Information processing method and information processing system

Abstract

The energy consumption of a vehicle during operation is predicted by considering the relationship between the road surface conditions on which the vehicle travels and rolling resistance. [Solution] The information processing system according to this embodiment identifies the planned route of the target vehicle, determines the road surface condition of the planned route, calculates the rolling resistance when the target vehicle travels along the planned route based on the road surface condition, and predicts the energy consumption of the target vehicle.

Description

Technical Field

[0001] The present invention relates to an information processing method and an information processing system.

Background Art

[0002] Conventionally, a technique for predicting energy consumption of a vehicle during traveling in consideration of rolling resistance is known. For example, Patent Document 1 listed below discloses an energy consumption prediction device that calculates rolling resistance in accordance with an outside air temperature and predicts energy consumption of a vehicle during traveling in consideration of the calculated rolling resistance.

Prior Art Literature

Patent Literature

[0003]

Patent Document 1

Summary of the Invention

Problem to be Solved by the Invention

[0004] The above-described conventional technology considers the relationship between outside air temperature and rolling resistance (rolling resistance coefficient), but cannot consider the relationship between the road surface condition on which the vehicle travels and rolling resistance. Therefore, there is room for improvement in order to accurately predict energy consumption.

[0005] In one aspect, the present invention has been made in view of such circumstances, and an object of the present invention is to provide an information processing method and an information processing system that predict energy consumption of a vehicle during traveling in consideration of the relationship between the road surface condition on which the vehicle travels and rolling resistance.

Means for Solving the Problem

[0006] To solve the above-mentioned problems, an information processing method according to one aspect of the present invention is an information processing method that causes a processor to perform a process to predict the energy consumption of a target vehicle while it is traveling. In the information processing method according to one aspect of the present invention, a first processor identifies the planned travel route of the target vehicle. A second processor capable of sending and receiving information with the first processor determines the target road surface condition, which is the road surface condition of the planned travel route identified by the first processor. A third processor capable of sending and receiving information with the second processor calculates the target rolling resistance, which is the rolling resistance when the target vehicle travels along the planned travel route, from the target road surface condition determined by the second processor. The first processor is capable of sending and receiving information with the third processor, and the first processor uses the target rolling resistance calculated by the third processor to predict the energy consumption when the target vehicle travels along the planned travel route. [Effects of the Invention]

[0007] According to the present invention, it is possible to provide an information processing method and information processing system that predict the energy consumption of a vehicle while it is running, taking into account the relationship between the road surface conditions on which the vehicle is running and the rolling resistance. [Brief explanation of the drawing]

[0008] [Figure 1] This is a block diagram showing the schematic configuration of the information processing system according to the embodiment. [Figure 2] An example of the configuration of the target vehicle according to the embodiment is schematically shown. [Figure 3] An example of the configuration of the determination database according to the embodiment is schematically shown. [Figure 4] A schematic example of the configuration of the calculation database according to the embodiment is shown below. [Figure 5] An example of the processing procedure for the information processing system according to the embodiment is shown. [Modes for carrying out the invention]

[0009] Hereinafter, an embodiment relating to one aspect of the present invention (hereinafter also referred to as "this embodiment") will be described based on the drawings. However, this embodiment described below is merely illustrative in all respects of the present invention. Needless to say, various improvements and modifications can be made without departing from the scope of the present invention. In other words, in carrying out the present invention, specific configurations according to the embodiment may be appropriately adopted. Although the data appearing in this embodiment is described in natural language, more specifically, it is specified in pseudo-language, commands, parameters, machine code, etc., that can be recognized by a computer.

[0010] §1 Examples of Application To facilitate understanding of the information processing system (information processing system 1) according to this embodiment, the overview of information processing system 1 is summarized below. Specifically, information processing system 1 predicts the energy consumption (energy consumption EC) when the target vehicle 10 travels along the planned route PR, using the target rolling resistance RST, which is the rolling resistance when the target vehicle 10 travels along the planned route PR. In particular, information processing system 1 identifies the planned route PR of the target vehicle 10, determines the target road surface condition CST, which is the road surface condition (road surface conditions) of the identified planned route PR, and calculates the target rolling resistance RST from the determined target road surface condition CST. Then, information processing system 1 predicts the energy consumption EC using the calculated target rolling resistance RST. Therefore, the information processing system 1 can accurately predict the energy consumption (energy consumption EC) of the target vehicle 10 while it is driving, by considering the relationship between the road surface condition (target road surface condition CST) and the rolling resistance (target rolling resistance RST) of the road on which the target vehicle 10 is driving.

[0011] In this embodiment, an example is described in which the target vehicle 10 is an electric vehicle (electric vehicle) that runs on the driving force of a motor. However, it is not necessary for the target vehicle 10 to be an electric vehicle. In addition to the motor, or in place of the motor, the target vehicle 10 may be equipped with an internal combustion engine such as an engine, or for example, it may be equipped with both an internal combustion engine and a motor. If the target vehicle 10 is equipped with both an internal combustion engine and a motor, the target vehicle 10 may be a series hybrid vehicle or a parallel hybrid vehicle. Furthermore, the target vehicle 10 does not have to be a hybrid vehicle, and the target vehicle 10 may be a vehicle in which the drive wheels are driven by an internal combustion engine.

[0012] Information processing system 1 includes at least three processors: a first processor, a second processor, and a third processor, all capable of sending and receiving information from each other (in other words, inputting and outputting information). In information processing system 1, the first processor identifies the planned route PR of the target vehicle 10. The second processor determines the road surface condition (target road surface condition CST) for the planned route PR identified by the first processor. The third processor calculates the rolling resistance (target rolling resistance RST) when the target vehicle 10 travels along the planned route PR, based on the target road surface condition CST determined by the second processor. The first processor then uses the target rolling resistance RST calculated by the third processor to predict the energy consumption EC. By distributing the three processes—identification of the planned route PR of the target vehicle 10, determination of the target road surface condition CST of the planned route PR, and calculation of the target rolling resistance RST based on the target road surface condition CST—among three processors, information processing system 1 achieves the following effects. In other words, the information processing system 1 can reduce the processing load on each processor and perform high-speed prediction of energy consumption EC using the target rolling resistance RST.

[0013] Figure 1 illustrates the schematic configuration of the information processing system 1. As illustrated in Figure 1, the information processing system 1 according to this embodiment includes a target vehicle 10, a judgment database 20, and a calculation database 30, all of which are capable of communicating with each other. In Figure 1, the judgment database and the calculation database are referred to as "Judgment DB" and "Calculation DB," respectively. The same notation will be used in Figures 3 and 4 described later.

[0014] In particular, this embodiment describes an example in which the target vehicle 10 is equipped with a first processor, the decision database 20 is equipped with a second processor, and the calculation database 30 is equipped with a third processor. However, it is not essential that the first processor, the second processor, and the third processor in the information processing system 1 are each located in three different devices. For example, the information processing system 1 may include an integrated database that combines the decision database 20 and the calculation database 30, and the target vehicle 10, and such integrated database may be equipped with a second processor and a third processor, and the target vehicle 10 may be equipped with a first processor. In other words, any two of the first processor, the second processor, and the third processor may be located in one device, and the remaining processors may be located in another device. Furthermore, all of the first processor, the second processor, and the third processor may be located in a single device. For example, the first processor, the second processor, and the third processor may be implemented as a multi-core processor including cores corresponding to each of the first processor, the second processor, and the third processor. In other words, the first processor, the second processor, and the third processor may be implemented as a single multi-core processor. For example, the target vehicle 10 may be equipped with such a multi-core processor. The information processing system 1 only needs to include the first processor, the second processor, and the third processor, and the devices in which each processor is located in the information processing system 1 are not particularly limited.

[0015] In this embodiment, the target vehicle 10 identifies its planned travel route PR. The determination database 20 then determines the target road surface condition CST, which is the road surface condition of the planned travel route PR identified by the target vehicle 10. Furthermore, the calculation database 30 calculates the target rolling resistance RST, which is the rolling resistance when the target vehicle 10 travels along the planned travel route PR, from the target road surface condition CST determined by the determination database 20. Specifically, the calculation database 30 calculates the target rolling resistance RST, which is optimal for the target vehicle 10 traveling along the planned travel route PR, based on at least one of the vehicle weight and vehicle type of the target vehicle 10 and the target road surface condition CST. The target vehicle 10 then uses the target rolling resistance RST calculated by the calculation database 30 to predict its energy consumption EC.

[0016] The information processing system 1 may divide the planned route PR into multiple sections DS, determine the road surface condition (target road surface condition CST) for each of the divided sections DS, and calculate the rolling resistance (target rolling resistance RST) for each section DS from the target road surface condition CST for each section DS. The information processing system 1 may, for example, obtain external organization information Iea from an external organization 40, and use the obtained external organization information Iea to divide the planned route PR into multiple sections DS. In the example shown in Figure 1, the information processing system 1 (in particular, the determination database 20) obtains external organization information Iea from an external organization 40.

[0017] External organizations 40 are, for example, public organizations such as the Japan Meteorological Agency, the Japan Road Traffic Information Center (JARTIC), the Road Traffic Information and Communication System Center (VICS® Center), and the Geospatial Information Authority of Japan. However, it is not necessary for external organizations 40 to be public organizations. Information processing system 1 may acquire weather information (information showing weather, temperature, etc. at each location) such as AMeDAS observation data provided periodically by the Japan Meteorological Agency as external organization information Iea. Information processing system 1 may acquire road information (information showing road conditions, vehicle driving conditions, etc. at each location) provided by JARTIC, etc. as external organization information Iea. Information processing system 1 may acquire map information (information showing elevation, road gradient, etc. at each location) provided by the Geospatial Information Authority of Japan as external organization information Iea. In other words, in this embodiment, external organization information Iea is information provided by external organizations 40 that shows the conditions at each location, such as weather conditions, topographic conditions, and vehicle driving conditions. For example, external information sources like the IEA provide information for each location, including weather (which may include temperature), altitude, the average speed of vehicles traveling at the location, and the road gradient at that location.

[0018] For example, after identifying the planned travel route PR of the target vehicle 10, the information processing system 1 acquires, from an external organization 40, external organization information Iea indicating the situation of each of one or more points included in the planned travel route PR (including the vicinity of the planned travel route PR). In the present embodiment, after the target vehicle 10 identifies the planned travel route PR, the determination database 20 acquires, from the external organization 40, the external organization information Iea indicating the situation of each of one or more points included in the planned travel route PR (including the vicinity of the planned travel route PR). The determination database 20 can use the acquired external organization information Iea to grasp at least one of the weather and the elevation for each of one or more points included in the planned travel route PR (including the vicinity of the planned travel route PR) of the target vehicle 10. The determination database 20 may store the external organization information Iea acquired from the external organization 40 as external organization information 242. As will be described in detail later, the external organization information 242 is information that can be used by the determination database 20 to divide the planned travel route PR into a plurality of sections DS, and is an example of the "external organization information" in the present invention. Further, the external organization information 242 can also be used by the determination database 20 to determine the target road surface condition CST of the planned travel route PR, and is also an example of the "stockpiled information" in the present invention.

