JP2021193583A - Driving evaluation model adaptation device, terminal equipment, control method, program, and storage medium - Google Patents

Abstract

To provide a driving evaluation model adaptation device capable of distributing a driving evaluation model as a learning result adapted to a use situation.SOLUTION: A server device 1 stores a driving evaluation model learnt on the basis of a photographic image of the vicinity of a mobile body and an evaluation value related to driving determined on the basis of the photographic image by a determining person. In this case, the driving evaluation model is generated for each group of determining persons classified in accordance with commonality of profiles. The server device 1 acquires profile information Ip related to a driver who drives a mobile body, and extracts the driving evaluation model in the adapted group adapted to the acquired profile information Ip from a driving evaluation model DB 10.SELECTED DRAWING: Figure 1

Description

The present invention relates to a technique for utilizing a model obtained by learning.

Conventionally, there has been known a system that aggregates data from a plurality of vehicles into a server device and distributes information about learning results obtained by the server device by machine learning. For example, Patent Document 1 discloses a system that collects conditions such as the weather and time zone when each vehicle is traveling as environmental data and learns each situation indicated by the environmental data.

Japanese Unexamined Patent Publication No. 2015-135552

Regarding situational awareness and situational judgment during driving, there are individual differences based on the driver's experience, etc., and the recognition and judgment may differ depending on the driver. On the other hand, Patent Document 1 does not take into consideration such individual differences.

The present invention has been made to solve the above-mentioned problems, and receives a driving evaluation model conforming device and a driving evaluation model capable of delivering a driving evaluation model which is a learning result suitable for the usage situation. The main purpose is to provide a terminal device to be used.

The invention according to claim 1 is a driving evaluation model conforming device, which includes an information acquisition unit for acquiring driver information about a driver who drives a moving body, a photographed image of the surroundings of the moving body, and a determiner having a profile. Is driving in a conforming group that matches the driver information from the driving evaluation model learned for each group of the judges classified by the commonality of the profiles based on the evaluation information about the driving evaluated based on the captured image. It is provided with an extraction unit for extracting an evaluation model.

The invention according to claim 8 is the determination person classified according to the commonality of the profile based on the photographed image around the moving body and the evaluation information regarding the driving evaluated by the determination person having the profile based on the photographed image. It is a control method executed by a driving evaluation model conforming device having a storage unit for storing a driving evaluation model learned for each group, and is an information acquisition process for acquiring driver information about a driver who drives a moving body. It has an extraction step of extracting a driving evaluation model in a conforming group that matches the driver information.

The invention according to claim 9 is the determination person classified according to the commonality of the profile based on the photographed image around the moving body and the evaluation information regarding the driving evaluated by the determination person having the profile based on the photographed image. A computer-executed program that refers to a storage unit that stores a driving evaluation model learned for each group, and is an information acquisition unit that acquires driver information about a driver who drives a moving object, and the driver information. The computer is made to function as an extraction unit for extracting an operation evaluation model in a conforming group suitable for.

It is a schematic configuration of the operation evaluation system. The block configuration of the server device and the terminal device is shown. It is a correspondence table between the identification number of the driving evaluation model and each item representing the profile of the judge corresponding to the driving evaluation model. This is an example of a flowchart showing the procedure of distribution processing of the operation evaluation model. This is an example of a flowchart showing a processing procedure related to reception and utilization of a driving evaluation model. A display example output by the terminal device is shown.

According to a preferred embodiment of the present invention, the driving evaluation model conforming device includes an information acquisition unit for acquiring driver information about a driver who drives a moving body, a photographed image around the moving body, and a determiner having a profile. Is driving in a conforming group that matches the driver information from the driving evaluation model learned for each group of the judges classified by the commonality of the profiles based on the evaluation information about the driving evaluated based on the captured image. It is provided with an extraction unit for extracting an evaluation model.

The operation evaluation model conforming device includes an information acquisition unit, a storage unit, and an extraction unit. The storage unit stores a driving evaluation model learned based on the captured image around the moving body and the evaluation information regarding the driving evaluated by the determiner based on the captured image. Here, the driving evaluation model stored in the storage unit is generated for each group of judges classified according to the commonality of the profiles. The information acquisition unit acquires driver information about the driver who drives the moving body. The extraction unit extracts a driving evaluation model in a conforming group that matches the acquired driver information. Here, the "conformity group" refers to a group that conforms to the acquired driver information among the groups of judges classified according to the commonality of profiles. According to this aspect, the driving evaluation model conforming device can suitably extract a driving evaluation model that matches the characteristics of the driver indicated by the acquired driver information.

