Vehicle weight estimation device, vehicle weight estimation method, program, and storage medium

Abstract

To provide a vehicle weight estimation device, etc., capable of estimating vehicle weight with high accuracy. [Solution] The vehicle weight estimation device has an estimation means. The estimation means estimates the vehicle weight of the vehicle based on driving information including the vehicle's speed and acceleration, energy consumption information including measured values ​​of the energy consumption actually consumed by the vehicle, and vehicle information of the vehicle.

Description

Technical Field

[0001] The present invention relates to a technique for estimating vehicle weight.

Background Art

[0002] Techniques for estimating the state of a vehicle have been proposed.

[0003] Specifically, for example, Patent Document 1 discloses an unmanned transport vehicle having a traveling device, and discloses a viewpoint of estimating the weight of a transported object loaded on a loading platform of the traveling device based on a current value flowing through an electric motor provided in the traveling device.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, according to the viewpoint disclosed in Patent Document 1, for example, when the road surface condition on which the unmanned transport vehicle travels is poor, the estimation accuracy of the weight of the transported object may decrease. Further, according to the viewpoint disclosed in Patent Document 1, for example, when factors that affect the current value flowing through the electric motor, such as air resistance, are taken into account, the estimation accuracy of the weight of the transported object may decrease.

[0006] In view of the above problems, a main object of the present invention is to provide a vehicle weight estimation device capable of estimating vehicle weight with high accuracy.

Means for Solving the Problems

[0007] The invention described in the claim is a vehicle weight estimation device comprising estimation means for estimating the vehicle weight of a vehicle based on driving information including the speed and acceleration of the vehicle, energy consumption information including a measured value of the amount of energy actually consumed by the vehicle, and vehicle information of the vehicle.

[0008] Furthermore, the invention described in the claims is a method for estimating vehicle weight performed by a computer, which estimates the vehicle weight of the vehicle based on driving information including the vehicle's speed and acceleration, energy consumption information including a measured value of the energy consumption actually consumed by the vehicle, and vehicle information of the vehicle.

[0009] Furthermore, the invention described in the claims is a program executed by a computer that causes the computer to perform a process of estimating the vehicle weight based on driving information including the vehicle's speed and acceleration, energy consumption information including a measured value of the energy consumption actually consumed by the vehicle, and vehicle information of the vehicle. [Brief explanation of the drawing]

[0010] [Figure 1] A diagram showing an example of the configuration of the vehicle weight estimation system according to the embodiment. [Figure 2] A diagram showing an example of the configuration of an information processing device according to the embodiment. [Figure 3] A figure showing an example of the time change in the estimated average vehicle weight. [Figure 4] A figure showing an example of the time evolution of the standard deviation of the estimated vehicle weight. [Figure 5] A diagram illustrating an example of the relationship between an estimation model used in vehicle weight estimation and the values ​​associated with that estimation model. [Figure 6] A flowchart showing an example of processing performed by the information processing device according to the embodiment. [Figure 7] A diagram showing an example configuration of a vehicle weight estimation system related to a modified example. [Figure 8] A diagram showing the schematic configuration of a modified server device. [Figure 9] A diagram illustrating an example of the processing involved in the application. [Modes for carrying out the invention]

[0011] In one preferred embodiment of the present invention, the vehicle weight estimation device has estimation means for estimating the vehicle weight of the vehicle based on driving information including the vehicle's speed and acceleration, energy consumption information including a measured value of the energy consumption actually consumed by the vehicle, and vehicle information of the vehicle.

[0012] The above-described vehicle weight estimation device includes an estimation means. The estimation means estimates the vehicle weight based on driving information including the vehicle's speed and acceleration, energy consumption information including measured values ​​of the energy consumption actually consumed by the vehicle, and vehicle information of the vehicle. This makes it possible to estimate the vehicle weight with high accuracy.

[0013] In one embodiment of the vehicle weight estimation device described above, the device further includes a data selection means for acquiring estimation data by excluding data acquired when the vehicle is traveling downhill and data acquired when the vehicle is decelerating from the data included in the driving information and the energy consumption information, and the estimation means estimates the vehicle weight of the vehicle using the estimation data and the vehicle information.

[0014] In one embodiment of the vehicle weight estimation device described above, the estimation means includes a first calculation means that uses the estimation data set and the vehicle information to calculate a plurality of vehicle weight estimates as the vehicle weight of the vehicle at each acquisition timing of each data included in the estimation data set, and a second calculation means that calculates a moving average of a predetermined number of vehicle weight estimates from the plurality of vehicle weight estimates as the average vehicle weight estimate.

[0015] In one embodiment of the vehicle weight estimation device described above, the estimation means further includes a determination means that determines whether or not to recalculate the estimated vehicle weight based on the time change of the estimated vehicle weight average value and the time change of the standard deviation corresponding to a predetermined number of estimated vehicle weight values.

[0016] In one embodiment of the above-described information processing device, the second calculation means sets weights according to the content of each data used to calculate the plurality of vehicle weight estimates, and calculates the average vehicle weight estimate by performing calculations using these weights.

[0017] In one embodiment of the vehicle weight estimation device described above, the estimation means includes a third calculation means that calculates a plurality of residuals by using the estimation data group and the vehicle information, and calculating the residuals of the value calculated from each data included in the estimation data group and the value calculated from an estimation model using the said data and the estimated vehicle weight at each acquisition timing; a fourth calculation means that calculates the vehicle weight of the vehicle when the residual with the smallest sum of squares among the plurality of residuals is used as the estimated vehicle weight; and a fifth calculation means that calculates the average value of a predetermined number of the estimated vehicle weights as the average estimated vehicle weight.

[0018] In one embodiment of the vehicle weight estimation device described above, the estimation means further includes a determination means that determines whether or not to recalculate the estimated vehicle weight based on the time change of the estimated vehicle weight average value and the time change of the standard deviation corresponding to a predetermined number of estimated vehicle weight values.

[0019] In one embodiment of the vehicle weight estimation device described above, the fifth calculation means sets weights according to the content of the data used to calculate the estimated vehicle weight, and calculates the average estimated vehicle weight by performing calculations using these weights.

[0020] One embodiment of the vehicle weight estimation device described above further includes a transport volume calculation means that calculates the transport volume for each travel section for each of a plurality of vehicles using the vehicle weight obtained from the vehicle information and the estimated average vehicle weight.

[0021] In another preferred embodiment of the present invention, a vehicle weight estimation method performed by a computer estimates the vehicle weight based on driving information including the vehicle's speed and acceleration, energy consumption information including measured values ​​of the energy consumption actually consumed by the vehicle, and vehicle information of the vehicle. This allows for highly accurate estimation of the vehicle weight.

[0022] In yet another preferred embodiment of the present invention, a program executed by a computer causes the computer to perform a process of estimating the vehicle's weight based on driving information including the vehicle's speed and acceleration, energy consumption information including measured values ​​of the actual energy consumption consumed by the vehicle, and vehicle information of the vehicle. By executing this program on a computer, the above-described vehicle weight estimation device can be realized. This program can be stored on a storage medium and used. This makes it possible to estimate the vehicle weight with high accuracy. [Examples]

[0023] Preferred embodiments of the present invention will be described below with reference to the drawings.