[0019] The information processing system 1 may acquire other vehicle information Iov from other vehicles 50 that are vehicles other than the target vehicle 10, and use the acquired other vehicle information Iov to determine the target road surface condition CST of the planned travel route PR or calculate the target rolling resistance RST. The information processing system 1 may acquire other vehicle information Iov from each of one or more other vehicles 50. In the example shown in FIG. 1, the determination database 20 and the calculation database 30 acquire other vehicle information Iov from each of one or more other vehicles 50.

[0020] The other vehicle information Iov acquired by the determination database 20 may be, for example, information in which a captured image obtained by the other vehicle 50 capturing the surroundings of the other vehicle 50 with an on-board camera provided in the other vehicle 50 is associated with the imaging position (and imaging time) at which the captured image was captured. If there is another vehicle 50 traveling in the vicinity of the planned travel route PR at the timing when the determination database 20 attempts to determine the target road surface condition CST of the planned travel route PR, the determination database 20 may acquire the other vehicle information Iov from said another vehicle 50. The determination database 20 may determine the target road surface condition CST of the planned travel route PR using the other vehicle information Iov acquired from another vehicle 50 traveling in the vicinity of the planned travel route PR. Through said processing, the information processing system 1 can precisely determine the target road surface condition CST of the planned travel route PR.

[0021] When there are "a plurality" of other vehicles 50 traveling in the vicinity of the planned travel route PR at the timing when the determination database 20 attempts to determine the target road surface condition CST, the determination database 20 may acquire the other vehicle information Iov from the following other vehicle 50. That is, among the plurality of other vehicles 50, the determination database 20 may acquire the other vehicle information Iov from, for example, the other vehicle 50 that is closest to the target vehicle 10. Through said processing, the information processing system 1 can determine the target road surface condition CST of the planned travel route PR with higher accuracy.

[0022] The other vehicles 50 from which the judgment database 20 acquires other vehicle information Iov are not limited to "other vehicles 50 that are traveling near the planned route PR at the time the judgment database 20 attempts to determine the target road surface condition CST". The judgment database 20 may acquire other vehicle information Iov from each of several other vehicles 50 in advance, extract the following other vehicle information Iov from the acquired multiple other vehicle information Iov as "stockpiled information", and use the extracted stockpiled information to determine the target road surface condition CST. In other words, the judgment database 20 may extract other vehicle information Iov acquired from "other vehicles 50 that were traveling near the planned route PR before the time the judgment database 20 attempts to determine the target road surface condition CST" as "stockpiled information". The judgment database 20 may use the other vehicle information Iov (stockpiled information) of such other vehicles 50 to determine the target road surface condition CST. For example, the judgment database 20 obtains other vehicle information Iov from each of several other vehicles 50 in advance and stores the obtained other vehicle information Iov as other vehicle information 244 in the storage device 240. If there are no other vehicles 50 traveling near the planned route PR at the time the judgment database 20 attempts to determine the target road surface condition CST, the judgment database 20 extracts "stockpiled information" from the stored other vehicle information 244. That is, the judgment database 20 extracts the other vehicle information Iov of "other vehicles 50 that were traveling near the planned route PR before the time the judgment database 20 attempts to determine the target road surface condition CST" as "stockpiled information" from the other vehicle information 244. The judgment database 20 may then use the extracted stockpiled information (other vehicle information Iov) to determine the target road surface condition CST.

[0023] The stored information is, for example, information used by the information processing system 1 (in this embodiment, the judgment database 20) when there are no other vehicles 50 traveling near the planned route PR at the time the judgment database 20 attempts to determine the target road surface condition CST. As described above, the information processing system 1 may use the other vehicle information Iov received from "other vehicles 50 that were traveling near the planned route PR" as "stored information" to determine the target road surface condition CST. Alternatively, the information processing system 1 may use the external organization information 242 as "stored information" to determine the target road surface condition CST. For example, the information processing system 1 may determine the target road surface condition CST from "external organization information Iea" (external organization information 242) provided by the external organization 40, which indicates the conditions near the planned route PR (e.g., weather, temperature, etc.). The information processing system 1 can determine the target road surface condition CST with high accuracy even when there are no other vehicles 50 traveling near the planned route PR at the time it attempts to determine the target road surface condition CST, by utilizing the stored information (at least one of external organization information 242 and other vehicle information 244).

[0024] In contrast, the other vehicle information Iov acquired by the calculation database 30 indicates, for example, the weight of the other vehicle 50, the type of vehicle, and at least one of the other vehicle 50's driving resistance (especially rolling resistance). The other vehicle information Iov acquired by the calculation database 30 may also be information in which at least two of the weight of the other vehicle 50, the type of vehicle, and the other vehicle 50's driving resistance are associated with each other. For example, when the target vehicle 10 identifies a planned driving route PR, the calculation database 30 may acquire the other vehicle information Iov of the other vehicle 50 from the other vehicle 50 that is driving on the identified "planned driving route PR of the target vehicle 10 (including the vicinity of the planned driving route PR)". The calculation database 30 may also use the rolling resistance, vehicle weight, and vehicle type of other vehicles 50 traveling along the planned route PR of the target vehicle 10 (including the vicinity of the planned route PR) to calculate the rolling resistance (target rolling resistance RST) when the target vehicle 10 travels along the planned route PR.

[0025] As illustrated in Figure 1, the calculation database 30 may obtain target vehicle information Iv from the target vehicle 10, which indicates the weight of the target vehicle 10. The calculation database 30 may use the obtained target vehicle information Iv to calculate the target rolling resistance RST. For example, the calculation database 30 may calculate the target rolling resistance RST by adjusting the rolling resistance of "another vehicle 50 traveling along the planned route PR of the target vehicle 10" based on the differences between the weight of the other vehicle 50 and the weight of the target vehicle 10. In addition to the weight of the target vehicle 10, or instead of the weight of the target vehicle 10, the target vehicle information Iv may indicate the type of vehicle of the target vehicle 10.

[0026] As explained above with reference to Figure 1, the information processing system 1 identifies the planned route PR of the target vehicle 10, determines the road surface condition (target road surface condition CST) of the identified planned route PR, and calculates the target rolling resistance RST, which is the rolling resistance when the target vehicle 10 travels along the planned route PR, from the determined target road surface condition CST. Then, the information processing system 1 uses the calculated target rolling resistance RST to predict the energy consumption EC when the target vehicle 10 travels along the planned route PR. Through this process, the information processing system 1 can predict the energy consumption (energy consumption EC) of the target vehicle 10 when it travels along the planned route PR, taking into account the relationship between the road surface condition (target road surface condition CST) of the planned route PR and the rolling resistance (target rolling resistance RST) when the target vehicle 10 travels along that route PR. In particular, the information processing system 1 calculates the optimal rolling resistance (specific to the target vehicle 10) as the target rolling resistance RST by adjusting the rolling resistance (rolling resistance coefficient) for each target road surface condition CST using at least one of the vehicle weight and vehicle type of the target vehicle 10. In other words, the information processing system 1 calculates the rolling resistance (target rolling resistance RST) specific to the target vehicle 10 traveling along the planned route PR from at least one of the vehicle weight and vehicle type of the target vehicle 10 and the target road surface condition CST. By using this target rolling resistance RST, the information processing system 1 can predict the energy consumption EC when the target vehicle 10 travels along the planned route PR with high accuracy. The information processing system 1 outlined above will now be explained in detail using Figures 2 to 8.

[0027] §2 Example Configuration (Target vehicles) Figure 2 schematically illustrates an example of the hardware configuration of the target vehicle 10 according to this embodiment. As shown in Figure 2, the target vehicle 10 according to this embodiment includes, as hardware, a CPU (Central Processing Unit) 110, RAM (Random Access Memory) 120, ROM (Read-Only Memory) 130, an in-vehicle storage device 140, a navigation system 150, and an in-vehicle communication device 160. In Figure 2, the navigation system is referred to as "NaviSys" and the vehicle control program as "Vehicle Control PG".

[0028] The navigation system 150 is a system that, once a destination is set for the target vehicle 10, can provide information about the route from the current location of the target vehicle 10 to the destination, and is an example of the "car navigation system" of the present invention. In addition to information about the route to the destination, the navigation system 150 may also provide information indicating the distance to the destination, the road gradient at each point along the route, the (statistical) average speed, the speed limit, etc. The navigation system 150 may also provide information indicating the current location of the target vehicle 10. The information that the navigation system 150 can provide may include information provided by an external organization 40 (external organization information Iea), for example, road information provided by JARTIC, etc. The navigation system 150 includes an HMI (Human Machine Interface) that receives "various information (operations) input from the occupants of the target vehicle 10" and "outputs various information to the occupants of the target vehicle 10".

[0029] The in-vehicle storage device 140 is equipped with a storage medium that stores various types of information and is readable and writable. In this embodiment, the in-vehicle storage device 140 may have information pre-stored that can be used to identify (estimate) the planned driving route PR of the target vehicle 10. For example, the in-vehicle storage device 140 may have driving history information 142 showing the driving history of the target vehicle 10 pre-stored. The in-vehicle communication device 160 is a device for the target vehicle 10 to send and receive (communicate) information with the determination database 20 and the calculation database 30, respectively, via a network.

[0030] In the example shown in Figure 2, the ROM 130 stores the vehicle control program 132. The vehicle control program 132 is a program that causes the target vehicle 10 (particularly the CPU 110) to perform information processing such as identifying the planned route PR of the target vehicle 10 and predicting the energy consumption EC when the target vehicle 10 travels along the planned route PR. The vehicle control program 132 includes a series of instructions for said information processing.

[0031] The CPU 110 is an example of a processor resource, and in particular, an example of the "first processor" of the present invention. The CPU 110 executes the vehicle control program 132 read from the ROM 130, using the RAM 120 as a work area, thereby performing information processing such as identifying the planned route PR of the target vehicle 10. Specifically, the CPU 110 loads the vehicle control program 132 stored in the ROM 130 into the RAM 120. Then, the CPU 110 interprets and executes the instructions contained in the vehicle control program 132 loaded into the RAM 120 to control each component. As a result, as shown in Figure 2, the target vehicle 10 according to this embodiment operates as a computer equipped with a route identification unit 111, a target rolling resistance acquisition unit 112, and a consumption calculation unit 113 as software modules. That is, in this embodiment, each software module of the target vehicle 10 is realized by the CPU 110.

