Flood detection device, flood detection system, flood detection program storage medium, and flood detection method

A flood detection device includes a detection result acquisition section, a weather information acquisition section and an identification section. The detection result acquisition section acquires a detection result that, on the basis of a plurality of kinds of running state data relating to running of a vehicle, detects flooding of a road on which the vehicle is running. The weather information acquisition section acquires weather information including at least one of rainfall information representing a measured rainfall amount in a region in which the vehicle is running or rainfall estimation information representing an estimated rainfall amount. The identification section uses the respective acquisition results of the detection result acquisition section and the weather information acquisition section to identify flooding of the road.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based on and claims priority under 35 USC 119 from Japanese Patent Application No. 2019-167664 filed on Sep. 13, 2019, the disclosure of which is incorporated by reference herein.

BACKGROUND

Technical Field

The present disclosure relates to a flood detection device, a flood detection system, a flood detection program storage medium, and a flood detection method.

Related Art

A road may be flooded by a large quantity of rainfall, an inflow of rainwater that has fallen in another location or the like. Technologies that detect this flooding of a road include technologies proposed in, for example, Japanese Patent Application Laid-Open (JP-A) Nos. 2004-341795 and 2012-216103.

JP-A No. 2004-341795 proposes equipping, in the vehicle, a vehicle with a flood sensor configured to be capable of detecting the presence of a liquid substance, detecting flooding of roads, transmitting detection results to a server in a center, and specifying a route that does not pass through an impassable flood and providing with another vehicle guidance for the diverted route.

JP-A No. 2012-216103 proposes estimating a rainfall amount of a location a vehicle is running through on the basis of a wiping speed and operation duration of a wiper of the vehicle, and estimating whether or not there is flooding in a running route on the basis of estimated rainfall amounts provided from other vehicles.

However, the technology of JP-A No. 2004-341795 requires a rainfall sensor, and there is scope for improvement in more easily assessing flooding.

With the technology of JP-A No. 2012-216103, not all drivers necessarily operate wipers at the same wiper speeds for the same rainfall amounts, and there is scope for improvement in accurately identifying flooding.

SUMMARY

The present disclosure is made in consideration of the circumstances described above and provides a flood detection device, a flood detection system, a flood detection program storage medium, and a flood detection method that may identify flooding of a road simply and accurately.

A first aspect of the present disclosure is a flood detection device including: a detection result acquisition section that acquires a detection result detecting flooding of a road on which a vehicle is running, on the basis of plural kinds of running state data relating to the running of the vehicle; a weather information acquisition section that acquires weather information including at least one of rainfall information representing a measured rainfall amount in a region in which the vehicle is running, or rainfall estimation information representing an estimated rainfall amount; and an identification section that identifies flooding of the road by using the acquisition results of the detection result acquisition section and the weather information acquisition section.

According to the first aspect, on the basis of the plural kinds of running state data relating to the running of the vehicle, the detection result acquisition section acquires the detection result detecting flooding of the road on which the vehicle is running.

The weather information acquisition section acquires the weather information that includes at least one of the rainfall information representing measured rainfall amounts in the region in which the vehicle is running or the rainfall estimation information.

The identification section uses the respective acquisition results of the detection result acquisition section and the weather information acquisition section to identify flooding of the road. Thus, flooding may be detected easily without using a flood detection sensor. Moreover, flooding of a road may be identified more accurately than in a case of directly detecting flooding of the road.

The first aspect may further include a collection section that collects post information from a social networking service, wherein the identification section identifies flooding of the road by using the acquisition results of the detection result acquisition section and the weather information acquisition section, and a collection result of the collection section. Thus, the accuracy of identification of flooding of the road may be further improved.

The first aspect may further include a detection section that detects flooding of the road on which the vehicle is running, on the basis of the running state data, wherein the detection result acquisition section acquiring the detection result from the detection section.

In the first aspect, the vehicle may be equipped with a detection section that detects flooding of the road on which the vehicle is running on the basis of the running state data, and the detection result acquisition section may acquire the detection result from the vehicle.

