Driving diagnostic device

Abstract

To provide a drive diagnosis device capable of drive diagnosis using a proper standard for drive diagnosis.SOLUTION: A plurality of representative road places differing in road item are determined as shown with comparison places 1 to 8. A plurality of vehicles having traveled at the determined representative road places are regarded as teacher vehicles, vehicle information on the teacher vehicles is acquired to find a distribution of the vehicle information on the teacher vehicles, and ideal travel information is generated based upon the distribution. At each representative road places, travel information on a vehicle 14 as an object of drive diagnosis is compared with the generated ideal travel information so as to diagnose the driver.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to a driving diagnosis device for diagnosing a driver's driving. [Background technology]

[0002] Patent Document 1 discloses a method of storing a preset reference road shape and an ideal driving locus corresponding to the reference road shape in a memory unit, acquiring the driving conditions of a vehicle traveling on a road, acquiring from the memory unit a reference road shape corresponding to the road shape and an ideal driving locus corresponding to the reference road shape based on the road shape on which the vehicle has traveled obtained from the acquired driving conditions of the vehicle, generating an ideal locus corresponding to the road shape on which the vehicle has traveled based on the acquired reference road shape and ideal driving locus, and diagnosing the driving of the vehicle by comparing the generated ideal locus with the driving driving locus of the vehicle obtained from the driving conditions of the vehicle. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2014-194620 A Summary of the Invention [Problem to be solved by the invention]

[0004] In the technology described in Patent Document 1, an ideal driving locus is generated based on the road shape obtained from the driving conditions of the vehicle traveling on the road, so the ideal driving locus changes depending on the driving conditions. If the ideal driving locus changes depending on the driving conditions, the standard for driving diagnosis will change, and there is room for improvement in order to perform appropriate driving diagnosis.

[0005] The present invention has been made in consideration of the above circumstances, and has an object to provide a driving diagnosis device capable of performing driving diagnosis using appropriate driving diagnosis criteria. [Means for solving the problem]

[0006] In order to achieve the above object, the driving diagnosis device according to claim 1 includes a processor, the processor generating ideal driving information relating to ideal driving of a representative vehicle corresponding to road specifications, The ideal driving information is converted for a vehicle type to be diagnosed, Acquiring vehicle information related to traveling from a vehicle, and the acquired vehicle information; Converted The vehicle driver is diagnosed by comparing the ideal driving information with the actual driving information.

[0007] According to the invention described in claim 1, ideal driving information relating to the ideal driving of a representative vehicle corresponding to the road specifications is generated, so that ideal driving information can be generated that matches specifications such as road shape.

[0008] and, Convert the ideal driving information to match the model of the vehicle being diagnosed, Vehicle information related to driving is obtained from the vehicle being diagnosed, and the vehicle information is compared with ideal driving information that is matched to specifications such as road shape to diagnose the vehicle's driver, making it possible to perform driving diagnosis using appropriate driving diagnosis standards.

[0009] The processor may generate the ideal driving information using a distribution of the vehicle information acquired from a plurality of vehicles that have traveled on a predetermined representative road, and may acquire the vehicle information from the vehicle to be diagnosed when it traveled on the representative road to perform the diagnosis. This makes it possible to generate ideal driving information from vehicle information of vehicles other than the vehicle to be diagnosed and perform driver diagnosis.

[0010] The processor may generate the derived vehicle information as the ideal driving information by using a driver model that is trained in advance using a plurality of road information related to the specifications of roads with different specifications and the vehicle information acquired from a vehicle that has traveled on a road corresponding to the plurality of road information as teacher data to derive the vehicle information corresponding to the road information of a road on which a vehicle to be diagnosed has traveled. This makes it possible to diagnose the driver at any position.

[0011] The processor may generate a plurality of patterns of the ideal driving information corresponding to a plurality of roads having different characteristics, thereby increasing the variation of diagnosis.