[0032] The route identification unit 111 identifies the planned route PR of the target vehicle 10. The route identification unit 111 outputs information indicating the identified planned route PR to the judgment database 20, and in this embodiment, transmits it to the judgment database 20 via the in-vehicle communication device 160. The route identification unit 111 may also include information indicating the position of its own vehicle (target vehicle 10) (for example, current position, position at the start of driving, etc.) in the "information indicating the planned route PR" transmitted to the judgment database 20. The route identification unit 111 may also transmit the same "information indicating the planned route PR" that it transmits to the judgment database 20 to the calculation database 30.

[0033] For example, the route identification unit 111 may identify the planned route PR of the target vehicle 10 based on information provided by the navigation system 150. For instance, the route identification unit 111 may obtain information from the navigation system 150 about "the route of the target vehicle 10 to its destination" and identify "the route of the target vehicle 10 to its destination" indicated by the obtained information as the planned route PR. For example, if the driver of the target vehicle 10 is traveling to a destination for the first time, or is driving on unfamiliar roads, the driver is likely to use the navigation system 150 to search for a route to that destination and drive the target vehicle 10 along the searched route. Therefore, the route identification unit 111 may identify the planned route PR of the target vehicle 10 based on information provided by the navigation system 150. Through this process, the route identification unit 111 can identify (estimate) the planned route PR with high accuracy, such as when the driver of the target vehicle 10 selects a route using the navigation system 150. Furthermore, the route identification unit 111 can reduce the computational load and shorten the computation time by identifying the planned route PR based on the information provided by the navigation system 150.

[0034] Alternatively, the route identification unit 111 may use driving history information 142, which shows the driving history of the target vehicle 10, to identify the planned driving route PR for the target vehicle 10. For example, the route identification unit 111 first obtains driving history information 142 by referring to the in-vehicle storage device 140. From the acquired driving history information 142, the route identification unit 111 may predict the destination of the target vehicle 10 and use the driving history information 142 to identify the driving route with the highest driving frequency among one or more driving routes to the predicted destination as the planned driving route PR. The route identification unit 111 may also obtain information showing the current location of the target vehicle 10 from a navigation system 150 or the like, and predict the destination of the target vehicle 10 from the current location of the target vehicle 10 and the driving history information 142. The route identification unit 111 can identify the planned driving route PR with high accuracy from the driving history information 142, which shows the driving history of the target vehicle 10.

[0035] The target rolling resistance acquisition unit 112 acquires the target rolling resistance RST calculated by the calculation database 30, and in this embodiment, receives the target rolling resistance RST calculated by the calculation database 30 from the calculation database 30 via the in-vehicle communication device 160. The target rolling resistance acquisition unit 112 may store the received target rolling resistance RST in the in-vehicle storage device 140. In particular, the target rolling resistance acquisition unit 112 may associate the target rolling resistance RST with the planned driving route PR of the target vehicle 10 identified by the route identification unit 111 and store them in the in-vehicle storage device 140. Also, if the target vehicle 10 is notified by the determination database 20 of the road surface condition (target road surface condition CST) of the planned driving route PR determined by the determination database 20, the target rolling resistance acquisition unit 112 may associate the planned driving route PR, the target road surface condition CST, and the target rolling resistance RST with each other and store them in the in-vehicle storage device 140.

[0036] As described above, in the information processing system 1, the target vehicle 10 (first processor), the judgment database 20 (second processor), and the calculation database 30 (third processor) can send and receive information (input / output) from each other and can communicate with each other. Therefore, the on-board storage device 140 is a storage device (storage area) that is accessible (i.e., can send and receive information) from each of the target vehicle 10, the judgment database 20, and the calculation database 30. In the information processing system 1, the planned driving route PR, the target road surface condition CST, and the target rolling resistance RST may be associated with each other and stored in the on-board storage device 140. With this configuration, the information processing system 1 can reduce the computational load related to re-prediction and improve the computation speed, for example, when it becomes necessary to re-predict the target rolling resistance RST.

[0037] As described above, in this embodiment, the planned route PR of the target vehicle 10 may be divided into multiple sections DS, and the determination database 20 may determine the road surface condition (target road surface condition CST) for each of the multiple sections DS. The calculation database 30 may then calculate the rolling resistance (target rolling resistance RST) for each of the multiple sections DS from the target road surface condition CST for each of the multiple sections DS. Accordingly, the target rolling resistance acquisition unit 112 may acquire the "target rolling resistance RST for each of the multiple sections DS of the planned route PR" calculated by the calculation database 30 from the calculation database 30. Furthermore, "each of the multiple sections DS of the planned route PR", "the road surface condition (target road surface condition CST) for each of the multiple sections DS of the planned route PR", and "the rolling resistance (target rolling resistance RST) for each of the multiple sections DS of the planned route PR" may be associated with each other and stored in the on-board storage device 140.

[0038] The energy consumption calculation unit 113 uses the target rolling resistance RST acquired by the target rolling resistance acquisition unit 112 to predict the energy consumption (energy consumption EC) when the target vehicle 10 travels along the planned route PR. Here, the energy consumption EC includes, for example, at least the following four types of energy (consumption energy): the energy required to drive (travel) the target vehicle 10 (driving force), the energy consumed by the air conditioner of the target vehicle 10 (air conditioner power), the energy consumed by the electrical equipment of the target vehicle 10 (electrical equipment power), and the energy consumed by each unit other than the "air conditioner and electrical equipment" of the target vehicle 10 (unit losses). Examples of such electrical equipment include headlights, wipers, defoggers, and door locks. Examples of units other than the "air conditioner and electrical equipment" include the ECU (Electronic Control Unit). In this embodiment, the energy consumption calculation unit 113 predicts the sum of the above four types of energy as the energy consumption EC.

[0039] Of the four energies mentioned above, the driving force of the target vehicle 10 includes driving resistance (also called "RL (Road Load)"), acceleration resistance, and gradient resistance, and is predicted, for example, as the sum of these three resistances. Furthermore, the aforementioned driving resistance includes rolling resistance, energy that is "proportional to vehicle speed (in other words, calculated by multiplying vehicle speed by a predetermined coefficient)" such as rotational losses, and energy that is "proportional to the square of vehicle speed (in other words, calculated by multiplying the square of vehicle speed by a predetermined coefficient)" such as air resistance. As will be explained in more detail later, rolling resistance may also be calculated by multiplying the rolling resistance coefficient by the vehicle weight.

[0040] In this embodiment, the energy consumption calculation unit 113 can obtain information indicating the "vehicle speed (predicted vehicle speed) of the target vehicle 10 on the planned route PR" from, for example, the navigation system 150, the driving history information 142, or a driving control ECU (not shown). From the "predicted vehicle speed of the target vehicle 10 on the planned route PR" indicated by the acquired information, the energy consumption calculation unit 113 predicts the "energy proportional to vehicle speed," such as rotational losses, and the "energy proportional to the square of vehicle speed," such as air resistance. The energy consumption calculation unit 113 then predicts the driving resistance as the sum (total) of the predicted "energy proportional to vehicle speed" and "energy proportional to the square of vehicle speed" and the target rolling resistance RST acquired by the target rolling resistance acquisition unit 112. The energy consumption calculation unit 113 also calculates (predicts) acceleration resistance and gradient resistance, and calculates acceleration resistance and gradient resistance, respectively, using, for example, information provided by the navigation system 150, the driving history information 142, etc. For example, the consumption calculation unit 113 calculates acceleration resistance and gradient resistance using the road gradient of the planned route PR and indicators (statistical indicators) of acceleration and deceleration performed by the vehicle traveling on the planned route PR. In other words, the consumption calculation unit 113 calculates acceleration resistance and gradient resistance using, for example, the road gradient of the planned route PR. The consumption calculation unit 113 predicts the driving force of the target vehicle 10 as the sum of the predicted driving force and the calculated acceleration resistance and gradient resistance. Furthermore, the consumption calculation unit 113 predicts the energy consumption EC as the sum of the predicted driving force of the target vehicle 10, the aforementioned air conditioning power, electrical equipment power, and each unit loss.

[0041] As described above, the energy consumption calculation unit 113 predicts the energy consumption EC using the target rolling resistance RST calculated by the calculation database 30 (third processor), the predicted vehicle speed of the target vehicle 10 on the planned route PR, and at least one of the road gradient of the planned route PR. Through this process, the energy consumption calculation unit 113 can predict the energy consumption EC with high accuracy.

[0042] Here, the consumption calculation unit 113 may notify the occupants of the target vehicle 10 of the predicted energy consumption EC. For example, it may control the in-vehicle HMI (for instance, the HMI included in the navigation system 150) to notify the occupants of the energy consumption EC. Alternatively, the consumption calculation unit 113 may obtain the "current SOC (State of Charge) at the present time (for example, at the start of driving)" from, for example, a power management ECU that manages the charge rate (SOC, State of Charge) of the battery that supplies power to the motor of the target vehicle 10. The consumption calculation unit 113 may predict the "SOC at the time of arrival of the target vehicle 10 at its destination" (arrival SOC) from the obtained current SOC and the predicted energy consumption EC, and notify the occupants of the target vehicle 10 of the predicted arrival SOC. Through such notification, the occupants can be aware of the arrival SOC in advance.

[0043] Furthermore, the consumption calculation unit 113 may store the predicted energy consumption EC in the on-board storage device 140. Here, as described above, in this embodiment, the target rolling resistance acquisition unit 112 may store the planned driving route PR, the target road surface condition CST, and the target rolling resistance RST in the on-board storage device 140 in association with each other. Therefore, the consumption calculation unit 113 may store the predicted energy consumption EC in the on-board storage device 140 in association with at least one of the planned driving route PR, the target road surface condition CST, and the target rolling resistance RST.

[0044] Furthermore, the energy consumption calculation unit 113 may transmit the predicted energy consumption EC to at least one of the determination database 20 and the calculation database 30 via the in-vehicle communication device 160. The energy consumption calculation unit 113 may also transmit the energy consumption EC to at least one of the determination database 20 and the calculation database 30 in association with at least one of the planned driving route PR, the target road surface condition CST, and the target rolling resistance RST.

