JP7609682B2 - Wear amount estimation system, computation model generation system, and wear amount estimation method - Google Patents

Description

The present invention relates to a wear amount estimation system, a calculation model generation system, and a wear amount estimation method for estimating the amount of wear of tires mounted on a vehicle.

In general, tires wear down depending on the driving conditions and distance traveled. Recently, devices have been commercialized that attach sensors to tires to measure tire pressure and temperature and display the measured pressure and temperature.

Patent Document 1 describes a conventional tire maintenance support system that estimates tire wear. This tire maintenance support system includes a wear progress acquisition unit that acquires the wear progress of the tread and side at each mounting position, a wear amount calculation unit that calculates a predicted wear amount, which is the amount of wear of the tread and side at each mounting position until the vehicle maintenance time based on the acquired wear progress, and a rotation plan selection unit that selects at least one rotation plan from among a plurality of rotation plans for swapping tires from a specified mounting position to another mounting position based on the calculated predicted wear amount.

JP 2018-156125 A

In the tire maintenance support system described in Patent Document 1, the predicted amount of tire wear is calculated based on the degree of wear of the tread and side at each mounting position. The inventor realized that there is room for improvement in tire wear estimation for vehicles that transport passengers and other cargo, such as route buses, because the amount of wear of each tire varies depending on the position of the cargo. In addition, the amount of tire wear varies depending on factors such as the distance of each section separated by stops such as bus stops, and the acceleration and deceleration of the vehicle at stops, so the inventor realized that tire wear estimation could be improved by taking these factors into account.

The present invention has been made in consideration of the above circumstances, and its purpose is to provide a wear amount estimation system, a calculation model generation system, and a wear amount estimation method that can improve the accuracy of estimating the amount of tire wear.

The wear amount estimation system of one embodiment of the present invention includes a vehicle information acquisition unit that acquires data detecting cargo loaded inside a vehicle for transportation, a recognition unit that recognizes the position of the cargo from the data acquired by the vehicle information acquisition unit, a distribution calculation unit that calculates the distribution of the weight of the cargo in each section separated by stops based on the position of the cargo recognized by the recognition unit, and a wear amount calculation unit that has a calculation model that calculates the amount of tire wear based on input information and inputs the distribution calculated by the distribution calculation unit into the calculation model to calculate the amount of wear of the tire.

Another aspect of the present invention is a computational model generation system. The computational model generation system includes a vehicle information acquisition unit that acquires data detecting cargo loaded inside a vehicle for transportation, a recognition unit that recognizes the position of the cargo from the data acquired by the vehicle information acquisition unit, a distribution calculation unit that calculates the distribution of the weight of the cargo in each section separated by stops based on the position of the cargo recognized by the recognition unit, a wear amount calculation unit that has a computational model that calculates the amount of wear of tires based on input information and inputs the distribution calculated by the distribution calculation unit to the computational model to calculate the amount of wear of the tires, and a learning processing unit that learns the computational model by comparing the amount of wear measured on the tires with the amount of wear calculated by the wear amount calculation unit.

Another aspect of the present invention is a wear amount estimation method. The wear amount estimation method includes a vehicle information acquisition step of acquiring data detecting a load loaded inside a vehicle for transportation, a recognition step of recognizing the position of the load from the data acquired by the vehicle information acquisition step, a distribution calculation step of calculating the distribution of the load weight in each section separated by stops based on the position of the load recognized by the recognition step, and a wear amount calculation step having a calculation model for calculating the amount of tire wear based on input information, inputting the distribution calculated by the distribution calculation step into the calculation model to calculate the amount of wear of the tire.

The present invention can improve the accuracy of estimating tire wear.

1 is a block diagram showing a functional configuration of a wear amount estimation system according to an embodiment; FIG. 2 is a block diagram showing a functional configuration of the vehicle-mounted measuring device. FIG. 2 is a plan view of the interior of the vehicle as viewed from above. 4 is a table showing an example of tire severity information generated by a tire severity generating unit. FIG. 1 is a schematic diagram showing an example of a route on which a vehicle travels. 10 is a schematic diagram for explaining wear amount estimation and learning of a computation model. FIG. FIG. 2 is a block diagram showing a functional configuration of the computation model generation system. 1 is a flowchart showing a procedure for generating a computation model by the computation model generation system.

