Information processing system, information processing method, and storage medium

Abstract

This invention provides an information processing system, information processing method, and storage medium capable of effectively utilizing a wide variety of drivers to transport people and goods efficiently and safely. The information processing system comprises: an estimation unit that estimates the driving skill level of a driver of a vehicle; a decision unit that determines the difficulty of a task involving the transport of people and / or goods using the vehicle; and a matching processing unit that matches the driver with a suitable task based on the skill level and the difficulty level.

Description

Technical Field

[0001] This invention relates to information processing systems, information processing methods, and storage media. Background Technology

[0002] In on-demand services (such as transportation services), there is a known network computer system that determines the optimal boarding location and supports the efficient meeting of users and service providers (see, for example, Patent Document 1).

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Publication No. 2017-524195 Summary of the Invention

[0006] The problem that the invention aims to solve

[0007] However, in the existing technology, when drivers are entrusted with the task of transporting people or goods, tasks that are not suitable for the drivers' driving skills are assigned. As a result, people and goods are sometimes not transported efficiently and safely.

[0008] This invention was made in consideration of such circumstances, and one of its objectives is to provide an information processing system, information processing method, and storage medium that can effectively utilize a wide variety of drivers and transport people and goods efficiently and safely.

[0009] Solution for solving the problem

[0010] The information processing system, information processing method, and storage medium of the present invention adopt the following structure.

[0011] (1) The first example of the present invention is an information processing system comprising: an estimation unit that estimates the level of driving skills of a driver of a vehicle; a determination unit that determines the difficulty of a task involving the transportation of people and / or goods using the vehicle; and a matching processing unit that matches a suitable task to the driver based on the level and the difficulty.

[0012] (2) In the second example of the present invention, based on the information processing system of the first example, the estimation unit obtains driving data representing the behavior of the vehicle driven by the driver, and the estimation unit calculates a score for the driver's driving skills for multiple viewpoints based on the driving data, and the estimation unit estimates the level based on the scores calculated for the multiple viewpoints.

[0013] (3) In the third example of the present invention, based on the information processing system of the first or second example, the decision unit determines the difficulty level based on the destination of the person and / or the item, the time spent on transporting the person and / or the item, the distance required for transporting the person and / or the item, the geographical information of the area where the person and / or the item is transported, the meteorological information of the area where the person and / or the item is transported, and / or the congestion prediction information of the area where the person and / or the item is transported.

[0014] (4) In the fourth example of the present invention, based on the information processing system of the first or second example, the matching processing unit matches the first driver with a first task, and the matching processing unit matches the second driver, whose level is lower than that of the first driver, with a second task whose difficulty is lower than that of the first task.

[0015] (5) In the fifth example of the present invention, based on the information processing system of the second example, the information processing system further includes a providing unit that provides feedback to the driver for improving the score.

[0016] (6) In the sixth example of the present invention, based on the information processing system of the fifth example, the providing unit selects the viewpoint with the lowest score from a plurality of viewpoints, and the providing unit provides the driver with driving training for improving the score of the selected viewpoint as feedback.

[0017] (7) In the seventh example of the present invention, based on the information processing system of the sixth example, the matching processing unit matches the driver who has performed the driving training specified by the client with the client's task.

[0018] (8) The eighth example of the present invention is an information processing method, which is an information processing method using a computer, wherein the information processing method includes the following processes: estimating the driving skill level of the driver of the vehicle; determining the difficulty of the task of using the vehicle to transport people and / or goods; and matching the driver with a suitable task based on the skill level and the difficulty.

[0019] (9) The ninth example of the present invention is a storage medium storing a program for execution by a computer, wherein the program includes the following processes: estimating the driving skill level of a driver of a vehicle; determining the difficulty of a task involving the transportation of people and / or goods using the vehicle; and matching the driver with a suitable task based on the skill level and the difficulty.

[0020] Invention Effects

[0021] Based on the above examples, it is possible to effectively utilize a wide variety of driver talents and transport people and goods efficiently and safely. Attached Figure Description

[0022] Figure 1 This is a diagram illustrating an example of the structure of the information processing system 1 in the implementation method.

