Control device for driver training device

Abstract

To allow a driver to experience an appropriate driving operation in accordance with driving characteristics of each driver, thereby improving driving skills of the drivers.SOLUTION: A control apparatus to be applied to a driving training apparatus that allows a trainee to perform driving operation while displaying a running scene video on a display device is configured to: acquire information on driving characteristics related to driving orientation or tendency of driving operation of the trainee; select a model driver similar to the characteristics of the trainee from among a plurality of model drivers stored in advance; set driving operation of the model driver, as model driving operation; display a running scene video on the display device; and present at least one of operation of an operation pedal, operation of a steering wheel, and a gazing position, in the model driving operation, to the trainee so that the trainee may experience the model driving operation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a control device for a driving training device that allows a trainee to experience model vehicle driving actions to improve driving skills. [Background technology]

[0002] Conventionally, there has been known a driving simulator device used for training and evaluation of vehicle driving skills. For example, Patent Document 1 discloses a driving course selection system including an operation information database that stores driving operation information of a trainee, a driving skill determination means that acquires the driving operation information from the operation information database and compares the driving operation information with a predetermined reference value to determine the driving skill, a traffic law result database that stores results of a traffic law test, and a driving course database that stores training course information, and a driving course selection means that selects training course information suitable for the trainee based on the determined driving skill and results of the traffic law test.

[0003] Furthermore, Patent Document 2 discloses a driving training system that uses a driving simulator and includes a driving operation information acquisition means for acquiring information on operating devices corresponding to the driving operations performed by the trainee in a predetermined standard training program, a driving operation determination means for determining which driving operations the trainee is weak at by comparing the information with predetermined reference values, a training program creation means for creating a dedicated training program that includes many images of situations where the determined driving operations are required, and an image control means for projecting images of the created dedicated training program onto a screen. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2004-361782 [Patent Document 2] Japanese Patent Application Laid-Open No. 2003-263098 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the systems disclosed in Patent Documents 1 and 2 teach drivers by displaying courses and traffic scenes that include weak points that the driver needs to learn and having the driver perform driving operations, making it difficult for the driver to understand what driving operations are appropriate. Also, driving characteristics such as the driving style that a driver aims for when driving a vehicle and the way they perceive the burden of driving vary from driver to driver. However, the systems disclosed in Patent Documents 1 and 2 select training courses and traffic scenes without taking into consideration the individual driver's driving inclinations and driving behavior tendencies, so there is a risk that the systems may not be able to teach the optimal driving operations for each driver.

[0006] The present disclosure has been made in consideration of the above problems, and an object of the present disclosure is to provide a control device for a driving training device that allows a driver to relive appropriate driving actions that are tailored to the driving characteristics of each driver, thereby improving the driver's driving skills. [Means for solving the problem]

[0007] In order to solve the above problems, according to one aspect of the present disclosure, there is provided a control device applicable to a driving training device that has a trainee drive a vehicle while displaying an image of a driving scene on a display device, the control device including one or more processors and one or more memories communicably connected to the one or more processors, wherein the processor acquires information on driving characteristics related to the trainee's driving inclination or driving behavior tendencies, selects a model driver who is similar to the trainee's driving characteristics from a plurality of model drivers stored in advance, sets the driving behavior of the model driver as model driving behavior, displays an image of the driving scene on the display device, and presents to the trainee at least one of operation of the control pedals, operation of the steering wheel, or eye position in the model driving behavior, thereby allowing the trainee to experience the model driving behavior. [Effects of the Invention]

