Snow removal support system, snow removal support method, and snow removal support program

Abstract

This snow removal support system comprises an acquisition unit, an identification unit, an estimation unit and an output unit. The acquisition unit acquires an image with depicts a road on which snow has accumulated, and measurement data which measures the state of travel of a vehicle traveling the road on which snow has accumulated. The identification unit identifies the state of the snow surface on the basis of the image which depicts the road on which snow has accumulated. The estimation unit estimates the priority for snow removal at each point along the road on the basis of the identified snow surface state and the measurement data. The output unit outputs the estimated snow removal priority.

Description

[Technical Field]

[0001] The present invention relates to a snow removal assistance system and the like. [Background technology]

[0002] When snow falls, road administrators grasp the condition of the snow surface on the road, for example, by patrolling with vehicles. Then, the road administrators determine whether snow removal is necessary based on the condition of the snow surface and dispatch snowplows to areas where snow removal is necessary. On the other hand, for example, when the number of snowplows or the number of workers is limited, the road administrators must determine which points on the roads under their management have a high priority for snow removal. For this reason, it is desirable to have a system that can confirm the condition of the snow surface on the road and support the determination of whether snow removal is necessary.

[0003] The road surface assessment method of Patent Document 1 detects boundaries of areas where the height of the road surface changes based on a radio wave image that is based on electromagnetic waves emitted from objects around the vehicle, and assesses the condition of the road surface.

[0004] The snow transportation and removal plan planning support system in Patent Document 2 calculates the amount of snow to be transported and removed based on satellite images. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2018-84535 [Patent Document 2] Japanese Patent Application Laid-Open No. 2012-203495 Summary of the Invention [Problem to be solved by the invention]

[0006] In the road surface determination method of Patent Document 1 and the snow transportation and removal plan planning support system of Patent Document 2, it is sometimes difficult to determine the locations with high priority for snow removal.

[0007] In order to solve the above problem, an object of the present invention is to provide a snow removal support system that can easily estimate points on roads that have a high priority for snow removal. [Means for solving the problem]

[0008] In order to solve the above problems, the snow removal support system of the present invention comprises an acquisition means for acquiring an image of a snow-covered road and measurement data measuring the driving conditions of a vehicle traveling on the snow-covered road, an identification means for identifying the condition of the snow surface from the image of the snow-covered road, an estimation means for estimating the priority of snow removal at each point on the road based on the identified snow surface condition and the measurement data, and an output means for outputting the estimated snow removal priority.

[0009] The snow removal support method of the present invention acquires an image of a snow-covered road and measurement data measuring the driving conditions of a vehicle traveling on the snow-covered road, identifies the condition of the snow surface from the image of the snow-covered road, estimates the priority of snow removal at each point on the road based on the identified snow surface condition and the measurement data, and outputs the estimated snow removal priority.

[0010] The recording medium of the present invention non-temporarily records a snow removal assistance program that causes a computer to execute the following processes: acquiring an image of a snow-covered road and measurement data measuring the driving conditions of a vehicle traveling on the snow-covered road; identifying the condition of the snow surface from the image of the snow-covered road; estimating the priority of snow removal at each point on the road based on the identified snow surface condition and the measurement data; and outputting the estimated priority of snow removal. [Effects of the Invention]

[0011] According to the present invention, points on a road that have a high priority for snow removal can be easily estimated. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 is a diagram illustrating an example of a configuration according to an embodiment of the present invention. [Figure 2] FIG. 1 is a diagram schematically illustrating an example in which a road is photographed while a vehicle is traveling and the traveling state of the vehicle is measured. [Figure 3] 1 is a diagram illustrating an example of the configuration of a snow removal support system according to an embodiment of the present invention. [Figure 4] FIG. 2 is a diagram illustrating an example of a display screen according to an embodiment of the present invention. [Figure 5] FIG. 2 is a diagram illustrating an example of a display screen according to an embodiment of the present invention. [Figure 6] FIG. 2 is a diagram illustrating an example of a display screen according to an embodiment of the present invention. [Figure 7] FIG. 2 is a diagram illustrating an example of a display screen according to an embodiment of the present invention. [Figure 8] FIG. 2 is a diagram illustrating an example of a display screen according to an embodiment of the present invention. [Figure 9] FIG. 2 is a diagram illustrating an example of an operation flow of the snow removal support system according to the embodiment of the present invention. [Figure 10] FIG. 10 is a diagram illustrating another example of the configuration of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0013] An embodiment of the present invention will be described in detail with reference to the drawings. FIG. 1 is a diagram showing an example of the configuration of a road management system in an embodiment of the present invention. The road management system includes a snow removal assistance system 10, an on-board device 20, and a terminal device 30. The snow removal assistance system 10 is connected to the on-board device 20 via a network, for example. Data input / output between the snow removal assistance system 10 and the on-board device 20 may be performed via a storage device. For example, data input / output between the snow removal assistance system 10 and the on-board device 20 may be performed via a non-volatile semiconductor storage device. The snow removal assistance system 10 is also connected to the terminal device 30 via the network. There may be multiple on-board devices 20 and multiple terminal devices 30.

[0014] The snow removal support system 10 is a system that estimates the priority of snow removal at each point on a road during snowfall, for example. A road administrator performs snow removal on the road based on the estimation results of the snow removal support system 10, for example.

[0015] For example, the snow removal assistance system 10 identifies the condition of the snow surface from an image of a snow-covered road. Then, the snow removal assistance system 10 estimates the priority of snow removal at each point on the road based on the identified snow surface condition and measurement data obtained by measuring the driving conditions of vehicles traveling on the snow-covered road. The vehicle is, for example, an automobile. The vehicle may also be a two-wheeled motor vehicle such as a motorcycle or scooter. The vehicle may also be a bicycle. The vehicle is not limited to the above.

[0016] The priority of snow removal is, for example, an index that indicates the degree to which snow removal is necessary. A high priority of snow removal means, for example, that snow removal is more necessary than at other points. The necessity of snow removal means, for example, that snow removal is necessary for smooth and safe vehicle passage. A point with a high priority for snow removal is, for example, a point where snow on the road surface is likely to cause obstruction or danger to vehicle passage. A point with a high priority for snow removal may also be a point where snow on the road surface is likely to cause obstruction or danger to pedestrian passage.