[0045] When the target rolling resistance acquisition unit 112 acquires the "target rolling resistance RST for each of the multiple sections DS of the planned travel route PR" from the calculation database 30, the consumption calculation unit 113 may predict the energy consumption EC when the target vehicle 10 travels along the planned travel route PR in the following manner. That is, the consumption calculation unit 113 may predict the energy consumption of the target vehicle 10 when it travels for each of the multiple sections DS from the "target rolling resistance RST for each of the multiple sections DS of the planned travel route PR". Then, the consumption calculation unit 113 may predict the energy consumption EC from the sum of the energy consumption predicted for each of the multiple sections DS of the planned travel route PR. The consumption calculation unit 113 can predict the energy consumption of the target vehicle 10 when it travels for each section DS from the target rolling resistance RST of each section DS by applying the above-described method of "predicting energy consumption EC using the target rolling resistance RST (target rolling resistance RST for the entire planned travel route PR)".

[0046] (Judgment Database) Figure 3 schematically illustrates an example of the hardware configuration of the determination database 20 according to this embodiment. As shown in Figure 3, the determination database 20 according to this embodiment includes a CPU 210, RAM 220, ROM 230, storage device 240, and communication device 250 as hardware. In Figure 3, the determination database control program is referred to as "determination DB control PG".

[0047] The storage device 240 is equipped with a storage medium that stores various types of information and is readable and writable. In this embodiment, the storage device 240 may store information that can be used to divide the planned route PR of the target vehicle 10 into multiple sections DS, or to determine the road surface condition (target road surface condition CST) of the planned route PR (each section DS).

[0048] For example, the storage device 240 may store external agency information Iea obtained from the external agency 40 as external agency information 242. In this embodiment, external agency information Iea indicating the conditions of one or more points included in the planned driving route PR of the target vehicle 10 (including the vicinity of the planned driving route PR) is stored as external agency information 242. The external agency information 242 indicates the weather conditions, terrain conditions, vehicle driving conditions, etc. for one or more points included in the planned driving route PR (including the vicinity of the planned driving route PR). For example, the external agency information 242 may indicate the weather (which may include temperature), altitude, vehicle speed (average speed) of the vehicle traveling at the point, road gradient at the point, etc. for one or more points included in the planned driving route PR (including the vicinity of the planned driving route PR).

[0049] The external organization information 242 (external organization information Iea) is an example of the "external organization information" of the present invention. Furthermore, the external organization information 242 (external organization information Iea) is an example of "stockpiled information" that can be used by the judgment database 20 to determine the target road surface condition CST when there are no other vehicles 50 traveling near the planned route PR at the time the judgment database 20 attempts to determine the target road surface condition CST for the planned route PR.

[0050] External agency information 242 for each of the one or more points included in the planned route PR (including the vicinity of the planned route PR) may be managed (stored) in the storage device 240 in association with the road surface condition (estimated road surface condition) of the point in question. For example, if the external agency information 242 for a certain point in the planned route PR indicates that the weather at that point is "sunny", then the external agency information 242 may be associated with the road surface condition at that point as "dry". Similarly, if the external agency information 242 for a certain point in the planned route PR indicates that the weather at that point is "rainy", then the external agency information 242 may be associated with the road surface condition at that point as "wet".

[0051] Furthermore, for example, the storage device 240 may have vehicle information Iov acquired from the other vehicle 50 pre-stored as vehicle information 244. This vehicle information 244 (vehicle information Iov) is information that associates, for example, an image captured by the other vehicle 50 around the vehicle itself with the position where the image was captured (in other words, the position of the other vehicle 50 at the time the image was captured). In vehicle information 244 (vehicle information Iov), the captured image and the position where the image was captured (position of the other vehicle 50) may also be associated with the time when the other vehicle 50 captured the image (capture time). It is desirable that the image captured in vehicle information 244 (vehicle information Iov) captures the road surface on which the other vehicle 50 is traveling (the condition of the road surface), but it is not essential that the image captures the road surface on which the other vehicle 50 is traveling.

[0052] The other vehicle information 244 for each other vehicle 50 may be managed (stored) in the storage device 240 in association with the "road surface condition (estimated road surface condition) at the imaging location (i.e., the position of the other vehicle 50 at the time the other vehicle 50 took the image)." For example, other vehicle information 244 that includes an image showing that the surroundings of the other vehicle 50 are "sunny" may be associated with "dry" as the road surface condition at the position of the other vehicle 50 (imaging location) at the time the image was taken. Similarly, other vehicle information 244 that includes an image showing that it is "raining" around the other vehicle 50 may be associated with "wet" as the road surface condition at the position of the other vehicle 50 (imaging location) at the time the image was taken.

[0053] The other vehicle information Iov (other vehicle information 244) obtained from "other vehicles 50 traveling near the planned route PR at the time the judgment database 20 attempts to determine the target road surface condition CST for the planned route PR" is an example of the "other vehicle information" of the present invention. In addition, the other vehicle information Iov (other vehicle information 244) obtained in advance from "other vehicles 50 that were traveling near the planned route (before the time the judgment database 20 attempts to determine the target road surface condition CST)" is an example of the "stored information" of the present invention.

[0054] In this embodiment, the stockpiled information (at least one of the external organization information 242 and other vehicle information 244 described above) may be reviewed (in other words, updated) if terrain changes (for example, natural disasters such as earthquakes) occur near the planned route PR. For example, the external organization information 242 for each of the one or more points included in the planned route PR (including the vicinity of the planned route PR) may be reviewed if terrain changes occur near the planned route PR. Similarly, the correspondence between the external organization information 242 for each of the one or more points included in the planned route PR (including the vicinity of the planned route PR) and the road surface condition (estimated road surface condition) of those points may also be reviewed if terrain changes occur near the planned route PR. In addition, the other vehicle information Iov (other vehicle information 244) obtained in advance from "other vehicles 50 that were traveling near the planned route (before the timing when the judgment database 20 attempts to determine the target road surface condition CST)" may be reviewed if terrain changes occur near the planned route PR. Similarly, the correspondence between the other vehicle information 244 previously acquired from the other vehicle 50 and the road surface condition (estimated road surface condition) at the location where the other vehicle 50 captured the image (imaging location) may also be reviewed if terrain changes occur near the planned route PR. Through this process, the information processing system 1 can review the "stockpiled information" that can be used to determine the road surface condition (target road surface condition CST) of the planned route PR at an appropriate time, and can determine the target road surface condition CST with high accuracy using this "stockpiled information".

[0055] The communication device 250 is a device for the determination database 20 to send and receive information (communicate) with the target vehicle 10 and the calculation database 30 via the network. In this embodiment, the communication device 250 also sends and receives information with the external organization 40 and other vehicles 50 (in particular, each of the one or more other vehicles 50) as illustrated in Figure 1.

[0056] In the example shown in Figure 3, the ROM 230 stores the determination database control program 232. The determination database control program 232 is a program that causes the determination database 20 (specifically the CPU 210) to perform information processing such as dividing the planned travel route PR into multiple sections DS and determining the road surface condition (target road surface condition CST) of the planned travel route PR (specifically each section DS). The determination database control program 232 includes a series of instructions for said information processing.

[0057] The CPU 210 is an example of a processor resource, and in particular, an example of the "second processor" of the present invention. The CPU 210 executes the above-mentioned information processing by executing the judgment database control program 232 read from the ROM 230, using the RAM 220 as a work area. Specifically, the CPU 210 loads the judgment database control program 232 stored in the ROM 230 into the RAM 220. Then, the CPU 210 interprets and executes the instructions contained in the judgment database control program 232 loaded into the RAM 220 to control each component. As a result, as shown in Figure 3, the judgment database 20 according to this embodiment operates as a computer equipped with a planned route information acquisition unit 211, an external organization information acquisition unit 212, another vehicle information acquisition unit 213, a route division unit 214, and a road surface condition determination unit 215 as software modules. That is, in this embodiment, each software module of the judgment database 20 is realized by the CPU 210.

[0058] The planned route information acquisition unit 211 acquires various information from the target vehicle 10 via the communication device 250, and receives (acquires) at least information indicating the "planned driving route PR of the target vehicle 10" identified by the target vehicle 10. This information indicating the "planned driving route PR of the target vehicle 10" may include information indicating the location of the target vehicle 10 (such as its current location). The planned route information acquisition unit 211 may further acquire information indicating the vehicle type, vehicle weight, etc. (target vehicle information Itv) from the target vehicle 10.

[0059] The external agency information acquisition unit 212 receives external agency information Iea from the external agency 40 via the communication device 250. In particular, the external agency information acquisition unit 212 receives external agency information Iea from the external agency 40 that indicates the status of one or more points included in the planned driving route PR (including the vicinity of the planned driving route PR) indicated by the information acquired by the planned route information acquisition unit 211. In this embodiment, the external agency information acquisition unit 212 stores the received external agency information Iea as external agency information 242 in the storage device 240. The external agency information acquisition unit 212 may also store the external agency information Iea for each of the one or more points included in the planned driving route PR (including the vicinity of the planned driving route PR) in the storage device 240 in association with the road surface condition (estimated road surface condition) of the relevant point.

[0060] The other vehicle information acquisition unit 213 receives other vehicle information Iov from each of one or more other vehicles 50 via the communication device 250. As described above, the other vehicle information Iov includes an image captured by the other vehicle 50 around the vehicle itself, and may also indicate the position where the image was captured, the time it was captured, etc. In this embodiment, the other vehicle information acquisition unit 213 stores the received other vehicle information Iov as other vehicle information 244 in the storage device 240. The other vehicle information acquisition unit 213 may also store the other vehicle information Iov of each other vehicle 50 in the storage device 240 in association with the road surface condition (estimated road surface condition) at the position where each other vehicle 50 captured the image (image capture position).

[0061] Furthermore, the other vehicle information acquisition unit 213 may assign a priority to the other vehicle information Iov of each other vehicle 50 and store the other vehicle information Iov with such priority assigned as other vehicle information 244 in the storage device 240. For example, the other vehicle information acquisition unit 213 may assign a priority to each image (each other vehicle information Iov) such that the image (other vehicle information Iov) taken at a time closer to the "time when the target vehicle 10 is predicted to travel along the planned route PR" has a higher priority. Alternatively, for example, the other vehicle information acquisition unit 213 may assign a priority to each image (each other vehicle information Iov) such that the image (other vehicle information Iov) taken at an imaging position closer to the "point where the road surface condition is determined" has a higher priority.

[0062] The route division unit 214 divides the planned route PR of the target vehicle 10 into multiple sections DS. In this embodiment, the route division unit 214 divides the planned route PR indicated by the information acquired by the planned route information acquisition unit 211 into multiple sections DS. The route division unit 214 may also divide the planned route PR into multiple sections DS based on "at least one of the weather and altitude of each of the one or more points included in the planned route PR (including the vicinity of the planned route PR)" indicated by the external organization information 242. For example, if "a number of consecutive (in other words, adjacent) points included in the planned route PR (including the vicinity of the planned route PR)" have the same weather and altitude, the route division unit 214 may divide the planned route PR into multiple sections DS so that these consecutive points are included in the same section DS.