In one aspect of the driving evaluation model conforming device, the driving evaluation model conforming device further includes a transmission unit that transmits information about the driving evaluation model in the conforming group to a communication terminal provided in the mobile body. According to this aspect, the driving evaluation model conforming device can suitably supply a driving evaluation model that matches the characteristics of the driver based on the driver information of the driver of the moving body.

In another aspect of the driving evaluation model conforming device, the driver information and the profile include at least the residential area information regarding the residential area of the driver and the determiner, and the driving evaluation extracted by the extraction unit. The model is a driving evaluation model in a group that matches the residential area information included in the driver information among the groups of the determiners classified according to the commonality of the residential area information. According to this aspect, the driving evaluation model conforming device can suitably extract a driving evaluation model specialized for the residential area of the driver of the moving body.

In another aspect of the driving evaluation model conforming device, the driver information and the profile further include driving history information of the driver and the determiner, and the driving evaluation model extracted by the extracting unit is This is a driving evaluation model in a group that matches the residential area information and the driving history information included in the driver information among the groups of the determiners classified according to the commonality of the residential area information and the driving history information. According to this aspect, the driving evaluation model conforming device can suitably extract a driving evaluation model suitable for the living area and driving experience of the driver of the moving body.

According to another preferred embodiment of the present invention, the terminal device comprises a transmitter that transmits driver information about a driver driving a moving object to the above-described driving evaluation model conforming device, and the driving evaluation model conforming device. By applying the driving evaluation model to the receiving unit that receives information about the driving evaluation model in the conforming group that matches the driver information, the photographing unit that generates the captured image, and the captured image. It is provided with an evaluation information generation unit that generates evaluation information regarding the above. According to this aspect, the terminal device can suitably receive the driving evaluation model generated by the matching group of the determiners who match the driver information as a teacher, and generate the evaluation information according to the driver's feeling.

In one aspect of the terminal device, the terminal device further includes an output unit that displays or outputs voice based on the evaluation information generated by the evaluation information generation means. According to this aspect, the operation evaluation model conforming device can suitably notify the driver and the like of the evaluation information generated by the acquired operation evaluation model. Preferably, the evaluation information is information indicating the degree of difficulty in driving or information indicating the goodness of the view.

According to another preferred embodiment of the present invention, the image is classified according to the commonality of the profile based on the photographed image around the moving body and the evaluation information regarding the driving evaluated by the determiner having the profile based on the photographed image. It is a control method executed by a driving evaluation model conforming device including a storage unit for storing a driving evaluation model learned for each group of the above-mentioned judges, and is information for acquiring driver information about a driver who drives a moving body. It has an acquisition step and an extraction step of extracting a driving evaluation model in a conforming group that matches the driver information. By executing this control method, the driving evaluation model conforming device can suitably extract a driving evaluation model that matches the characteristics of the driver indicated by the acquired driver information.

According to another preferred embodiment of the present invention, the image is classified according to the commonality of the profile based on the photographed image around the moving body and the evaluation information regarding the driving evaluated by the determiner having the profile based on the photographed image. A program executed by a computer that refers to a storage unit that stores a driving evaluation model learned for each group of judges, and is an information acquisition unit that acquires driver information about a driver who drives a moving body. The computer functions as an extraction unit for extracting a driving evaluation model in a conforming group that matches the driver information. By executing this program, the computer can suitably extract a driving evaluation model that matches the characteristics of the driver indicated by the acquired driver information. Preferably, the program is stored in a storage medium.

Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings.

[Overview of driving evaluation system]
FIG. 1 is a schematic configuration of an operation evaluation system according to this embodiment. The driving evaluation system is a system that evaluates the driving of the road on which the vehicle travels, and includes a server device 1 and a terminal device 2.

The server device 1 provides a driving evaluation model DB 10 which is a database of a model (also referred to as a “driving evaluation model”) for determining an evaluation regarding road driving displayed on the captured image from a captured image captured on the road. I remember. The driving evaluation model is a pattern classifier obtained by supervised machine learning, and is, for example, learning data learned by applying a neural network (deep learning). In this embodiment, a combination of a photographed image taken on the road in advance and an evaluation value of driving difficulty determined by a person who refers to the photographed image (also referred to as a "determiner") is learned as teacher data. By doing so, a driving evaluation model is generated. The evaluation value of the driving difficulty may be, for example, a two-stage evaluation value of "0" indicating that it is easy to drive or "1" indicating that it is difficult to drive, or an evaluation value of three or more stages. good. The evaluation value of the driving difficulty level is an example of "evaluation information" in the present invention.