[0012] The processor may generate the ideal driving information using the vehicle information of a vehicle that has traveled on a road that is traveled at a frequency equal to or greater than a predetermined frequency, thereby enabling a diagnosis with higher accuracy than when using vehicle information of a vehicle that has traveled on a road that is traveled at a low frequency.

[0013] The processor also converts the ideal driving information for the vehicle model, and compares the vehicle information with the converted ideal driving information to diagnose the driver of the vehicle. So, It is possible to diagnose the driver taking into account the characteristics of the vehicle model.

[0014] The processor may also set the vehicle to be diagnosed to be autonomous and tune the autonomous driving using the diagnosis result, which makes it possible to tune the autonomous driving to suit regional characteristics. Effect of the Invention

[0015] As described above, according to the present invention, it is possible to provide a driving diagnosis device capable of diagnosing driving using appropriate driving diagnosis criteria. [Brief description of the drawings]

[0016] [Figure 1] 1 is a diagram showing a schematic configuration of a driving diagnosis system according to an embodiment of the present invention; [Diagram 2] 2 is a functional block diagram showing the functional configuration of an in-vehicle device and a cloud server in the driving diagnosis system according to the embodiment. FIG. [Diagram 3] 2 is a block diagram showing the configuration of a control unit of an in-vehicle device and a central processing unit of a cloud server. [Figure 4] 3A to 3C are diagrams for explaining a method for generating ideal driving information and a driving diagnosis method according to the first embodiment. [Diagram 5]5 is a flowchart showing an example of a flow of processing performed in an ideal driving information generating unit according to the first embodiment. [Figure 6] 5 is a flowchart showing an example of a flow of processing performed in a driving diagnosis unit according to the first embodiment. [Figure 7] 13 is a diagram for explaining a method for generating ideal driving information and a driving diagnosis method according to a second embodiment. FIG. [Figure 8] 10 is a flowchart showing an example of a flow of processes performed by an ideal driving information generating unit and a driving diagnosis unit according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] An example of an embodiment of the present invention will be described in detail below with reference to the drawings. In this embodiment, a driving diagnosis system for evaluating the driving of a driver will be described. Fig. 1 is a diagram showing a schematic configuration of the driving diagnosis system according to this embodiment.

[0018] In the driving diagnosis system 10 according to the present embodiment, an on-board device 16 mounted on a vehicle 14, a cloud server 12 as an example of a driving diagnosis device, and an external server 13 are connected via a communication network 18. In the driving diagnosis system 10 according to the present embodiment, vehicle information indicating the state of each vehicle 14 is transmitted to the cloud server 12, and the cloud server 12 accumulates the vehicle information. The cloud server 12 also acquires road information related to road specifications from the external server 13 and accumulates the information. Then, the cloud server 12 performs a process of diagnosing the driver's driving based on the accumulated information. The road specifications indicate the shape and characteristics of the road, and examples of the road information include, for example, the length of the road, the entrance angle, the exit angle, the vertical axis angle, the speed limit, the average speed, the junction type, traffic lights, and signs.

[0019] FIG. 2 is a functional block diagram showing the functional configuration of the vehicle-mounted device 16 and the cloud server 12 in the driving diagnosis system 10 according to this embodiment.

[0020] The vehicle-mounted device 16 includes a control unit 20, a vehicle information detection unit 22, an image capture unit 24, a communication unit 26, and a display unit .