[0063] The road surface condition determination unit 215 determines the road surface condition (target road surface condition CST) of the planned route PR, and determines, for example, whether the road surface of the planned route PR is "dry, wet, frozen, or covered in compacted snow" as the target road surface condition CST. In this embodiment, the road surface condition determination unit 215 determines the road surface condition (target road surface condition CST) for each of the "multiple sections DS" obtained by the route division unit 214 dividing the planned route PR. For example, the planned route PR may include "multiple locations" with different road surface conditions. Therefore, the determination database 20 (second processor) divides the planned route PR into multiple sections DS such that, for example, "multiple adjacent locations where at least one of the weather and altitude is the same" are included in the same section DS. Then, the determination database 20 (second processor) determines the road surface condition of each of the multiple sections DS as the target road surface condition CST for each of the multiple sections DS. Through this process, the judgment database 20 can precisely determine the road surface condition (target road surface condition CST) for multiple sections DS, each containing "multiple adjacent points where at least one of the weather and altitude is the same," even when the planned route PR includes "multiple points" with different road surface conditions.

[0064] In particular, in this embodiment, the planned route PR is divided based on "at least one of the weather and altitude of each of the one or more points included in the planned route PR (including the vicinity of the planned route PR)." For example, even if there are "multiple adjacent points included in the planned route PR (including the vicinity of the planned route PR)," if the weather at each of these points is different from that of the other points, the road surface conditions at each of those points are considered to be different. Also, even if the weather at each of the multiple adjacent points included in the planned route PR (including the vicinity of the planned route PR) is the same, if the altitude (or temperature) at each of those points is different from that of the other points, the road surface conditions at each of those points may be different. Therefore, the determination database 20 (second processor) divides the planned route PR into multiple sections DS based on "at least one of the weather and altitude of each of the one or more points included in the planned route PR (including the vicinity of the planned route PR)." The judgment database 20 then determines the road surface condition of each of the multiple sections DS as the target road surface condition CST for each of the multiple sections DS. Through this process, the judgment database 20 can precisely determine the road surface condition (target road surface condition CST) for each of the "multiple sections DS" obtained by dividing the planned route PR based on "at least one of weather and altitude" which is considered to affect the road surface condition.

[0065] In this embodiment, the road surface condition determination unit 215 may determine the target road surface condition CST for the planned driving route PR (in particular, the target road surface condition CST for each section DS) using the following other vehicle information Iov (other vehicle information 244). That is, the road surface condition determination unit 215 may determine the target road surface condition CST using other vehicle information Iov obtained from "other vehicles 50 traveling near the planned driving route PR (at the time when the road surface condition determination unit 215 is trying to determine the target road surface condition CST)". For example, the road surface condition determination unit 215 first grasps the "planned driving route PR of the target vehicle 10" from the information obtained by the planned route information acquisition unit 211. Next, the road surface condition determination unit 215 refers to the storage device 240 and extracts the other vehicle information Iov obtained from other vehicles 50 traveling near the grasped "planned driving route PR" from the other vehicle information 244. The road surface condition determination unit 215 uses the extracted other vehicle information Iov, that is, the other vehicle information 244 of other vehicles 50 that are "traveling near the planned route PR (at the time when the road surface condition determination unit 215 attempts to determine the target road surface condition CST)" to determine the target road surface condition CST.

[0066] For example, if the captured image included in the other vehicle information 244 indicates that "the planned driving route PR (a certain section DS within the planned driving route PR) is sunny," the road surface condition determination unit 215 may determine from the other vehicle information 244 that the target road surface condition CST for the planned driving route PR (the target road surface condition CST for the certain section DS) is "dry." The information processing system 1 (second processor) can determine the road surface condition (target road surface condition CST) of the planned driving route PR with high accuracy by using "an image captured by another vehicle 50 traveling near the planned driving route PR, capturing the area around its own vehicle" (other vehicle information Iov).

[0067] If, at the time the road surface condition determination unit 215 attempts to determine the target road surface condition CST, multiple other vehicles 50 are traveling near the planned route PR, the road surface condition determination unit 215 may determine the target road surface condition CST using the other vehicle information Iov of the following other vehicles 50. That is, the road surface condition determination unit 215 may determine the target road surface condition CST using the other vehicle information Iov (other vehicle information 244) obtained from the other vehicle 50 that is closest in distance from the target vehicle 10 among the multiple other vehicles 50. Through this process, the information processing system 1 (second processor) can determine the target road surface condition CST of the planned route PR with higher accuracy.

[0068] If there are no other vehicles 50 traveling near the planned route PR (at the time the road surface condition determination unit 215 attempts to determine the target road surface condition CST), the road surface condition determination unit 215 may determine the target road surface condition CST using pre-prepared "stockpiled information". That is, the road surface condition determination unit 215 may determine the target road surface condition CST using at least one of the external organization information 242 and other vehicle information 244 as "stockpiled information".

[0069] For example, the road surface condition determination unit 215 determines the target road surface condition CST from other vehicle information Iov (other vehicle information 244) obtained from "other vehicles 50 that were traveling near the planned route PR (before the timing when the road surface condition determination unit 215 attempts to determine the target road surface condition CST)". In other words, the road surface condition determination unit 215 uses "images taken by other vehicles 50 that were traveling near the planned route PR" as "stored information" to determine the target road surface condition CST. In this embodiment, the road surface condition determination unit 215 extracts the other vehicle information Iov (other vehicle information 244) of other vehicles 50 that were traveling near the planned route PR from the other vehicle information 244 of each other vehicle 50 stored in the storage device 240 as "stored information". Then, the road surface condition determination unit 215 determines the target road surface condition CST from the other vehicle information 244 extracted as "stored information". For example, if the captured image included in the other vehicle information 244 extracted as "stockpiled information" indicates that "the planned driving route PR (a certain section DS in the planned driving route PR) was sunny," the road surface condition determination unit 215 may determine from the other vehicle information 244 that the target road surface condition CST for the planned driving route PR (the target road surface condition CST for the certain section DS) is "dry." Furthermore, the road surface condition determination unit 215 may refer to the priority assigned to the other vehicle information 244 of each other vehicle 50, extract the other vehicle information 244 with a higher priority as "stockpiled information," and determine the target road surface condition CST from the extracted other vehicle information 244.

[0070] Furthermore, for example, the road surface condition determination unit 215 determines the target road surface condition CST from "external agency information Iea" (external agency information 242) provided by an external agency 40, which indicates the conditions near the planned route PR (e.g., weather, temperature, etc.). For example, if the external agency information 242 for the planned route PR (a certain section DS within the planned route PR) indicates that the weather for the planned route PR (the relevant section DS) is "sunny", the road surface condition determination unit 215 determines that the target road surface condition CST for the planned route PR (the relevant section DS) is "dry".

[0071] The information processing system 1 can determine the target road surface condition CST with high accuracy even when there are no other vehicles 50 traveling near the planned route PR at the time it attempts to determine the target road surface condition CST, by utilizing the aforementioned stored information (at least one of the external organization information 242 and other vehicle information 244).

[0072] As described above, the stored information may have pre-associated road surface conditions at each point (or each imaging location). For example, the other vehicle information 244 of another vehicle 50 that was traveling near the planned route PR may have the road surface conditions of "the position of the other vehicle 50 at the time the other vehicle 50 captured the image" associated with it. Also, for example, the external organization information 242 for each of the one or more points included in the planned route PR (including the vicinity of the planned route PR) may have the road surface conditions of those points associated with it. The road surface condition determination unit 215 may determine the target road surface condition CST of the planned route PR from the "road surface conditions near the planned route PR" that are pre-associated with the stored information. Because the stored information has pre-associated road surface conditions at each point (or each imaging location), the information processing system 1 can use the stored information to determine the target road surface condition CST with high accuracy even when there are no other vehicles 50 traveling near the planned route PR. The information processing system 1 can use the stored information to suppress the computational load required to determine the target road surface condition CST and determine the target road surface condition CST at high speed.

[0073] The road surface condition determination unit 215 transmits information indicating the target road surface condition CST (in this embodiment, the target road surface condition CST for each section DS) of the determined planned driving route PR to the calculation database 30 via the communication device 250. The road surface condition determination unit 215 may also transmit information to the calculation database 30 that associates the planned driving route PR (each section DS) with the "road surface condition (target road surface condition CST) of the planned driving route PR (each section DS)". The road surface condition determination unit 215 may further transmit information indicating the target road surface condition CST of the planned driving route PR (each section DS) to the target vehicle 10. In particular, the road surface condition determination unit 215 may transmit information to the target vehicle 10 that associates the planned driving route PR (each section DS) with the "road surface condition (target road surface condition CST) of the planned driving route PR (each section DS)".

[0074] The road surface condition determination unit 215 may store information indicating the target road surface condition CST (in this embodiment, the target road surface condition CST for each section DS) of the determined planned driving route PR in the storage device 240. In particular, the road surface condition determination unit 215 may store information in the storage device 240 that associates the planned driving route PR (each section DS) with the "road surface condition (target road surface condition CST) of the planned driving route PR (each section DS)". Furthermore, when the determination database 20 is notified by the calculation database 30 of the target rolling resistance RST (in this embodiment, the target rolling resistance RST for each section DS) of the planned driving route PR calculated by the calculation database 30, the road surface condition determination unit 215 may store the following information in the storage device 240. In other words, the road surface condition determination unit 215 may associate the planned travel route PR (each section DS), the target road surface condition CST for the planned travel route PR (each section DS), and the target rolling resistance RST for the planned travel route PR (each section DS) with each other and store them in the storage device 240.

[0075] As described above, in the information processing system 1, the target vehicle 10 (first processor), the judgment database 20 (second processor), and the calculation database 30 (third processor) can send and receive information (input / output) from each other and can communicate with each other. Therefore, the storage device 240 is a storage device (storage area) that is accessible (i.e., can send and receive information) from each of the target vehicle 10, the judgment database 20, and the calculation database 30. In the information processing system 1, the planned driving route PR, the target road surface condition CST, and the target rolling resistance RST may be associated with each other and stored in the storage device 240. With this configuration, the information processing system 1 can reduce the computational load related to re-judgment and re-prediction and improve the computation speed, for example, when it is necessary to re-judgment the target road surface condition CST or to re-predict the target rolling resistance RST.

[0076] (Computational database) Figure 4 schematically illustrates an example of the hardware configuration of the calculation database 30 according to this embodiment. As shown in Figure 4, the calculation database 30 according to this embodiment includes a CPU 310, RAM 320, ROM 330, storage device 340, and communication device 350 as hardware. In Figure 4, the calculation database control program is referred to as "Calculation DB Control PG".