[0017] "Passport obstruction" refers to, for example, the impossibility of travel due to snow on the road surface. "Passport obstruction" refers to, for example, the time it takes to travel compared to when there is no snow. "Passport danger" refers to, for example, the occurrence of accidents due to snow on the road surface.

[0018] The priority of snow removal is set, for example, so that the higher the risk that the snow surface condition will affect vehicle traffic and safety, the higher the priority. The snow surface condition is, for example, the condition of snow on the road surface when there is snow on the road. In other words, the snow surface condition is the condition of snow on the vehicle's driving surface when there is snow on the road. The snow surface condition is, for example, the condition of snow accumulated on the road surface in the portion of the road where vehicles travel. The portion of the road where vehicles travel may include portions where vehicles can pass. For example, the portion of the road where vehicles travel may include centerlines, lane boundaries, road shoulders, shoulder strips, and guidance strips.

[0019] The priority of snow removal may be set higher as the snow surface condition is more likely to affect pedestrian traffic and safety. In this case, the snow surface condition may include the condition of snow on the sidewalk of the road.

[0020] The condition of the snow surface may be, for example, one or more of the amount of snow, the width of the ruts on the snow surface, the step of the ruts on the snow surface, the spacing between the ruts on the snow surface, whether the snow is frozen, whether the snow is melted, and the passable width.

[0021] When the snow surface condition includes the amount of accumulated snow, the priority of snow removal is set to be higher, for example, the greater the amount of accumulated snow. Also, when the snow surface condition includes the step of a rut on the snow surface, the priority of snow removal is set to be higher, for example, the greater the width of the rut on the snow surface and the step.

[0022] The priority of snow removal may be set higher at points where it is important to pass through. Furthermore, the priority of snow removal may be set higher at points where there is a high possibility of danger to passing through. Points where it is important to pass through may be, for example, points on major roads with heavy traffic. Points where it is important to pass through may also be, for example, points where emergency vehicles frequently pass through and where there is a hospital, police station, or fire station nearby. Points where it is important to pass through are not limited to the above.

[0023] Points where danger to travel is likely to occur include, for example, intersections where vehicles frequently stop and start and where the snow surface condition is likely to change, and the vicinity of railroad crossings. Points where danger to travel is likely to occur may also be bridges and tunnel entrances and exits. Points where danger to travel is likely to occur are not limited to the above.

[0024] FIG. 2 is a diagram schematically illustrating an example in which road images are captured and the vehicle's driving conditions are measured while the vehicle is traveling. In the example of FIG. 2, the vehicle is equipped with an on-board device 20. The on-board device 20 captures images of the road surface while the vehicle is traveling, for example, using an imaging device. The on-board device 20 also measures the vehicle's driving conditions, for example, using a sensor that measures the vehicle's driving conditions. The on-board device 20 then outputs the captured images and measurement data to, for example, the snow removal assistance system 10.

[0025] The sensor that measures the vehicle's running state is, for example, an acceleration sensor. The in-vehicle device 20 is equipped with, for example, an acceleration sensor that can measure the vehicle's acceleration in the vertical direction. An acceleration sensor that can measure the vehicle's acceleration in the vertical direction can measure, for example, the vehicle's vibration in the vertical direction. The acceleration sensor may also measure acceleration in the vehicle's traveling direction and in a direction perpendicular to the vehicle's traveling direction and vertical direction. The vehicle's vertical direction is a direction perpendicular to the road surface. In other words, the vehicle's vertical direction is a direction perpendicular to the traveling surface. The direction perpendicular to the vehicle's traveling direction and vertical direction is the vehicle width direction. The sensor that measures the vehicle's running state is not limited to an acceleration sensor.

[0026] The vertical acceleration of the vehicle changes, for example, due to vertical vibration of the vehicle. The vertical vibration of the vehicle is caused, for example, by unevenness in the road surface. The vertical vibration of the vehicle is caused, for example, by unevenness in the snow surface caused by ruts. Therefore, the vertical acceleration of the vehicle can reflect the condition of the snow surface. The measurement data obtained by measuring the driving condition of the vehicle is not limited to the vertical acceleration of the vehicle.

[0027] The snow removal assistance system 10, for example, identifies the condition of the snow surface in an image acquired from the in-vehicle device 20. The snow removal assistance system 10 estimates the priority of snow removal at each point on the road based on the snow surface condition obtained by identifying the image and the snow surface condition obtained from the measurement data. The snow removal assistance system 10 then outputs the estimated snow removal priority to, for example, the terminal device 30. The road manager, for example, refers to the estimated snow removal priority and performs snow removal on the road.

[0028] Here, we will explain the configuration of the snow removal support system 10. Fig. 3 is a diagram showing an example of the configuration of the snow removal support system 10 in an embodiment of the present invention.

[0029] The snow removal assistance system 10 basically includes an acquisition unit 11, a recognition unit 12, an estimation unit 13, and an output unit 14. The snow removal assistance system 10 further includes a storage unit 15, for example.

[0030] The acquisition unit 11 acquires images of snow-covered roads and measurement data obtained by measuring the driving conditions of vehicles traveling on snow-covered roads. For example, information about the shooting location is added to the images of snow-covered roads and the measurement data obtained by measuring the driving conditions of vehicles. Information about the shooting date and time may be added to the images of roads, images of snow-covered roads, and measurement data obtained by measuring the driving conditions of vehicles.

[0031] The acquisition unit 11 acquires, for example, from the in-vehicle device 20, an image of a snow-covered road and measurement data measuring the driving state of a vehicle traveling on the snow-covered road. The acquisition unit 11 acquires, for example, the image of a snow-covered road and measurement data measuring the driving state of a vehicle traveling on the snow-covered road from the in-vehicle device 20 via a network. The acquisition unit 11 may acquire, from the in-vehicle device 20, the image of a snow-covered road and measurement data measuring the driving state of a vehicle traveling on the snow-covered road via a storage device. For example, a non-volatile semiconductor storage device may be used as the storage device. The storage device is not limited to a non-volatile semiconductor storage device. The acquisition unit 11 may acquire, from a server connected via a network, an image of a snow-covered road and measurement data measuring the driving state of a vehicle traveling on the snow-covered road. The acquisition unit 11 stores, for example, in the storage unit 15, an image of a snow-covered road and measurement data obtained by measuring the driving state of a vehicle driving on a snow-covered road.