The present invention will be described below based on a preferred embodiment with reference to Figures 1 to 8. The same or equivalent components and parts shown in each drawing are given the same reference numerals, and duplicated explanations will be omitted as appropriate. Furthermore, the dimensions of the parts in each drawing are enlarged or reduced as appropriate to facilitate understanding. Furthermore, some of the parts that are not important for explaining the embodiment will be omitted in each drawing.

(Embodiment)
1 is a block diagram showing a functional configuration of a wear amount estimation system 100 according to an embodiment. The wear amount estimation system 100 includes an on-board measurement device 70 mounted on a vehicle 6, a weather information server device 80, and a wear amount estimation device 10 that estimates the amount of wear of each tire 7 mounted on the vehicle 6.

The wear amount estimation system 100 estimates the wear amount of each tire 7 mounted on a vehicle 6 that travels along a set route to transport passengers and other cargo. The vehicle 6 is, for example, a route bus, which includes a starting stop and a terminal stop as stops, and transports passengers by boarding and disembarking at each stop between the starting stop and the terminal stop. The starting stop and the terminal stop may be the same or different. The vehicle 6 is also a transport truck with a preset route, which loads and unloads cargo such as luggage at stops including the starting point, the arrival point, and intermediate stops along the way. The following description will be given using a route bus as an example of the vehicle 6, but the same applies when the vehicle 6 is a transport truck, and the stops of the route bus are replaced with the stops of the transport truck, and the passengers loaded onto the vehicle 6 are replaced with luggage loaded onto the transport truck. In the present invention, the stops of the vehicle 6 are the departure point, arrival point, and loading and unloading stops in the case of a transport truck, and each bus stop in the case of a route bus.

The route is made up of sections connected together, each separated by a bus stop. The wear amount estimation system 100 estimates the wear amount of each tire 7 using information such as the known travel distance, number of turns, direction of turns, and elevation difference in each section, as well as information such as the acceleration of the vehicle 6 measured while traveling and the distribution of passenger weight, as described below. Note that the route is not limited to a predetermined one, and when a service plan is created that includes a route from a departure point to a destination, information such as the travel distance and number of turns for that route may be obtained from an external computer-assisted map routing service.

The wear amount estimation device 10 acquires vehicle measurement information such as the speed, acceleration, and position information of the vehicle 6 from an on-board measurement device 70 mounted on the vehicle 6 via a communication network 9 such as the Internet, as well as tire measurement information measured on the tires 7. The wear amount estimation device 10 also acquires weather information from a weather information server device 80. The wear amount estimation device 10 estimates the wear amount of each tire 7 by performing calculations using a learning-type calculation model based on the acquired information.

Figure 2 is a block diagram showing the functional configuration of the on-board measurement device 70. The on-board measurement device 70 comprises a vehicle measurement unit 71, a tire measurement unit 72, an in-vehicle image capture unit 73, an information acquisition unit 74, and a communication unit 75. Each unit in the on-board measurement device 70 can be realized in hardware terms by electronic elements and mechanical parts such as a computer CPU, and in software terms by a computer program, but here, functional blocks realized by the cooperation of these are depicted. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various forms by combining hardware and software.

The vehicle measurement unit 71 has a speedometer 71a and an acceleration sensor 71b mounted on the vehicle 6. The speedometer 71a measures the speed of the vehicle. The acceleration sensor 71b measures the acceleration of the vehicle 6 in three axial directions. The vehicle measurement unit 71 may also have a device that measures position information of the vehicle 6.

The tire measurement unit 72 has a temperature sensor 72a and a pressure sensor 72b. The temperature sensor 72a and the pressure sensor 72b are disposed on the air valve of the tire 7 mounted on the vehicle 6, or are firmly wrapped around the wheel with a belt or the like and fixed thereto, and measure the temperature and air pressure of the tire 7. The temperature sensor 72a may be disposed on the inner liner of the tire 7, or the like. The tire measurement unit 72 may also include an acceleration sensor in its configuration to obtain more detailed tire information.

The interior imaging unit 73 is a camera device having a detector such as a CCD, and captures images of the interior of the vehicle to generate image data of the interior of the vehicle. The interior imaging unit 73 may add information such as the time of capture and arrival, departure and stop times at bus stops to the image data captured of the interior of the vehicle.

The information acquisition unit 74 acquires vehicle measurement information (speed, acceleration, etc.) measured by the vehicle measurement unit 71, tire measurement information (tire temperature, air pressure, etc.) measured by the tire measurement unit 72, and video data captured by the in-vehicle image capture unit 73. The information acquisition unit 74 associates measured time information or acquired time information with each piece of measurement data included in the vehicle measurement information and tire measurement information.