In the first aspect, the weather information may further include a recording section that records a flood point at which flooding of the road is identified by the identification section together with the weather information used at the time when the flooding is identified by the identification section; and a flood prediction section that predicts flooding of the flood point recorded at the recording section, by using an estimated rainfall amount represented by rainfall estimation information, the rainfall estimation information being acquired by the weather information acquisition section as the weather information, and the weather information of the flood point recorded at the recording section.

A second aspect of the present disclosure is a flood detection system including: a running detection section that detects plural kinds of running state data relating to running of a vehicle; an acquisition section that acquires the plural kinds of running state data detected by the running detection section from plural the vehicle; a derivation section that derives a pre-specified vehicle movement model that finds a physical quantity that changes in accordance with running of the vehicle, by using the plural kinds of running state data acquired from the plural vehicles by the acquisition section at a previous time, and a pre-specified learning model; a detection section that detects flooding of a road on which the vehicle of interest is running by using the vehicle movement model derived by the derivation section and the plural kinds of running state data acquired from a pre-specified vehicle of interest by the acquisition section at a current time to predict the physical quantity, and using the predicted physical quantity and a physical quantity obtained from the running state data acquired from the vehicle of interest by the acquisition section at the current time; a weather information acquisition section that acquires weather information including at least one of rainfall information representing a measured rainfall amount in a region in which the vehicle is running, or rainfall estimation information representing an estimated rainfall amount; and an identification section that identifies flooding of the road by using a detection result from the detection section and an acquisition result from the weather information acquisition section.

According to the second aspect, the detection section detects plural kinds of running state data relating to running of the vehicle.

The acquisition section acquires the plural kinds of running state data detected by detection sections from the plural vehicles.

The derivation section uses the plural kinds of running state data previously acquired from the plural vehicles by the acquisition section and the pre-specified learning model to derive the vehicle movement model for finding the physical quantity that changes in accordance with running of the vehicle.

The detection section uses the vehicle movement model derived by the derivation section and the plural kinds of current running state data acquired by the acquisition section from the pre-specified vehicle of interest to predict the physical quantity. The detection section uses the predicted physical quality and the physical quantity that is obtained from the current running state data acquired from the vehicle of interest by the acquisition section to detect flooding of a road on which the vehicle of interest is running.

The weather information acquisition section acquires the weather information that includes at least one of the rainfall information representing measured rainfall amounts in the region in which the vehicle is running or the rainfall estimation information representing an estimated rainfall amount.

The identification section uses each of the detection result from the detection section and the acquisition result from the weather information acquisition section to identify flooding of the road. Thus, flooding may be detected easily without using a flood detection sensor. Moreover, because flooding is identified using not only running state data but also weather information, flooding of a road may be identified accurately.

A third aspect of the present disclosure is a non-transitory computer readable storage medium storing a program that causes a computer to execute flood detection processing, the flood detection processing including: acquiring a detection result detecting flooding of a road on which a present vehicle is running, on the basis of plural kinds of running state data relating to the running of the present vehicle; acquiring weather information including at least one of rainfall information representing a measured rainfall amount in a region in which the present vehicle is running, or rainfall estimation information representing an estimated rainfall amount; and identifying flooding of the road by using the detection result and the weather information.

A fourth aspect of the present disclosure is a flood detection method including: acquiring a detection result detecting flooding of a road on which a present vehicle is running on the basis of plural kinds of running state data relating to the running of the present vehicle; acquiring weather information including at least one of rainfall information representing a measured rainfall amount in a region in which the present vehicle is running, or rainfall estimation information representing an estimated rainfall amount; and identifying flooding of the road by using the detection result and the weather information.

According to the present disclosure as described above, a flood detection device, a flood detection system, a flood detection program storage medium, and a flood detection method that may assess flooding of a road simply and accurately may be provided.

DETAILED DESCRIPTION

Herebelow, an exemplary embodiment of the present disclosure is described in detail with reference to the attached drawings.FIG.1is a block diagram illustrating a schematic configuration of a flood detection system according to the exemplary embodiment.