[0008] As described above, according to the present disclosure, the driver can relive appropriate driving actions that are tailored to the driving characteristics of each driver, thereby improving the driver's driving skills. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a schematic diagram illustrating a configuration example of a driving training device according to a first embodiment of the present disclosure. [Figure 2] FIG. 2 is a block diagram showing an example of the configuration of a control device of the driving training apparatus according to the embodiment. [Figure 3] 3 is an explanatory diagram showing an example of data of driving characteristics recorded in a driving characteristics database according to the embodiment; FIG. [Figure 4] 4 is a flowchart showing a main routine of a processing operation executed by the control device according to the embodiment; [Figure 5] 10 is a flowchart showing a process of selecting a model driver performed by the control device according to the embodiment. [Figure 6] 10 is a flowchart showing a process for setting a re-experience item by the control device according to the embodiment. [Figure 7] 4 is an explanatory diagram showing data of the calculation results of the deviation degree and the driving skill level by the control device according to the embodiment. FIG. [Figure 8] FIG. 10 is a block diagram illustrating a configuration example of a driving training device according to a second embodiment of the present disclosure. [Figure 9] 4 is a flowchart showing a processing operation executed by the control device according to the embodiment. [Figure 10] 10 is a flowchart showing a processing operation of a modified example of the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In this specification and drawings, components having substantially the same functional configurations are designated by the same reference numerals, and redundant description will be omitted.

[0011] <<1. First Embodiment>> <1-1. Overall configuration of the driver training device> First, an example of the overall configuration of a driving training device according to a first embodiment of the present disclosure will be described.

[0012] 1 is a schematic diagram showing an example of the configuration of a driving training device 1 according to this embodiment. The driving training device 1 is constructed as a device that trains the trainee H in driving behavior by displaying on a head-mounted display (HMD) 29 an image of the trainee H's field of vision of a vehicle traveling on an experience course in accordance with the trainee H's simulated driving behavior, thereby improving the driving skill of the trainee H. The driving training device 1 includes a seat 11, a steering wheel 17, and operating pedals (accelerator pedal 21, brake pedal 23), and is configured to simulate a driver's seat of a vehicle.

[0013] The driving training device 1 includes a monitor device 13, a speaker system 15, an input device 27, a head-mounted display (HMD) 29, and a control device 50. The control device 50 is configured with one or more processors and one or more memories communicatively connected to the processors, and executes various arithmetic processes. The input device 27 is operated by a user, including the trainee H, and accepts input operations for the control device 50. The input device 27 may be a touch panel display, or may include at least one of a keyboard, a mouse, a button, and a switch. The input device 27 may also be an audio input device equipped with a microphone that receives the user's voice.

[0014] The HMD 29 is an image display device worn on the head of the trainee H. In this embodiment, the driving training device 1 is configured as a device that presents an image of a trial course to the trainee H using VR (Virtual Reality) technology, and the HMD 29 displays a VR image. That is, the HMD 29 includes a display panel, an angular velocity sensor, and an HMD driver (not shown), and displays the surrounding environment of the vehicle traveling on the trial course according to the orientation of the trainee H's face. The HMD 29 also includes a gaze detection sensor that detects the direction of the trainee H's gaze. The gaze detection sensor detects the movement of the trainee H's eyeballs, pupils, or eyelids, converts the gaze position into two-dimensional coordinates on the display panel, and outputs the coordinates. The gaze detection sensor may be, for example, a sensor that detects the movement of the eyeballs, pupils, or eyelids based on image data captured by a camera, or a sensor that detects the movement of the eyeballs or pupils based on reflected light obtained by irradiating the eyeballs with infrared light. Sensor signals from the angular velocity sensor and gaze detection sensor are transmitted to the control device 50.

[0015] The speaker system 15 includes five speakers 15a to 15e in total, located in the front, left and right, and left and right rear portions, and is configured as a stereophonic speaker system 15 that outputs sound by being driven by the control device 50, so that sound can be heard from the position of a virtual sound source. Note that the number of speakers included in the speaker system 15 is not particularly limited. Furthermore, if the HMD 29 includes headphones, the speaker system 15 may be omitted.

[0016] The monitor device 13 includes speakers 13a and 13b, and is driven by the control device 50 to output sound and display images. The speakers 13a and 13b of the monitor device 13 may be omitted. In this embodiment, the monitor device 13 is an image display device that mainly presents the image displayed on the HMD 29 to an instructor and the like other than the trainee H, and may be omitted.

[0017] The steering wheel 17, accelerator pedal 21, and brake pedal 23 are each configured to be operable by the trainee H. The steering wheel 17, accelerator pedal 21, and brake pedal 23 are equipped with a steering angle sensor 17a, accelerator sensor 21a, and brake sensor 23a, which detect the amount of operation, respectively. Sensor signals from the steering angle sensor 17a, accelerator sensor 21a, and brake sensor 23a are transmitted to the control device 50. For example, the steering angle sensor 17a, accelerator sensor 21a, and brake sensor 23a are each rotation sensors which detect the rotation angle, and are configured to be able to detect the left and right steering angle of the steering wheel 17 and the amount of depression of the accelerator pedal 21 and brake pedal 23.