[0077] The storage device 340 is equipped with a storage medium that stores various types of information and is readable and writable. In this embodiment, the storage device 340 may have information pre-stored that can be used to calculate the rolling resistance (target rolling resistance RST) when the target vehicle 10 travels along the planned route PR, based on the road surface condition (target road surface condition CST) of the planned route PR.

[0078] For example, the storage device 340 may store the target vehicle information Iv obtained from the target vehicle 10 as target vehicle information 342. This target vehicle information 342 (target vehicle information Iv) indicates the vehicle weight of the target vehicle 10. The target vehicle information 342 may indicate the vehicle type of the target vehicle 10, either together with the vehicle weight of the target vehicle 10 or in lieu of the vehicle weight of the target vehicle 10. If the target vehicle information 342 indicates the vehicle type of the target vehicle 10, this target vehicle information 342 may be associated with a predetermined vehicle weight (for example, an average vehicle weight) for each vehicle type. The target vehicle information 342 (target vehicle information Iv) may further indicate the "planned driving route PR of the target vehicle 10" identified by the target vehicle 10.

[0079] Furthermore, for example, the storage device 340 may store other vehicle information Iov obtained from other vehicles 50 traveling along the planned route PR (or near the planned route PR) as other vehicle information 344. Such other vehicle information 344 (other vehicle information Iov) indicates, for example, at least one of the vehicle weight, vehicle type, and driving resistance (especially rolling resistance) of other vehicles 50 traveling along the planned route PR (or near the planned route PR).

[0080] Furthermore, for example, the storage device 340 may pre-store learning result data 346 relating to at least one of the first trained machine learning model TM1 and the second trained machine learning model TM2 generated (constructed) by machine learning. The learning result data 346 indicates, for example, the structure and computational parameter values ​​of each component of at least one of the first trained machine learning model TM1 and the second trained machine learning model TM2. The structure may be specified, for example, by the number of layers from the input layer to the output layer in the neural network, the type of each layer, the number of neurons included in each layer, the connectivity between neurons in adjacent layers, etc.

[0081] The first trained machine learning model TM1 is a trained (in other words, a pre-trained) machine learning model that, when given the target road surface condition CST of the planned route PR (in this embodiment, each section DS) as input, has acquired the ability to output the target rolling resistance coefficient RCT, which is the rolling resistance coefficient of the planned route PR (each section DS). For example, the first trained machine learning model TM1 is generated by performing machine learning using multiple training datasets (first training datasets) each composed of a combination of the road surface condition of the route (routing section) and the rolling resistance coefficient of the said route (routing section).

[0082] The second trained machine learning model TM2 is a trained (in other words, a pre-trained) machine learning model that, when given at least one of the vehicle weight and vehicle type of the target vehicle 10 and the target road surface condition CST of the planned travel route PR (in this embodiment, each section DS), has acquired the ability to output the target rolling resistance RST of the planned travel route PR (each section DS). For example, the second trained machine learning model TM2 is generated by performing machine learning using multiple training datasets (second training datasets), each consisting of a combination of at least one of the vehicle weight and vehicle type, the road surface condition of the travel route (travel section) on which the vehicle travels, and the rolling resistance when the vehicle travels along the travel route (travel section).

[0083] The communication device 350 is a device for the calculation database 30 to send and receive information (communicate) with the target vehicle 10 and the judgment database 20 via the network. In this embodiment, the communication device 350 also sends and receives information with other vehicles 50 as illustrated in Figure 1 (in particular, each of the one or more other vehicles 50 traveling along the planned travel route PR (or near the planned travel route PR)).

[0084] In the example shown in Figure 4, the ROM 330 stores the calculation database control program 332. The calculation database control program 332 is a program that causes the calculation database 30 (particularly the CPU 310) to perform information processing to calculate the rolling resistance (target rolling resistance RST) when the target vehicle 10 travels along the planned route PR. In particular, in this embodiment, the calculation database control program 332 causes the calculation database 30 to perform information processing to calculate the rolling resistance (target rolling resistance RST) for each of the multiple sections DS of the planned route PR. The calculation database control program 332 includes a series of instructions for this information processing.

[0085] The CPU 310 is an example of a processor resource, and in particular, an example of the "third processor" of the present invention. The CPU 310 executes the above-mentioned information processing by executing the arithmetic database control program 332 read from the ROM 330, using the RAM 320 as a work area. Specifically, the CPU 310 loads the arithmetic database control program 332 stored in the ROM 330 into the RAM 320. Then, the CPU 310 interprets and executes the instructions contained in the arithmetic database control program 332 loaded into the RAM 320 to control each component. As a result, as shown in Figure 4, the arithmetic database 30 according to this embodiment operates as a computer equipped with a target vehicle information acquisition unit 311, another vehicle information acquisition unit 312, road surface condition acquisition unit 313, and rolling resistance calculation unit 314 as software modules. That is, in this embodiment, each software module of the arithmetic database 30 is realized by the CPU 310.

[0086] The target vehicle information acquisition unit 311 receives target vehicle information Iv from the target vehicle 10 via the communication device 250, which indicates the type of vehicle, vehicle weight, etc. of the target vehicle 10. The target vehicle information acquisition unit 311 may also acquire information from the target vehicle 10 indicating the "planned driving route PR of the target vehicle 10" identified by the target vehicle 10. In this embodiment, the target vehicle information acquisition unit 311 stores the received target vehicle information Iv (and the information indicating the "planned driving route PR of the target vehicle 10") as target vehicle information 342 in the storage device 340.

[0087] The other vehicle information acquisition unit 312 receives other vehicle information Iov from each of the one or more other vehicles 50 that are traveling along the planned travel route PR (or near the planned travel route PR) via the communication device 250. For example, the other vehicle information acquisition unit 312 obtains target vehicle information 342 (target vehicle information 342 including information indicating "the planned travel route PR of the target vehicle 10") by referring to the storage device 340, and determines the planned travel route PR from the obtained target vehicle information 342. The other vehicle information acquisition unit 312 uses the determined planned travel route PR to communicate with the other vehicles 50 that are traveling along the planned travel route PR (or near the planned travel route PR), and obtains other vehicle information Iov from the other vehicles 50. In this embodiment, the other vehicle information acquisition unit 312 stores the received other vehicle information Iov as other vehicle information 344 in the storage device 340. The other vehicle information 344 (other vehicle information Iov) indicates the vehicle weight, vehicle type, and driving resistance (especially rolling resistance) of other vehicles 50 traveling along the planned driving route PR (or near the planned driving route PR).

[0088] The road surface condition acquisition unit 313 receives (acquires) information from the determination database 20 via the communication device 350 indicating the "target road surface condition CST for the planned driving route PR" determined by the determination database 20. In this embodiment, the road surface condition acquisition unit 313 acquires information indicating the "target road surface condition CST for each of the multiple sections DS of the planned driving route PR" determined by the determination database 20.

[0089] The rolling resistance calculation unit 314 calculates the target rolling resistance RST, which is the rolling resistance when the target vehicle 10 travels along the planned route PR, from the "target road surface condition CST" determined by the determination database 20. In this embodiment, the rolling resistance calculation unit 314 calculates the rolling resistance (target rolling resistance RST) for each of the multiple sections DS of the planned route PR from the "target road surface condition CST" for each of the multiple sections DS of the planned route PR indicated by the information acquired by the road surface condition acquisition unit 313.

[0090] In this embodiment, the rolling resistance calculation unit 314 may calculate the target rolling resistance RST for the planned route PR (each section DS) using at least one of the first trained machine learning model TM1 and the second trained machine learning model TM2 generated by machine learning. The rolling resistance calculation unit 314 includes at least one of the first trained machine learning model TM1 and the second trained machine learning model TM2 by referring to the storage device 340 to acquire the learning result data 346 and holding the acquired learning result data 346.

[0091] As described above, the first pre-trained machine learning model TM1 is generated by performing machine learning using multiple first training datasets, each composed of a combination of the road surface condition of the travel route (travel section) and the rolling resistance coefficient of the travel route (travel section). Therefore, the first pre-trained machine learning model TM1 has acquired the ability to output the target rolling resistance coefficient RCT, which is the rolling resistance coefficient of the planned travel route PR (each section DS), when the target road surface condition CST of the planned travel route PR (each section DS) is input. The rolling resistance calculation unit 314 then first calculates the target rolling resistance coefficient RCT of the planned travel route PR (each section DS) from the target road surface condition CST of the planned travel route PR (each section DS) using the first pre-trained machine learning model TM1. The rolling resistance calculation unit 314 then calculates the target rolling resistance RST of the planned travel route PR (each section DS) from the calculated "target rolling resistance coefficient RCT of the planned travel route PR (each section DS)" and the vehicle weight of the target vehicle 10. In this embodiment, the rolling resistance calculation unit 314 obtains target vehicle information 342 by referring to the storage device 340, and calculates the target rolling resistance RST for the planned driving route PR (each section DS) by multiplying the vehicle weight of the target vehicle 10 indicated by the target vehicle information 342 by the calculated "target rolling resistance coefficient RCT for the planned driving route PR (each section DS)".

[0092] The second pre-trained machine learning model TM2 is: The second trained machine learning model TM2 is generated by performing machine learning using multiple second training datasets, each composed of a combination of at least one of the vehicle's weight and vehicle type, the road surface condition of the travel route (travel section) on which the vehicle travels, and the rolling resistance of the travel route (travel section). As a result, the second trained machine learning model TM2 acquires the ability to output the target rolling resistance RST of the planned travel route PR (each section DS) when it receives at least one of the vehicle's weight and vehicle type and the target road surface condition CST of the planned travel route PR (each section DS) as input. The rolling resistance calculation unit 314 then uses the second trained machine learning model TM2 to calculate the target rolling resistance RST of the planned travel route PR (each section DS) from at least one of the vehicle's weight and vehicle type and the target road surface condition CST of the planned travel route PR (each section DS).

[0093] As described above, the information processing system 1 may use a trained machine learning model (at least one of the first trained machine learning model TM1 and the second trained machine learning model TM2) generated by machine learning to calculate the target rolling resistance RST for the planned route PR (each section DS) from the target road surface condition CST for each section DS. Through this process, the information processing system 1 can calculate the target rolling resistance RST from the target road surface condition CST with high accuracy using a trained machine learning model generated by machine learning.

[0094] However, for the information processing system 1, it is not essential to use a trained machine learning model to calculate the target rolling resistance RST from the target road surface condition CST. The information processing system 1 only needs to be able to calculate the target rolling resistance RST from the target road surface condition CST. In particular, the information processing system 1 only needs to be able to calculate the target rolling resistance RST, which is optimal for the target vehicle 10 traveling along the planned route PR (each section DS), from the target road surface condition CST and at least one of the vehicle weight and vehicle type of the target vehicle 10.