[0024] <System Configuration> [Overall structure] Figure 1 shows an example of the configuration of a vehicle weight estimation system according to an embodiment. The vehicle weight estimation system 100 has an information processing device 1 that moves together with the vehicle Ve in which the user is riding. The vehicle Ve can be treated as an example of a moving object.

[0025] [Information Processing Device] The information processing device 1 functions as a vehicle weight estimation device. The information processing device 1 estimates the vehicle weight of vehicle Ve based on the vehicle Ve's driving information, energy consumption information, and vehicle information. Furthermore, the information processing device 1 can output information indicating the estimated vehicle weight of vehicle Ve as vehicle weight estimation information EJ to the outside. In this embodiment, for the sake of explanation, the total weight of vehicle Ve with occupants and / or cargo will be referred to as the vehicle weight of vehicle Ve.

[0026] The vehicle Ve's driving information (hereinafter also referred to as driving information RJ) includes data indicating the vehicle Ve's position, speed, and acceleration during its operation. In other words, driving information RJ includes the vehicle Ve's speed and acceleration.

[0027] The energy consumption information of vehicle Ve (hereinafter also referred to as energy consumption information SJ) includes information related to the energy consumed by the vehicle Ve during its operation. Specifically, energy consumption information SJ includes data showing, for example, the energy consumption per unit time during acceleration and driving of vehicle Ve. In addition, energy consumption information SJ includes information showing, for example, the energy consumption of vehicle Ve during idling. In other words, energy consumption information SJ includes measured values ​​of the energy consumption actually consumed by vehicle Ve.

[0028] The vehicle information for vehicle Ve (hereinafter also referred to as vehicle information VJ) includes information indicating parameters related to the specifications of vehicle Ve. Specifically, vehicle information VJ includes information such as the vehicle weight, which corresponds to the weight of vehicle Ve when there are no occupants or cargo.

[0029] The information processing device 1 may be a device installed in the vehicle Ve, or it may be a portable terminal such as a smartphone carried by the user. Alternatively, the information processing device 1 may be integrated into the vehicle Ve.

[0030] Figure 2 shows an example of the configuration of an information processing device according to an embodiment. The information processing device 1 includes a communication unit 11, a storage unit 12, an input unit 13, a control unit 14, a sensor group 15, and a display unit 16. Each element of the information processing device 1 is interconnected via a bus line 10.

[0031] The communication unit 11 performs data communication with external devices based on the control of the control unit 14. The communication unit 11 can also acquire, for example, map data and road data from external devices.

[0032] The memory unit 12 is composed of various storage media such as RAM (Random Access Memory), ROM (Read Only Memory), and non-volatile memory (including hard disk drives, flash memory, etc.). The memory unit 12 also stores programs for the information processing device 1 to execute predetermined processes. Furthermore, the memory unit 12 is used as the working memory for the control unit 14. Note that the programs executed by the information processing device 1 may be stored in storage media other than the memory unit 12.

[0033] The memory unit 12 stores the database 4, driving information RJ, energy consumption information SJ, and vehicle information VJ. The memory unit 12 also stores known physical parameters that can be used in the vehicle weight estimation process described later.

[0034] Database 4 stores map data and road data obtained by the communication unit 11. The map data includes, for example, data necessary for displaying a map based on a predetermined location such as the current location of vehicle Ve. The road data includes, for example, data representing the road network using combinations of nodes and links. The map data and road data contained in Database 4 can be updated to the latest data at regular intervals according to the control of the control unit 14. In this embodiment, links can be set as sections that divide the road network in any way. For example, links in this embodiment can be set as sections of any length and / or any shape. Also, links in this embodiment may be set as sections that include nodes, or as sections that do not include nodes.

[0035] The input unit 13 has a user interface that accepts user input. The input unit 13 may include at least one user interface, such as a button, a touch panel, and a remote controller. The display unit 16 displays information based on the control of the control unit 14. The display unit 16 may include at least one device, such as a display and a projector.

[0036] The sensor group 15 includes various sensors that perform sensing of the vehicle Ve's state or the external environment. The sensor group 15 comprises an external sensor 20 and an internal sensor 21.

[0037] The external sensor 20 has one or more sensors for recognizing the surrounding environment of the vehicle Ve. The external sensor 20 may include, for example, a lidar, radar, ultrasonic sensor, infrared sensor, sonar, and camera.

[0038] The internal sensor 21 has one or more sensors for positioning the vehicle Ve. The internal sensor 21 may include, for example, a GNSS (Global Navigation Satellite System) receiver, a gyro sensor, a tilt sensor, an acceleration sensor, an IMU (Inertial Measurement Unit), and a vehicle speed sensor.

[0039] The internal sensor 21 has one or more sensors capable of measuring parameters related to the energy consumption of the vehicle Ve. The internal sensor 21 may include, for example, a current sensor, a voltage sensor, and a fuel sensor.

[0040] Furthermore, the sensor group 15 only needs to include sensors from which the control unit 14 can directly or indirectly derive the vehicle's speed and acceleration from the output of the sensor group 15. Also, the sensor group 15 only needs to include sensors from which the control unit 14 can directly or indirectly derive the vehicle's energy consumption from the output of the sensor group 15.

[0041] The control unit 14 includes a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and other components, and controls the entire information processing device 1. For example, the control unit 14 acquires driving information RJ and energy consumption information SJ based on the output of one or more sensors included in the sensor group 15. The control unit 14 can be treated as an example of a computer. Furthermore, the control unit 14 has the functions of estimation means, calculation means, determination means, and transport volume calculation means.

[0042] Furthermore, the processing performed by the control unit 14 is not limited to being implemented by software through a program, but may also be implemented by any combination of hardware, firmware, and software. Also, the processing performed by the control unit 14 may be implemented using a user-programmable integrated circuit, such as an FPGA (Field-Programmable Gate Array) or a microcontroller. In this case, the program that the control unit 14 performs in this embodiment may be implemented using this integrated circuit. Thus, the control unit 14 may be implemented using hardware other than a processor.

[0043] The configuration of the information processing device 1 shown in Figure 2 is an example, and various modifications may be made to the configuration shown in Figure 2. For example, instead of the storage unit 12 storing map data and road data, the control unit 14 may receive information equivalent to map data and road data from a server device (not shown) via the communication unit 11. In another example, the input unit 13 may be provided inside the target vehicle as an external device of the information processing device 1, and the generated signals may be supplied to the information processing device 1. Also, at least some of the sensors in the sensor group 15 may be sensors installed on the vehicle Ve. In this case, the information processing device 1 may acquire information output by the sensors installed on the vehicle Ve from the vehicle Ve based on a communication protocol such as CAN (Controller Area Network).

[0044] <Specific example> Next, we will describe specific examples of processing performed by the information processing device 1. Unless otherwise specified, the processing described in the following examples will be described as processing performed while the vehicle Ve is in motion.