The information acquisition unit 74 transmits the vehicle measurement information and tire measurement information together with time information associated with each measurement data from the communication unit 75 to the wear amount estimation device 10. The information acquisition unit 74 also transmits the video data captured by the in-vehicle image capture unit 73 from the communication unit 75 to the wear amount estimation device 10.

If the vehicle 6 is equipped with a device such as a digital tachometer, the information acquisition unit 74 may acquire information such as the speed and acceleration of the vehicle 6 collected by that device. The communication unit 75 connects to the communication network 9 by wireless communication such as Wi-Fi (registered trademark), and transmits the video data, vehicle measurement information, and tire measurement information (including time information) acquired by the information acquisition unit 74 to the wear amount estimation device 10 via the communication network 9.

Returning to FIG. 1, the weather information server device 80 provides weather information for various locations. The weather information provided by the weather information server device 80 includes information such as the amount of precipitation, snowfall, temperature, and sunshine hours for various locations. The wear amount estimation device 10 obtains weather information for the location where the vehicle 6 is traveling from the weather information server device 80.

The wear amount estimation device 10 includes a communication unit 11, a vehicle information acquisition unit 12, an image recognition unit 13, a distribution calculation unit 14, a tire severity generation unit 15, a wear amount calculation unit 16, and a memory unit 17. Each unit in the wear amount estimation device 10 can be realized in hardware terms by electronic elements and mechanical parts such as a computer CPU, and in software terms by a computer program, but here, functional blocks realized by the cooperation of these are depicted. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various forms by combining hardware and software.

The communication unit 11 is connected to the communication network 9 by wireless or wired communication, and communicates with the communication unit 75 of the on-board measurement device 70. The communication unit 11 also communicates with the weather information server device 80 via the communication network 9.

The vehicle information acquisition unit 12 acquires vehicle measurement information, tire measurement information, and video data captured inside the vehicle, transmitted from the on-board measurement device 70 mounted on the vehicle 6. The vehicle information acquisition unit 12 outputs the acquired video data to the image recognition unit 13. The vehicle information acquisition unit 12 outputs the acquired tire measurement information (tire temperature, air pressure, etc.) to the wear amount calculation unit 16. The vehicle information acquisition unit 12 outputs vehicle measurement information (speed and acceleration) to the tire severity generation unit 15.

The vehicle information acquisition unit 12 acquires data used to estimate the amount of wear of the tires 7 from among the vehicle specification data 17a, tire specification data 17b, and tire position data 17c from the storage unit 17, and outputs the data to the wear amount calculation unit 16. The vehicle information acquisition unit 12 also acquires route data 17d from the storage unit 17, and outputs the data to the tire severity generation unit 15. The route data 17d may be acquired from an external computer-assisted map routing service based on each stop including the departure point, intermediate points, and arrival point.

The memory unit 17 is a storage device, such as a solid state drive (SSD), a hard disk, a CD-ROM, a DVD, etc., and stores data provided in advance regarding the specifications of various vehicles and tires 7, as well as route data regarding the routes on which the vehicle 6 travels.

The vehicle specification data 17a includes information on vehicle performance such as the manufacturer, vehicle name, vehicle model, vehicle weight, drive train, overall length, vehicle width, vehicle height, and maximum load capacity. The tire specification data 17b includes information on tire 7 performance such as the manufacturer, product name, tire size, tire width, aspect ratio, wear resistance, tire strength, static stiffness, dynamic stiffness, tire outer diameter, load index, and manufacturing date. The tire position data 17c includes information on the position of the tire on the vehicle whose wear is predicted and the axle on which it is attached. The route data 17d includes data on the distance of each section between stops, information on turns, the amount of altitude change, the number of altitude changes, and the amount of altitude change.

The image recognition unit 13 performs image recognition of passengers and their positions within the vehicle from the video data captured inside the vehicle. When there are multiple passengers, the image recognition unit 13 recognizes the position of each passenger. The image recognition unit 13 outputs the image-recognized position of each passenger to the distribution calculation unit 14. The image recognition unit 13 can use various known image recognition methods in the technical field of image recognition for recognizing people from captured video data, for example, an image recognition method using a learning model. The image recognition unit 13 corresponds to the recognition unit in the present invention. The video data used by the image recognition unit 13 corresponds to the data used by the recognition unit to recognize the position of the load. The configuration for detecting the position of the passengers is not limited to the configuration using the image recognition unit 13, and may be a configuration including a sensor device that uses ultrasonic waves or radio waves (millimeter waves) to detect objects, for example, by echolocation technology.