[0032] The acquisition unit 11 may acquire an image of a snow-covered road from a roadside camera. When acquiring an image of a snow-covered road from a roadside camera, the acquisition unit 11 acquires, for example, measurement data from the in-vehicle device 20, which measures the driving state of a vehicle traveling on a snow-covered road.

[0033] The acquisition unit 11 may acquire information used to estimate the priority of snow removal from a server connected via a network. The information used to estimate the priority of snow removal is information that affects the estimation of the priority of snow removal. The acquisition unit 11 acquires, for example, weather information. The acquisition unit 11 acquires, for example, map data. The map data may include data on topography and road structure. The acquisition unit 11 may also acquire information on road importance, traffic volume, event implementation plans, and surrounding facilities. The information used to estimate the priority of snow removal may be input by an operator to the snow removal assistance system 10 or the terminal device 30. If the information is input to the terminal device 30, the acquisition unit 11 acquires the information used to estimate the priority of snow removal from the terminal device 30.

[0034] The acquisition unit 11 may acquire the selection of the range for estimating the priority of snow removal from the terminal device 30. The selection of the range for estimating the priority of snow removal is performed on the terminal device 30 by, for example, an operation by an operator.

[0035] The identification unit 12 identifies the condition of the snow surface from an image of a snow-covered road. The identification unit 12, for example, uses an identification model to identify the condition of the snow surface from an image of a snow-covered road. The identification model, for example, identifies the condition of the snow surface by image recognition. If the snow surface is rutted, the identification model, for example, identifies the width of the ruts on the snow surface. The identification model may also identify the passable width of the road. The identification model may also identify the amount of snow accumulation. For example, the identification model identifies the amount of snow accumulation based on the height of a portion of a structure on the road buried in snow. For example, the identification model identifies the amount of snow accumulation based on the height of a portion of a pole indicating the road width buried in snow. If the poles indicating the road width are painted in different colors at each set height, the identification model may, for example, estimate the height of the portion buried in snow from the color of the pole that appears on the snow surface and identify the amount of snow accumulation based on the estimated height. The identification model may also identify whether or not the snow on the road surface is frozen.

[0036] The identification model is generated, for example, by machine learning. The identification model is generated, for example, by deep learning using a neural network. The identification model is generated, for example, by learning the relationship between an image of a snow surface and the state of the snow surface. The identification model is generated, for example, in a system external to the snow removal assistance system 10.

[0037] The estimation unit 13 estimates the priority of snow removal at each point on the road based on the state of the snow surface identified by the identification unit 12 and the measurement data. The estimation unit 13 estimates the priority of snow removal using, for example, a score based on the state of the snow surface identified by the identification unit 12 and a score based on the measurement data of the vehicle's driving state.

[0038] The estimation unit 13 estimates a score for the snow surface condition based on the snow surface condition identified by the identification unit 12, for example. The score for the snow surface condition is, for example, an index indicating the poorness of the snow surface condition. A poor snow surface condition means, for example, that the snow on the road is in a state in which snow removal is highly necessary. In other words, the higher the priority of snow removal, the higher the score for the snow surface condition. The estimation unit 13 estimates the score for the snow surface condition identified by the identification unit 12, for example, based on a set criterion. The criterion for estimating the score for the snow surface condition is set, for example, so that the higher the snow surface condition in a state in which snow removal is highly necessary. The criterion for estimating the score for the snow surface condition is set, for example, based on the classification of the snow surface condition and the degree of the snow surface condition. When the classification of the snow surface condition is ruts, the degree of the snow surface condition is, for example, the width of the ruts. When the classification of the snow surface condition is ruts, the criterion for estimating the score indicating the snow surface condition is set, for example, so that the wider the rut width, the higher the score.

[0039] The estimation unit 13 also estimates a score for the measurement data based on, for example, measurement data of the vehicle's driving state and a set criterion. The measurement data score is, for example, an index indicating the impact of the snow surface condition on vehicle driving. The measurement data score is, for example, an index indicating the impact of poor snow surface conditions on vehicle driving. The criterion used when estimating a score from the measurement data is set, for example, so that the greater the impact of the snow surface condition on vehicle driving, the higher the score. The criterion used when estimating a score from the measurement data is set, for example, so that the greater the acceleration, the higher the score. When the measurement data is the vehicle's vertical acceleration, the score estimated from the measurement data indicates, for example, the height difference of steps on the snow surface. When the measurement data is the vehicle's acceleration in the traveling direction, the score estimated from the measurement data indicates, for example, the frozen state and melted state of the snow surface. Steps on the snow surface are caused, for example, by ruts on the snow surface. The criterion used when estimating a score from the measurement data may be set so that the greater the change in acceleration, the higher the score.

[0040] The estimation unit 13 estimates the priority of snow removal based on the snow surface condition and the measurement data. For example, the estimation unit 13 determines the priority of snow removal as the sum of the score of the snow surface condition and the score of the measurement data. The priority of snow removal is not limited to the sum of the score of the snow surface condition and the score of the measurement data. The estimation unit 13 may estimate the priority of snow removal using weighted values ​​for the score of the snow surface condition and the score of the measurement data. The estimation unit 13 may also estimate the priority of snow removal using a weighted value for either the score of the snow surface condition or the score of the measurement data. The estimation unit 13 may estimate the priority of snow removal using a weighted value for the score estimated for each classification of snow surface condition.

[0041] The estimation unit 13 may further use the road importance to estimate the snow removal priority. The estimation unit 13 may score the road importance based on, for example, a set criterion. Then, the estimation unit 13 may determine the snow removal priority as the sum of, for example, a score estimated from the snow surface condition, a score estimated from the measurement data, and a score estimated from the road importance. The estimation unit 13 may estimate the snow removal priority using the score estimated from the road importance as a weight. The road importance score is, for example, an index indicating the necessity of snow removal according to the road priority. For example, the higher the road importance, the greater the necessity of snow removal. The criterion used when estimating the snow removal priority based on the road importance is, for example, set so that the higher the road importance, the higher the snow removal priority. The road importance is, for example, an index indicating the importance of a road in terms of traffic. For example, the road importance is set so that the greater the impact on logistics and pedestrian flow when the road is impassable, the higher the road importance. The importance of a road may be set to be higher for a main road, for example, or may be set to be higher for a road without a detour.