[0045] [Getting parameters] The control unit 14 obtains the gravitational acceleration g from the known parameters stored in the memory unit 12.

[0046] The control unit 14 acquires the vehicle's speed v and acceleration α at regular intervals based on the output from the sensor group 15. The control unit 14 also uses the vehicle's current position VP, identified based on the output from the sensor group 15, and road data read from the database 4 to calculate a correction coefficient γ corresponding to the distance from the most recently passed traffic light to the next traffic light the vehicle will pass. The correction coefficient γ can be calculated, for example, using a method similar to that described in Japanese Patent Publication No. 6122935. The control unit 14 also acquires the road gradient θ on the road the vehicle is traveling on by referring to the map data and / or road data read from the database 4. The control unit 14 also stores the data including the speed v, acceleration α, correction coefficient γ, and road gradient θ as driving information RJ in the storage unit 12.

[0047] The control unit 14 calculates the actual energy consumption P per unit time during acceleration and driving of the vehicle Ve based on the output from the sensor group 15. t And the actual energy consumption P of the vehicle Ve during idling. idle The and are acquired at regular intervals. In addition, the control unit 14 acquires the energy consumption P t and P idle The data including the above is stored in the storage unit 12 as energy consumption information SJ. In this specific example, the data includes velocity v, acceleration α, and energy consumption P. t and P idle This explanation assumes that both data points are acquired at the same time, and that the information that allows for the identification of this timing is included in the driving information RJ and energy consumption information SJ.

[0048] The control unit 14 obtains the vehicle body weight m of the vehicle Ve, the rolling resistance coefficient μ of the vehicle Ve, the net thermal efficiency ε of the vehicle Ve, the total transfer efficiency η of the vehicle Ve, the air resistance coefficient k of the vehicle Ve, and the recovery rate β of the vehicle Ve by referring to the vehicle information VJ read from the database 4.

[0049] The value obtained by multiplying the net thermal efficiency ε by the total transfer efficiency η can be rephrased as the energy consumption efficiency of the vehicle Ve. The drag coefficient k is obtained as a constant calculated in advance based on, for example, the height and width of the vehicle Ve. Alternatively, the drag coefficient k is obtained as a constant calculated based on, for example, the frontal projected area of ​​the vehicle Ve. The recovery rate β is obtained as a constant representing the ratio of the energy recovered to the energy consumed when the vehicle Ve is running, for example, if the vehicle Ve is an electric vehicle. Furthermore, if the recovery rate β is obtained as a constant representing the ratio of the energy consumed when the vehicle Ve is accelerating to the amount of energy cut off when the vehicle Ve is decelerating, for example, if the vehicle Ve is a gasoline vehicle. The control unit 14 may obtain the recovery rate β by performing a calculation using, for example, the same method as described in Japanese Patent Publication No. 6122935, instead of referring to the vehicle information VJ.

[0050] [Specific examples of processing related to vehicle weight estimation] Next, we will explain a specific example of the process related to vehicle weight estimation.

[0051] (Data selection) For example, during periods when the vehicle Ve is decelerating, and during periods when the vehicle Ve is traveling downhill, the acceleration α tends not to fall within a certain range, which may lead to a decrease in the accuracy of the estimation results obtained by vehicle weight estimation. Therefore, before performing the processing related to the vehicle weight estimation of the vehicle Ve, the control unit 14 identifies unused data ND from the data contained in the driving information RJ and energy consumption information SJ stored in the storage unit 12 that will not be used in the processing related to the vehicle weight estimation of the vehicle Ve.

[0052] Specifically, the control unit 14 identifies the data acquired when the road gradient θ is less than 0° and the data acquired when the acceleration α is less than 0 as unused data group ND, from among the data included in the driving information RJ and energy consumption information SJ. Then, the control unit 14 uses the data excluding the data identified as unused data group ND (hereinafter also referred to as the estimation data group ED) to perform processing related to the estimation of the vehicle weight of the vehicle Ve. That is, the control unit 14 can obtain the estimation data ED by excluding the data acquired when the vehicle Ve is driving downhill and the data acquired when the vehicle Ve is decelerating from among the data included in the driving information RJ and energy consumption information SJ.

[0053] (Estimated vehicle weight) In this specific example, the control unit 14 can obtain an estimated result of the vehicle weight of the vehicle Ve by using the estimation data set ED and processing it according to one of the two estimation methods described below.

[0054] (First estimation method) The first estimation method for vehicle weight estimation is described below. Unless otherwise specified, the following explanation will use the example of a case where the vehicle weight estimation process is performed using n sets of data included in the estimation data set ED. Furthermore, the following explanation will assume that each parameter applied to the right-hand side of the following equation (1) is included in each of the n sets of data.

[0055] The vehicle weight M of vehicle Ve, excluding deceleration and downhill driving, can be expressed, for example, as shown in formula (1) below. Note that the vehicle weight M expressed by formula (1) below shall be treated as the total weight of vehicle Ve with occupants and / or cargo.

[0056]

number

[0057] The control unit 14 controls the time ti By applying each parameter included in a set of data obtained at (1 ≤ i ≤ n) to the right side of the above formula (1), the vehicle weight estimated value Mt of the vehicle Ve corresponding to the time t i can be calculated. The time t i For example, can be rephrased as the acquisition timing of a set of data. i

[0058] The control unit 14 performs an operation using the above formula (1) for each of the n sets of data obtained from time t1 to time t n to calculate n vehicle weight estimated values Mt1 to Mt n . The vehicle weight estimated value Mt n at time t n is calculated as a value corresponding to the latest data included in the estimation data group ED.

[0059] The control unit 14 calculates the moving average of a predetermined k (k ≤ n) vehicle weight estimated values including the vehicle weight estimated value Mt n at time t n as the vehicle weight estimated average value Ma. The control unit 14 calculates the vehicle weight estimated average value Ma by performing an operation using, for example, the following formula (2). Also, the control unit 14 calculates the standard deviation σ da indicating the degree of variation with respect to the vehicle weight estimated average value Ma for each of the k vehicle weight estimated values used for calculating the vehicle weight estimated average value Ma by performing an operation using, for example, the following formula (3).

[0060]

Equation

Equation

[0061] ​Here, if data obtained during a period in which the vehicle Ve's speed v and / or acceleration α change sharply, such as during a period when the vehicle Ve is suddenly accelerating and / or braking sharply, is applied to the above formula (1), the calculated value as the estimated vehicle weight may have an unnecessary influence on the estimated vehicle weight of Ve. Therefore, in this estimation method, if the estimated vehicle weight is calculated using data that is presumed to have been obtained during a period in which the vehicle Ve's speed v and / or acceleration α change sharply, the average estimated vehicle weight Ma is recalculated. Details of this process are explained below.

[0062] The control unit 14 controls the estimated average vehicle weight Ma and the standard deviation σ. da By monitoring the time-dependent changes in the vehicle weight, a determination is made as to whether or not the estimated average vehicle weight Ma needs to be recalculated.