The distribution calculation unit 14 calculates the distribution of passenger weight based on the position of each passenger inside the vehicle recognized by the image recognition unit 13, and outputs the calculated weight to the wear amount calculation unit 16. FIG. 3 is a plan view of the interior of the vehicle 6 seen from above. A driver's seat 61 is provided at the front right side of the vehicle 6, and seats 62 are provided from behind the driver's seat 61 to the rearmost part of the vehicle 6. Tires 7 are mounted on the left and right sides of the front and rear of the vehicle 6.

In the example shown in FIG. 3, the interior of the vehicle is divided into four areas: area A1 including the right front wheel, area A2 including the left front wheel, area A3 including the right rear wheel, and area A4 including the left rear wheel. The distribution calculation unit 14 determines which of areas A1 to A4 each passenger belongs to based on their position, and calculates the number of passengers present in each area. Note that the driver is included in the passengers in area A1 when calculating the number of passengers.

The distribution calculation unit 14 calculates the weight distribution of passengers assuming that the passengers have the same weight. For example, when the distribution calculation unit 14 determines that there are four passengers in area A1, three in area A2, three in area A3, and two in area A4, it calculates the passenger weight distribution as 3 in area A1, 3 in area A2, 3 in area A3, and 2 in area A4. If the image recognition unit 13 is capable of image recognition of adults and children, the distribution calculation unit 14 may set the weight of an adult to 1 and the weight of a child to 0.5, for example. In addition, the distribution calculation unit 14 may calculate that the weight distribution in area A1 is 240 kg when the passenger weight is, for example, 60 kg and there are four passengers in area A1. Furthermore, a device capable of measuring the weight of passengers may be installed at the entrance of the vehicle to measure the actual weight of the passengers and calculate a more detailed weight distribution.

The tire severity generation unit 15 generates severity information on tire wear (hereinafter referred to as tire severity information) based on information such as the distance of each section on the route traveled by the vehicle 6, and outputs it to the wear amount calculation unit 16.

Figure 4 is a chart showing an example of tire severity information generated by the tire severity generation unit 15. The tire severity information is classified into four categories: driving-related, turning-related, starting/stopping-related, and elevation-related. For driving-related information, the tire severity generation unit 15 generates the distance of each section separated by stops and the vehicle speed as tire severity information. The longer the distance of each section and the higher the vehicle speed in each section, the greater the amount of wear on the tires 7. The remarks column in Figure 4 indicates the source of the data used as tire severity information. The distance of each section is known depending on the route and is obtained from route data 17d. The vehicle speed is measured by a speedometer 71a and obtained from an on-board measuring device 70.

Figure 5 is a schematic diagram showing an example of a route on which vehicle 6 travels. In the example route shown in Figure 5, vehicle 6 starts and ends at bus stop ST1, and travels through bus stops ST2 and ST3. For example, turning points P1 to P3 exist between bus stops ST1 and ST2. Turning points P1 to P9 on which vehicle 6 travels are known according to the route, and are included in route data 17d.

In relation to turning, the tire severity generating unit 15 generates the turning radius, turning direction, turning speed, and lateral acceleration at turning points in each section separated by bus stops as tire severity information. The smaller the turning radius and the greater the turning speed and lateral acceleration at a turning point, the greater the amount of wear on the tires 7. In addition, the amount of wear on the right or left side tires 7 of the vehicle 6 increases depending on the turning direction. The turning speed and lateral acceleration at each turning point are measured by the speedometer 71a and acceleration sensor 71b, and are obtained from the on-board measuring device 70.

The tire severity generating unit 15 generates tire severity information related to departure and stopping, including the speed and acceleration when the vehicle is stopped at each stop, and the speed and acceleration when the vehicle is departing. The greater the speed and lateral acceleration when the vehicle is stopped and when the vehicle is departing, the greater the amount of wear on the tires 7. The speed and acceleration when the vehicle is stopped at each stop, and the speed and acceleration when the vehicle is departing, are measured by the speedometer 71a and acceleration sensor 71b, and are obtained from the on-board measuring device 70.