[0023] Figure 2 This is a diagram illustrating an example of the structure of the first terminal device 10 and the second terminal device 20 in the embodiments.

[0024] Figure 3 This is a diagram illustrating an example of the structure of the information processing apparatus 100 according to an embodiment.

[0025] Figure 4 This is a flowchart illustrating an example of a series of processing flows of the information processing apparatus 100 in an embodiment.

[0026] Figure 5 This is a diagram illustrating an example of a method for estimating driving skill levels.

[0027] Figure 6 This is a diagram illustrating an example of how difficulty levels are determined.

[0028] Figure 7 This is a schematic diagram showing the interior of vehicle V.

[0029] Figure 8 This is a diagram illustrating an example of an image displayed on a first display 30 or a second display 31 during driving training.

[0030] Figure 9 This is a diagram illustrating an example of an image displayed on a first display 30 or a second display 31 during driving training.

[0031] Figure 10 This is a diagram showing an example of a screen displayed on the first terminal device 10 in the embodiment.

[0032] Figure 11 This is a diagram showing an example of a screen displayed on the first terminal device 10 in the embodiment.

[0033] Symbol explanation: 1…Information processing system, 10…First terminal device, 20…Second terminal device, V…Vehicle, 100…Information processing device, 110…Communication interface, 120…Storage unit, 130…Processing unit, 131…Acquisition unit, 132…Estimation unit, 133…Decision unit, 134…Matching processing unit, 135…Providing unit. Detailed Implementation

[0034] Hereinafter, embodiments of the information processing system, information processing method, and storage medium of the present invention will be described with reference to the accompanying drawings.

[0035] [Structure of an Information Processing System]

[0036] Figure 1 This diagram illustrates an example of the structure of the information processing system 1 in this embodiment. The information processing system 1 in this embodiment is a system that delegates various tasks to other users U2 based on requests from a user U1. The tasks delegated to user U2 include the task of transporting objects. The objects can be people or items.

[0037] Information processing system 1 includes, for example, a first terminal device 10, a second terminal device 20, a vehicle V, and an information processing device 100. These devices are connected, for example, via networks (NW) such as LAN (Local Area Network) and WAN (Wide Area Network).

[0038] The first terminal device 10 can be, for example, a general-purpose device such as a smartphone, tablet computer, personal computer, or wearable device. The first terminal device 10 is used by user U1. The first terminal device 10 can also be an edge information processing terminal device.

[0039] User U1 can be a specific individual, such as a sole proprietor, or an organization or group consisting of multiple people, such as a delivery company. Additionally, User U1 can also be a system or robot, including those using AI (Artificial Intelligence).

[0040] For example, a task is input into the first terminal device 10, which is used to entrust (provide) user U1 and their family members to user U2, or to entrust user U2 to transport goods purchased by user U1 from physical stores or online stores. Hereinafter, the task of transporting people or goods will be specifically referred to as a transport task. User U1 is an example of a "consignor".

[0041] The second terminal device 20 is similar to the first terminal device 10, and can be a common device such as a smartphone, tablet, personal computer, wearable device, or dashcam. The second terminal device 20 is used by user U2. The functions of the second terminal device 20 can also be integrated into an in-vehicle navigation system installed in vehicle V, or a network-connected device. Like the first terminal device 10, the second terminal device 20 can also be an edge information processing terminal device.

[0042] User U2 can be a specific individual driver, such as a sole proprietor, a driver employed by a delivery company, or a group of drivers working together. User U2, who has entrusted a delivery task to User U1, uses vehicle V to transport people and goods.

[0043] Vehicle V can be a vehicle owned by user U2 (a private car) or a shared vehicle (a vehicle used for vehicle sharing). Alternatively, vehicle V can be a vehicle leased from operators such as taxi companies or bus companies.