[0063] Figure 3 shows an example of the time change of the estimated average vehicle weight. The control unit 14 determines, for example, that a recalculation of the estimated average vehicle weight Ma is necessary when the amount of change in the estimated average vehicle weight Ma exceeds the threshold TH1. In addition, when such a determination is made, the control unit 14 identifies the k vehicle weight estimates used to calculate the estimated average vehicle weight Ma, and uses a new set of k data, excluding the data on which each of the identified vehicle weight estimates was based, to calculate the estimated vehicle weight Mt i The control unit 14 also recalculates the estimated vehicle weight average value Ma. The control unit 14 can obtain, for example, the absolute value of the difference between the estimated vehicle weight average value calculated as the j(2≦j)th moving average and the estimated vehicle weight average value calculated as the (j-1)th moving average as the change in the estimated vehicle weight average value Ma. Furthermore, the control unit 14 determines, for example, that recalculation of the estimated vehicle weight average value Ma is unnecessary if the change in the estimated vehicle weight average value Ma is less than the threshold TH1.

[0064] Figure 4 shows an example of the time change in the standard deviation of the vehicle weight estimate. The control unit 14 controls, for example, the standard deviation σ da When the threshold TH2 or higher, the standard deviation σ daThe control unit determines that the average vehicle weight estimate Ma used in the calculation needs to be recalculated. Furthermore, if such a determination is made, the control unit 14 identifies the k vehicle weight estimates used in calculating the average vehicle weight estimate Ma, and uses a new set of k data, excluding the data on which each of the identified vehicle weight estimates was based, to calculate the vehicle weight estimate Mt i The control unit 14 also recalculates the estimated average vehicle weight Ma. Furthermore, the control unit 14 uses the estimated average vehicle weight Ma obtained from the recalculation to calculate the standard deviation σ da The control unit 14 calculates, for example, the standard deviation σ. da If the value is less than the threshold TH2, it is determined that recalculation of the estimated average vehicle weight Ma is unnecessary.

[0065] The control unit 14 obtains the estimated average vehicle weight Ma obtained by performing the processing described above as the estimated vehicle weight of vehicle Ve. Subsequently, the control unit 14 sets the permissible weight range WR based on the vehicle body weight m, etc., included in the vehicle information VJ, and determines whether the estimated average vehicle weight Ma obtained as the estimated vehicle weight of vehicle Ve falls within the permissible weight range WR. The lower limit of the permissible weight range WR can be set, for example, as a value corresponding to the vehicle body weight m of vehicle Ve. The upper limit of the permissible weight range WR can be set, for example, as a value obtained by adding the number of occupants and the maximum load capacity of vehicle Ve to the vehicle body weight m of vehicle Ve.

[0066] If the control unit 14 determines that the average estimated vehicle weight Ma obtained as the result of estimating the vehicle weight of vehicle Ve does not fall within the allowable weight range WR, it corrects the average estimated vehicle weight Ma to fall within the allowable weight range WR and obtains the corrected value as the final estimated vehicle weight of vehicle Ve. Specifically, for example, the control unit 14 corrects the average estimated vehicle weight Ma so that it falls within a range that is greater than the vehicle weight stated on the vehicle registration certificate of vehicle Ve and less than the gross vehicle weight. If the control unit 14 determines that the average estimated vehicle weight Ma obtained as the result of estimating the vehicle weight of vehicle Ve falls within the allowable weight range WR, it obtains the average estimated vehicle weight Ma as the final estimated vehicle weight of vehicle Ve. The control unit 14 also outputs vehicle weight estimation information EJ, which includes the average estimated vehicle weight Ma obtained as the final estimated vehicle weight of vehicle Ve, to the outside. The control unit 14 may store the vehicle weight estimation information EJ in the storage unit 12.

[0067] According to the first estimation method described above, for example, the estimated vehicle weight Mt i The weights w are set according to the content of the data used in the calculation. i The estimated vehicle weight Mt i By multiplying by a moving average, the accuracy of the final estimated vehicle weight average value Ma obtained can be improved. i How to set it, and the weight w i The details of the processing using this method are explained below. For simplicity, specific explanations of the parts to which the previously described processing methods can be applied will be omitted as appropriate.

[0068] Here, when both acceleration α and road gradient θ are small, the energy consumption in the vehicle Ve decreases, which is why the estimated vehicle weight Mt i The accuracy of the calculation results tends to be low. Therefore, the weight w described below i In the setting method, the estimated vehicle weight Mt is calculated using data where either acceleration α or road gradient θ is larger. i A large weight is assigned to this. Also, the weight w described below iIn the setting method, the estimated vehicle weight Mt is calculated using data with small acceleration α and road gradient θ. i A small weight is assigned to each of the k vehicle weight estimates Mt that formed the basis of the average vehicle weight estimate Ma. i Depending on the content of the data used in the calculation, the weight w will be set according to one of the following three setting methods. i Set it.

[0069] In the first setting method, the control unit 14 uses data where the acceleration α is greater than a predetermined value, or data where the road gradient θ is greater than a predetermined value, to estimate the vehicle weight Mt i When calculating the estimated vehicle weight Mt i The corresponding weight w i Set to "10". In the first setting method, the control unit 14 uses data where the acceleration α is less than or equal to a predetermined value and the road gradient θ is less than or equal to a predetermined value to estimate the vehicle weight Mt i When calculating the estimated vehicle weight Mt i The corresponding weight w i Set it to "1".

[0070] In the second setting method, the control unit 14 uses data where the acceleration α is greater than a predetermined value, or data where the road gradient θ is greater than a predetermined value, to estimate the vehicle weight Mt i When calculating the estimated vehicle weight Mt i The corresponding weight w i Set to "1". In the second setting method, the control unit 14 uses data where the acceleration α is less than or equal to a predetermined value and the road gradient θ is less than or equal to a predetermined value to estimate the vehicle weight Mt i When calculating the estimated vehicle weight Mt i The corresponding weight w i Set it to "0".

[0071] In the third setting method, the control unit 14 sets the estimated vehicle weight Mt i By applying the acceleration α and road gradient θ used in the calculation to the following formula (4), the estimated vehicle weight Mt i The corresponding weight w iSet the following. In formula (4) below, u1 and u2 represent coefficients.

[0072]

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[0073] The coefficient u1 is preferably set to a relatively high value when, for example, applying the highly accurate acceleration α obtained from the output of the sensor group 15. Conversely, the coefficient u1 is preferably set to a relatively low value or "0" when, for example, applying the acceleration α obtained when the vehicle Ve is traveling on a curve.

[0074] The coefficient u2 is preferably set to a relatively high value when applying a highly accurate road gradient θ obtained from, for example, map data for autonomous driving. Furthermore, the coefficient u2 is preferably set to a relatively high value when, for example, the current position VP of the vehicle Ve is detected with high accuracy from the output of the sensor group 15, and the road gradient θ corresponding to the detected current position VP is applied. In addition, the coefficient u2 may be set to a value that takes elevation correction into account when, for example, elevation data is included in the map data. For example, when the elevation data is represented as mesh data, the correction made according to the difference between the elevation of the mesh corresponding to the current position VP of the vehicle Ve and the elevation of the next mesh that the vehicle Ve will pass through can be treated as elevation correction. Also, for example, when the elevation data is represented as data associated with each link of the road network, the correction made according to the difference between the elevation of the link corresponding to the current position VP of the vehicle Ve and the elevation of the next link that the vehicle Ve will pass through can be treated as elevation correction.