Here, each driving evaluation model registered in the driving evaluation model DB 10 is generated for each group of judges having a common profile such as gender and driving history, as will be described later. Then, when the server device 1 receives the information indicating the profile of the user of the terminal device 2 (also referred to as "profile information Ip") from the terminal device 2, the server device 1 obtains a driving evaluation model that matches the profile indicated by the profile information Ip. Extracted from the operation evaluation model DB10. Then, the server device 1 transmits information indicating the extracted operation evaluation model (also referred to as “extracted model information Im”) to the terminal device 2. The server device 1 is an example of the "operation evaluation model conforming device" in the present invention.

The terminal device 2 is, for example, a stationary navigation device or a mobile terminal such as a smartphone, and performs route guidance to a destination and display of a map around the current position. Further, the terminal device 2 transmits the profile information Ip indicating the user profile of the terminal device 2 to the server device 1, and receives the extracted model information Im related to the driving evaluation model matching the user profile from the server device 1. .. Then, the terminal device 2 inputs the image taken by the camera into the driving evaluation model indicated by the above-mentioned extraction model information Im, obtains the evaluation value of the driving difficulty of the road displayed in the image, and obtains the evaluation value of the driving difficulty. Output based on the evaluation value of the degree. The terminal device 2 is an example of the "communication terminal" and the "terminal device" in the present invention.

[Device configuration]
FIG. 2A shows a schematic configuration of the server device 1. As shown in FIG. 2A, the server device 1 has a communication unit 11, a storage unit 12, and a control unit 15. The communication unit 11, the storage unit 12, and the control unit 15 are connected to each other via a bus line.

Based on the control of the control unit 15, the communication unit 11 receives the profile information Ip transmitted from the terminal device 2 and transmits the extraction model information Im to the terminal device 2. The communication unit 11 is an example of the "transmission unit" in the present invention. The storage unit 12 stores a program for controlling the operation of the server device 1 and holds information necessary for the operation of the server device 1. Further, the storage unit 12 stores the operation evaluation model DB 10. The data structure of the operation evaluation model DB 10 will be described later.

The control unit 15 includes a CPU, ROM, RAM, and the like (not shown), and performs various controls on each component in the server device 1. When the communication unit 11 receives the profile information Ip, the control unit 15 extracts the operation evaluation model from the operation evaluation model DB 10 based on the profile indicated by the profile information Ip, and communicates the extracted model information Im related to the extracted operation evaluation model. It is transmitted to the terminal device 2 by the unit 11. The control unit 15 is an example of the “information acquisition unit”, the “extraction unit”, and the computer that executes the program in the present invention.

FIG. 2B shows a schematic configuration of the terminal device 2. As shown in FIG. 2B, the terminal device 2 mainly has a communication unit 21, a storage unit 22, a sensor unit 23, an input unit 24, a control unit 25, and an output unit 26. The communication unit 21, the storage unit 22, the sensor unit 23, the input unit 24, the control unit 25, and the output unit 26 are connected to each other via a bus line.

Based on the control of the control unit 25, the communication unit 21 transmits the profile information Ip to the server device 1 and receives the extraction model information Im transmitted from the server device 1. The communication unit 21 is an example of the “transmitter unit” and the “receiver unit” in the present invention.

The storage unit 22 stores a program executed by the control unit 25 and information necessary for the control unit 25 to execute a predetermined process. In this embodiment, the storage unit 22 stores the map DB 20 including road data, facility information, and the like. Further, the storage unit 22 stores the profile information Ip generated by the control unit 25 based on the input to the input unit 24 or the like. Profile information Ip is an example of "driver information" in the present invention. Further, the storage unit 22 stores the extraction model information Im received from the server device 1 by the communication unit 21.

The sensor unit 23 includes a camera 31 that captures a landscape in the traveling direction of the vehicle, a GPS receiver 32 that generates absolute position information of the vehicle, and an autonomous positioning device 33 such as a gyro sensor and a speed sensor. The sensor unit 23 supplies the generated output signal to the control unit 25. The camera 31 is an example of the "shooting unit" in the present invention.

The input unit 24 is a button, a touch panel, a remote controller, a voice input device, etc. for the user to operate, and receives and generates an input for designating a destination for a route search, an input for a driver's profile, and the like. The signal is supplied to the control unit 25. The output unit 26 is, for example, a display, a speaker, or the like that outputs under the control of the control unit 25.