In relation to elevation difference, the tire severity generation unit 15 generates the amount of altitude change, the number of altitude changes, and the direction of altitude change as tire severity information for each section. The amount of altitude change is, for example, the total amount of change obtained by accumulating the absolute values of the altitude difference due to ascents and descents on the road in each section. The number of altitude changes is, for example, the total number of changes obtained by accumulating the number of ascents and descents on the road in each section. The direction of altitude change indicates, for example, whether each section is generally upward or downward.

The greater the amount of altitude change and the number of altitude changes in each section, the greater the amount of wear on the tires 7. Also, depending on the direction of the altitude change, the amount of wear on the front or rear tires 7 increases. The amount of altitude change, the number of altitude changes, and the direction of altitude change in each section are known and are included in the route data 17d.

The wear amount calculation unit 16 has a calculation model 16a and estimates the wear amount of the tire 7. The calculation model 16a is a learning model that calculates the wear amount of the tire 7 based on input information. Figure 6 is a schematic diagram for explaining the wear amount estimation and learning of the calculation model 16a. The input data to the calculation model 16a is roughly classified into the following categories: vehicle measurement information, tire measurement information, passenger weight distribution status, tire severity information, and other information.

The input data related to the vehicle measurement information includes the speed and acceleration of the vehicle 6. The tire severity generation unit 15 uses the speed and acceleration when starting and stopping as tire severity information, but the speed and acceleration while traveling within a section may also be used as input data to the calculation model 16a. The input data related to the tire measurement information includes the temperature and air pressure of the tire 7.

The input data related to tire severity information is tire severity information calculated by the tire severity generation unit 15. As described above, the tire severity information is data related to driving, turning, starting and stopping, and elevation difference.

The input data from other information includes road surface conditions, temperature, precipitation, etc. estimated based on meteorological information, the maximum vehicle load included in vehicle specification data 17a, and the wear resistance performance of tires 7 included in tire specification data 17b. The wear resistance performance of tires 7 is measured using, for example, a tire wear index value based on a Lambourn wear test, where the standard blend is set at 100 and the wear resistance performance of various tread blends is indexed. The input data from other information is also information related to the position of tires 7 and axles included in tire position data 17c.

The computation model 16a uses a learning model such as a neural network. The computation model 16a is constructed using a method such as a deep neural network (DNN) or a decision tree. The computation model 16a may also be a multiple linear regression model for input information, for example, which is generated by learning.

Figure 7 is a block diagram showing the functional configuration of the computational model generation system 110. In addition to the configuration of the wear amount estimation system 100, the computational model generation system 110 includes a tire wear amount measurement device 60 and a computational model generation device 20 having a learning processing unit 21.

The tire wear measuring device 60 directly measures the depth of the grooves in the tread of the tire 7 to obtain the wear of the tire 7. An operator may measure the depth of each groove using a measuring tool, a camera, or visually, and the tire wear measuring device 60 may store the measurement data input by the operator. The tire wear measuring device 60 may also be a dedicated device that measures the groove depth using a mechanical or optical method and stores the wear amount.

Specifically, for example, if a tire has four grooves, the tire wear measuring device 60 measures at four locations in the width direction, and then measures three locations in the circumferential direction of the same groove, for example at 120° intervals. As a result, uneven wear data in the width direction or circumferential direction of the tire is also stored in the tire wear measuring device 60. Note that the tire wear measuring device 60 may indirectly measure groove depth by calculation from information on the mileage and the tire rotation speed and speed, since tire diameter changes due to wear. In addition, a device that directly measures groove depth may be used in combination with a device that predicts groove depth by calculation from the mileage and the tire rotation speed and speed.

The computational model generating device 20 has a learning processing unit 21 in addition to each component of the wear amount estimating device 10. The parts of the computational model generating device 20 that correspond to each component of the wear amount estimating device 10 have the same functions as those of the wear amount estimating device 10, but the computational model 16a is either before learning or is currently being learned.

The learning processing unit 21 acquires the wear amount of the tire 7 from the tire wear amount measuring device 60 via the communication unit 11. Referring to FIG. 6, in the learning process of the calculation model 16a, the wear amount of the tire 7 is estimated as output data by the calculation model 16a based on the input information, and compared with the teacher data. The teacher data uses the wear amount of the tire 7 measured by the tire wear amount measuring device 60.