[0042] The estimation unit 13 may further estimate the priority of snow removal using information about the topography at each point on the road. The estimation unit 13 may, for example, score the topography at each point on the road based on a set criterion. The estimation unit 13 may then determine the priority of snow removal by adding, for example, a score estimated from the snow surface condition, a score estimated from the measurement data, and the score of the topography at each point on the road. The estimation unit 13 may estimate the priority of snow removal using the score of the topography at each point on the road as a weight. The topography score is, for example, an index indicating the influence of the topography around the road on the deterioration of the snow surface condition. For example, the greater the influence on the deterioration of the snow surface condition, the higher the topography score. The estimation unit 13 estimates the priority of snow removal using, for example, a criterion that gives a higher priority to snow removal for topography more likely to deteriorate in snow surface condition. An example of a topography more likely to deteriorate in snow surface condition is topography where snow is easily accumulated by wind. A topography more likely to deteriorate in snow surface condition may be topography that is shaded even during the day and does not melt easily.

[0043] The estimation unit 13 may further estimate the priority of snow removal using information about the structure at each point on the road. The estimation unit 13 may, for example, score the structure at each point on the road based on a set criterion. The estimation unit 13 may then determine the priority of snow removal by adding, for example, a score estimated from the snow surface condition, a score estimated from the measurement data, and a score of the structure at each point on the road. The estimation unit 13 may estimate the priority of snow removal using the score of the structure at each point on the road as a weight. The structure score is, for example, an index indicating the influence of the road structure on the deterioration of the snow surface condition. For example, the greater the influence of the terrain on the deterioration of the snow surface condition, the higher the terrain score. The estimation unit 13 estimates the priority of snow removal using, for example, a criterion that gives a higher priority to snow removal for structures that are more likely to deteriorate the snow surface condition. A structure that is more likely to deteriorate the snow surface condition may be, for example, a location where the tire force acting on the road surface is large. A structure that is more likely to deteriorate the snow surface condition is, for example, a bridge, a tunnel entrance or exit, an intersection, a junction, a fork, and a slope. In locations where the structure makes it easy for the snow surface to deteriorate, the snow surface condition is likely to deteriorate due to, for example, starting, stopping, and lane changes of the vehicle. The structures where the snow surface condition is likely to deteriorate are not limited to those mentioned above.

[0044] The estimation unit 13 may further use information about surrounding facilities at each point on the road to estimate the priority of snow removal. The estimation unit 13 may, for example, score surrounding facilities at each point on the road based on set criteria. The estimation unit 13 may then determine the priority of snow removal by adding, for example, a score estimated from the state of the snow surface, a score estimated from the measurement data, and the score of the surrounding facilities at each point on the road. The estimation unit 13 may estimate the priority of snow removal using the scores of surrounding facilities at each point on the road as weights. The score of the surrounding facility is, for example, an index indicating the necessity of snow removal depending on the type of surrounding facility. For example, the higher the importance of the surrounding facility, the greater the necessity of snow removal. For example, the estimation unit 13 estimates the priority of snow removal using criteria that increase the priority of snow removal when there are important surrounding facilities. Important surrounding facilities are, for example, facilities that, if unusable, would have a significant impact on life and safety due to snow accumulation. Examples of important surrounding facilities include, but are not limited to, schools, nurseries, kindergartens, hospitals, police stations, fire stations, train stations, bus terminals, ports, industrial parks, and distribution parks.

[0045] The estimation unit 13 may further estimate the priority of snow removal using reports from citizens. The citizens may include drivers. For example, the estimation unit 13 estimates the priority of snow removal so that a location where a citizen has reported the need for snow removal is given a higher priority.

[0046] The estimation unit 13 may estimate the priority of snow removal based on the snow surface condition predicted using weather forecast data. For example, the estimation unit 13 predicts the snow surface condition at the scheduled time of snow removal using weather forecast data. Then, the estimation unit 13 estimates the priority of snow removal based on the prediction results. The estimation unit 13 may estimate the priority of snow removal based on the prediction of the snow surface condition for each time period.

[0047] The estimation unit 13, for example, uses weather forecast data to predict the amount of snowfall, and predicts the state of the snow surface based on the predicted amount of snowfall. The estimation unit 13 may also predict the state of the snow surface based on the prediction results of wind speed, wind direction, rainfall, and temperature. The wind speed and wind direction are used, for example, to predict snowdrifts caused by the influence of wind. The estimation unit 13, for example, uses weather forecast data to predict the width and depth of ruts on the snow surface. The estimation unit 13, for example, uses the amount of snowfall and temperature to predict the width and depth of ruts on the snow surface. The estimation unit 13 may also predict the state of the snow surface using, for example, traffic volume by time period in addition to weather forecast data.

[0048] The estimation unit 13 may further use the road surface condition when there is no snow to estimate the priority of snow removal. For example, the estimation unit 13 further uses the road surface deterioration level diagnosed when there is no snow to estimate the priority of snow removal. For example, the estimation unit 13 estimates the priority of snow removal so that a location where the deterioration level is above a standard is given a low priority of snow removal. For example, the estimation unit 13 estimates the priority of snow removal for a location where the deterioration level is above a standard by multiplying the snow surface condition and a score estimated from the measurement data by a coefficient less than 1. For example, the estimation unit 13 may estimate the priority of snow removal by multiplying the snow surface condition and the score estimated from the measurement data by a coefficient less than 1 that decreases as the deterioration level increases. By estimating the priority of snow removal so that a location where the deterioration level is above a standard is given a low priority of snow removal, for example, it is possible to suppress the progression of road surface deterioration due to snow removal. The progression of road surface deterioration due to snow removal is caused, for example, by contact of snow removal equipment with the road surface. Furthermore, the estimation unit 13 may exclude points where the degree of road surface deterioration is equal to or higher than a standard from targets for estimating the priority of snow removal.