The control unit 25 includes a CPU that executes a program and controls the entire terminal device 2. For example, the control unit 25 generates profile information Ip based on user input by the input unit 24 and stores it in the storage unit 22. Further, the control unit 25 transmits the profile information Ip to the server device 1 by the communication unit 21, so that the extraction model information Im is received by the communication unit 21 and stored in the storage unit 22. Further, the control unit 25 inputs an image generated by the camera 31 (also simply referred to as a “camera image”) to the driving evaluation model indicated by the received extraction model information Im, so that the control unit 25 takes a picture in front of the camera 31. Obtain an evaluation value indicating the difficulty of driving on the road. The control unit 25 is an example of the "evaluation information generation unit" in the present invention.

[Driving evaluation model DB]
Each driving evaluation model registered in the driving evaluation model DB 10 is generated for each group of judges having a common profile such as gender and driving history, and is associated with the classification information of the common profile. This will be described with reference to FIG.

FIG. 3 is a correspondence table between the identification number (also referred to as “model ID”) of each driving evaluation model included in the driving evaluation model DB 10 and the classification information of the profile of the judge corresponding to the driving evaluation model. The corresponding table in FIG. 3 has each item of "gender", "driving history", "place of residence", "age", and "model ID".

Here, the driving evaluation model of the model ID "M001" is a photographed image referred to by a judge whose gender is male, driving history is less than 5 years, residence is Tokyo, and age is 25 to 39 years old. A driving evaluation model obtained by learning a combination with an evaluation value of driving difficulty determined by a judge by referring to a photographed image as teacher data is shown. Similarly, the driving evaluation model of the model ID "M002" includes a photographed image referred to by a judge whose gender is male, driving history is less than 5 years, residence is Tokyo, and age is 40 to 59 years old. A driving evaluation model obtained by learning a combination with an evaluation value of driving difficulty determined by a judge by referring to a photographed image as teacher data is shown. In addition, the driving evaluation model of the model ID "M251" is a photographed image referred to by a judge whose gender is female, driving history is less than 5 years, residence is Saitama prefecture, and age is 60 to 69 years old, and the judgment is made. A driving evaluation model obtained by learning a combination with an evaluation value of driving difficulty determined by a person with reference to a photographed image as teacher data is shown. In addition, the driving evaluation model of model ID "M342" is a photographed image referred to by a judge whose gender is female, driving history is 5 years or more, residence is Kanagawa prefecture, and age is 24 years or younger, and the judge is A driving evaluation model obtained by learning a combination with an evaluation value of driving difficulty determined by referring to a photographed image as teacher data is shown. In addition, the driving evaluation model of model ID "M445" is a photographed image referred to by a judge whose gender is female, driving history is 5 years or more, residence is Hokkaido, and age is 70 years or older, and a photographed image by the judge. A driving evaluation model obtained by learning a combination with an evaluation value of driving difficulty determined by referring to an image as teacher data is shown.

In this way, the driving evaluation model is generated for each group when the judges are grouped based on gender, driving history, place of residence, and age. Generally, the driving difficulty perceived by a driver varies depending on the driver's gender, driving history, place of residence (that is, the area where he / she often drives), age, and the like. In consideration of the above, in this embodiment, the judges are grouped based on the profile items that affect the driving difficulty felt by the driver, and the server device 1 is a pre-generated operation evaluation for each divided group. The model is stored as the operation evaluation model DB10.

In the example of Fig. 3, the driving history was classified into two categories, less than 5 years and 5 years or more, the place of residence was classified by prefecture, and the age was classified by 5 stages, but each item was classified. The method is not limited to this. For example, the driving history may be divided into three or more stages, and the place of residence may be classified by a wider area (for example, 7 regions) or a more detailed area. Similarly, age may be categorized by stages other than the five stages. In addition to or in addition to the driving history, the corresponding table in FIG. 3 is provided with items related to the driving history other than the driving history (for example, the number or frequency of accidents, the number or frequency of sudden braking, etc.). May be good.

[Delivery of driving evaluation model]
FIG. 4 is an example of a flowchart showing a procedure of distribution processing of the operation evaluation model executed by the server device 1. The server device 1 repeatedly executes the process of the flowchart of FIG.