[0075] The control unit 14 uses the weight w in the manner described above. i If this is set, the estimated average vehicle weight Ma is calculated by performing the calculation using the following formula (5) instead of the above formula (2). Furthermore, the control unit 14 calculates the weight w in the manner described above. iIf this setting is selected, the calculation will be performed using the following formula (6) instead of the above formula (3), and the standard deviation σ corresponding to each of the k vehicle weight estimates used to calculate the average vehicle weight Ma will be calculated. da Calculate.

[0076]

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[0077] According to the calculations using the above formulas (5) and (6), the control unit 14 determines the vehicle weight estimate Mt, which is estimated to have high accuracy in the calculation results. i The process involves a weighted moving average with increased weight. Therefore, the calculation using the above formulas (5) and (6) can improve the stability of the calculation result of the estimated vehicle weight average value Ma. Furthermore, the calculation using the above formulas (5) and (6) can improve the stability of the estimated vehicle weight average value Ma and the standard deviation σ. da The amount of change over time can be suppressed. Therefore, by using the above formulas (5) and (6), for example, the number of times the estimated average vehicle weight Ma is recalculated can be reduced.

[0078] (Second estimation method) The second estimation method for vehicle weight estimation is described below. Unless otherwise specified, the following explanation uses the example of a case where the vehicle weight estimation process is performed using n sets of data included in the estimation data set ED. Furthermore, the following explanation assumes that each parameter applied to the right-hand side of the above formula (1) is included in each of the n sets of data. The second estimation method described below can also be described as a vehicle weight estimation method using the least squares method.

[0079] The vehicle weight M of vehicle Ve can be expressed, for example, as shown in equation (1) above, excluding the weight during deceleration and downhill driving. Furthermore, if the denominator of the right-hand side of equation (1) is the variable x, and the numerator of the right-hand side of equation (1) is the variable y, then equation (1) can be transformed into equation (7) below. Equation (7) below can be used as an estimation model for estimating the vehicle weight of vehicle Ve.

[0080]

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[0081] The control unit 14 operates from time t1 to time t n By applying each of the n sets of data obtained up to this point as elements of the variables x and y in equation (7) above, we can obtain a system of equations like the one shown in equation (8) below. In equation (8) below, Mt represents a common vehicle weight estimate corresponding to each of the n data points. Also, in equation (8) below, z1, z2, ..., z n This represents the residual z in the estimation model of equation (7) above. Also, z1, z2, ..., z in equation (8) below n This corresponds to the difference between the left and right sides of the estimation model for each acquisition timing of each data point included in the estimation data set ED, when the estimated vehicle weight Mt is applied to the vehicle weight M of the estimation model in formula (7) above.

[0082]

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[0083] Figure 5 shows an example of the relationship between the estimation model in vehicle weight estimation and the values ​​associated with the estimation model. According to the above formula (8), the control unit 14 determines the time t i By applying the data obtained at the above equation (7) to the estimation model, the time t i The variable x corresponds to this variable. i , variable y i and residual z iAn equation containing the above formula (7) and the variable x can be obtained. i and y i And the residual z i The relationship between and can be represented, for example, as shown in Figure 5.

[0084] The control unit 14 obtains a mathematical expression represented by a matrix, as shown in equation (9) below, from the system of equations in equation (8) above. Furthermore, by setting the vector on the left side of equation (9) below as Y, the vector of the first term on the right side of equation (9) below as X, and the vector of the second term on the right side of equation (9) below as Z, the control unit 14 can obtain a mathematical expression represented by a matrix, as shown in equation (10) below.

[0085]

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number

[0086] The control unit 14 obtains the sum of squares RSS of residuals z, expressed as shown in formula (11) below, by performing calculations based on the above formulas (9) and / or (10).

[0087]

number

[0088] Considering the relationship shown in Figure 5, the estimated vehicle weight Mt at which the sum of squares RSS is minimized can be treated as the solution to the vehicle weight estimation using the least squares method. Furthermore, the condition for minimizing the sum of squares RSS can be rephrased as the condition that the value obtained by partially differentiating both sides of the above equation (11) with respect to the estimated vehicle weight Mt is zero.

[0089] The control unit 14 can obtain a relational expression as shown in the following formula (12) by transforming the above formula (11) based on the condition that the value when both sides of the above formula (11) are partially differentiated with respect to the vehicle weight estimated value Mt becomes 0. Further, the control unit 14 multiplies both sides of the following formula (12) by (X T X) -1 from the left side, thereby obtaining a relational expression as shown in the following formula (13). Then, the control unit 14 can calculate a vehicle weight estimated value Mt corresponding to the solution of vehicle weight estimation using the least squares method by performing an operation using the following formula (13).

[0090] [Number] [Number]

[0091] The control unit 14 calculates p (p < n) vehicle weight estimated values Mt1, Mt2,... Mt p by performing an operation using the above formula (13). Specifically, the control unit 14 performs an operation using the above formula (13) each time the value of n increases by 1 or a predetermined value, thereby calculating p vehicle weight estimated values Mt1 to Mt p .

[0092] The control unit 14 calculates a vehicle weight estimated average value Ma corresponding to p vehicle weight estimated values Mt1 to Mt p by performing an operation using, for example, the following formula (14). Further, the control unit 14 calculates a standard deviation σ da indicating the degree of variation with respect to the vehicle weight estimated average value Ma for each of the p vehicle weight estimated values used in the calculation of the vehicle weight estimated average value Ma by performing an operation using, for example, the following formula (15). Note that the variable q (1 ≤ q ≤ p) in the following formulas (14) and (15) represents the order of the vehicle weight estimated value Mt calculated by the operation using the above formula (13). Also, the vehicle weight estimated value Mt q in the following formulas (14) and (15) is the p vehicle weight estimated values Mt1 to Mt pThis represents the q-th calculated estimated vehicle weight, Mt.

[0093]

number

number

[0094] The control unit 14 controls the estimated average vehicle weight Ma and the standard deviation σ. da By monitoring the time-dependent changes in the vehicle weight, a determination is made as to whether or not the estimated average vehicle weight Ma needs to be recalculated.

[0095] The control unit 14 determines, for example, that recalculation of the estimated vehicle weight average value Ma is necessary if the change in the estimated vehicle weight average value Ma exceeds the threshold TH3. If such a determination is made, the control unit 14 identifies the p vehicle weight estimates used to calculate the estimated vehicle weight average value Ma, and recalculates the estimated vehicle weight Mt and the estimated vehicle weight average value Ma using new data excluding the data on which the identified p vehicle weight estimates were based. The control unit 14 can, for example, obtain the absolute difference between the r(2≦r≦q)th calculated estimated vehicle weight average value and the (r-1)th calculated estimated vehicle weight average value as the change in the estimated vehicle weight average value Ma. Furthermore, the control unit 14 determines, for example, that recalculation of the estimated vehicle weight average value Ma is unnecessary if the change in the estimated vehicle weight average value Ma is less than the threshold TH3.