[0044] Vehicle V can be equipped with advanced safety features, advanced driver assistance features, and / or autonomous driving features. Specifically, Vehicle V can be equipped with adaptive cruise control, collision mitigation braking system, lane keeping assist system, parking assist, blind spot monitoring, road sign recognition system, driver fatigue detection system, and Level 1-5 autonomous driving functions, etc.

[0045] For example, the second terminal device 20 displays a screen for selecting whether to accept a delivery request for people or goods based on the delivery task input to the first terminal device 10. When user U2 accepts the request and actually delivers people or goods, user U2 is paid a reward.

[0046] Information processing device 100 obtains driving data of user U2 from vehicle V and second terminal device 20 via a network, and classifies user U2's driving skills based on the driving data. That is, information processing device 100 estimates the level of user U2's driving skills (hereinafter referred to as driving skill level). Information processing device 100 may be a cloud-based information processing device.

[0047] In addition to the user U2's driving data, the information processing device 100 can also estimate the user U2's driving skill level based on the user U2's health data. The health data includes not only the user U2's health data during driving but also health data before and after driving. Specifically, the health data may include heart rate, blood pressure, stress level, sleep data, blood oxygen concentration, body temperature, activity level, electroencephalogram (EEG) data, and health diagnosis results.

[0048] In addition to the driving data of user U2, the information processing device 100 can also estimate the driving skill level of user U2 based on the equipment of user U2's vehicle V. The equipment of vehicle V may include, for example, an anti-accelerator pedal and brake pedal anti-misoperation device, driving assistance device, etc.

[0049] Driving data may include data related to the behavior and state of vehicle V when driven by user U2. Specifically, driving data may include engine speed, secondary battery charging rate, accelerator pedal input (push amount), frequency, brake pedal input (push amount), frequency, steering wheel input (push amount), frequency, vehicle V speed, acceleration, jerk, etc.

[0050] Furthermore, driving data can also include data from various sensors (cameras, radar, lidar, GNSS receivers, etc.) set up to enable driving assistance functions and autonomous driving functions to function. Specifically, driving data can include various information such as images of the front, rear, and sides of vehicle V, distances to other vehicles around vehicle V (vehicle-to-vehicle distances), and location information.

[0051] Furthermore, the information processing device 100 obtains task information from the first terminal device 10 via the network, and determines the difficulty of the delivery task entrusted to the user U2 based on the task information.

[0052] Task information is information input into the first terminal device 10 as a delivery task. For example, task information includes the destination of the person or goods being delivered, the time required for delivery (time constraints), the distance required for delivery (travel distance), the geographical information of the area where the person or goods are being delivered, the weather information of the area where the person or goods are being delivered, and / or traffic congestion prediction information for the area where the person or goods are being delivered. Geographical information may include, for example, various information such as altitude, the number and angle of slopes, the number of winding roads, road width, whether the road is paved, traffic volume, snow accumulation, whether there is standing water, whether there are schools, and whether there are routes to and from school.

[0053] The information processing device 100 matches a suitable delivery task to the user U2 based on the difficulty of the delivery task and the user U2's driving skill level.

[0054] [Structure of the terminal device]

[0055] Figure 2 This is a diagram illustrating an example of the structure of the first terminal device 10 and the second terminal device 20 in the embodiments. The structures of these devices are common, therefore the first terminal device 10 is used as an example in the description. That is, the structure of the first terminal device 10 can be appropriately read as the structure of the second terminal device 20. Symbols in parentheses indicate the structure of the second terminal device 20.

[0056] The first terminal device 10 (20) includes, for example, a communication interface 11 (21), an input interface 12 (22), an output interface 13 (23), a storage unit 14 (24), and a processing unit 15 (25).

[0057] The communication interface 11 includes, for example, a NIC (Network Interface Card), a wireless communication module, etc. The wireless communication module includes a receiver and a transmitter. The communication interface 11 communicates with the information processing device 100 via a network.

[0058] Input interface 12 accepts various input operations from the user and converts the received input operations into electrical signals, which are then output to processing unit 15. For example, input interface 12 can be a mouse, keyboard, trackball, switch, button, joystick, touch panel, etc. Alternatively, input interface 12 can also be a voice user interface that accepts voice input, such as a microphone.