First, the server device 1 determines whether or not the profile information Ip transmitted from the terminal device 2 has been received (step S101). Here, the profile information Ip includes, for example, profile information such as the gender, driving history, place of residence, and age of the user of the terminal device 2. Then, when the server device 1 receives the profile information Ip (step S101; Yes), the server device 1 proceeds to the process to step S102. On the other hand, when the server device 1 does not receive the profile information Ip (step S101; No), the server device 1 ends the processing of the flowchart.

After receiving the profile information Ip in step S101, the server device 1 refers to the profile indicated by the profile information Ip, and obtains an operation evaluation model corresponding to the group of judges (conformity group) conforming to the profile from the operation evaluation model DB10. Extract (step S102). Specifically, the server device 1 refers to the correspondence table shown in FIG. 3, and the operation evaluation corresponding to the model ID recorded in the record corresponding to the gender, the operation history, the place of residence, and the age included in the profile information Ip. The model is extracted from the operation evaluation model DB10. For example, if the gender indicated by the profile information Ip is "male", the driving history is "3 years", the place of residence is "Tokyo", and the age is "45 years old", it is recorded in the second record in FIG. The driving evaluation model of the model ID “M002” is extracted from the driving evaluation model DB10.

Then, the server device 1 transmits the extracted model information Im regarding the driving evaluation model extracted from the driving evaluation model DB 10 to the terminal device 2 that is the transmission source of the profile information Ip (step S103). Thereby, the server device 1 can suitably supply the operation evaluation model suitable for the user's profile to the terminal device 2 which is the transmission source of the profile information Ip.

[Reception and utilization of driving evaluation model]
FIG. 5 is an example of a flowchart showing a processing procedure related to reception and utilization of the operation evaluation model executed by the terminal device 2. The terminal device 2 repeatedly executes the process of the flowchart of FIG.

First, the terminal device 2 determines whether or not the driving evaluation model has not been acquired, or whether or not the profile has been changed since the driving evaluation model was acquired (step S201). In this case, the terminal device 2 has the profile information Ip changed from the time when the extraction model information Im received from the server device 1 is stored in the storage unit 22 and the extraction model information Im is stored in the storage unit 22. Is determined.

Then, when the terminal device 2 has not acquired the driving evaluation model or the profile has been changed since the driving evaluation model was acquired (step S201; Yes), the terminal device 2 transmits the profile information Ip to the server device 1. (Step S202). After that, the server device 1 that has received the profile information Ip executes the flowchart of FIG. 4, and transmits the extracted model information Im indicating the driving evaluation model that matches the profile indicated by the profile information Ip to the terminal device 2. Then, the terminal device 2 receives the extraction model information Im from the server device 1 (step S203).

On the other hand, when it is determined that the operation evaluation model has been acquired and the profile has not been changed since the acquisition of the operation evaluation model (step S201; No), the terminal device 2 proceeds to the process to step S204.

Next, the terminal device 2 determines whether or not the vehicle is running (step S204). Then, when the terminal device 2 determines that the vehicle is running (step S204; Yes), the terminal device 2 inputs the camera image generated by the camera 31 into the driving evaluation model indicated by the extraction model information Im, so that the driving difficulty is achieved. The evaluation value of the degree is calculated (step S205). Then, the terminal device 2 outputs based on the evaluation value of the driving difficulty level determined in step S205 (step S206). In this case, the terminal device 2 displays / and displays according to the evaluation value of the driving difficulty calculated in step S205 by the output unit 26. On the other hand, when the vehicle is stopped (step S204; No), the terminal device 2 ends the processing of the flowchart. This prevents the evaluation value of the driving difficulty level from being repeatedly calculated based on the camera images taken at the same point.

FIG. 6 is a display example of the terminal device 2 in step S206. In the example of FIG. 6, the terminal device 2 displays the camera image on the display and displays the guide images 81 and 82 on the camera image.

In the example of FIG. 6, the terminal device 2 displays a guidance image 81 indicating that the driver should turn right 300 m ahead based on the information of the set guidance route, and sets the evaluation value of the driving difficulty level determined in step S205. The guide image 82 based on this is displayed. In the example of FIG. 6, since the terminal device 2 has a value indicating that the evaluation value of the driving difficulty level determined in step S205 is difficult to drive, the guidance image 82 indicating a warning that driving should be noted is provided. it's shown. In addition to the guidance image 82, the terminal device 2 may output a voice warning that the driver should be careful about driving.