The learning processing unit 21 performs learning by comparing the wear amount of the tire 7 estimated by the computation model 16a with the teacher data, setting various coefficients in the computation process such as weighting anew in the computation model 16a, and repeatedly updating the model. The wear amount estimation system 100 estimates the wear amount of the tire 7 using the computation model 16a that has been learned by the computation model generation system 110. The learning processing unit 21 can use a known learning method such as gradient boosting. Also, known verification methods such as random data sampling and cross verification can be used to verify the computation model 16a.

Next, the operation of the wear amount estimation system 100 and the calculation model generation system 110 will be described. FIG. 8 is a flowchart showing the procedure for generating a calculation model by the calculation model generation system 110. The vehicle information acquisition unit 12 of the calculation model generation device 20 starts acquiring vehicle measurement information, tire measurement information, and video data (S1). In step S1, the vehicle information acquisition unit 12 also reads out other necessary information such as vehicle specifications, tire specifications, tire position, maximum vehicle load, tire wear resistance, and route data from the storage unit 17. The image recognition unit 13 performs image recognition of passengers and their positions based on the video data (S2). The distribution calculation unit 14 calculates the distribution of passenger weights based on the image-recognized positions of each passenger (S3).

The tire severity generation unit 15 generates tire severity information based on each of the related data of driving, turning, starting and stopping, and elevation difference (S3). As described above, the tire severity information is generated using the speed and acceleration information of the vehicle 6 measured by the on-board measuring device 70, and the data included in the route data 17d.

The wear amount calculation unit 16 acquires input data from the vehicle information acquisition unit 12 and the tire severity generation unit 15, and calculates and estimates the wear amount of the tire 7 using the calculation model 16a (S5). When the road surface condition or the like is used as input data for the calculation model 16a, a processing unit (not shown) that estimates the road surface condition is provided, and the estimated road surface condition is input from the processing unit to the wear amount calculation unit 16.

The learning processing unit 21 compares the amount of wear of the tire 7 calculated by the computation model 16a with the amount of wear of the tire 7 measured as teacher data by the tire wear amount measuring device 60 (S6). The learning processing unit 21 updates the computation model 16a based on the comparison result in step S6 (S7) and ends the process. The computation model generating device 20 repeats these processes to update the computation model 16a and improve the accuracy of the estimation of the amount of tire wear.

The wear amount estimation system 100 estimates the wear amount of the tire 7 by using the trained computation model 16a generated by the computation model generating device 20. The wear amount estimation system 100 estimates the wear amount of the tire 7 by executing the processes from step S1 to step S5 in the flowchart shown in FIG. 8.

The wear amount estimation system 100 calculates the distribution of passenger weight based on video data captured inside the vehicle and uses the calculated weight as input data for the computational model 16a, thereby improving the accuracy of the wear amount estimation for the tire 7. Similarly, the computational model generation system 110 calculates the distribution of passenger weight based on video data captured inside the vehicle and uses the calculated weight as input data for the computational model 16a, thereby generating a computational model 16a that accurately estimates the wear amount for the tire 7.

The wear amount estimation system 100 can also construct a calculation model 16a that outputs the amount of uneven wear of the tire 7 and estimate the amount of uneven wear. In this case, the wear amount estimation system 100 estimates the uneven wear of the tire 7 caused by the weight distribution of passengers, etc. The calculation model generation system 110 can also generate a calculation model 16a that estimates the amount of uneven wear by having the calculation model 16a learn using the amount of wear measured in each groove of the tire as training data for uneven wear.

The tire severity generation unit 15 generates the distance of each section separated by bus stops as severity information for tire wear. The wear amount estimation system 100 can further improve the accuracy of estimating the amount of wear of the tires 7 by using the distance of each section and the weight distribution status of passengers as input data for the calculation model 16a.

As described above, the wear amount estimation system 100 can further improve the accuracy of estimating the wear amount of the tire 7 by using each piece of data that contributes to the wear amount of the tire 7 in relation to driving, turning, starting and stopping, and elevation difference. The wear amount estimation system 100 can further improve the accuracy of estimating the wear amount of the tire 7 by using the distance in each section and the vehicle speed in relation to driving, and the turning radius, turning direction, turning speed, and lateral acceleration in relation to turning, as tire severity information.

When it comes to starting and stopping, the wear amount estimation system 100 can further improve the accuracy of estimating the amount of wear on the tires 7 by using the speed and acceleration when the vehicle is stopped, and the speed and acceleration when the vehicle is starting, as tire severity information. When it comes to elevation difference, the wear amount estimation system 100 can further improve the accuracy of estimating the amount of wear on the tires 7 by using the amount of altitude change in each section, the number of altitude changes, and the direction of altitude change, etc., as tire severity information.