[0096] The control unit 14, for example, calculates the standard deviation σ da When the threshold TH4 or higher, the standard deviation σ da The control unit determines that the average estimated vehicle weight Ma used in the calculation needs to be recalculated. Furthermore, if such a determination is made, the control unit 14 identifies the p vehicle weight estimates used in calculating the average estimated vehicle weight Ma, and recalculates the vehicle weight estimate Mt and the average estimated vehicle weight Ma using new data excluding the data on which each of the identified vehicle weight estimates was based. The control unit 14 also uses the standard deviation σ obtained from the recalculation of the average estimated vehicle weight Ma. daThe control unit 14 calculates, for example, the standard deviation σ. da If the value is less than the threshold TH4, it is determined that recalculation of the estimated average vehicle weight Ma is unnecessary.

[0097] The control unit 14 obtains the estimated average vehicle weight Ma obtained by performing the processing described above as the estimated vehicle weight of vehicle Ve. Subsequently, the control unit 14 sets the permissible weight range WR based on the vehicle body weight m etc. included in the vehicle information VJ, and determines whether the estimated average vehicle weight Ma obtained as the estimated vehicle weight of vehicle Ve falls within the said permissible weight range WR.

[0098] If the control unit 14 determines that the average estimated vehicle weight Ma obtained as the result of estimating the vehicle weight of vehicle Ve does not fall within the allowable weight range WR, it corrects the average estimated vehicle weight Ma to fall within the allowable weight range WR and obtains the corrected value as the final estimated vehicle weight of vehicle Ve. Specifically, for example, the control unit 14 corrects the average estimated vehicle weight Ma so that it falls within a range that is greater than the vehicle weight stated on the vehicle registration certificate of vehicle Ve and less than the gross vehicle weight. If the control unit 14 determines that the average estimated vehicle weight Ma obtained as the result of estimating the vehicle weight of vehicle Ve falls within the allowable weight range WR, it obtains the average estimated vehicle weight Ma as the final estimated vehicle weight of vehicle Ve. The control unit 14 also outputs vehicle weight estimation information EJ, which includes the average estimated vehicle weight Ma obtained as the final estimated vehicle weight of vehicle Ve, to the outside. The control unit 14 may store the vehicle weight estimation information EJ in the storage unit 12.

[0099] According to the second estimation method described above, for example, a weight w set according to the content of the data used to calculate the estimated vehicle weight i By multiplying the estimated vehicle weight by this weight w, the accuracy of the final estimated average vehicle weight Ma obtained can be improved. i How to set it, and the weight w i Details of the process using this method are explained below.

[0100] The control unit 14 calculates the estimated vehicle weight Mtq A weight w set according to the content of the data used for the calculation of q is multiplied by the vehicle weight estimated value Mt q to perform a calculation, thereby improving the accuracy of the average vehicle weight estimated value Ma obtained as the final estimation result. Such a weight w q setting method, and details of the process using the weight w q will be described below.

[0101] The control unit 14 sets the weight w according to the content of the data used for the calculation of p vehicle weight estimated values Mt that are the basis of the average vehicle weight estimated value Ma q in the same manner as one of the three setting methods described in the first estimation method. According to such a process, the control unit 14 can set weights w1 to w corresponding to each of the p vehicle weight estimated values Mt1 to Mt q respectively. Also, according to the above-described process, the control unit 14 can obtain a weight matrix W as shown in the following mathematical formula (16). p p When the control unit 14 obtains the weight matrix W shown in the above mathematical formula (16), instead of the above mathematical formula (11), it performs an operation using the following mathematical formula (17) to obtain the sum of squares of residuals z, RSS.

[0102]

Equation

[0103] When the control unit 14 obtains the weight matrix W shown in the above mathematical formula (16), instead of the above mathematical formula (11), it performs an operation using the following mathematical formula (17) to obtain the sum of squares of residuals z, RSS.

[0104]

Equation

[0105] The control unit 14 can obtain a relational expression as shown in the following mathematical formula (18) by transforming the above mathematical formula (17) based on the condition that the value when the both sides of the above mathematical formula (17) are partially differentiated with respect to the vehicle weight estimated value Mt becomes 0. Also, the control unit 14 multiplies both sides of the following mathematical formula (18) from the left side by (X T WX) -1 ​By multiplying by , a relational expression like the following equation (19) can be obtained. Then, the control unit 14 can calculate the estimated vehicle weight Mt, which corresponds to the solution of the vehicle weight estimation using the least squares method, by performing calculations using the following equation (19).

[0106]

number

number

[0107] According to the calculation using the above formula (19), the control unit 14 performs processing related to a weighted least squares method in which the weight of highly reliable data is increased. Therefore, according to the calculation using the above formula (19), the stability of the calculation result of the estimated average vehicle weight Ma can be improved. Furthermore, according to the calculation using the above formula (19), the amount of change in the estimated average vehicle weight Ma over time can be suppressed. Accordingly, according to the calculation using the above formula (19), for example, the number of times the estimated average vehicle weight Ma is recalculated can be reduced. Furthermore, according to the calculation using the above formula (19), for example, for data with relatively large values ​​as acceleration α or road gradient θ, the residual z in the estimation model of the above formula (7) can be made relatively smaller.

[0108] [Processing flow] Next, we will explain the processing flow performed by the information processing device 1. Figure 6 is a flowchart showing an example of processing performed by the information processing device according to the embodiment.

[0109] First, the information processing device 1 acquires the parameters necessary for estimating the vehicle weight of vehicle Ve based on the driving information RJ, energy consumption information SJ, and vehicle information VJ stored in the memory unit 12 (step S11). As part of the process in step S11, the information processing device 1 may, for example, perform processing to acquire each parameter included in the right-hand side of the above formula (1).

[0110] Next, the information processing device 1 obtains a group of estimation data to be used for processing related to the estimation of the vehicle weight of vehicle Ve from among multiple data including each parameter acquired in step S11 (step S12).

[0111] Next, the information processing device 1 obtains the estimated weight of vehicle Ve by performing processing related to vehicle weight estimation using the estimation data set acquired in step S12 (step S13). As the processing in step S13, the information processing device 1 may, for example, perform processing according to either the first estimation method or the second estimation method described above.

[0112] Next, the information processing device 1 determines whether the vehicle weight of the vehicle Ve obtained as an estimation result in step S13 falls within the permissible weight range WR (step S14). The permissible weight range WR may be set based on the vehicle weight m, etc., included in the vehicle information VJ, or it may be set as a predetermined weight range.

[0113] If the information processing device 1 determines that the vehicle weight of vehicle Ve obtained as an estimation result in step S13 does not fall within the permissible weight range WR (step S14: NO), it corrects the vehicle weight to fall within the permissible weight range WR (step S15). Specifically, the control unit 14 corrects the vehicle weight of vehicle Ve obtained as an estimation result in step S13, for example, so that it falls within a range that is greater than the vehicle weight listed on the vehicle registration certificate of vehicle Ve and less than the gross vehicle weight. Then, the information processing device 1 obtains the corrected vehicle weight in step S15 as the final estimated result of the vehicle weight of vehicle Ve (step S16), and terminates the series of processes.