In a flood detection system10according to the exemplary embodiment, an information provision device14is respectively installed in plural vehicles12. The information provision devices14, a flood assessment center36, a weather information center60and a social networking service (SNS) server62are respectively connected via a communications network34.

The weather information center60provides various kinds of weather data relating to weather to devices connected to the communications network34in the form of weather information. The weather information includes, for example, weather data of at least one of measured rainfall amounts or estimated rainfall amounts at respective locations. In the descriptions below, the weather data that is described is mainly measured rainfall amounts. However, estimated rainfall amounts may be used in areas where measured rainfall amounts cannot be identified.

The SNS server62provides a service that enables communications between users who are registered in advance. For example, communications between users are enabled by the users collectively posting various kinds of information to the SNS server62in the form of post information.

The flood assessment center36collects running state data of the plural vehicles12from the information provision devices14installed in the vehicles12, in the form of Controller Area Network (CAN) data, and acquires weather data from the weather information center60. The flood assessment center36executes processing to assess flooding of roads on which the vehicles12are running, using the collected CAN data, weather data, SNS post information and the like. The flood assessment center36also uses a history of flood points assessed to have flooded and the weather data to predict flooding.

The information provision device14installed in each vehicle12is provided with a calculation section16, a GPS reception section18, an acceleration sensor20, a display section22, a vehicle speed sensor24, a communications section26, a gradient sensor28, an accelerator pedal sensor30and a brake pedal sensor32. The acceleration sensor20, vehicle speed sensor24, gradient sensor28, accelerator pedal sensor30and brake pedal sensor32correspond to a running detection section.

The calculation section16is constituted by a general microcomputer including a central processing unit (CPU), read-only memory (ROM), random access memory (RAM) and so forth.

The GPS reception section18receives signals from Global Positioning System (GPS) satellites and outputs the received GPS signals to the calculation section16. Hence, the calculation section16determines the position of the vehicle12on the basis of the GPS signals received from plural GPS satellites.

The acceleration sensor20senses accelerations applied to the present (local) vehicle12as running state data and outputs sensing results to the calculation section16. Accelerations may be sensed in each of a front-and-rear direction, a width direction and a vertical direction of the vehicle12, or only accelerations of the vehicle12in the front-and-rear direction may be sensed.

The display section22displays information (for example, map information or the like) about flood points that are assessed to be flooded by the flood assessment center36, and various other kinds of information.

The vehicle speed sensor24senses running speeds of the present vehicle12as running state data and outputs sensing results to the calculation section16.

The communications section26conducts wireless communications with the communications network34, and thus communicates with the flood assessment center36and the information provision devices14installed in others of the vehicles12. The communications network34includes, for example, a wireless communications circuit network such as a portable telephony circuit network or the like.

By sensing tilting of the vehicle12, the gradient sensor28senses gradients along which the vehicle12is running as running state data and outputs sensing results to the calculation section16. The gradients that are sensed may only be gradients in the front-and-rear direction of the vehicle12, or gradients in the vehicle width direction may also be sensed.

The accelerator pedal sensor30senses pressing amounts of an accelerator pedal as running state data and outputs sensing results to the calculation section16.

The brake pedal sensor32senses pressing amounts of a brake pedal as running state data and outputs sensing results to the calculation section16.

In the exemplary embodiment, an example is described in which sensing results from the acceleration sensor20, the vehicle speed sensor24, the gradient sensor28, the accelerator pedal sensor30and the brake pedal sensor32are detected as an example of the running state data, but this is not limiting.

The calculation section16transmits the plural kinds of running state data acquired from the sensors and a vehicle type ID, which identifies vehicle types, to the flood assessment center36via the communications section26and the communications network34.

The flood assessment center36is provided with a central processing section38, a central communications section48, a model storage section50, a CAN database52and a flood point database56.

The central communications section48communicates with the information provision devices14installed at the vehicles12, by conducting wireless communications with the communications network34.

The model storage section50stores a vehicle movement model that finds a physical quantity (described in detail below) that changes in accordance with running of the vehicle12, and a table of coefficients that are specified for each of the vehicle types.