[0021] The vehicle information detection unit 22 detects vehicle information related to the vehicle 14. Examples of the vehicle information include vehicle information such as the position information of the vehicle 14, vehicle speed, acceleration, steering angle, accelerator opening, distance to obstacles around the vehicle, and route. Specifically, the vehicle information detection unit 22 can apply a plurality of types of sensors and devices that acquire information indicating the state of the surrounding environment of the vehicle 14. Examples of the sensors and devices include sensors mounted on the vehicle 14 such as a vehicle speed sensor, a steering angle sensor, and an acceleration sensor, a GNSS (Global Navigation Satellite System) device, an in-vehicle communication device, a navigation system, and a radar device. The GNSS device receives GNSS signals from a plurality of GNSS satellites to measure the position of the vehicle 14. The positioning accuracy of the GNSS device improves as the number of GNSS signals that the GNSS device can receive increases. The in-vehicle communication device is a communication device that performs at least one of vehicle-to-vehicle communication with another vehicle 14 and road-to-vehicle communication with a roadside device via the communication unit 26. The navigation system includes a map information storage unit that stores map information, and performs processing such as displaying the position of the vehicle 14 on a map and guiding the route to the destination based on the position information obtained from the GNSS device and the map information stored in the map information storage unit. The radar device includes a plurality of radars with different detection ranges, detects objects such as pedestrians and other vehicles 14 present around the vehicle 14, and acquires the relative position and relative speed of the detected objects and the vehicle 14. The radar device also includes a processing device that processes the detection results of the surrounding objects. The processing device excludes noise, roadside objects such as guardrails, etc. from the monitoring target based on the changes in the relative position and relative speed with respect to each object included in the most recent detection results, and tracks and monitors pedestrians, other vehicles 14, etc. as the monitoring target objects. The radar device then outputs information such as the relative position and relative speed with respect to each monitoring target object. In this embodiment, vehicle information including at least one of these pieces of information is detected.

[0022] In this embodiment, the image capturing unit 24 is mounted on the vehicle 14, captures images of the vehicle periphery, such as the front of the vehicle 14, and generates moving image data representing the captured images of a moving image as image information. For example, a camera such as a drive recorder can be used as the image capturing unit 24. The image capturing unit 24 may further capture images of at least one of the vehicle periphery on the sides and rear of the vehicle 14. The image capturing unit 24 may further capture images of the interior of the vehicle cabin.

[0023] The communication unit 26 establishes communication with the cloud server 12 via the communication network 18, and transmits and receives information such as image information obtained by the photographing unit 24 and vehicle information detected by the vehicle information detection unit 22 as CAN (Controller Area Network) data.

[0024] The display unit 28 provides various information to the occupant by displaying information. In the present embodiment, for example, information provided by the cloud server 12 is displayed.

[0025] As shown in FIG. 3, the control unit 20 is composed of a general microcomputer including a CPU (Central Processing Unit) 20A, a ROM (Read Only Memory) 20B, a RAM (Random Access Memory) 20C, a storage 20D, an interface (I / F) 20E, and a bus 20F.

[0026] The control unit 20 performs control such as uploading the vehicle information detected by the vehicle information detection unit 22 to the cloud server 12. When uploading the vehicle information, identification information for identifying the vehicle 14 and the driver is added and transmitted. The information for identifying the driver may be, for example, a photographed image of the driver, identification information of a smart key carried by the driver, or other information capable of identifying the driver.

[0027] On the other hand, the cloud server 12 includes a central processing unit 30, a central communication unit 36, and a DB (database) 38.

[0028] As shown in FIG. 3, the central processing unit 30 is configured as a general microcomputer including a CPU 30A as an example of a processor, a ROM 30B, a RAM 30C, a storage 30D, an interface (I / F) 30E, and a bus 30F.

[0029] The central processing unit 30 has the following functions by the CPU 30A expanding a program stored in the ROM 30B into the RAM 30C and executing it.

[0030] That is, the central processing unit 30 has the functions of a vehicle information acquisition unit 44 , a road information acquisition unit 46 , an ideal driving information generation unit 48 , and a driving diagnosis unit 50 .

[0031] The vehicle information acquisition unit 44 acquires vehicle information detected by each of the vehicle-mounted devices 16 of the multiple vehicles 14 and stores the information in the DB 38. The collected vehicle information is information detected by various sensors mounted on the vehicles 14. The detected vehicle information may include, for example, information such as the position, speed, acceleration, vibration, and steering angle of the vehicle 14.

[0032] The road information acquisition unit 46 acquires road information related to road specifications from the external server 13 that manages road information. For example, road information such as the length of the road, the entrance angle, the exit angle, the vertical axis angle, the speed limit, the average speed, the junction type, traffic lights, and signs is acquired.