[0114] On the other hand, if the information processing device 1 determines that the vehicle weight of vehicle Ve obtained as an estimation result in step S13 falls within the permissible weight range WR (step S14: YES), it acquires the said vehicle weight as the final estimated result of the vehicle weight of vehicle Ve (step S16) and terminates the series of processes. The information processing device 1 can output vehicle weight estimation information EJ, which includes the final estimated result of the vehicle weight of vehicle Ve obtained in step S16, to the outside. The information processing device 1 may also store the vehicle weight estimation information EJ in the storage unit 12.

[0115] The information processing device 1 repeatedly performs the series of processes shown in Figure 6 while the vehicle Ve is in motion. Furthermore, when the information processing device 1 repeatedly performs the series of processes shown in Figure 6, it may, for example, retain the constant parameters included in the right-hand side of equation (1) while newly acquiring parameters other than the constants included in the right-hand side of equation (1).

[0116] As described above, according to this embodiment, by using driving information RJ, energy consumption information SJ, and vehicle information VJ, and performing processing related to vehicle weight estimation while excluding data that may reduce estimation accuracy, it is possible to obtain an estimated result of the total weight of the vehicle Ve while it is in motion. Therefore, according to this embodiment, the vehicle weight can be estimated with high accuracy.

[0117] <Variation> Next, we will describe some suitable modifications of the above-described embodiments. Furthermore, the following modifications may be applied in combination to the above-described embodiments.

[0118] [Example 1] The control unit 14 may, for example, display alert information on the display unit 16, including a string of characters indicating that the vehicle Ve is overloaded, if the average estimated vehicle weight Ma obtained as the final estimated result of the vehicle weight of the vehicle Ve exceeds the upper limit of the permissible weight range WR.

[0119] [Differentiation 2] According to the embodiment described above, at least some of the processes performed by the information processing device 1 may be performed by a server device that communicates with the information processing device 1.

[0120] Figure 7 shows an example configuration of a modified vehicle weight estimation system. The vehicle weight estimation system 100A includes an information processing device 1A and a server device 200. The information processing device 1A and the server device 200 communicate data via a network 150.

[0121] The information processing device 1A has the same configuration as the information processing device 1 described in the above embodiment (see Figure 2). Information that does not change while the vehicle Ve is in motion, such as constants applied to the above formula (1), may be stored in either the information processing device 1A or the server device 200. The information processing device 1A transmits the information input at the input unit 13 and the information obtained by the sensor group 15 to the server device 200. For example, the information processing device 1A receives as information obtained while the vehicle Ve is in motion includes velocity v, acceleration α, current position VP, and energy consumption P. t and P idle The information including this is sent to the server device 200.

[0122] Figure 8 shows a schematic configuration of a modified server device. As shown in Figure 8, the server device 200 includes a communication unit 301, a storage unit 302, and a control unit 304. The communication unit 301, the storage unit 302, and the control unit 304 are interconnected via a bus line 300.

[0123] The communication unit 301 transmits and receives various data via the network 150 based on the control of the control unit 304. The storage unit 302 is composed of, for example, an HDD. The storage unit 302 also stores data that can be used to estimate the vehicle weight of vehicle Ve, such as database 4, driving information RJ, energy consumption information SJ, and vehicle information VJ. The control unit 304 has memory such as a CPU, ROM, and RAM, and performs overall control of the server device 200 by executing programs stored in memory. The control unit 304 can also be treated as an example of a computer.

[0124] With the configuration described above, the control unit 304 can store information about the vehicle Ve in motion, transmitted from the information processing device 1A, in the storage unit 302. Furthermore, with the configuration described above, the control unit 304 can use the data, including the driving information RJ, energy consumption information SJ, and vehicle information VJ, stored in the storage unit 302 to perform processing related to estimating the vehicle weight of the vehicle Ve.

[0125] According to this modified example, the same processing performed in server device 200 may be performed in a server system having multiple server devices.

[0126] <Application Examples> Cases in which the above embodiment can be applied will be described below. In the following, an example will be described in which a vehicle weight estimation system similar to that of Modification 2 is used. In the following, the final estimated result of vehicle weight obtained by the above embodiment will also be referred to as the gross vehicle weight ML. The gross vehicle weight ML may include, for example, the average estimated vehicle weight Ma obtained by the first estimation method or the second estimation method described above.

[0127] [Calculation of transport volume] The control unit 304 uses information transmitted from the information processing device 1A of each of the multiple vehicles Ve in motion to obtain the total vehicle weight ML for each of the multiple vehicles Ve. The control unit 304 also obtains the body weight m corresponding to each of the multiple vehicles Ve from the vehicle information VJ stored in the storage unit 302. The control unit 304 also calculates the transport volume MY for each of the multiple vehicles Ve by, for example, subtracting the body weight m and the occupant weight MH from the total vehicle weight ML. The occupant weight MH can be calculated, for example, as a value obtained by multiplying a constant representing the weight of the occupants of the vehicle Ve by the number of occupants of the vehicle Ve. The transport volume MY can be calculated as a value representing the weight of the cargo carried by the vehicle Ve. Furthermore, if the vehicle Ve is a swap body container vehicle, the transport volume MY can be calculated as the weight of the container portion of the vehicle Ve. Furthermore, if the vehicle Ve is a trailer vehicle, the transport volume MY can be calculated as the weight of the object towed by the vehicle Ve. Furthermore, the transport volume MY can be calculated for each section traveled by the vehicle Ve, such as a section that includes one or more links.

[0128] The control unit 304 may, for example, calculate the transport volume MZ of the vehicle Ve by subtracting the weight of the remaining fuel in the vehicle Ve and / or the weight of the transport pallets loaded on the vehicle Ve from the transport volume MY. Furthermore, in the following description, the transport volume of the vehicle Ve calculated using a method similar to either of the above-described methods for calculating transport volume MY and transport volume MZ will be referred to as transport volume MX.

[0129] [Calculation of workload and charges] The control unit 304 can calculate the transport load TL generated by the transport of vehicle Ve by multiplying the transport volume MX of vehicle Ve by the transport distance DY of vehicle Ve. The control unit 304 can also calculate the charge amount CN according to the transport load TL.

[0130] Figure 9 is a diagram illustrating an example of the processing involved in the application. Below, we will explain how to calculate the transportation load and charges in a delivery area DE with four delivery destinations D1 to D4, as shown in Figure 9. In this application, the delivery destinations can be rephrased as, for example, the final destination or transit points.

[0131] For example, if vehicle Ve1 transports a load of MX1 from delivery destination D1 to D2, the control unit 304 sets the actual distance traveled along the route from delivery destination D1 to D2 as the transport distance DY1. In such a case, the control unit 304 can calculate the transport load TL1 corresponding to vehicle Ve1 by multiplying the transport load MX1 by the transport distance DY1. Furthermore, the control unit 304 can calculate the charge amount CN1 corresponding to vehicle Ve1 by multiplying the transport load TL1 by a predetermined constant, for example.