The computational model generation system 110 can generate a computational model 16a that accurately estimates the amount of tire wear by using each piece of data that contributes to the amount of tire wear in relation to driving, turning, starting and stopping, and elevation change as input data for the computational model 16a.

When the wear amount estimation system 100 is equipped with a GPS sensor that acquires position information from a GPS (Global Positioning Satellite), the wear amount estimation system 100 may be configured to use the position information acquired by the GPS sensor. That is, the wear amount estimation system 100 may calculate information such as turning radius, turning direction, turning speed, speed at departure and stopping, amount of altitude change, number of altitude changes, and direction of altitude change, instead of input data related to vehicle measurement information, based on the position information acquired by the GPS sensor, and use the information as input data to the calculation model 16a. Similarly, the calculation model generation system 110 may calculate information such as turning radius, turning direction, turning speed, speed at departure and stopping, amount of altitude change, number of altitude changes, and direction of altitude change, instead of input data related to vehicle measurement information, based on the position information acquired by the GPS sensor, and use the information as input data to the calculation model 16a.

Next, features of the wear amount estimation system 100 and the computation model generation system 110 according to each embodiment will be described.
The wear amount estimation system 100 includes a vehicle information acquisition unit 12, an image recognition unit 13 as a recognition unit, a distribution calculation unit 14, and a wear amount calculation unit 16. The vehicle information acquisition unit 12 acquires video data detecting passengers, which are cargo loaded in the vehicle 6 for transportation. The distribution calculation unit 14 calculates the distribution of passenger weights in each section separated by bus stops as stops, based on the positions of the passengers recognized by the image recognition unit 13. The wear amount calculation unit 16 has a calculation model 16a that calculates the wear amount of the tire 7 based on input information, and inputs the distribution calculated by the distribution calculation unit 14 to the calculation model 16a to calculate the wear amount of the tire 7. As a result, the wear amount estimation system 100 can improve the accuracy of the wear amount estimation of the tire 7 by using the distribution of passenger weights in the vehicle as input data for the calculation model 16a.

The wear amount estimation system 100 further includes a tire severity generation unit 15 that generates the distance of the sections separated by bus stops as severity information for tire wear. The wear amount calculation unit 16 inputs the severity information generated by the tire severity generation unit 15 into a calculation model 16a to calculate the wear amount of the tires 7. This allows the wear amount estimation system 100 to further improve the accuracy of the wear amount estimation of the tires 7 by using the distance of each section and the distribution of weights by passengers as input data for the calculation model 16a.

The tire severity generation unit 15 also generates information about the turning of the vehicle 6 in the section separated by bus stops as severity information. This allows the wear amount estimation system 100 to use the information about the turning of the vehicle 6 as input data for the calculation model 16a, thereby further improving the accuracy of the wear amount estimation of the tire 7.

The tire severity generation unit 15 also generates the acceleration when the vehicle stops at a bus stop and when it departs as severity information. This allows the wear amount estimation system 100 to use the acceleration when the vehicle stops and when it departs as input data for the calculation model 16a, thereby further improving the accuracy of the wear amount estimation of the tire 7.

The tire severity generation unit 15 also generates information on the elevation difference in the section separated by bus stops as severity information. This allows the wear amount estimation system 100 to use the information on the elevation difference as input data for the calculation model 16a, thereby further improving the accuracy of the wear amount estimation of the tire 7.

The computation model generation system 110 includes a vehicle information acquisition unit 12, an image recognition unit 13 as a recognition unit, a distribution calculation unit 14, a wear amount calculation unit 16, and a learning processing unit 21. The vehicle information acquisition unit 12 acquires video data that detects passengers, which are cargo loaded into the vehicle 6 for transportation. The distribution calculation unit 14 calculates the distribution of passenger weights in each section separated by bus stops as stops, based on the positions of the passengers recognized by the image recognition unit 13. The wear amount calculation unit 16 has a computation model 16a that calculates the wear amount of the tire 7 based on the input information, and inputs the distribution calculated by the distribution calculation unit 14 to the computation model 16a to calculate the wear amount of the tire 7. The learning processing unit 21 compares the wear amount measured on the tire 7 with the wear amount calculated by the wear amount calculation unit 16 to train the computation model 16a. As a result, the computation model generation system 110 can generate a computation model 16a that accurately estimates the wear amount of the tire 7.