[0033] The ideal driving information generating unit 48 generates ideal driving information related to ideal driving of the representative vehicle 14 on the road corresponding to the road information. That is, the ideal driving information is generated as ideal vehicle information that serves as a reference for driving diagnosis. In this embodiment, the ideal driving information is generated using vehicle information of multiple vehicles.

[0034] The driving diagnosis unit 50 performs driving diagnosis by comparing the ideal driving information with the vehicle information of the vehicle 14 to be diagnosed. That is, since the ideal driving information is generated from the vehicle information of a plurality of vehicles, the driving diagnosis is performed relative to the other vehicles. For example, the driving diagnosis unit 50 performs driving diagnosis based on the magnitude of the difference between the ideal driving information and the vehicle information of the vehicle 14 to be diagnosed.

[0035] The central communication unit 36 ​​establishes communication with the vehicle-mounted device 16 via the communication network 18, and transmits and receives information such as image information and vehicle information.

[0036] The DB 38 stores vehicle information collected by a vehicle information acquisition unit 44 and road information collected by a road information acquisition unit 46 .

[0037] In addition, the cloud server 12 provides various services, such as a service of feeding back the driving diagnosis result of the driving diagnosis unit 50 to the driver.

[0038] (First embodiment) Here, a first embodiment of a method for generating ideal driving information by the ideal driving information generating unit 48 and a driving diagnosis method by the driving diagnosis unit 50 will be described. Fig. 4 is a diagram for explaining a method for generating ideal driving information and a driving diagnosis method of the first embodiment.

[0039] In this embodiment, driving diagnosis is performed by comparing the vehicle behavior distribution of the driver with the vehicle behavior of the vehicle 14 to be diagnosed at a plurality of representative road locations.

[0040] For example, as shown in FIG. 4, a plurality of representative road locations with different road specifications are determined, such as comparison locations 1 to 8. The representative roads are determined, for example, based on the size and curvature of the intersection. The representative road locations may be determined manually, or may be determined by extracting locations where road information such as the size and curvature of the intersection are of predetermined values. In addition, when determining the representative road locations, highly accurate driving diagnosis is possible by determining roads on which the vehicle 14 travels at a frequency equal to or higher than a predetermined threshold.

[0041] In addition, a plurality of vehicles 14 that have traveled along the determined representative road locations are set as teacher vehicles, vehicle information of the teacher vehicles is acquired, a distribution of the vehicle information of the teacher vehicles is obtained, and ideal travel information is generated based on the distribution. As an example, the average value of the distribution obtained is generated as the ideal travel information. Note that the ideal travel information is not limited to the average value, and the median value of the distribution may be generated as the ideal travel information, or another value may be used as the ideal travel information.

[0042] Then, for each representative road location, the generated ideal driving information is compared with the driving information of the vehicle 14 that is the subject of the driving diagnosis to diagnose the driver. Note that a comprehensive driving diagnosis may be performed based on the comparison results for each representative road location.

[0043] Next, specific processing performed by the cloud server 12 of the driving diagnosis system 10 according to the first embodiment will be described.

[0044] First, a specific process performed by the ideal driving information generating unit 48 will be described. Fig. 5 is a flowchart showing an example of the flow of the process performed by the ideal driving information generating unit 48 according to the first embodiment. Note that the process in Fig. 5 starts when, for example, an operator issues an instruction to generate ideal driving information.

[0045] In step 100, the CPU 30A performs a driving diagnosis location determination process, and proceeds to step 102. The driving diagnosis location determination process is performed, for example, by an operator designating a plurality of representative road locations such as comparison locations 1 to 8 as shown in Fig. 4. Alternatively, the locations may be determined by extracting locations where the size, curvature, etc. of an intersection have predetermined values ​​from the road information acquired by the road information acquisition unit 46.

[0046] In step 102, the CPU 30A focuses on one of the driving diagnosis points determined in step 100, and proceeds to step 104.