[0132] For example, if vehicle Ve2 transports cargo with transport volume MX2 from delivery destination D1 to D2, and then transports cargo with transport volume MX3 from delivery destination D2 to D3, the control unit 304 sets the actual distance traveled along the route from delivery destination D1 to D3 as the transport distance DY2. In such a case, the control unit 304 obtains the total transport volume WM2 by adding the transport volumes MX2 and MX3, and calculates the transport load TL2 corresponding to vehicle Ve2 by multiplying the total transport volume WM2 by the transport distance DY2. Furthermore, the control unit 304 can calculate the charge amount CN2 corresponding to vehicle Ve2 by multiplying the transport load TL2 by a predetermined constant, for example.

[0133] For example, if vehicle Ve3 transports cargo with transport volume MX4 from delivery destination D2 to D3, and then transports cargo with transport volume MX5 from delivery destination D3 to D4, the control unit 304 sets the actual distance traveled along the route from delivery destination D2 to D4 as the transport distance DY3. In such a case, the control unit 304 obtains the total transport volume WM3 by adding the transport volumes MX4 and MX5, and calculates the transport load TL3 corresponding to vehicle Ve3 by multiplying the total transport volume WM3 by the transport distance DY3. Furthermore, the control unit 304 can calculate the charge amount CN3 corresponding to vehicle Ve3 by multiplying the transport load TL3 by a predetermined constant, for example.

[0134] For example, if vehicle Ve4 transports cargo with a transport volume of MX6 from delivery destination D1 to D2, cargo with a transport volume of MX7 from delivery destination D2 to D3, and cargo with a transport volume of MX8 from delivery destination D3 to D4, the control unit 304 sets the actual distance traveled along the route from delivery destination D1 to D4 as the transport distance DY4. In such a case, the control unit 304 obtains the total transport volume WM4 by adding the transport volumes MX6, MX7, and MX8, and calculates the transport load TL4 corresponding to vehicle Ve4 by multiplying the total transport volume WM4 by the transport distance DY4. Furthermore, the control unit 304 can calculate the charge amount CN4 corresponding to vehicle Ve4 by multiplying the transport load TL4 by a predetermined constant, for example.

[0135] The processing described in this application example can be suitably applied, for example, when the actual transport volume for each delivery destination cannot be measured, or when the transport volume increases or decreases for each delivery destination. Furthermore, according to the processing described in this application example, the control unit 304 can acquire information relating to the change in transport volume for each section in which the cargo is transported, such as the amount collected in garbage collection.

[0136] In the embodiments described above, the program can be stored using various types of non-transitory computer-readable medium and supplied to a control unit, which is a computer. Non-transitory computer-readable medium includes various types of tangible storage medium. Examples of non-transitory computer-readable medium include magnetic storage medium (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical storage medium (e.g., magneto-optical disks), CD-ROM (Read Only Memory), CD-R, CD-R / W, and semiconductor memory (e.g., mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, RAM (Random Access Memory)).

[0137] Although the present invention has been described above with reference to embodiments, the present invention is not limited to the above embodiments. Various modifications to the structure and details of the present invention can be made that are understandable to those skilled in the art within the scope of the present invention. That is, the present invention naturally includes the full disclosure, including the claims, and various modifications and alterations that those skilled in the art could make in accordance with the technical idea. Furthermore, each disclosure of the above-mentioned patent documents and other references is incorporated herein by reference. [Explanation of Symbols]

[0138] 1. 1A Information Processing Device 11, 301 Communications Department 12, 302 Storage section 14, 304 Control Unit 15 Sensor Groups

Claims

1. Estimation means for estimating the vehicle weight based on driving information including vehicle speed and acceleration, energy consumption information including measured values ​​of energy consumption actually consumed by the vehicle, and vehicle information of the vehicle. A vehicle weight estimation device having the following features.

2. The system further includes a data selection means for obtaining estimation data sets by excluding data acquired when the vehicle is traveling downhill and data acquired when the vehicle is decelerating from the data included in the aforementioned driving information and energy consumption information. The vehicle weight estimation device according to claim 1, wherein the estimation means estimates the vehicle weight of the vehicle using the estimation data set and the vehicle information.

3. The estimation means is, A first calculation means that uses the estimation data set and the vehicle information to calculate a plurality of vehicle weight estimates as the vehicle weight for each acquisition timing of the data included in the estimation data set, A second calculation means for calculating the average vehicle weight by taking the moving average of a predetermined number of vehicle weight estimates from the aforementioned plurality of vehicle weight estimates, A vehicle weight estimation device according to claim 2, having the following features.

4. The vehicle weight estimation device according to claim 3, further comprising a determination means for determining whether or not to recalculate the estimated vehicle weight average value based on the time change of the estimated vehicle weight average value and the time change of the standard deviation corresponding to a predetermined number of estimated vehicle weight values.

5. The vehicle weight estimation device according to claim 3, wherein the second calculation means sets weights according to the content of each data used to calculate the plurality of vehicle weight estimates, and calculates the average vehicle weight estimate by performing calculations using the weights.

6. The estimation means is, A third calculation means for calculating multiple residuals by using the estimation data set and the vehicle information, and calculating the residuals of the values ​​calculated from each data included in the estimation data set and the values ​​calculated from an estimation model using each of the said data and the estimated vehicle weight, at each acquisition timing. A fourth calculation means for calculating the vehicle weight estimate of the vehicle when the sum of squares of the residuals is the smallest among the aforementioned multiple residuals, A fifth calculation means for calculating the average value of a predetermined number of vehicle weight estimates as the average vehicle weight estimate, A vehicle weight estimation device according to claim 2, having the following features.

7. The vehicle weight estimation device according to claim 6, further comprising a determination means for determining whether or not to recalculate the estimated vehicle weight average value based on the time change of the estimated vehicle weight average value and the time change of the standard deviation corresponding to a predetermined number of estimated vehicle weight values.

8. The vehicle weight estimation device according to claim 6, wherein the fifth calculation means sets weights according to the content of the data used to calculate the estimated vehicle weight, and calculates the average estimated vehicle weight by performing calculations using the weights.

9. The vehicle weight estimation device according to claim 3 or 6, further comprising a transport volume calculation means for calculating the transport volume for each travel section using the vehicle weight obtained from the vehicle information and the estimated average vehicle weight for each of the multiple vehicles.

10. A method for estimating vehicle weight performed by a computer, A method for estimating the weight of a vehicle, based on driving information including the vehicle's speed and acceleration, energy consumption information including measured values ​​of the energy consumed by the vehicle, and vehicle information of the vehicle.

11. A program executed by a computer, A program that causes a computer to perform a process to estimate the vehicle weight based on driving information including the vehicle's speed and acceleration, energy consumption information including measured values ​​of the actual energy consumed by the vehicle, and vehicle information of the vehicle.

12. A storage medium storing the program described in claim 11.

Images