The CAN database52stores, as CAN data, the running state data acquired from the information provision devices14installed at the vehicles12.

The central processing section38is constituted by a general microcomputer including a central processing unit (CPU), read-only memory (ROM), random access memory (RAM) and so forth. The central processing section38features the functions of a prediction section40, an assessment section42, a model update section46, an information collection section54, and a flood prediction section44. These functions are realized by the CPU of the central processing section38executing a program stored in the ROM or the like. The functions of the central processing section38correspond to a detection result acquisition section, a weather information acquisition section, an identification section, a collection section, a detection section, a recording section, a flood prediction section, an acquisition section and a derivation section. The functions of the central processing section38correspond to processing that is described in detail below.

The information collection section54collects information such as flood information contained in SNS post information exchanged between users via various SNS servers62, weather data provided by the weather information center60and so forth.

The prediction section40reads out a vehicle movement model stored in advance in the model storage section50, identifies a vehicle type from each vehicle type ID, and derives a vehicle movement model for the respective vehicle type by selecting coefficients corresponding to the vehicle type and applying the coefficients to the vehicle movement model. Predicted values of a physical quantity are calculated by substituting CAN data into the derived vehicle movement model. In the exemplary embodiment, vehicle speed is employed as the physical quantity to be predicted. Coefficients corresponding to the vehicle type that have been found beforehand are applied to a vehicle movement model for finding vehicle speed that has been derived beforehand, and predicted values of vehicle speed are calculated. A vehicle movement model for finding vehicle speed is described in detail below.

The assessment section42compares vehicle speeds predicted by the prediction section40with actual vehicle speeds acquired from the information provision device14and assesses whether or not a road is flooded. More specifically, the assessment section42detects flooding of a road by assessing that the road is flooded in a case in which a difference between a predicted speed and a measured speed is at least a pre-specified threshold value. For example, as illustrated inFIG.2, in a case in which the measured values and predicted values are changing with the passing of time, the assessment section42determines that there is an error (a flood) in an area where a state in which the differences between the measured values and the predicted values are at least the pre-specified threshold value continues for a predetermined duration (for example, 5 seconds or more). The assessment section42also executes processing to use SNS post information, weather data and the like collected by the information collection section54to improve the accuracy of this flood assessment based on CAN data.

The model update section46uses CAN data stored in the CAN database52to derive the coefficients of the vehicle movement model by machine learning, stores the coefficients in the model storage section50, and continuously updates a model coefficients table.

Now, an example of the above-mentioned vehicle movement model that finds vehicle speed is described in detail. In the exemplary embodiment, a vehicle movement model that uses equations of motion to find the physical quantity that is vehicle speed is derived.

First, an equation of motion is represented by the following expression (1).
M×(dv/dt)=F(1)

Here, M represents vehicle weight, dv/dt represents acceleration, and F represents a forward progress force of the vehicle12.

Here, v(t+Δt) is a vehicle speed Δt seconds later (a predicted vehicle speed), t is time, and v(t) is the vehicle speed at a current time.

Substituting expression (2) into expression (1) provides the following expression (3).
M×(v(t+Δt)−v(t))/Δt=F(3)

Rearranging for v(t+Δt) provides the following expression (4).
v(t+Δt)=v(t)+(F/M)×Δt(4)

The term F is made up of F=F1 (driving force of the vehicle12)−F2 (resistance acting on the vehicle12). Using CAN data provides the following expression (5).
F1=C1×R(5)

Here, C1 is a coefficient and R is an accelerator pressing amount, which is obtained from the CAN data.
F2=air resistance+slope resistance+rolling resistance+acceleration resistance  (6)

Here, C21, C22, C23 and C24 are coefficients, θ is a road gradient, v(t) is the vehicle speed, and a(t) is acceleration; these are obtained from the CAN data.