[0095] For example, the storage device 340 may store information (for example, a function showing the relationship between the road surface condition and the rolling resistance coefficient) associated with each road surface condition. The rolling resistance calculation unit 314 may use this information to calculate the target rolling resistance coefficient RCT from the target road surface condition CST. The rolling resistance calculation unit 314 may then calculate the target rolling resistance RST from the calculated target rolling resistance coefficient RCT and the weight of the target vehicle 10. Furthermore, the storage device 340 may store information associated with rolling resistance for each combination of "rolling resistance coefficient" and "vehicle weight". The rolling resistance calculation unit 314 may use this information to calculate the target rolling resistance RST from the target rolling resistance coefficient RCT and the weight of the target vehicle 10. In addition, the storage device 340 may store information associated with rolling resistance for each combination of "road surface condition" and "vehicle weight". The rolling resistance calculation unit 314 may use this information to calculate the target rolling resistance RST from the target road surface condition CST and the vehicle weight of the target vehicle 10.

[0096] Furthermore, for example, if the other vehicle information Iov (target vehicle information 342) obtained from another vehicle 50 traveling along the planned route PR of the target vehicle 10 indicates the rolling resistance of the other vehicle 50 when it is traveling along the planned route PR and the weight of the other vehicle 50, the rolling resistance calculation unit 314 may calculate the target rolling resistance RST as follows. That is, the rolling resistance calculation unit 314 may calculate the target rolling resistance RST by adjusting the "rolling resistance of the other vehicle 50 when it is traveling along the planned route PR" based on the difference between the weight of the other vehicle 50 and the weight of the target vehicle 10. Through this process, the information processing system 1 can accurately calculate the target rolling resistance RST (the rolling resistance specific to the target vehicle 10 when it travels along the planned route PR) from the "rolling resistance of the other vehicle 50 when it is traveling along the planned route PR of the target vehicle 10," taking into account the weight of the target vehicle 10.

[0097] As explained above, the information processing system 1 determines the road surface condition (target road surface condition CST) of the planned route PR of the target vehicle 10, and calculates the rolling resistance (target rolling resistance RST) when the target vehicle 10 travels along the planned route PR from the determined target road surface condition CST. Using the calculated target rolling resistance RST, the information processing system 1 can predict the energy consumption EC when the target vehicle 10 travels along the planned route PR, according to the target road surface condition CST of the planned route PR. The information processing system 1 may identify the planned route PR, for example, based on information provided by the navigation system 150, or it may predict the destination of the target vehicle 10 from the vehicle's travel history and identify the route based on the predicted destination. The information processing system 1 may divide the planned route PR into multiple sections DS. The information processing system 1 may determine the road surface condition (target road surface condition CST) of the planned route PR (each section DS) using captured images (other vehicle information Iov) of other vehicles 50 traveling near the planned route PR. The information processing system 1 may also determine the target road surface condition CST using "external organization information Iea" provided by an external organization 40, which indicates the conditions near the planned route PR (e.g., weather, temperature, etc.). The information processing system 1 may also determine the target road surface condition CST using "stockpiled information" (at least one of other vehicle information Iov and external organization information Iea) that has been prepared in advance. The information processing system 1 may calculate the target rolling resistance RST from the determined target road surface condition CST using a trained machine learning model. The trained machine learning model may be a second trained machine learning model TM2 that has acquired the ability to output the target rolling resistance RST when at least one of the vehicle weight and vehicle type of the target vehicle 10 and the target road surface condition CST are input.

[0098] The information processing system 1 can predict the energy consumption EC when the target vehicle 10 travels along the planned route PR with high accuracy by using the target rolling resistance RST corresponding to the target road surface condition CST of the planned route PR. The information processing system 1 may also calculate the target rolling resistance RST from the vehicle weight (and target rolling resistance coefficient RCT) of the target vehicle 10 by using information that associates rolling resistance with the "vehicle weight" (in particular, information that associates rolling resistance with combinations of "rolling resistance coefficient" and "vehicle weight").

[0099] §3 Example of Operation Figure 5 is a flowchart showing an example of the processing procedure of the information processing system 1 according to this embodiment. The processing procedure described below is an example of the processing procedure of the information processing method PM which "predicts the energy consumption EC when the target vehicle 10 travels along the planned route PR". However, the processing procedure described below is merely an example, and each step may be changed as much as possible. Furthermore, depending on the embodiment, steps in the processing procedure described below can be omitted, replaced, and added as appropriate. Note that in Figure 5, the first processor, second processor, and third processor are referred to as "first CPU", "second CPU", and "third CPU", respectively.

[0100] (Step S10) In step S10, the first processor (in this embodiment, the CPU 110 of the target vehicle 10) operates as a route identification unit 111 and identifies the planned route PR of the target vehicle 10. In step S10, the first processor may identify the planned route PR based on information provided by the navigation system 150 of the target vehicle 10. In step S10, the first processor may predict the destination of the target vehicle 10 using driving history information 142 that shows the driving history of the target vehicle 10. The first processor may use the driving history information 142 to extract one or more driving routes to the predicted destination, and among the one or more extracted driving routes, the driving route with the highest driving frequency may be identified as the planned route PR.

[0101] (Step S20) In step S20, the second processor (in this embodiment, the CPU 210 of the determination database 20) operates as a planned route information acquisition unit 211 and acquires information indicating the planned travel route PR identified by the first processor in step S10.

[0102] (Step S30) In step S30, the second processor operates as a vehicle information acquisition unit 213 and an external engine information acquisition unit 212. The second processor, operating as a vehicle information acquisition unit 213, acquires vehicle information Iov from each of the one or more other vehicles 50. The second processor, operating as an external engine information acquisition unit 212, acquires external engine information Iea from the external engine 40.

[0103] (Step S40) In step S40, the second processor operates as a route division unit 214 and divides the "planned route PR of the target vehicle 10" indicated by the information acquired in step S20 into multiple section DS. In step S40, the second processor may also divide the planned route PR into multiple section DS based on "at least one of the weather and altitude of each of the one or more points included in the planned route PR" indicated by the external organization information Iea acquired in step S30.

[0104] (Step S50) In step S50, the second processor operates as a road surface condition determination unit 215 and determines the road surface condition (target road surface condition CST) of the planned route PR. In particular, in step S50, the second processor determines the target road surface condition CST for each of the "multiple sections DS" obtained by dividing the planned route PR in step S40.

[0105] In step S50, the second processor may determine the target road surface condition CST using the following other vehicle information Iov from among the other vehicle information Iov acquired in step S30. That is, the second processor may determine the target road surface condition CST using other vehicle information Iov acquired from "other vehicles 50 traveling near the planned route PR (at the time the second processor attempts to determine the target road surface condition CST)". If multiple other vehicles 50 are traveling near the planned route PR at the time the second processor attempts to determine the target road surface condition CST, the second processor may determine the target road surface condition CST using other vehicle information Iov acquired from the other vehicle 50 that is closest in distance from the target vehicle 10 among the multiple other vehicles 50.

[0106] If there are no other vehicles 50 traveling near the planned route PR (at the time when the second processor attempts to determine the target road surface condition CST), the second processor may determine the target road surface condition CST from pre-prepared "stockpiled information". The second processor may use at least one of the other vehicle information Iov and external organization information Iea acquired in step S30 as "stockpiled information" and determine the target road surface condition CST from said "stockpiled information". For example, the second processor may use "images taken near the planned route PR" (other vehicle information Iov) taken by "another vehicle 50 that was traveling near the planned route PR (before the time when the second processor attempts to determine the target road surface condition CST)" as "stockpiled information" to determine the target road surface condition CST. Furthermore, the second processor may use "external agency information Iea" provided by the external agency 40, which indicates the conditions near the planned route PR (e.g., weather, temperature, etc.), as "stockpiled information" to determine the target road surface condition CST.

[0107] (Step S60) In step S60, the third processor (in this embodiment, the CPU 310 of the calculation database 30) operates as a road surface condition acquisition unit 313 and acquires information indicating the road surface condition (target road surface condition CST) of the planned travel route PR (each section DS) determined by the second processor in step S50.

[0108] (Step S70) In step S70, the third processor operates as a rolling resistance calculation unit 314 and calculates the rolling resistance (target rolling resistance RST) of the planned route PR (each section DS). Specifically, in step S70, the third processor calculates the target rolling resistance RST of the planned route PR (each section DS) from the "target road surface condition CST of the planned route PR (each section DS)" indicated by the information acquired in step S60.

[0109] In step S70, the third processor may use at least one of the first trained machine learning model TM1 and the second trained machine learning model TM2 to calculate the target rolling resistance RST for the planned route PR (each section DS) from the "target road surface condition CST for the planned route PR (each section DS)". For example, the third processor may use the first trained machine learning model TM1, which has acquired the capability to output the target rolling resistance coefficient RCT when the target road surface condition CST is input, to calculate the target rolling resistance coefficient RCT for the planned route PR (each section DS) from the target road surface condition CST for the planned route PR (each section DS). Then, the third processor may calculate the target rolling resistance RST for the planned route PR (each section DS) from the calculated "target rolling resistance coefficient RCT for the planned route PR (each section DS)" and the vehicle weight of the target vehicle 10. Alternatively, for example, the third processor may use a second trained machine learning model TM2, which has acquired the capability to output a target rolling resistance RST when at least one of the vehicle weight and vehicle type of the target vehicle 10 and the target road surface condition CST are input, to calculate the target rolling resistance RST for the planned route PR (each section DS) from at least one of the vehicle weight and vehicle type of the target vehicle 10 and the target road surface condition CST of the planned route PR (each section DS).

[0110] (Step S80) In step S80, the first processor operates as a target rolling resistance acquisition unit 112 and acquires information indicating the "target rolling resistance RST of the planned travel route PR (each section DS)" calculated by the third processor in step S70.

[0111] (Step S90) In step S90, the first processor operates as a consumption calculation unit 113 and predicts the energy consumption EC. Specifically, in step S90, the first processor uses the "target rolling resistance RST of the planned route PR" indicated by the information acquired in step S80 to predict the energy consumption (energy consumption EC) when the target vehicle 10 travels along the planned route PR.

[0112] In this embodiment, the first processor first uses the "target rolling resistance RST for each section DS" indicated by the information acquired in step S80 to predict the energy consumption of the target vehicle 10 when it travels through each section DS. Then, the first processor predicts the energy consumption EC of the target vehicle 10 when it travels along the planned route PR (the entire planned route PR) from the sum of the predicted "energy consumption of the target vehicle 10 when it travels through each section DS".