As described above, the server device 1 according to the present embodiment stores the operation evaluation model learned based on the photographed image around the moving body and the evaluation value related to the operation determined by the determiner based on the photographed image. Here, the driving evaluation model is generated for each group of judges classified according to the commonality of profiles. The server device 1 acquires the profile information Ip about the driver who drives the vehicle, and extracts the driving evaluation model in the conforming group conforming to the acquired profile information Ip from the driving evaluation model DB 10. As a result, the server device 1 can suitably extract a driving evaluation model that matches the characteristics of the driver indicated by the acquired profile information Ip.

[Modification example]
Next, a modification suitable for the embodiment will be described. The following modifications may be applied to the above-described embodiment in any combination.

(Modification 1)
The driving evaluation model may be further classified and generated based on the driving environment of the vehicle.

In this case, in the corresponding table of FIG. 3, in addition to the items related to the profile such as gender, driving history, place of residence, and age, items related to the driving environment such as weather, road type, and time zone are further provided. Then, the driving evaluation model is generated in advance for each classification based on the items related to the driving environment, and is stored in the driving evaluation model DB 10. In this case, the driving evaluation model corresponding to each driving environment evaluates the photographed image and the driving difficulty level determined by the judge with reference to the photographed image for each photographed image taken on the road in each driving environment. It is generated by learning the combination with the value as teacher data.

Then, for example, in step S203 of FIG. 5, the terminal device 2 receives from the server device 1 a driving evaluation model corresponding to each driving environment conforming to the profile indicated by the profile information Ip. After that, when the vehicle is traveling, the terminal device 2 refers to the output of the sensor unit 23 such as a raindrop sensor (not shown), the map DB 20, and the like, and recognizes the current traveling environment of the vehicle. Then, the terminal device 2 selects a driving evaluation model corresponding to the recognized driving environment, and calculates the evaluation value of the driving difficulty level in step S205. Instead of the above example, the terminal device 2 recognizes the current driving environment of the vehicle before acquiring the driving evaluation model, and transmits information indicating the driving environment together with the profile information Ip to the server device 1. A driving evaluation model corresponding to the current driving environment of the vehicle may be received from the server device 1.

(Modification 2)
The object to be judged by the driving evaluation model is not limited to the driving difficulty level. For example, the object to be determined by the driving evaluation model may be a good view. In this case, each driving evaluation model is generated by learning as teacher data a combination of a photographed image taken on the road and an evaluation value of good visibility judged by a judge who referred to the photographed image. .. Then, each of these driving evaluation models is generated for each group of judges having a common profile such as gender and age, and is associated with the classification information of the common profile, as in the embodiment, for the driving evaluation model DB10. Is remembered in. Then, the terminal device 2 receives the extracted model information Im from the server device 1 by transmitting the profile information Ip to the server device 1 based on the flowchart of FIG. 5, as in the embodiment. In this case, the server device 1 extracts a driving evaluation model matching the profile indicated by the profile information Ip received from the terminal device 2 from the driving evaluation model DB 10 based on the flowchart of FIG. 4, and the terminal device 2 uses the extracted model information Im as the extracted model information Im. Send to. Then, after receiving the extraction model information Im, the terminal device 2 acquires an evaluation value regarding the goodness of the view by inputting a camera image into the operation evaluation model indicated by the extraction model information Im, and displays the evaluation value based on the evaluation value. Or / and perform audio output.

According to this modification, since the terminal device 2 displays and / or outputs audio based on the evaluation value regarding the good view while the vehicle is traveling, the terminal device 2 travels on a road preferable from the viewpoint of tourism or safety. It can be suitably notified to the driver. Here, a road preferable from the viewpoint of tourism corresponds to, for example, a road having an excellent landscape in a tourist spot, and a road preferable from the viewpoint of safety corresponds to, for example, a safe road where pedestrians can be relatively easily found even from a distance. do. In addition, for example, if there is an indication that the evaluation value regarding the good view is low and / or a voice output, the driver determines that he / she is driving on an unfavorable road from the viewpoint of tourism or safety. , It is possible to take measures such as changing the driving route and increasing the concentration on driving.

1 Server device 2 Terminal device 10 Operation evaluation model DB
11, 21 Communication unit 12, 22 Storage unit 15, 25 Control unit 23 Sensor unit 24 Input unit 26 Output unit

Claims (1)

The information acquisition unit that acquires driver information about the driver who drives the mobile body,
In the conforming group that matches the driver information from the driving evaluation model learned for each group of the judge based on the photographed image around the moving body and the evaluation information about the driving evaluated by the judge based on the photographed image. An extraction unit that extracts the operation evaluation model and
Driving evaluation model conforming device.

Images