[0049] The road surface deterioration may be, for example, one or more of cracks, ruts, potholes, and irregularities in road surface roughness. The road surface deterioration is not limited to the above. When the road surface deterioration is cracks, the degree of road surface deterioration may be, for example, the crack rate. The crack rate is, for example, a value indicating the ratio between the area of ​​the deterioration included in an image captured at a certain point and the area of ​​the road included in the image. When the road surface deterioration is ruts, the degree of deterioration may be, for example, the amount of ruts. When the road surface deterioration is irregularities in road surface roughness, the degree of deterioration may be, for example, the International Roughness Index (IRI). The IRI is an index indicating the irregularities in road surface roughness. The IRI may be calculated based on the vertical acceleration of the vehicle. The measured value of the vertical acceleration may reflect, for example, the vertical vibration of the vehicle when traveling over ruts. The vertical acceleration may be measured, for example, by an acceleration sensor attached to the vehicle. Alternatively, the degree of deterioration may be expressed as a Maintenance Control Index (MCI), which is a composite deterioration index calculated from, for example, the crack rate, the amount of rutting, and flatness.

[0050] The estimation unit 13 may estimate the priority of snow removal using an estimation model for estimating the priority of snow removal. The estimation model estimates the priority of snow removal based on, for example, the state of the snow surface and measurement data. The estimation model is, for example, a learning model that receives the state of the snow surface and the measurement data as input and outputs an estimation result of the priority of snow removal.

[0051] The estimation model may further use weather forecast data as an input to estimate the priority of snow removal. The estimation model may further use road importance as an input to estimate the priority of snow removal. The estimation model may also further use the topography and structure of each point on the road as an input to estimate the priority of snow removal. The inputs to the estimation model are not limited to those described above.

[0052] The estimation model is generated, for example, in a system external to the snow removal assistance system 10. The estimation model is generated, for example, by learning the relationship between past snow surface conditions and measurement data and whether or not snow removal has been performed. When data other than the snow surface conditions and measurement data is used as input, for example, the data other than the snow surface conditions and measurement data is also used as learning data.

[0053] The estimation model is generated, for example, by deep learning using a neural network. The estimation model is generated, for example, by learning the relationship between the snow surface condition and the measurement data of the vehicle driving state during past snowfalls and the relationship between whether snow removal was performed or not. The estimation model is generated, for example, by training a neural network using the snow surface condition identified by the identification unit 12 and the measurement data as input data, and the presence or absence of snow removal as a label. The snow surface condition used as input data is, for example, the snow surface condition classification identified by the identification unit 12 and the degree of snow surface condition. The snow surface condition classification and the degree of snow surface condition are converted, for example, into feature quantities and input to the neural network. Even when data other than the relationship between the snow surface condition and the measurement data of the vehicle driving state is used as input data, for example, each data is converted, for example, into feature quantities and input to the neural network. The estimation model generated in this way estimates, for example, the probability that snow removal is necessary based on the input data. The estimation model outputs, for example, the probability that snow removal is necessary as the snow removal priority.

[0054] The estimation model may be generated using a machine learning algorithm based on factorized asymptotic Bayesian inference. When learning is performed using a machine learning algorithm based on factorized asymptotic Bayesian inference, case classification is performed using decision tree rules, with the snow surface condition identified by the identification unit 12 and measurement data as input data and the presence or absence of snow removal as labels. Then, an estimation model that estimates the priority of snow removal is generated using a linear model that combines different explanatory variables for each case. Using the generated estimation model, a trained estimation model is generated by sequentially optimizing the data classification conditions, generating an estimation model by optimizing the combination of explanatory variables, and deleting unnecessary estimation models. In addition, the estimation model generated using a machine learning algorithm based on factorized asymptotic Bayesian inference can output the reason for the estimation of the priority of snow removal.

[0055] When based on factorized asymptotic Bayesian inference, the estimation model outputs the reason for estimating the priority of snow removal based on, for example, the weights of the explanatory variables included in the linear model used to estimate the priority of snow removal. The estimation model may output the reason for estimating the priority of snow removal based on the fluctuation in the estimation result of snow removal priority when each item of input data is changed. For example, when each item of input data is changed, the estimation model outputs an item that causes a larger fluctuation in the estimation result of snow removal priority than other items as the reason for estimating the priority of snow removal. The machine learning algorithm used to generate the estimation model is not limited to the above.

[0056] The output unit 14 outputs the snow removal priority estimated by the estimation unit 13. The output unit 14 outputs the snow removal priority to, for example, the terminal device 30. The output unit 14 may also output the snow removal priority to a display device (not shown) connected to the snow removal support system 10.

[0057] The output unit 14 outputs, for example, the snow removal priority by superimposing it on a map. The output unit 14 outputs, for example, a numerical value indicating the snow removal priority of each point by superimposing it on the map. The output unit 14 may also output, for example, a snow removal priority level by superimposing it on the map. The snow removal priority level indicates, for example, which level of multiple levels divided into numerical ranges the snow removal priority estimated by the estimation unit 13 falls into.

[0058] The output unit 14 may output the estimated results of snow removal priority by coloring roads on the map in different colors according to the level of snow removal priority. The output unit 14 may also output the estimated results of snow removal priority as a list of snow removal priorities for each location. The output format of the estimated results of snow removal priority is not limited to the above.

[0059] The output unit 14 may output a reason for estimating the priority of snow removal in addition to the priority of snow removal. For example, the output unit 14 outputs the item that contributes most to the numerical value of the priority of snow removal as the reason for estimating the priority of snow removal. The item that contributes most to the numerical value of the priority of snow removal is, for example, the item with the highest score among the items used to estimate the priority of snow removal. For example, if the score of the snow surface condition is the highest, the output unit 14 outputs the classification of the snow surface as the reason for estimation. Furthermore, if an estimation model capable of outputting the reason for estimation is used, the output unit 14 outputs the reason for estimation by the estimation model as the reason for estimation. For example, if the estimation model has a large contribution to the estimation result of the snow surface condition, it outputs the classification of the snow surface condition as the reason for estimation. A large contribution to the estimation result means, for example, that the weight of the variable used in the estimation is large.

[0060] The output unit 14 may output the estimated reasons for the snow removal priority by superimposing them on a map. For example, the output unit 14 outputs the estimated reasons by superimposing an icon set for each estimated reason on the map.