[0059] Output interface 13 may include, for example, a display and a speaker. The display shows images generated by the processing unit 15 and a GUI (Graphical User Interface) for accepting various input operations from the user. For example, the display may be an LCD (Liquid Crystal Display) or an OLED (Electro Luminescence) display. The speaker outputs the information input from the processing unit 15 as sound. If the input interface 12 is a touch panel, the input interface 12 and the output interface 13 may be integrated.

[0060] The storage unit 14 is implemented, for example, by an HDD (Hard Disc Drive), flash memory, EEPROM (Electrically Erasable Programmable Read Only Memory), ROM (Read Only Memory), RAM (Random Access Memory), etc. The storage unit 14 stores firmware, applications, etc.

[0061] The processing unit 15 is implemented by executing the program stored in the storage unit 14, for example, using a processor such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit). Alternatively, the processing unit 15 can be implemented using hardware such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or SOC (System on Chip), or it can be implemented through a combination of software and hardware.

[0062] [Structure of the information processing device]

[0063] The structure of the information processing device 100 will now be described. The information processing device 100 can be a single device or a system in which multiple devices connected via a network cooperate to operate. That is, the information processing device 100 can be implemented using multiple computers (processors) included in a distributed computing system or a cloud computing system.

[0064] Figure 3 This is a diagram illustrating an example of the structure of the information processing apparatus 100 according to an embodiment. The information processing apparatus 100 includes, for example, a communication interface 110, a storage unit 120, and a processing unit 130.

[0065] The communication interface 110 includes, for example, a NIC, a wireless communication module, etc., and the wireless communication module includes a receiver and a transmitter. The communication interface 110 communicates with the first terminal device 10 and the second terminal device 20 via a network.

[0066] The storage unit 120 is implemented, for example, by an HDD, flash memory, EEPROM, ROM, RAM, etc. The storage unit 120 stores firmware, applications, etc.

[0067] The processing unit 130 includes, for example, an acquisition unit 131, an estimation unit 132, a decision unit 133, a matching processing unit 134, and a provision unit 135. These components of the processing unit 130 are implemented, for example, by a processor such as a CPU or GPU executing a program stored in the storage unit 120. Furthermore, some or all of the components of the processing unit 130 can also be implemented using hardware such as an LSI, ASIC, FPGA, or SOC, or through a combination of software and hardware.

[0068] [Processing flow of the information processing device]

[0069] The following is a flowchart illustrating the processing flow of each component of the processing unit 130. Figure 4 This is a flowchart illustrating an example of a series of processing steps performed by the information processing apparatus 100 in this embodiment. The processing described in this flowchart can, for example, be repeatedly executed according to a predetermined cycle.

[0070] First, the acquisition unit 131 acquires driving data and task information of vehicle V that has been driven by user U2 at least once (step S100).

[0071] As mentioned above, driving data includes data related to the behavior and state of vehicle V, as well as data from sensors used to enable driving assistance and autonomous driving functions. Task information includes the destination of the person or goods being transported, the time taken for transport (time constraints), the distance required for transport (travel distance), and / or the geographical information of the area where the person or goods are being transported.

[0072] For example, the acquisition unit 131 can access the vehicle V via the communication interface 110 and obtain driving data from the vehicle V.

[0073] Similarly, the acquisition unit 131 can access the first terminal device 10 via the communication interface 110 and obtain task information from the first terminal device 10.

[0074] In addition, when driving data and mission information are stored in a cloud server connected via a network, the acquisition unit 131 can also access the cloud server via the communication interface 110 to obtain driving data and mission information from the cloud server.

[0075] Next, the estimation unit 132 estimates the driving skill level of user U2 based on user U2's driving data (step S102).

[0076] For example, the estimation unit 132 calculates a score for user U2's driving skills for multiple viewpoints, and estimates the driving skill level based on the score for each viewpoint.

[0077] Figure 5 This diagram illustrates an example of a method for estimating driving skill levels. As shown, multiple perspectives may include, for example, safety, punctuality, fuel efficiency, and experience points, with scores calculated for each perspective.