[0047] In step 104, the CPU 30A acquires vehicle information of the vehicle 14 that has passed through the driving diagnosis location, and proceeds to step 106. That is, the ideal driving information generating unit 48 extracts vehicle information of the teacher vehicle that has passed through the relevant location from the multiple pieces of vehicle information acquired by the vehicle information acquiring unit 44.

[0048] In step 106, the CPU 30A generates ideal driving information and proceeds to step 108. That is, the distribution of the vehicle information of the acquired teacher vehicle is obtained, and the ideal driving information is generated based on the distribution. As an example, the average value of the obtained distribution may be generated as the ideal driving information, or the median value of the distribution may be generated as the ideal driving information.

[0049] In step 108, the CPU 30A judges whether or not ideal driving information has been generated for all locations. If the judgment is negative, the process proceeds to step 110, and if the judgment is positive, the series of processes of the ideal driving information generating unit 48 ends.

[0050] In step 110, the CPU 30A focuses on another portion and returns to step 104 to repeat the above-mentioned processing.

[0051] Next, a specific process performed by the driving diagnosis unit 50 will be described. Fig. 6 is a flowchart showing an example of the flow of the process performed by the driving diagnosis unit 50 according to the first embodiment. Note that the process in Fig. 6 starts when, for example, the driver or an operator issues an instruction to start driving diagnosis.

[0052] In step 200, the CPU 30A focuses on one diagnosis point, and proceeds to step 202. That is, of the driving diagnosis points determined in step 100 described above, the CPU 30A focuses on one diagnosis point.

[0053] In step 202, the CPU 30A acquires ideal driving information, and the process proceeds to step 204. That is, the CPU 30A acquires ideal driving information generated in the process of FIG. 5, which corresponds to the diagnosis location.

[0054] In step 204, CPU 30A acquires vehicle information of the vehicle to be diagnosed, and proceeds to step 206. That is, vehicle information on a diagnosis portion of interest is acquired from the vehicle information of vehicle 14 to be diagnosed.

[0055] In step 206, CPU 30A compares the vehicle information of the diagnosis target with the ideal driving information, and proceeds to step 208. For example, a deviation from the ideal driving information, a deviation value, or the like may be calculated as the driving diagnosis result.

[0056] In step 208, the CPU 30A judges whether the comparison of all the points is completed. This judges whether the comparison of all the points determined as the driving diagnosis points in step 206 is completed. If the judgement is negative, the process proceeds to step 210, and if the judgement is positive, the process proceeds to step 212.

[0057] In step 210, CPU 30A focuses on another portion and returns to step 202 to repeat the above-mentioned processing.

[0058] On the other hand, in step 212, the CPU 30A outputs the diagnosis result and ends the series of processes. The diagnosis result is sent to the requester of the driving diagnosis, for example, to a mobile terminal device or a personal computer of the driver who drives the vehicle 14 to be diagnosed.

[0059] In this manner, in this embodiment, ideal driving information that serves as a standard for driving diagnosis is generated from the distribution of vehicle information of the instructor vehicle that has traveled on the representative road, and driving diagnosis is performed by comparing it with the vehicle information of the vehicle 14 to be diagnosed. This makes it possible to perform driving diagnosis using an appropriate driving diagnosis standard. In addition, it becomes possible to perform driving diagnosis without being affected by road shapes, etc.

[0060] Furthermore, although the way the vehicle 14 runs varies depending on the region, ideal driving information can be generated according to the characteristics of each region, making it possible to perform driving diagnosis according to the characteristics of the region. This can also be applied to autonomous driving, and tuning autonomous driving to suit the characteristics of the region can be achieved by tuning autonomous driving using the results of driving diagnosis.

[0061] Second embodiment Next, a second embodiment of a method for generating ideal driving information by the ideal driving information generating unit 48 and a driving diagnosis method by the driving diagnosis unit 50 will be described. Fig. 7 is a diagram for explaining a method for generating ideal driving information and a driving diagnosis method of the second embodiment.

[0062] In this embodiment, driving diagnosis is performed by comparing the vehicle behavior distribution of the driver on all roads.