Substituting expressions (5) and (6) into expression (4) may provide the following multiple regression equation to serve as a vehicle movement model.
v(t+Δt)=v(t)+{C1×R−(C21×v(t)2+C22×sin θ+C23×v(t)+C24×a(t))}×(Δt/M)  (7)

The coefficients are found by a multiple regression analysis learning model using large volumes of CAN data collected from the plural vehicles12and stored in the CAN database, and the coefficients are stored in the model storage section50as a coefficient table. Upon new CAN data is acquired, the coefficients stored in the model storage section50are updated. Because the coefficients differ between different vehicle types, the coefficients are found and updated for each vehicle type. For example, as illustrated inFIG.3, the coefficients stored in the model storage section50are stored in the form of a table associating the vehicle types with the model coefficients.

Now, processing of the central processing section38of the flood assessment center36that derives the vehicle movement model in the flood detection system10according to the exemplary embodiment configured as described above is described.FIG.4is a flowchart illustrating an example of a flow of processing that is executed by the central processing section38of the flood assessment center36of the flood detection system10according to the exemplary embodiment to derive a vehicle movement model by machine learning. The processing inFIG.4is executed when initially deriving coefficients of the vehicle movement model and each time CAN data is collected into the CAN database52.

In step100, the model update section46acquires CAN data serving as running state data that has been collected into the CAN database52via the central communications section48, and proceeds to step102. Note that step100corresponds to the processing of the acquisition section.

In step102, the model update section46performs preprocessing of the acquired CAN data, and the central processing section38proceeds to step104. The preprocessing is, for example, sorting the CAN data by date/time and vehicle type, and categorizing the CAN data into time sets and vehicle type sets. The preprocessing may include processing each set of CAN data to standardize times, interpolate missing data and the like.

In step104, the model update section46sets a model equation, stores the model equation in the model storage section50, and the central processing section38ends this sequence of processing. That is, the model update section46uses the CAN data to derive the coefficients of the aforementioned multiple regression equation serving as the vehicle movement model by machine learning, and stores the coefficients in the model storage section50. If coefficients have been stored previously, the coefficients are updated. Note that step104corresponds to the processing of the derivation section.

Now, processing that is executed by the central processing section38of the flood assessment center36to assess flooding on the basis of CAN data from the vehicles12is described.FIG.5is a flowchart illustrating an example of a flow of processing that is executed by the central processing section38of the flood assessment center36of the flood detection system10according to the exemplary embodiment to assess flooding. The processing inFIG.5is executed during processing to improve the accuracy of flood assessment, which is described below.

In step200, the central processing section38acquires CAN data from one of the information provision devices14via the central communications section48and the communications network34, and the central processing section38proceeds to step202. Note that step200corresponds to the processing of the acquisition section, and the subsequent processing of steps202to210corresponds to the processing of the detection section.

In step202, the prediction section40uses the acquired CAN data and the vehicle movement model to calculate a predicted value of vehicle speed, and the central processing section38proceeds to step204. That is, the prediction section40reads out the vehicle movement model stored in the model storage section50, identifies the vehicle type from the vehicle type ID, selects the coefficients corresponding to that vehicle type, and deploys the coefficients in the vehicle movement model. Thus, the predicted value of vehicle speed is calculated by substituting the acquired CAN data into the vehicle movement model.

In step204, the assessment section42compares the predicted value of vehicle speed with a measured value of vehicle speed in actual CAN data acquired from the information provision device14, and the central processing section38proceeds to step206.

In step206, the assessment section42makes a determination as to whether a difference between the predicted value and the measured value is at least the pre-specified threshold value. In a case in which the result of this determination is negative, the central processing section38proceeds to step208, and in a case in which the result is affirmative, the central processing section38proceeds to step210.

In step208, the assessment section42assesses that a road on which the vehicle12that acquired the CAN data is running is free of flooding and not flooded, and the central processing section38ends this sequence of processing.

Alternatively, in step210the assessment section42assesses that the road on which the vehicle12that acquired the CAN data is running is flooded, and the central processing section38ends the sequence of processing.

Next, processing in the flood detection system10according to the exemplary embodiment to utilize SNS post information, weather data and the like collected by the information collection section54and improve the accuracy of flood assessment by the central processing section38is described.FIG.6is a flowchart illustrating an example of a flow of processing that uses the SNS post information, and weather data collected by the information collection section54to improve the accuracy of the flood assessment performed by the central processing section38. The processing inFIG.7is started, for example, each time CAN data is acquired from the information provision device14of one of the vehicles12or each time a pre-specified amount of CAN data has been acquired.