[0078] For example, the estimation unit 132 evaluates safety by taking into account the number of times of rapid acceleration, rapid deceleration, sharp turns (sharp turns), and speeding, and assigns a weighted score to each item. Specifically, the estimation unit 132 deducts points in the form of 5 points for each instance of rapid acceleration and 3 points for each instance of speeding, and calculates the safety score with 100 points as the maximum score.

[0079] Presumption 132, based on the concept of punctuality, calculates a score by deducting 2 points for every minute of delay based on the difference between the scheduled arrival time of goods delivery and the actual arrival time of people picking up or dropping off.

[0080] The estimation unit 132 calculates a score based on the fuel utilization data and idling time of vehicle V, taking into account the concept of fuel-efficient driving performance. Specifically, the estimation unit 132 adds 5 points if the fuel utilization and electricity utilization efficiency is 10% higher than the benchmark value, or deducts 5 points if the idling time is long, thereby calculating the fuel-efficient driving performance score.

[0081] The presumption section 132, based on the concept of experience value, calculates scores based on past total driving distance, number of missions completed, accidents, and traffic violation history.

[0082] The estimation section 132 estimates the driving skill level based on the score calculated for each viewpoint. For example, the driving skill level is scored as follows: safety 85 points, punctuality 90 points, fuel-efficient driving 80 points, and experience 95 points.

[0083] In addition to estimating the driving skill level of user U2 based on the driving data of user U2 as described above, the estimation unit 132 can also estimate the driving skill level of user U2 based on the health data of user U2, or based on the equipment of user U2's vehicle V. For example, even if user U2 is an elderly driver and the driving skill level is estimated to be "low" based on the driving data of user U2, the driving skill level can be raised from "low" to "medium" if user U2's vehicle V is equipped with an anti-accidental pedal device.

[0084] Next, the decision unit 133 determines the difficulty of the delivery task entrusted from user U1 to user U2 based on the task information (step S104).

[0085] Figure 6 This diagram illustrates an example of a method for determining difficulty. As shown, the decision-making unit 133 can determine the difficulty of a transport mission based on factors such as time constraints, geographical factors, driving skills, and risk factors.

[0086] For example, in the logistics field, there are sometimes tasks involving the urgent transport of high-value equipment. Such transport tasks are characterized by high time constraints (arrival within a specified time), involve complex geographical factors (including routes through congestion-prone areas), and require advanced driving skills (precise driving skills to mitigate equipment vibration). Therefore, tasks involving the urgent transport of high-value equipment are classified as high-difficulty.

[0087] On the other hand, in the realm of passenger flow, there are sometimes tasks where inexperienced drivers pick up and drop off passengers over short distances. In such transport tasks, time constraints are low, the geographical factors are flat and simple, and advanced driving skills are not required. Therefore, tasks where inexperienced drivers pick up and drop off passengers over short distances are considered to be of low difficulty.

[0088] These difficulty levels can be quantified based on pre-defined weighted benchmarks. For example, "time constraints" can be assigned a score from 1 to 5, with higher scores for higher delay risks. Similarly, in "geographical factors," mountain roads, poor roads, and congested urban areas receive higher scores. The decision unit 133 can sum the scores of these elements and determine the difficulty level based on a threshold in three stages: "low," "medium," and "high," or in five or more stages.

[0089] Next, the matching processing unit 134 matches a suitable transport task to the user U2 based on the difficulty of the transport task and the user U2's driving skill level (step S106).

[0090] For example, the matching processing unit 134 matches a user U2-1 with a high-difficulty transport task Ta, and a user U2-2 with a lower-difficulty transport task Tb, which is less difficult than transport task Ta. User U2-1 is an example of "first driver", user U2-2 is an example of "second driver", transport task Ta is an example of "first task", and transport task Tb is an example of "second task".

