S14). As a result, the external device 3 can collect the conditions of the road surface on which the vehicle body 10 has traveled. For example, the external device 3 can update the state of the road surface on the map by reflecting the state of the road surface on which the vehicle body 10 has traveled on the map.

Next, the control unit 24 controls the driving force that assists the traveling of the vehicle body 10 by controlling the electric motor 31 according to the state of the traveling road surface estimated by the analysis unit 22 (S15). For example, when the vehicle body 10 is an electric bicycle, the control unit 24 controls the electric motor 31 in any of the assist mode, the push-walking mode, and the self-propelled mode to assist the traveling of the vehicle body 10. control power.

Then, the road surface condition detection system 20 ends the processing operation of FIG.

In this way, by notifying the external device 3 of the state of the road surface estimated by the analysis unit 22, the external device 3 can grasp the state of the road surface on which the vehicle body 10 has traveled. For example, if a road surface that is originally a flat surface is estimated to be an uneven surface, there is a possibility that the road surface is cracked or a fallen object is left unattended. If the user can know in advance such a problem spot on the road surface, the user can change the travel route and take a detour. In addition, if the road administrator is notified of the occurrence of such a problem on the road surface, the road administrator can immediately take action to resolve the problem that has occurred on the road surface.

<Effect>
Next, the effects of the road surface condition detection system 20 and the road surface condition detection method according to the present embodiment will be described.

As described above, the road surface condition detection system 20 according to the present embodiment includes the sensor 21 that acquires the vehicle body running information that is the information regarding the running of the vehicle body 10, the vehicle body running information that is acquired from the sensor 21, and the acquired vehicle body running. An analysis unit 22 for analyzing information and a control unit 24 are provided. Further, the analysis unit 22 estimates the state of the road surface on which the vehicle body 10 travels by analyzing the vehicle body travel information. Then, the control unit 24 controls traveling of the vehicle body 10 according to the estimated condition of the road surface.

Normally, the sensors that acquire the vehicle body running information are pre-installed in the vehicle body. Therefore, in the road surface condition detection system 20 of the present embodiment, existing sensors provided on the vehicle body 10 can be used without mounting a new sensor for detecting the condition of the road surface on the vehicle body 10. , the condition of the road surface on which the vehicle body 10 has traveled can be estimated. As a result, in the road surface condition detection system 20 of the present embodiment, it is not necessary to mount a new sensor for detecting the condition of the road surface on the vehicle body 10, thereby suppressing an increase in the manufacturing cost of the vehicle body 10. be able to.

In addition, since the running of the vehicle body 10 can be controlled according to the condition of the road surface, the user can drive the vehicle body 10 stably. Therefore, the user can drive the vehicle body 10 comfortably.

Further, in the road surface state detection method according to the present embodiment, the sensor 21 acquires vehicle body running information, which is information related to running of the vehicle body 10, the vehicle body running information is acquired from the sensor 21, and the acquired vehicle body running information is sent to the analysis unit. 22 analyzes and the analysis unit 22 analyzes the vehicle body traveling information to estimate the road surface condition related to the traveling of the vehicle body 10, and control the traveling of the vehicle body 10 according to the estimated traveling road surface condition. It includes that the unit 24 controls.

This road surface condition detection method also has the same effects as those described above.

In the road surface condition detection system 20 according to the present embodiment, the analysis unit 22 detects at least one of a flat surface, an uneven surface, a gravel road, and an icy road surface as the condition of the road surface on which the vehicle body 10 is traveling. It is estimated that the state of

According to this, it is possible to estimate that the road surface on which the vehicle is traveling is in at least one of a flat surface, an uneven surface, a gravel road, and an icy road surface, based on the vehicle body traveling information. Therefore, for example, the vehicle body 10 can control the driving force of the electric motor 31 mounted on the vehicle body 10 according to flat surfaces, uneven surfaces, gravel roads, and frozen road surfaces. Also, the user, the road administrator, etc. can grasp the condition of the road surface on which they are traveling.

In addition, in the road surface state detection system 20 according to the present embodiment, the analysis unit 22 estimates that the road surface on which the vehicle body 10 is running is running uphill or downhill.

According to this, it is possible to estimate whether the road surface is uphill or downhill based on the vehicle travel information. Therefore, for example, the vehicle body 10 can control the driving force of the electric motor 31 mounted on the vehicle body 10 depending on whether the vehicle is traveling uphill or downhill.

Further, the road surface condition detection system 20 according to the present embodiment further includes a notification unit 23 that notifies the external device 3 of traveling road surface condition information, which is information indicating the condition of the traveling road surface estimated by the analysis unit 22 .

According to this, the external device 3 can acquire the traveling road surface condition information, and therefore can grasp the condition of the traveling road surface. For example, the external device 3 can notify the user and the road administrator of the condition of the road surface that hinders the traveling of the vehicle body 10 .