[0063] Specifically, vehicle information such as the speed, accelerator opening, steering angle, and acceleration of the vehicle 14 changes depending on road specifications such as the road length, entrance angle, exit angle, vertical axis angle, speed limit, average speed, junction type, traffic lights, and signs.

[0064] Therefore, as a pre-processing step, a driver model is constructed to derive ideal driving information relating to the ideal driving of the representative vehicle 14 using road information relating to road specifications as an input.

[0065] The driver model is constructed by performing machine learning using as training data a data set of road information related to road specifications and vehicle information of the representative vehicle 14. For example, as shown in Fig. 7, a driver model 54 is constructed by training a learner 52 by machine learning such as a neural network using road information for a plurality of locations and vehicle information of the representative vehicle 14 when it traveled on a road corresponding to the road information as training data, as shown in Fig. 7.

[0066] As a result, vehicle information of the representative vehicle 14 at any position can be obtained as ideal driving information by inputting road information at any position to the driver model 54. That is, the ideal driving information generating unit 48 inputs road information at the position where the vehicle 14 to be diagnosed has traveled to the driver model 54, thereby generating ideal driving information at the position where the vehicle 14 to be diagnosed has traveled.

[0067] Then, the driving diagnosis unit 50 compares the generated ideal driving information with the vehicle information of the vehicle 14 to be diagnosed, thereby making it possible to perform driving diagnosis in the same manner as in the above embodiment.

[0068] Next, specific processing performed by the cloud server 12 of the driving diagnosis system 10 according to the second embodiment will be described. Fig. 8 is a flowchart showing an example of the flow of processing performed by the ideal driving information generating unit 48 and the driving diagnosis unit 50 according to the second embodiment. Note that the processing in Fig. 8 starts when, for example, a driver or an operator issues an instruction to start driving diagnosis.

[0069] In step 300, the CPU 30A acquires vehicle information of the vehicle 14 to be diagnosed, and proceeds to step 302. That is, the ideal driving information generating unit 48 acquires the vehicle information of the vehicle 14 to be diagnosed from the multiple pieces of vehicle information acquired by the vehicle information acquiring unit 44.

[0070] In step 302, the CPU 30A acquires road information of the road on which the vehicle 14 to be diagnosed has traveled, and proceeds to step 304. That is, the ideal driving information generating unit 48 acquires the road information of the road on which the vehicle 14 to be diagnosed has traveled, from the road information acquired by the road information acquiring unit 46.

[0071] In step 304, the CPU 30A generates ideal driving information from the road information, and proceeds to step 306. That is, the ideal driving information generating unit 48 inputs the road information at the position of the vehicle to be diagnosed to the driver model 54, thereby generating ideal driving information at the position of the vehicle 14 to be diagnosed.

[0072] In step 306, the CPU 30A compares the vehicle information of the vehicle to be diagnosed with the ideal driving information, and proceeds to step 308. That is, the driving diagnosis unit 50 performs driving diagnosis by comparing the vehicle information of the vehicle 14 to be diagnosed acquired in step 300 with the ideal driving information generated in step 304. For example, the deviation from the ideal driving information, a deviation value, or the like may be calculated as the driving diagnosis result.

[0073] In step 308, the CPU 30A outputs the diagnosis result and ends the series of processes. The diagnosis result is sent to the requester of the driving diagnosis, for example, to a mobile terminal device or a personal computer of the driver who drives the vehicle 14 to be diagnosed.

[0074] In this manner, in this embodiment, ideal driving information at the position of the vehicle 14 to be diagnosed is generated using the driver model 54, and driving diagnosis is performed by comparing it with the vehicle information of the vehicle 14 to be diagnosed. This makes it possible to perform driving diagnosis using appropriate driving diagnosis standards, as in the above embodiment. In addition, it becomes possible to perform driving diagnosis without being affected by road shapes, etc.