[0091] More specifically, within a certain delivery company, there are sometimes two tasks: "Designated delivery of precision equipment within city hours (high difficulty)" and "Delivery of documents to the suburbs (low difficulty)." The matching processing unit 134 matches user U2-1, who has a higher driving skill level, with the high-difficulty task of "Designated delivery of precision equipment within city hours," and matches user U2-2, who has a lower driving skill level, with the low-difficulty task of "Delivery of documents to the suburbs." This results in a high success rate for the tasks, minimizing risk while efficiently transporting people and goods.

[0092] Next, the providing unit 135 waits until the delivery task matched to user U2 is completed (step S108). When the delivery task is completed, the feedback used to improve the driving skill level is provided to the second terminal device 20 used by user U2 via the communication interface 110, or to the vehicle V used by user U2 (step S110).

[0093] Figure 7 This is a schematic diagram illustrating the interior of vehicle V. For example, a first display 30 is positioned near the front of the driver's seat (the seat closest to the steering wheel) in the instrument panel IP. The first display 30 is positioned so that the driver can visually confirm it from the gap in the steering wheel or over the steering wheel.

[0094] The first display 30 is, for example, an LCD or an organic EL display device. The first display 30 may display, for example, the vehicle V's speed, engine speed, fuel level, radiator temperature, distance traveled, and battery level. Furthermore, the first display 30 may also display, for example, the vehicle V's future trajectory, whether a lane change has occurred, the lane to which the lane change destination is located, identified lanes (marking lines), and other vehicles.

[0095] A second display 31 is located near the center of the instrument panel IP. The second display 31, like the first display 30, is an LCD, OLED, or similar display device. The second display 31 may display, for example, a route guided by a navigation device, a television program, content played from a DVD, or content downloaded via the internet.

[0096] For example, during the estimation of user U2's driving skill level, among the scores calculated according to multiple perspectives, the evaluation might be "frequent rapid acceleration (-10 points)," "3 speeding violations (-15 points)," and "fuel efficiency below the benchmark (-8 points)." In this case, the providing unit 135 can display the improvement points of "safety (avoidance of rapid acceleration)" and "fuel-efficient driving" as feedback on the first display 30 and the second display 31.

[0097] Alternatively, for example, the providing unit 135 may select the viewpoint with the lowest score from multiple viewpoints with calculated scores, and display the driving training used to improve the score of the selected viewpoint as feedback on the first display 30 and the second display 31.

[0098] Figure 8 and Figure 9 This diagram illustrates an example of an image displayed on the first display 30 or the second display 31 during driver training. For example... Figure 8 For example, during an acceleration training phase, to suppress rapid acceleration, specific instructions can be displayed such as "Pay attention to the smoothness of acceleration operation, and control the speed increase during acceleration to, for example, below 10 km / h per second." For instance, user U2 drives vehicle V while controlling the speed increase during acceleration to below 10 km / h per second according to the acceleration training. In this case, the first display 30 or the second display 31 indicates that the acceleration training phase has been completed. Furthermore, progress can be displayed such as "Rapid acceleration this month is 20% less than last month" or "Energy-saving driving score increased by 5 points," and rewards can be given to user U2 based on the completion of the training phase. In this way, by visualizing the driver's skill development process, the motivation of drivers who want to improve their driving skill level can be continuously enhanced with a game-like feel.

[0099] According to the implementation described above, the information processing device 100 estimates the driving skill level of the user U2 driving the vehicle V and determines the difficulty of the transportation task. Based on the driving skill level and the difficulty of the transportation task, the information processing device 100 matches a suitable transportation task to the user U2. By forming such a structure, a wide variety of driving personnel can be effectively utilized to transport people and goods efficiently and safely.

[0100] <Modifications of the Implementation>

[0101] Hereinafter, variations of the above-described embodiments will be described. In the above-described embodiments, the matching of user U2 with a transportation task is described based on driving skill level and the difficulty of the transportation task, but it is not limited to this. For example, in addition to driving skill level and the difficulty of the transportation task, the matching processing unit 134 may also match user U2 with a transportation task based on the degree of achievement of driving training.