Further, in the road surface condition detection system 20 according to the present embodiment, the sensor 21 detects the human power driving force based on the force applied to the pedal given by the state of the road surface on which the vehicle travels. Get it as information.

Normally, a sensor for detecting the human-powered driving force is pre-installed on the vehicle body 10, so if the human-powered driving force is detected using an existing sensor, the condition of the road surface can be determined based on the detected human-powered driving force. can be estimated. Therefore, in the road surface condition detection system 20 of the present embodiment, it is not necessary to mount a new sensor for detecting the condition of the road surface on the vehicle body 10, thereby suppressing an increase in the manufacturing cost of the vehicle body 10. be able to.

Further, in the road surface condition detection system 20 according to the present embodiment, the sensor 21 detects acceleration based on vibration generated in the vehicle body 10 traveling due to the condition of the road surface, and acquires the detected acceleration as vehicle body traveling information. do.

Normally, the sensor 21 for detecting the acceleration of the vehicle body 10 is mounted on the vehicle body 10, so if the acceleration of the vehicle body 10 is detected using an existing sensor, the traveling road surface can be detected based on the detected acceleration of the vehicle body 10. state can be estimated. Therefore, in the road surface condition detection system 20 of the present embodiment, it is not necessary to mount a new sensor for detecting the condition of the road surface on the vehicle body 10, thereby suppressing an increase in the manufacturing cost of the vehicle body 10. be able to.

Further, in the road surface condition detection system 20 according to the present embodiment, the analysis unit 22 uses at least one of a rule base and machine learning to analyze the vehicle traveling information and estimate the condition of the traveling road surface. do.

According to this, the condition of the road surface can be accurately estimated by using rule base and machine learning.

(Other modifications, etc.)
Although the present disclosure has been described above based on the embodiments, the present disclosure is not limited to these embodiments and the like.

For example, in the road surface condition detection system and road surface condition detection method according to the above embodiments, the sensor may include a speed sensor. In this case, the speed sensor detects the traveling speed of the vehicle body by detecting, for example, the number of revolutions of the wheels. The speed sensor may detect the traveling speed of the vehicle body when the vehicle body moves forward based on the user's pedaling force, and output speed information indicating the detected speed to the control unit. Thereby, the control unit may control the driving force with which the electric motor assists the running of the vehicle body based on the speed indicated by the speed information.

In addition, in the road surface condition detection system and road surface condition detection method according to the above embodiment, the control unit may control the brake according to the condition of the road surface estimated by the analysis unit. Specifically, when the vehicle body is an electric bicycle, the control unit may control the running of the vehicle body by controlling the brakes in any of the assist mode, push-walking mode, and self-propelled mode. . That is, the road surface condition detection system may have an antilock braking system. Antilock braking systems may allow the controller to determine whether the wheels are locked from rotation during braking. Specifically, when the sensor detects the rotation speed of the wheel, the control unit may determine that a strong brake is applied based on the rotation speed of the wheel detected by the sensor. In this case, the control unit may perform control to weaken the brake that has been applied with a strong brake. The control unit may perform such control individually for each wheel. As a result, it is possible to prevent the gripping force of the wheels from deteriorating on a frozen road surface, etc., so that slipping of the wheels can be suppressed. Further, the vehicle body may have a pressure valve, a hydraulic pump, and the like, and the control section may be capable of controlling the pressure valve, the hydraulic pump, and the like.

Further, in the road surface condition detection system and road surface condition detection method according to the above embodiments, the sensor may include a crank rotation sensor. In this case, the crank rotation sensor detects the rotation speed and rotation angle of the crank of the vehicle body. The crank rotation sensor detects the number of rotations and/or the rotation angle of the crank when the crankshaft rotates, and outputs crank information indicating the detected number of rotations and/or the rotation angle of the crank to the control unit. good too. Thereby, the control unit may control the driving force with which the electric motor assists the running of the vehicle body based on the number of rotations and/or the rotation angle of the crank indicated by the crank information.

Further, in the road surface condition detection system and road surface condition detection method according to the above embodiments, the sensor may include a motor torque sensor. In this case, the motor torque sensor detects the motor torque of the electric motor of the vehicle body. The motor torque sensor may detect the motor torque of the electric motor and output motor torque information indicating the detected motor torque to the control unit. Thereby, the control unit may control the driving force with which the electric motor assists the running of the vehicle body based on the motor torque indicated by the motor torque information.