In step300, the central processing section38executes the flood assessment processing based on CAN data, and proceeds to step302. That is, a flood assessment result is acquired by carrying out the flood assessment processing illustrated inFIG.5described above. Note that step300corresponds to the processing of the detection result acquisition section.

In step302, by accessing the SNS server62via the central communications section48and the communications network34, the information collection section54collects SNS post information containing flood information, and the central processing section38proceeds to step304. Note that step302corresponds to the processing of the collection section.

In step304, by accessing the weather information center60via the central communications section48and the communications network34, the information collection section54acquires weather data, and the central processing section38proceeds to step306. Note that step304corresponds to the processing of the weather information acquisition section.

In step306, for a location that is assessed to be flooded by the flood assessment processing based on CAN data in step300, the assessment section42makes a determination based on the weather data as to whether a measured rainfall at the location is at least a pre-specified threshold value. In a case in which the result of the determination is affirmative, the central processing section38proceeds to step308, and in a case in which the result is negative, the central processing section38ends this sequence of processing. The determination in step306may instead be a determination as to whether an assessment of flooding by the flood assessment processing based on CAN data in step300is within an area in which a measured rainfall is at least the pre-specified threshold value.

In step308, the assessment section42makes a determination as to whether there is a user report of the location assessed to be flooded at which the measured rainfall is at least the pre-specified threshold value of step306. This determination is a determination as to whether SNS post information indicating a flood report has been acquired for the location assessed to be flooded by the flood assessment processing based on CAN data in step300. In a case in which the result of the determination is affirmative, the central processing section38proceeds to step310, and in a case in which the result is negative, the central processing section38ends this sequence of processing.

In step310, the assessment section42records a flood point in the flood point database56, and the central processing section38ends the sequence of processing. When a flood point is being recorded, the assessment section42also records weather data such as the measured rainfall amount, an estimated rainfall amount or the like. The flood points recorded in the flood point database56may, for example, be distributed as flood information to each information provision device14connected to the communications network34via the central communications section48. Hence, at the vehicle12in which each information provision device14is installed, flood points may be known and a route that does not pass through a flood point may be selected. For example, in a case in which route guidance that passes through a flood point is being provided by a navigation device, the navigation device may re-route to a route that avoids the flood point. Alternatively, information on flood points may be distributed to weather forecasting companies and the like as necessary and be evaluated. Note that steps306to310correspond to the processing of the identification section and step310corresponds to the processing of the recording section.

Next, flood prediction processing that is executed by the flood prediction section44of the flood detection system according to the exemplary embodiment is described. FIG.7is a flowchart illustrating an example of a flow of the flood prediction processing that is executed by the flood prediction section44of the flood detection system10according to the exemplary embodiment. The processing inFIG.7may be started, for example, after an occupant of the vehicle12makes a request for a flood prediction from the information provision device14to the flood assessment center36. Alternatively, the processing inFIG.7may be started upon a warning of rainfall is issued from the weather information center60, such as a heavy rain warning, a flood warning, landslide warning information or the like.

In step400, the flood prediction section44acquires weather data, such as estimated rainfall amounts, estimated raincloud amounts and the like, from the weather information center60via the central communications section48and the communications network34, and the central processing section38proceeds to step402.

In step402, the flood prediction section44reads out flood records recorded in the flood point database56, and the central processing section38proceeds to step404.

In step404, the flood prediction section44checks an estimated rainfall amount against the flood records, and the central processing section38proceeds to step406. That is, the flood prediction section44checks the estimated rainfall amounts acquired as weather data against measured rainfall amounts at locations that have been recorded as flood points.

In step406, the flood prediction section44makes a determination, from the results of checking in step404, as to whether an estimated rainfall amount at a flood point exceeds a measured rainfall amount at a time of flooding. In a case in which the result of this determination is negative, the central processing section38simply ends this processing, and in a case in which the result is affirmative, the central processing section38proceeds to step408.