[0075] Furthermore, although the way the vehicle 14 runs varies depending on the region, if the driver model 54 is constructed for each region, ideal driving information according to the characteristics of each region can be generated, enabling driving diagnosis according to the characteristics of the region. This can also be applied to autonomous driving, and tuning autonomous driving to suit the characteristics of the region can be achieved by tuning autonomous driving using the results of driving diagnosis.

[0076] In addition, in the processing example of FIG. 8, driving diagnosis is performed at one location, but road information for multiple locations may be acquired to generate multiple patterns of ideal driving information and perform driving diagnosis for the multiple locations. In this case, comprehensive driving diagnosis may be performed based on the comparison results between the vehicle information of the vehicle 14 to be diagnosed at multiple locations and the ideal driving information. In addition, when constructing the driver model 54, highly accurate driving diagnosis is possible by acquiring road information for roads on which the vehicle 14 travels at a predetermined frequency or more.

[0077] In the above embodiment, the driving diagnosis is performed by comparing the ideal driving information with the vehicle information of the diagnosis target without considering the vehicle type, but this is not limited to this. Since the turning angle according to the steering angle and the speed according to the accelerator opening degree are different for each vehicle type, the ideal driving information corresponding to the vehicle type of the diagnosis target may be generated. For example, in the first embodiment, the distribution of vehicle information of the teacher vehicle for each vehicle type of the diagnosis target is obtained to generate the ideal driving information. In addition, in the second embodiment, the driver model 54 is constructed for each vehicle type to generate the ideal driving information. For a vehicle type with a small number of teacher vehicles, the ideal driving information generated without limiting the vehicle type may be converted for the vehicle type. The conversion of the ideal driving information for the vehicle type may be performed by, for example, deriving a coefficient for conversion in advance and converting the ideal driving information for the vehicle type using the coefficient.

[0078] In addition, the processing performed by the cloud server 12 in each of the above embodiments has been described as software processing performed by executing a program, but is not limited to this. For example, the processing may be performed by hardware such as a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), and an FPGA (Field-Programmable Gate Array). Alternatively, the processing may be a combination of both software and hardware. In addition, when the processing is performed by software, the program may be stored in various storage media and distributed.

[0079] Furthermore, the present invention is not limited to the above, and it goes without saying that various modifications can be made without departing from the spirit and scope of the present invention. [Explanation of symbols]

[0080] 10 Driving diagnostic system 12 Cloud server (driving diagnostic device) 30 Central Processing Unit 30A CPU (processor) 52 Learning Unit 54 Driver Model

Claims

1. a processor, the processor comprising: Generate ideal driving information relating to an ideal driving of a representative vehicle corresponding to road specifications; Converting the ideal driving information for a vehicle type of a vehicle to be diagnosed; A driving diagnosis device that acquires vehicle information relating to driving from the vehicle, and compares the acquired vehicle information with the converted ideal driving information to diagnose the driver of the vehicle.

2. The processor generates the ideal driving information using a distribution of the vehicle information acquired from a plurality of vehicles that have traveled along a predetermined representative road; The driving diagnosis device according to claim 1 , wherein the diagnosis is performed by acquiring the vehicle information from the vehicle to be diagnosed when the vehicle travels on the representative road.

3. 2. The driving diagnosis device according to claim 1, wherein the processor uses a driver model that has been trained in advance using a plurality of road information relating to specifications of roads with different specifications and the vehicle information acquired from a vehicle that has traveled on a road corresponding to the plurality of road information as teacher data to derive the vehicle information corresponding to the road information of a road on which a vehicle to be diagnosed has traveled, thereby generating the derived vehicle information as the ideal driving information.

4. 4. The driving diagnosis device according to claim 1, wherein the processor generates a plurality of patterns of the ideal driving information corresponding to a plurality of roads having different specifications.

5. The driving diagnosis device according to claim 3 , wherein the processor generates the ideal driving information by using the vehicle information of a vehicle that has traveled on a road with a frequency equal to or greater than a predetermined frequency.

6. The driving diagnosis device according to any one of claims 1 to 5, wherein the processor causes the vehicle to be diagnosed to operate autonomously and tunes the autonomous driving using a diagnosis result.

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