In addition, in the road surface condition detection system and road surface condition detection method according to the above embodiment, one sensor may detect the amount of vibration generated in the vehicle body due to the condition of the road surface and the acceleration based on the amount of vibration. According to this, the sensor can detect the vibration generated in the vehicle body, and can detect the acceleration based on the vibration generated in the vehicle body. That is, the sensor can achieve both detection of vibration and detection of acceleration. Further, the sensor can detect the road surface condition by detecting vibration and acceleration. Therefore, in the road surface condition detection system of the present embodiment, there is no need to install a new sensor for detecting the condition of the road surface on the vehicle body, so it is possible to suppress an increase in the manufacturing cost of the vehicle body. .

Further, in the road surface condition detection system and road surface condition detection method according to the above-described embodiments, when the external device acquires the result of estimating the condition of the road surface on which the vehicle is traveling, the external device estimates the condition of the road surface on a vehicle-by-vehicle basis. The results obtained may be stored as history information. In this case, the external device can estimate that an abnormality has occurred or may occur in the vehicle body if the vehicle body has traveled over a predetermined distance on uneven surfaces and gravel roads. As a result, the user and the servicer who lends the vehicle to the user can take measures such as replacing or repairing the vehicle.

In addition, the analysis unit, control unit, external devices, and the like used in the road surface condition detection system and road surface condition detection method according to each of the above embodiments are typically implemented as LSIs, which are integrated circuits. These may be made into one chip individually, or may be made into one chip so as to include part or all of them.

In addition, circuit integration is not limited to LSIs, and may be realized with dedicated circuits or general-purpose processors. An FPGA (Field Programmable Gate Array) that can be programmed after the LSI is manufactured, or a reconfigurable processor that can reconfigure the connections and settings of the circuit cells inside the LSI may be used.

It should be noted that in each of the above embodiments, each component may be implemented by dedicated hardware or by executing a software program suitable for each component. Each component may be realized by reading and executing a software program recorded in a recording medium such as a hard disk or a semiconductor memory by a program execution unit such as a CPU or processor.

In addition, the numbers used above are all examples for specifically describing the present disclosure, and the embodiments of the present disclosure are not limited to the illustrated numbers.

Also, the division of functional blocks in the block diagram is an example, and a plurality of functional blocks can be realized as one functional block, one functional block can be divided into a plurality of functional blocks, and some functions can be moved to other functional blocks. may Moreover, single hardware or software may process the functions of a plurality of functional blocks having similar functions in parallel or in a time-sharing manner.

Also, the order in which each step in the flowchart is executed is for illustrative purposes in order to specifically describe the present disclosure, and orders other than the above may be used. Also, some of the above steps may be executed concurrently (in parallel) with other steps.

In addition, a form obtained by applying various modifications that a person skilled in the art can think of to the embodiment, and a form realized by arbitrarily combining the constituent elements and functions in the embodiment within the scope of the present disclosure. Included in disclosure.

3 external device 10 vehicle body 20 road surface condition detection system 21 sensor 22 analysis unit 23 notification unit 24 control unit

Claims

a sensor for acquiring vehicle body running information, which is information relating to running of the vehicle body;
an analysis unit that acquires the vehicle body running information from the sensor and analyzes the acquired vehicle body running information;
and a control unit,
The analysis unit analyzes the vehicle body running information to estimate a road surface condition related to running of the vehicle body,
The road surface condition detection system, wherein the control unit controls traveling of the vehicle body according to the estimated condition of the road surface.
2. The analysis unit according to claim 1, wherein the state of the road surface on which the vehicle body is traveling is estimated to be at least one of a flat surface, an uneven surface, a gravel road, and an icy road surface. Road condition detection system.
The road surface condition detection system according to claim 1 or 2, wherein the analysis unit estimates that the road surface on which the vehicle body is traveling is traveling uphill or downhill.
The road surface condition detection system according to any one of claims 1 to 3, further comprising a notification unit that notifies an external device of road surface condition information indicating the condition of the road surface estimated by the analysis unit.
5. Any one of claims 1 to 4, wherein the sensor acquires the detected human driving force as the vehicle body traveling information by detecting the human driving force based on the pedaling force applied to the pedal according to the state of the running road surface. The road surface condition detection system according to the paragraph.
6. The sensor according to any one of claims 1 to 5, wherein the sensor acquires the detected acceleration as the vehicle body traveling information by detecting acceleration based on vibrations generated in the vehicle body traveling due to the state of the road surface. road condition detection system.
7. The analysis unit according to any one of claims 1 to 6, using at least one of a rule base and machine learning to analyze the vehicle body running information and estimate the condition of the road surface. road condition detection system.
The sensor acquires vehicle body running information, which is information related to the running of the vehicle body,
Acquiring the vehicle body running information from the sensor, analyzing the acquired vehicle body running information by an analysis unit,
The analysis unit analyzes the vehicle body traveling information to estimate the state of the road surface on which the vehicle travels,
A road surface state detection method, comprising: controlling travel of the vehicle body by a control unit according to the estimated state of the road surface.