[0061] The output unit 14 may output a snow removal route generated based on the snow removal priority. For example, the output unit 14 may generate a route that passes through points with a high snow removal priority and output the route as a snow removal route. For example, the output unit 14 may generate a route that passes through points with a snow removal priority equal to or higher than a set standard and output the route as a snow removal route.

[0062] The output unit 14 may output data related to a display screen used to select an area for estimating the priority of snow removal. For example, the output unit 14 outputs to the terminal device 30 a map on which an area for estimating the priority of snow removal is selected.

[0063] Fig. 4 is a diagram showing an example of a display screen that displays the estimated results of snow removal priority. In the example of the display screen in Fig. 4, the snow removal priority is displayed superimposed on a map. In the example of the display screen in Fig. 4, a numerical value indicating the snow removal priority is displayed on the map.

[0064] FIG. 5 is a diagram showing an example of a display screen that displays the estimated results of snow removal priority according to a plurality of levels of snow removal priority. In the example of the display screen in FIG. 5, snow removal priority is set to three levels: "H," "M," and "L." In the example of the display screen in FIG. 5, for example, the snow removal priority of a point "H" is the highest, and the snow removal priority of a point "L" is the lowest. The snow removal priority may be set to levels other than three. Furthermore, when snow removal priority is shown in levels, the display format of the snow removal priority is not limited to the example of the display screen in FIG. 5.

[0065] FIG. 6 is a diagram showing an example of a display screen displaying the priority of snow removal at a set time. In the example of the display screen of FIG. 6, similar to FIG. 5, the estimated results of snow removal priority are displayed according to the snow removal priority level, which is set in multiple levels. In the example of the display screen of FIG. 6, the set time is displayed as "estimated time 21:00." The example of the display screen of FIG. 6 is used, for example, when estimating the priority of snow removal by predicting the condition of the snow surface at a time later than the estimated time based on weather forecast data. The example of the display screen of FIG. 6 shows, for example, the result of estimating the priority of snow removal by predicting the condition of the snow surface at 21:00.

[0066] FIG. 7 shows an example of a display screen that outputs the reason for the estimation in addition to the estimated result of snow removal priority. In the example of the display screen in FIG. 7, the reason for the estimation is indicated by alphabets for points where the snow removal priority is above a standard. In the example of the display screen in FIG. 7, "J" indicates that the reason for the high snow removal priority is that the point is an intersection. In the example of the display screen in FIG. 7, "S" indicates that the reason for the high snow removal priority is that the point is a slope. When displaying the reason for the estimated snow removal priority, the display of the reason is not limited to the example of the display screen in FIG. 7. Furthermore, the reason for the estimated snow removal priority may be displayed for all points for which snow removal priority has been estimated.

[0067] FIG. 8 is a diagram showing an example of a display screen for selecting the range for estimating the snow removal priority. In the example of the display screen in FIG. 8, the range for estimating the snow removal priority is selected by selecting a range framed by a dashed line on a map. The range for estimating the snow removal priority is selected, for example, by an operator using a mouse on the terminal device 30. The terminal device 30 then outputs the selected range for estimating the snow removal priority to the snow removal assistance system 10. The estimation unit 13 estimates the snow removal priority for roads within the selected range, for example. The range for estimating the snow removal priority may also be selected by specifying each point on the map. The selection of the range for estimating the snow removal priority is not limited to a rectangle, and may be any shape.

[0068] The memory unit 15 stores, for example, data used to estimate the priority of snow removal. The memory unit 15 stores, for example, map data related to roads in an area to be cleared of snow. The map data may include data related to the topography and road structure. The memory unit 15 may store data on the importance of roads. The memory unit 15 may store weather forecast data. The memory unit 15 may also store photographed images of roads and measurement data on the vehicle's driving state. The memory unit 15 may also store the state of the snow surface identified by the identification unit 12.

[0069] The storage unit 15 stores, for example, criteria used to estimate the priority of snow removal. The storage unit 15 stores, for example, criteria used to estimate the score of the snow surface condition and the score of the measurement data. The storage unit 15 may also store criteria used to score the importance of roads, topography, and road structure.

[0070] When an estimation model is used to estimate the priority of snow removal, the estimation model is stored, for example, in the storage unit 15. The estimation model may be stored in a storage means other than the storage unit 15.

[0071] The in-vehicle device 20 includes, for example, a camera that captures an image in front of the vehicle. The camera of the in-vehicle device 20 captures an image including the road surface. The camera of the in-vehicle device 20 may also capture an image behind the vehicle. The in-vehicle device 20 adds, for example, information about the location where the image was captured to the captured image. The in-vehicle device 20 identifies the location of the vehicle when the image was captured, for example, using a Global Navigation Satellite System (GNSS). The in-vehicle device 20 may also identify the location of the vehicle based on a beacon that includes location information. The in-vehicle device 20 may also identify the location of the image based on map information and the travel distance from the location where the vehicle's location was identified. The in-vehicle device 20 also outputs the captured image to, for example, the snow removal assistance system 10.

[0072] The in-vehicle device 20 includes a sensor that measures the vehicle's driving state. The in-vehicle device 20 includes, for example, an acceleration sensor that can measure the vehicle's acceleration in the vertical direction. The vertical direction of the vehicle is the direction perpendicular to the driving surface. In an acceleration sensor, the vertical direction of the vehicle is also called the z-axis. The acceleration sensor may also measure acceleration in the vehicle's traveling direction and in a direction perpendicular to the vehicle's traveling direction and the vertical direction. The sensor that measures the vehicle's driving state may be a sensor that measures the load on the tires or brakes. The sensor that measures the vehicle's driving state may also be a sensor that measures the vehicle's speed. The sensor that measures the vehicle's driving state is not limited to the above. The in-vehicle device 20 may also acquire measurement data of the vehicle's driving state from a vehicle control device.

[0073] For example, the on-vehicle device 20 adds location information at the time of measurement to the measurement results obtained by a sensor that measures the vehicle's driving conditions. Then, the on-vehicle device 20 outputs the measurement results obtained by the sensor to the snow removal assistance system 10, for example, via a network. The on-vehicle device 20 may store the measurement data of the vehicle's driving conditions in a storage device. For example, the on-vehicle device 20 stores the measurement data of the vehicle's driving conditions in a non-volatile semiconductor storage device. For example, the on-vehicle device 20 has a slot for installing a removable non-volatile semiconductor storage device. For example, a flash memory is used as the non-volatile semiconductor storage device. The non-volatile semiconductor storage device is not limited to a flash memory.