[0113] In step S90, the first processor may predict the energy consumption EC using the "target rolling resistance RST for the planned route PR (each section DS)" calculated by the third processor in step 70, the predicted vehicle speed of the target vehicle 10 on the planned route PR, and at least one of the road gradient of the planned route PR. For example, the first processor may use the predicted vehicle speed of the target vehicle 10 on the planned route PR to predict "energy proportional to vehicle speed," such as rotational losses, and "energy proportional to the square of vehicle speed," such as air resistance. The first processor may then predict the driving resistance when the target vehicle 10 travels along the planned route PR by summing the predicted "energy proportional to vehicle speed" and "energy proportional to the square of vehicle speed" with the target rolling resistance RST for the planned route PR. The first processor may also use the road gradient of the planned route PR to calculate acceleration resistance and gradient resistance, respectively. The first processor may then predict the driving force of the target vehicle 10 when traveling along the planned route PR as the sum of the predicted driving resistance and the calculated acceleration resistance and gradient resistance. The first processor may also predict the energy consumption EC as the sum of the predicted driving force of the target vehicle 10 and the air conditioning power, electrical equipment power, and unit losses of the target vehicle 10 when traveling along the planned route PR.

[0114] [Features] As described above, the information processing system 1 according to this embodiment includes a CPU 110 (first processor) for the target vehicle 10, a CPU 210 (second processor) for the judgment database 20, and a CPU 310 (third processor) for the calculation database 30. The CPU 110 identifies the planned travel route PR for the target vehicle 10. The CPU 210 is capable of sending and receiving information with the CPU 110 and determines the road surface condition (target road surface condition CST) of the planned travel route PR identified by the CPU 110. The CPU 310 is capable of sending and receiving information with the CPU 210 and calculates the rolling resistance (target rolling resistance RST) when the target vehicle 10 travels along the planned travel route PR from the target road surface condition CST determined by the CPU 210. The CPU 110 is capable of sending and receiving information with the CPU 310 and predicts the energy consumption (energy consumption EC) when the target vehicle 10 travels along the planned travel route PR using the target rolling resistance RST calculated by the CPU 310.

[0115] Furthermore, the information processing method PM according to this embodiment causes the information processing system 1 to execute steps S10, S50, S70, and S90 as illustrated in Figure 5. In step S10, the CPU 110 of the target vehicle 10 identifies the planned travel route PR of the target vehicle 10. In step S50, the CPU 210 of the judgment database 20 determines the road surface condition (target road surface condition CST) of the planned travel route PR identified by the CPU 110. In step S70, the CPU 310 of the calculation database 30 calculates the rolling resistance (target rolling resistance RST) when the target vehicle 10 travels along the planned travel route PR from the target road surface condition CST determined by the CPU 210. In step S90, the CPU 110 predicts the energy consumption EC when the target vehicle 10 travels along the planned travel route PR using the target rolling resistance RST calculated by the CPU 310.

[0116] According to this configuration, the information processing system 1 (information processing method PM) calculates the rolling resistance (target rolling resistance RST) of the target vehicle 10 when it travels along the planned route PR, according to the road surface condition (target road surface condition CST) of the planned route PR of the target vehicle 10. Then, the information processing system 1 (information processing method PM) uses the calculated target rolling resistance RST to predict the energy consumption (energy consumption EC) when the target vehicle 10 travels along the planned route PR. Therefore, the information processing system 1 (information processing method PM) can predict the energy consumption (energy consumption EC) when the target vehicle 10 travels along the planned route PR by considering the relationship between the road surface condition (target road surface condition CST) of the planned route PR on which the target vehicle 10 (vehicle) will travel and the rolling resistance (target rolling resistance RST) of the target vehicle 10 when it travels along the planned route PR.

[0117] §4 Variant Although embodiments of the present invention have been described in detail above, the above description is merely illustrative in all respects of the present invention. Needless to say, various improvements or modifications can be made without departing from the scope of the present invention. For example, the following modifications are possible. In the following, the same reference numerals are used for components similar to those in the above embodiments, and explanations of points similar to those in the above embodiments have been omitted as appropriate. The following modifications can be combined as appropriate.

[0118] In the above embodiment, an example was described in which the target vehicle 10 is equipped with a first processor, the decision database 20 is equipped with a second processor, and the calculation database 30 is equipped with a third processor. However, the configuration of the information processing system 1 according to this embodiment is not limited to such an example and may be appropriately determined depending on the embodiment. For example, the information processing system 1 may have any two of the first processor, second processor, and third processor placed in one device, and the remaining processors placed in another device. Furthermore, the information processing system 1 may have all of the first processor, second processor, and third processor placed in a single device. The information processing system 1 only needs to include the first processor, second processor, and third processor, and the devices in which each processor is placed in the information processing system 1 are not particularly limited. Also, for example, at least one of the first processor, second processor, and third processor may be composed of multiple CPUs (CPU cores). [Explanation of Symbols]

[0119] 1... Information processing system, 10... Target vehicle, 50... Other vehicles 110...CPU (1st processor), 142...Driving history information, 150... Navigation system (car navigation system), 210...CPU (2nd processor), 242… External organization information (external organization information, stockpile information), 244...Other vehicle information (other vehicle information, stock information), 310...CPU (3rd processor), CST...Target road surface condition, DS... interval, EC... energy consumption, IEA… External agency information (external agency information, stockpile information), Iov...Other vehicle information (other vehicle information, stock information), PM...Information processing method, PR...Planned route, RCT...Target rolling resistance coefficient RST...Target rolling resistance, TM1...First trained machine learning model, TM2…Second pre-trained machine learning model

Claims

1. An information processing method that causes a processor to perform a process to predict the energy consumption of a target vehicle while it is in motion, The first processor is, Identify the planned route of the aforementioned vehicle, The second processor, which is capable of sending and receiving information with the first processor, The first processor determines the target road surface condition, which is the road surface condition of the planned route identified by the first processor. The third processor, which is capable of sending and receiving information with the second processor, From the target road surface condition determined by the second processor, the target rolling resistance, which is the rolling resistance when the target vehicle travels along the planned route, is calculated. The first processor is capable of sending and receiving information with the third processor, The first processor is Using the target rolling resistance calculated by the third processor, the energy consumption when the target vehicle travels along the planned route is predicted. Information processing methods.

2. The first processor is Based on information provided by the car navigation system installed in the aforementioned vehicle, the planned route is identified. The information processing method according to claim 1.

3. The first processor is Using the driving history information showing the driving history of the aforementioned vehicle, the destination of the aforementioned vehicle is predicted. Using the aforementioned driving history information, the driving route with the highest frequency of travel among one or more driving routes to the destination is identified as the planned driving route. The information processing method according to claim 1.

4. The second processor is, The aforementioned planned route is divided into multiple sections, The road surface condition of each of the aforementioned multiple sections is determined as the target road surface condition for each of the aforementioned multiple sections. The aforementioned third processor is From the target road surface conditions of each of the multiple sections determined by the second processor, the rolling resistance of each of the multiple sections is calculated as the target rolling resistance of each of the multiple sections. The first processor is Using the target rolling resistance of each of the plurality of sections calculated by the third processor, the energy consumption of the target vehicle during travel is predicted for each of the plurality of sections, and the energy consumption when the target vehicle travels along the planned route is predicted from the sum of the energy consumption predicted for each of the plurality of sections. The information processing method according to any one of claims 1 to 3.

5. The second processor is, Obtain external agency information indicating at least one of the weather and altitude for one or more points included in the planned route. The aforementioned planned route, The weather at each of the one or more locations as indicated by the information from the external organization, and, The elevation of each of the one or more points as indicated by the information from the external organization. Divide into the plurality of segments based on at least one of the following: The information processing method according to claim 4.

6. The second processor is, The condition of the target road surface is determined using information about other vehicles obtained from other vehicles that are traveling near the aforementioned planned route, other than the target vehicle. The information processing method according to any one of claims 1 to 3.

7. The second processor is, If multiple other vehicles are traveling near the planned route, the other vehicle information is obtained from the vehicle closest to the target vehicle. The information processing method according to claim 6.

8. If there are no vehicles other than the target vehicle traveling near the planned route, the second processor has pre-prepared stored information that can be used to determine the target road surface condition. The aforementioned stockpiling information is, A vehicle other than the target vehicle that was traveling near the aforementioned planned route captured images of the area near the aforementioned planned route, and External information provided by an external organization, showing the conditions near the planned route. Including at least one of the following, The second processor determines the target road surface condition from the stored information. The information processing method according to any one of claims 1 to 3.

9. The aforementioned stockpiling information is pre-associated with the road surface conditions near the planned route. The second processor is, The target road surface condition is determined from the road surface condition near the planned route, which is pre-associated with the aforementioned stockpiling information. The information processing method according to claim 8.

10. If terrain changes occur near the planned route, the stockpiling information will be reviewed. The information processing method according to claim 8.

11. The first processor is The target rolling resistance calculated by the third processor, At least one of the following: the predicted vehicle speed of the target vehicle along the planned route, and the road gradient of the planned route. Using this method, the amount of energy consumed is predicted. The information processing method according to any one of claims 1 to 3.

12. The aforementioned third processor is Using a first trained machine learning model generated by performing machine learning with multiple first training datasets, each composed of a combination of the road surface condition of the travel route and the rolling resistance coefficient of the said travel route, the target rolling resistance coefficient, which is the rolling resistance coefficient of the planned travel route, is calculated from the target road surface condition. The target rolling resistance is calculated from the calculated target rolling resistance coefficient and the vehicle weight of the target vehicle. or The target rolling resistance is calculated from the target vehicle's weight and type, and the target road surface conditions, using a second trained machine learning model generated by performing machine learning using multiple second training datasets, each consisting of a combination of at least one of the vehicle's weight and type, the road surface condition of the route the vehicle travels, and the rolling resistance when the vehicle travels along that route. The information processing method according to claim 11.

13. The planned travel route, the target road surface condition, and the target rolling resistance are associated with each other and stored in a memory area capable of sending and receiving information with at least one of the first processor, the second processor, and the third processor. The information processing method according to any one of claims 1 to 3.

14. A first processor that identifies the planned route of the target vehicle, A second processor is capable of sending and receiving information with the first processor and determines the target road surface condition, which is the road surface condition of the planned travel route identified by the first processor. A third processor is capable of sending and receiving information with the second processor, and calculates a target rolling resistance, which is the rolling resistance when the target vehicle travels along the planned route, from the target road surface condition determined by the second processor. Includes, The first processor is capable of sending and receiving information with the third processor, and uses the target rolling resistance calculated by the third processor to predict the energy consumption when the target vehicle travels along the planned route. Information processing system.

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