[0102] The following diagrams illustrate the matching between the achievement of driving training. Figure 10 and Figure 11 This is a diagram showing an example of a screen displayed on the first terminal device 10 in the embodiment.

[0103] like Figure 10 In this way, the screen of the first terminal device 10 displays a list of multiple drivers who can be assigned delivery tasks. At this time, drivers with higher driving skill levels may be displayed at the top of the screen. An assignment button (button B1 on the screen) is assigned to each driver. For example, when user U1 presses the assignment button for the driver at the top of the screen, the delivery task assigned to that driver is sent from the first terminal device 10 to the information processing device 100.

[0104] In addition, such as Figure 11 In this way, the screen of the first terminal device 10 can display an active area R1 and an inactive area R2. The active area R1 is where the user U1's operation is enabled, displaying only drivers who have completed the required driving training specified by the commissioner of the delivery task, i.e., the user U1 (or drivers with a completion rate above the threshold). The inactive area R2 is where the user U1's operation is disabled, displaying drivers who have not yet completed the required driving training (or drivers with a completion rate below the threshold). Thus, delivery tasks can be matched only to drivers who have completed the required driving training specified by the user U1.

[0105] Furthermore, in the above-described embodiments, the information processing device 100 can also score the user U2's driving skills from multiple perspectives in real time, or obtain the progress status of the delivery task, or reflect feedback after the completion of the delivery task. Additionally, the information processing device 100 can use machine learning to improve matching accuracy. Furthermore, it can flexibly respond to unexpected business assignments by dynamically adjusting priorities and business resources. Thus, optimal business allocation based on driving technology is achieved, resulting in improved efficiency and safety in logistics and passenger flow.

[0106] The above describes specific embodiments of the present invention, but the present invention is not limited to such embodiments in any way, and various modifications and substitutions can be made without departing from the spirit of the present invention.

Claims

1. An information processing system, wherein, The information processing system has the following features: The presumption department is responsible for presumpturing the level of driving skills of the driver of the vehicle. The decision-making department determines the difficulty of the task of using the vehicle to transport people and / or goods; and The matching processing unit matches the driver with a suitable task based on the level and the difficulty.

2. The information processing system according to claim 1, wherein, The estimation unit acquires driving data representing the behavior of the vehicle driven by the driver. Based on the driving data, the estimation unit calculates scores for the driver's driving skills from multiple perspectives. The estimation unit estimates the grade based on the scores calculated for each of the multiple viewpoints.

3. The information processing system according to claim 1 or 2, wherein, The decision-making unit determines the difficulty level based on the destination of the person and / or the item, the time spent transporting the person and / or the item, the distance required for transporting the person and / or the item, the geographical information of the area where the person and / or the item is transported, the meteorological information of the area where the person and / or the item is transported, and / or the congestion prediction information of the area where the person and / or the item is transported.

4. The information processing system according to claim 1 or 2, wherein, The matching processing unit matches the first driver with the first task. The matching processing unit matches the second driver, whose level is lower than that of the first driver, with a second task whose difficulty is lower than that of the first task.

5. The information processing system according to claim 2, wherein, The information processing system also includes a providing unit that provides feedback to the driver to improve the score.

6. The information processing system according to claim 5, wherein, The providing unit selects the viewpoint with the lowest score from among the multiple viewpoints. The providing unit will provide the driver with driving training as feedback to improve the score of the selected viewpoint.

7. The information processing system according to claim 6, wherein, The matching processing unit matches the driver who has performed the driving training specified by the client with the client's task.

8. An information processing method that uses a computer, wherein, The information processing method includes the following processes: To presuppose the level of driving skill of the driver of the vehicle; Determine the difficulty of the task of using the vehicle to transport people and / or goods; as well as Based on the level and the difficulty, a suitable task is matched to the driver.

9. A storage medium storing a program, said program being a program for causing a computer to execute, wherein, The procedure includes the following processing: To presuppose the level of driving skill of the driver of the vehicle; Determine the difficulty of the task of using the vehicle to transport people and / or goods; as well as Based on the level and the difficulty, a suitable task is matched to the driver.

Images