[0074] The in-vehicle device 20 is mounted, for example, in a road monitoring vehicle operated by a road administrator. The in-vehicle device 20 may also be mounted in a vehicle operated by someone other than the road administrator. For example, the in-vehicle device 20 may be mounted in a bus, a taxi, a truck, a public vehicle, a shuttle vehicle, or an emergency vehicle. The in-vehicle device 20 may also be mounted in a privately owned passenger car. Vehicles in which the in-vehicle device 20 is mounted are not limited to those described above. For example, a drive recorder is used as the in-vehicle device 20. The in-vehicle device 20 is not limited to a drive recorder.

[0075] The terminal device 30, for example, acquires the estimated results of snow removal priority generated by the snow removal assistance system 10. Then, the terminal device 30 outputs the acquired estimated results of snow removal priority to a display device (not shown). The terminal device 30 may also acquire the target range of snow removal input by an operator's operation. The terminal device 30 outputs the acquired target range of snow removal to the snow removal assistance system 10, for example.

[0076] For example, a personal computer, a tablet computer, or a smartphone can be used as the terminal device 30. The terminal device 30 is not limited to the above examples. Furthermore, the in-vehicle device 20 and the terminal device 30 may be integrated into one device.

[0077] The following describes the operation of the snow removal support system 10 when estimating the priority of snow removal. Fig. 9 shows an example of the operation flow when the snow removal support system 10 estimates the priority of snow removal.

[0078] The acquisition unit 11 acquires an image of a snow-covered road and measurement data obtained by measuring the driving state of a vehicle traveling on the snow-covered road (step S11).

[0079] When the image of the snow-covered road and the measurement data are acquired, the identification unit 12 identifies the state of the snow surface from the image of the snow-covered road (step S12).

[0080] If there is a point where the classification of the snow surface condition has not been completed (No in step S13), the classification unit 12 classifies the snow surface condition for the image where the classification of the snow surface condition has not been completed in step S12.

[0081] When the snow surface condition has been identified for all target points (Yes in step S13), the estimation unit 13 estimates the priority of snow removal at each point on the road based on the identified snow surface condition and the measurement data (step S14).

[0082] Once the priority of snow removal for the road has been estimated, the output unit 14 outputs the snow removal priority estimated by the estimation unit 13 (step S15). The output unit 14 outputs the estimated result of snow removal priority to, for example, the terminal device 30. Upon acquiring the estimated result of snow removal priority, the terminal device 30 outputs the acquired estimated result of snow removal priority to, for example, a display device (not shown).

[0083] The snow removal assistance system 10 of this embodiment identifies the condition of the snow surface from an image of a snow-covered road. The snow removal assistance system 10 then estimates the priority of snow removal for the road based on the identified snow surface condition and measurement data obtained by measuring the vehicle's driving condition. Therefore, by using the snow removal assistance system 10, it is possible to easily determine areas where snow removal is necessary. Furthermore, because the snow removal assistance system 10 estimates the priority of snow removal based on the condition of the snow surface and measurement data obtained by measuring the vehicle's driving condition, it is possible to estimate the priority of snow removal by taking into account the effect of the snow surface on the road on vehicle driving.

[0084] Furthermore, when the importance of roads is further used to estimate the priority of snow removal, the priority of snow removal can be estimated taking into account the impact on traffic. Therefore, by performing snow removal with reference to the snow removal priority estimated using the importance of roads further, the impact of snow accumulation on traffic can be reduced.

[0085] Furthermore, when weather information is further used to estimate the priority of snow removal, the priority of snow removal can be estimated based on, for example, changes in the state of the snow surface that may occur after the time of estimation. Therefore, by referring to the priority of snow removal estimated using weather information further, the effectiveness of snow removal when actually performing snow removal can be improved.

[0086] Furthermore, when the estimated snow removal priority and the reason for the estimation are output, for example, a road administrator can refer to the reason for the estimation and interpret the estimated snow removal priority. Therefore, outputting the estimated snow removal priority and the reason for the estimation makes it possible to set a more appropriate snow removal route, for example.

[0087] Each process in snow removal assistance system 10 can be realized by executing a computer program on a computer. Fig. 10 shows an example of the configuration of computer 100 that executes a computer program that performs each process in snow removal assistance system 10. Computer 100 includes a CPU (Central Processing Unit) 101, memory 102, a storage device 103, an input / output I / F (Interface) 104, and a communication I / F 105.

[0088] The CPU 101 reads and executes computer programs for performing each process from the storage device 103. The CPU 101 may be configured by a combination of multiple CPUs. Furthermore, the CPU 101 may be configured by a combination of a CPU and another type of processor. For example, the CPU 101 may be configured by a combination of a CPU and a graphics processing unit (GPU). The memory 102 is configured by a dynamic random access memory (DRAM) or the like, and temporarily stores computer programs executed by the CPU 101 and data being processed. The storage device 103 stores computer programs executed by the CPU 101. The storage device 103 is configured by, for example, a non-volatile semiconductor storage device. Other storage devices such as a hard disk drive may also be used for the storage device 103. The input / output I / F 104 is an interface that receives input from an operator and outputs display data, etc. The communication I / F 105 is an interface that transmits and receives data between the in-vehicle device 20, the terminal device 30, and other information processing devices. Furthermore, the terminal device 30 may have a configuration similar to that of the computer 100.

[0089] The computer program used to execute each process can also be stored and distributed on a computer-readable recording medium that non-temporarily stores data. Examples of recording media that can be used include magnetic tapes for recording data and magnetic disks such as hard disks. Optical disks such as CD-ROMs (Compact Disc Read Only Memory) can also be used as recording media. Non-volatile semiconductor storage devices can also be used as recording media.

[0090] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes.