In step408, the flood prediction section44predicts flooding and ends this sequence of processing. In a case in which flooding is predicted, flood prediction information may be distributed to each information provision device14connected to the communications network34via the central communications section48.

In the processing inFIG.6to improve the accuracy of flood assessment of the exemplary embodiment described above, the flood assessment is carried out using an assessment result of the flood assessment processing based on CAN data, weather data, and SNS post information, but this is not limiting. For example, a mode is possible in which steps302and308inFIG.6are omitted and the flood assessment is carried out without using SNS post information. Alternatively, a mode is possible in which steps304and306are omitted, step308makes the determination as to whether there is a user report of the location assessed to be flooded by the flood assessment processing based on CAN data, and thus flooding is assessed without using weather data.

In the processing inFIG.6to improve the accuracy of flood assessment of the exemplary embodiment described above, a location assessed to be flooded by the flood assessment processing based on CAN data is recorded in the flood point database56as a flood point in a case in which there is measured rainfall at the location and there is a user report, but this is not limiting. For example, a mode is possible in which a flood point is recorded in the flood point database56both in a case in which there is measured rainfall at a location assessed to be flooded by the flood assessment processing based on CAN data, and in a case in which there is SNS post information indicating a report of a flood location. In this configuration, the processing illustrated inFIG.8is executed.FIG.8is a flowchart illustrating an example of a flow of processing in which a flood point is recorded in the flood point database56both in a case in which there is measured rainfall at a location assessed to be flooded by the flood assessment processing based on CAN data, and in a case in which there is SNS post information indicating a flood report. Processing that is similar toFIG.6is assigned the same reference symbols and is not described in detail here.

The processing of steps300to304described above is executed in the same manner as inFIG.6, and then the central processing section38proceeds to step306.

In step306, similar toFIG.6, for a location that is assessed to be flooded by the flood assessment processing based on CAN data in step300, the assessment section42makes a determination based on the weather data as to whether a measured rainfall at the location is at least the pre-specified threshold value. In a case in which the result of this determination is negative, the central processing section38proceeds to step307, and in a case in which the result is affirmative, the central processing section38proceeds to step310.

In step307, the assessment section42makes a determination as to whether there is a user report indicating a location of flooding. This determination is a determination as to whether SNS post information indicating a flood report has been acquired. In a case in which the result of the determination is affirmative, the central processing section38proceeds to step310, and in a case in which the result is negative, the central processing section38ends this sequence of processing.

Alternatively, a mode is possible in which a flood point is recorded in the flood point database56in a case in which there is measured rainfall at a location assessed to be flooded by the flood assessment processing based on CAN data, and in a case in which there is SNS post information indicating a flood report of the location assessed to be flooded by the flood assessment processing based on CAN data. In this configuration, the processing illustrated inFIG.9is executed.FIG.9is a flowchart illustrating an example of a flow of processing in which a flood point is recorded in the flood point database56in a case in which there is measured rainfall at a location assessed to be flooded by the flood assessment processing based on CAN data, and in a case in which there is SNS post information indicating a flood report at the location assessed to be flooded by the flood assessment processing based on CAN data. Processing that is similar toFIG.8is assigned the same reference symbols and is not described in detail here.

That is, step309is executed instead of step307ofFIG.8. In step309, the assessment section42makes a determination as to whether there is a user report of the location assessed to be flooded by the flood assessment processing based on CAN data in step300. This determination is a determination as to whether SNS post information has been acquired that indicates a flood report at the location assessed to be flooded by the flood assessment processing based on CAN data in step300. In a case in which the result of the determination is affirmative, the central processing section38proceeds to step310, and in a case in which the result is negative, the central processing section38ends this sequence of processing.