The wear amount estimation method includes a vehicle information acquisition step, an image recognition step as a recognition step, a distribution calculation step, and a wear amount calculation step. The vehicle information acquisition step acquires video data detecting passengers, which are cargo loaded inside the vehicle 6 for transportation. The image recognition step recognizes the positions of the passengers from the video data acquired by the vehicle information acquisition step. The distribution calculation step calculates the distribution of passenger weights in each section separated by bus stops as stops, based on the positions of the passengers recognized by the image recognition step. The wear amount calculation step has a calculation model 16a that calculates the wear amount of the tire 7 based on input information, and inputs the distribution calculated by the distribution calculation step into the calculation model 16a to calculate the wear amount of the tire 7. According to this method, the distribution of passenger weights inside the vehicle can be used as input data for the calculation model 16a, thereby improving the accuracy of the wear amount estimation.

The above describes the embodiments of the present invention. These embodiments are illustrative, and it will be understood by those skilled in the art that various modifications and changes are possible within the scope of the claims of the present invention, and that such modifications and changes are also within the scope of the claims of the present invention. Therefore, the descriptions and drawings in this specification should be treated as illustrative rather than restrictive.

6 vehicle, 7 tire, 12 vehicle information acquisition unit, 13 image recognition unit (recognition unit),
14 Distribution calculation unit, 15 Tire severity generation unit, 16 Wear amount calculation unit,
16a computation model, 21 learning processing unit,
100 wear amount estimation system, 110 computation model generation system

Claims (6)

a vehicle information acquisition unit that acquires data on detected cargo loaded for transportation inside the vehicle;
a recognition unit that recognizes a position of a load from the data acquired by the vehicle information acquisition unit;
a distribution calculation unit that calculates a distribution state of weights of the cargo in each section divided by stops based on the position of the cargo recognized by the recognition unit;
a tire severity generating unit that generates the distance of the section as severity information for tire wear;
a wear amount calculation unit having a calculation model for calculating a wear amount of a tire based on input information, the wear amount calculation unit inputting the distribution state calculated by the distribution calculation unit and the severity information generated by the tire severity generation unit to the calculation model to calculate the wear amount of the tire;
A wear amount estimation system comprising: 2. The wear amount estimation system according to claim 1 , wherein the tire severity generation unit generates information regarding turning of the vehicle in the section as the severity information. 2. The wear amount estimation system according to claim 1 , wherein the tire severity generating unit generates, as the severity information, acceleration when the vehicle is stopped at a stop and when the vehicle is starting. 2. The wear amount estimation system according to claim 1 , wherein the tire severity generation unit generates information regarding an elevation difference in the section as the severity information. a vehicle information acquisition unit that acquires data on detected cargo loaded for transportation inside the vehicle;
a recognition unit that recognizes a position of a load from the data acquired by the vehicle information acquisition unit;
a distribution calculation unit that calculates a distribution state of weights of the cargo in each section divided by stops based on the position of the cargo recognized by the recognition unit;
a tire severity generating unit that generates the distance of the section as severity information for tire wear;
a wear amount calculation unit having a calculation model for calculating a wear amount of a tire based on input information, the wear amount calculation unit inputting the distribution state calculated by the distribution calculation unit and the severity information generated by the tire severity generation unit to the calculation model to calculate the wear amount of the tire;
a learning processing unit that learns the calculation model by comparing the amount of wear measured on the tire with the amount of wear calculated by the wear amount calculation unit;
A computational model generation system comprising: a vehicle information acquisition step in which the wear amount estimation system acquires data indicating that an object loaded for transportation inside the vehicle is detected;
a recognition step in which the wear amount estimation system recognizes a position of a load from the data acquired by the vehicle information acquisition step;
a distribution calculation step in which the wear amount estimation system calculates a distribution state of weight of the cargo in each section divided by stops based on the position of the cargo recognized in the recognition step;
a tire severity generation step in which the wear amount estimation system generates the distance of the section as severity information for tire wear;
a wear amount calculation step in which the wear amount estimation system inputs the distribution state calculated in the distribution calculation step and the severity information generated in the tire severity generation step into a calculation model that calculates the wear amount of the tire based on input information, and calculates the wear amount of the tire;
A wear amount estimating method comprising:

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