[0091] [Appendix 1] an acquisition means for acquiring an image of a snow-covered road and measurement data obtained by measuring the driving state of a vehicle driving on the snow-covered road; an identification means for identifying the state of a snow surface from an image of a snow-covered road; an estimation means for estimating the priority of snow removal at each point on the road based on the identified snow surface condition and the measurement data; an output means for outputting the estimated snow removal priority; A snow removal support system equipped with:

[0092] [Appendix 2] the estimation means estimates the priority of snow removal based on the state of the ruts on the snow surface identified by the identification means. 1. A snow removal assistance system as described in Appendix 1.

[0093] [Appendix 3] the estimation means estimates the priority of snow removal based on measurement data obtained by measuring the acceleration of a vehicle traveling on a snow-covered road; 3. The snow removal assistance system according to claim 1 or 2.

[0094] [Appendix 4] The estimation means further uses weather forecast data to predict the state of the snow surface, and estimates the priority of snow removal based on the prediction result. Attachment 1 to 3: A snow removal assistance system.

[0095] [Appendix 5] The estimation means estimates the priority of snow removal further using the road surface condition when there is no snow. Attachment 1 to 4 snow removal assistance system.

[0096] [Appendix 6] the estimation means estimates the priority of snow removal by further using the degree of deterioration of the road surface diagnosed when there is no snow. Attachment 5: A snow removal assistance system.

[0097] [Appendix 7] the estimation means estimates the priority of snow removal using the score based on the snow surface condition and the score based on the measurement data. 10. A snow removal assistance system as set forth in any one of appendixes 1 to 6.

[0098] [Appendix 8] the estimation means estimates the priority of snow removal using an estimation model for estimating the priority of snow removal based on the state of the snow surface and the measurement data; 10. A snow removal assistance system as set forth in either appendix 1 or 6.

[0099] [Appendix 9] the estimation means estimates the priority of snow removal by further using at least one of the following: importance of the road, information on topography, information on the structure of the road, and information on surrounding facilities; 10. A snow removal assistance system as set forth in any one of appendixes 1 to 8.

[0100] [Appendix 10] The output means outputs the estimated snow removal priority and the reason for the estimation by superimposing them on a map. 10. A snow removal assistance system as set forth in any one of appendixes 1 to 9.

[0101] [Appendix 11] the acquiring means acquires information on a map that indicates an area for estimating the priority of snow removal; The estimation means estimates the snow removal priority within a selected range. 11. A snow removal assistance system according to any one of appendixes 1 to 10.

[0102] [Appendix 12] Acquire an image of a snow-covered road and measurement data of the driving state of a vehicle traveling on the snow-covered road, Identify the condition of the snow surface from images of snow-covered roads, Based on the identified snow surface condition and the measurement data, a priority of snow removal at each point on the road is estimated; Output the estimated priority of the snow removal A snow removal support method comprising:

[0103] [Appendix 13] A process of acquiring an image of a snow-covered road and measurement data of a vehicle traveling on the snow-covered road; A process to identify the state of the snow surface from images of snow-covered roads, a process of estimating the priority of snow removal at each point on the road based on the identified snow surface condition and the measurement data; A process of outputting the estimated priority of snow removal. A recording medium for non-temporarily recording a snow removal assistance program that causes a computer to execute the above.

[0104] The present invention has been described above using the above-described embodiment as an example. However, the present invention is not limited to the above-described embodiment. In other words, the present invention can be applied in various aspects that can be understood by a person skilled in the art within the scope of the present invention. [Explanation of symbols]

[0105] 10 Snow removal support system 11 Acquisition Department 12 Identification unit 13 Estimation part 14 Output section 15 Storage section 20 Onboard equipment 30 Terminal Equipment 100 computers 101 CPU 102 memory 103 Storage device 104 Input / Output Interface 105 Communication I / F

Claims

1. an acquisition means for acquiring an image of a snow-covered road and measurement data obtained by measuring the driving state of a vehicle driving on the snow-covered road; an identification means for identifying the state of a snow surface from an image of a snow-covered road; a first estimation means for estimating a first score indicating the poorness of the snow surface condition based on the identified snow surface condition; a second estimation means for estimating a second score indicating an influence of the poor condition of the snow surface on the running of the vehicle based on the measurement data; a third estimation means for estimating a priority of snow removal at each point on the road based on the first score and the second score; an output means for outputting the estimated snow removal priority; A snow removal support system equipped with:

2. The third estimation means estimates the priority of snow removal at each point on the road such that the larger the sum of the first score and the second score, the higher the priority of snow removal. The snow removal assistance system according to claim 1 .

3. The state of the snow surface is a state of ruts on the snow surface. The snow removal assistance system according to claim 1 .

4. The measurement data is the acceleration of a vehicle traveling on a snow-covered road. The snow removal assistance system according to claim 1 .

5. the third estimation means further uses weather forecast data to predict the state of the snow surface, and estimates the priority of snow removal based on the prediction result; The snow removal support system according to any one of claims 1 to 4.

6. the third estimation means further uses the road surface condition when there is no snow to estimate the priority of snow removal. The snow removal support system according to any one of claims 1 to 4.

7. the third estimation means estimates the priority of snow removal by further using the degree of deterioration of the road surface diagnosed when there is no snow. The snow removal assistance system according to claim 6.

8. the third estimation means estimates the priority of snow removal using an estimation model for estimating the priority of snow removal based on the first score and the second score; The snow removal support system according to any one of claims 1 to 4.

9. A computer comprising: Acquire an image of a snow-covered road and measurement data of the driving state of a vehicle traveling on the snow-covered road, Identify the condition of the snow surface from images of snow-covered roads, Execute a process of estimating a first score indicating the poor condition of the snow surface based on the identified snow surface condition, and a process of estimating a second score indicating the effect of the poor condition of the snow surface on the running of the vehicle based on the measurement data, estimating a priority of snow removal at each point on the road based on the first score and the second score; outputting the estimated snow removal priority; Snow removal support method.

10. A process of acquiring an image of a snow-covered road and measurement data of a vehicle traveling on the snow-covered road; A process to identify the state of the snow surface from images of snow-covered roads, A process of estimating a first score indicating the poorness of the snow surface condition based on the identified snow surface condition; a process of estimating a second score indicating the influence of the poor condition of the snow surface on the running of the vehicle based on the measurement data; a process of estimating a priority of snow removal at each point on the road based on the first score and the second score; A process of outputting the estimated priority of snow removal. A snow removal assistance program that runs on a computer.

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