In the exemplary embodiment described above, an example is described in which the flooding is assessed using CAN data at the flood assessment center36side, but this is not limiting. For example, flood assessment using CAN data may be carried out at the information provision device14side installed in each vehicle12.FIG.10is a block diagram illustrating a configuration example of a flood detection system in a case in which flood assessment using CAN data is carried out at the information provision device14side installed in the vehicle12. In this configuration, as illustrated inFIG.10, the functions of the prediction section40, the assessment section42and the model storage section50are provided at the information provision device14. That is, a vehicle movement model corresponding to the vehicle type of the vehicle12in which the information provision device14is installed is derived in advance and stored in the model storage section50. Or, plural vehicle movement models for respective vehicle types are derived and stored in advance, and the vehicle movement model corresponding to the present vehicle is selected in a case in which the vehicle movement model is to be used. Due to the processing ofFIG.5being executed by the calculation section16of the information provision device14, the predicted values may be calculated by the prediction section40and flooding may be assessed by the assessment section42in the same manner as in the exemplary embodiment described above. In a case in which the flood assessment is carried out at the information provision device14side installed in the vehicle12, the processing ofFIG.5is switched as appropriate to processing that is executed by the calculation section16. The processing of step200that is executed by the calculation section16in this case corresponds to the processing of the acquisition section, and the processing of steps202to210corresponds to the processing of the detection section.

In the exemplary embodiment described above, an example is described in which weather data, SNS post information and running state data are collected at the flood assessment center36, but this is not limiting. For example, a mode is possible in which the weather information center60collects weather data, collects flood assessment information representing a flood assessment result from the flood assessment center36, and collects post information from the SNS server62.FIG.11is a diagram illustrating an example in which the weather information center60implements a service that collects the weather data, flood assessment information and SNS post information and provides a flood area map. In the example inFIG.11, the flood assessment center36assesses flooding by collecting CAN data from the vehicles12and aggregating the CAN data in the CAN database52. Similarly to the exemplary embodiment described above, the central processing section38uses the CAN data to implement flood assessment logic that assesses flooding. The weather information center60also collects flood assessment information representing flood assessment results from the flood assessment logic, and collects the weather data and SNS post information. Hence, the weather information center60executes the processing ofFIG.6using the flood assessment information, weather data and SMS post information, integrates the information and applies the information to a graphical user interface (GUI). Then, as illustrated inFIG.11, the weather information center60provides a flood area map70illustrating flooded places on a map to a portable terminal device of a customer or the like. The flood area map70is updated, for example, at a pre-specified time interval such as every five minutes or the like, or upon an update command is given.

In the exemplary embodiment described above, an example is described in which a multiple regression equation is used as the vehicle movement model. However, the vehicle movement model is not limited to machine learning based on a multiple regression equation. For example, as illustrated inFIG.12, a vehicle movement model may employ various kinds of prediction model that use CAN data (accelerator pressing amount R, vehicle speed v(t), road gradient θ, acceleration dv/dt and the like) as the described variable terms in a prediction equation to find a predicted value v(t+Δt) after Δt seconds. Various kinds of machine learning model other than a multiple regression equation may be employed as a prediction model, such as a neural network, support vector regression (SVR) or the like.

In the exemplary embodiment described above, the vehicle movement model that finds the physical quantity that is vehicle speed is used, but the physical amount is not limited thus. A vehicle movement model that finds an alternative physical quantity such as, for example, acceleration, rate of change of acceleration, or the like may be used.

In the exemplary embodiment described above, a vehicle movement model is derived in which air resistance, slope resistance, rolling resistance and acceleration resistance serve as a resistance F2 acting on the vehicle. However, the resistance F2 acting on the vehicle is not limited thus. For example, because rolling resistance and acceleration resistance are smaller than other kinds of resistance, one or more kinds of resistance may be omitted.

The processing executed by the respective sections of the flood detection system10according to the exemplary embodiment described above is described as software processing that is implemented by a program being executed, but this is not limiting. For example, the processing may be implemented in hardware. Alternatively, the processing may be a combination of both software and hardware processing. Further, in a case in which the processing is implemented in software, the program may be stored in any of various storage media and distributed.

The present disclosure is not limited by the above recitations. In addition to the above recitations, it will be clear that numerous modifications may be embodied within a technical scope not departing from the gist of the disclosure.