[0018] The steering wheel 17, accelerator pedal 21, and brake pedal 23 are each provided with a steering wheel drive unit 17b, an accelerator pedal drive unit 21b, and a brake pedal drive unit 23b, and are configured so that the operation amounts of the steering wheel 17, accelerator pedal 21, and brake pedal 23 can be controlled by the control device 50. For example, the accelerator pedal drive unit 21b and the brake pedal drive unit 23b are each a stepping motor, and are configured so that the left and right steering angle of the steering wheel 17 and the depression amount of the accelerator pedal 21 and the brake pedal 23 can be controlled, respectively.

[0019] <1-2.Control device> Next, the control device 50 of the driving training device 1 according to the first embodiment will be specifically described.

[0020] (1-2-1. Configuration example) 2 is a block diagram showing an example of the configuration of the control device 50 of the driving training device 1 according to this embodiment. The control device 50 includes a processing unit 51, a storage unit 53, an experience information database 55, and a driving characteristic database 57. The storage unit 53, the experience information database 55, and the driving characteristic database 57 are communicably connected to the processing unit 51. The processing unit 51 includes one or more processors such as CPUs (Central Processing Units). A part or all of the processing unit 51 may be configured with updatable firmware or the like, or may be a program module or the like executed by commands from the CPU or the like.

[0021] The processing unit 51 is communicatively connected to a steering angle sensor 19a and a steering drive unit 19b provided on the steering wheel 19, an accelerator sensor 21a and an accelerator pedal drive unit 21b provided on the accelerator pedal 21, and a brake sensor 23a and a brake pedal drive unit 23b provided on the brake pedal 23. The processing unit 51 is also communicatively connected to an angular velocity sensor 29a, an HMD drive unit 29b, and a gaze detection sensor 21c provided on the HMD 29. The processing unit 51 is also communicatively connected to the monitor device 13, the speaker system 15, and the input device 27.

[0022] The storage unit 53 is configured by a recording medium (memory) such as a RAM (Random Access Memory) or a ROM (Read Only Memory). However, there is no particular limitation on the number or type of storage units 53. The storage unit 53 records information such as computer programs executed by the processing unit 51, various parameters used in arithmetic processing, detection data, and arithmetic results.

[0023] (1-2-2. Database) The experience information database 55 and the driving characteristics database 57 are each configured by a storage element such as a RAM or a ROM, or a recording medium such as a hard disk drive (HDD), a compact disk (CD), a digital versatile disk (DVD), a solid state drive (SSD), a USB flash drive, or a storage device. However, the type of recording medium is not particularly limited. The experience information database 55 and the driving characteristics database 57 may be configured integrally with the control device 50, or may be stored in a server that can communicate with the control device 50 via wireless or wired communication means. Furthermore, the experience information database 55 and the driving characteristics database 57 may be configured as a single database.

[0024] (Experience information database) The experience information database 55 is a database that records data on exemplary driving on training courses. The training course data includes data on at least one training course, allowing the trainee H to experience various driving movements. The training course data includes multiple course data on different road types, such as courses for driving in urban areas, courses for driving on urban expressways, courses for driving on intercity expressways, courses for driving in mountainous areas, or parking lots, with speed limits, road widths, lane widths, road straightness or degree of turning, and parking space sizes. The training course data may also include multiple course data on different traffic environments, such as the volume of traffic and brightness of surrounding vehicles or pedestrians, weather conditions, and blind spots. Each training course is recorded in association with information on the driving scene items that can be experienced.

[0025] The model driving data for each training course is data on the driving behavior of multiple model drivers with high driving skills when they simulate driving each training course, and includes data on the amount of steering wheel operation, the amount of accelerator pedal operation, the amount of brake pedal operation, and the direction of the driver's eyes. The model driving data may also include data related to other driving behaviors. Each model driving data is recorded in association with the identification information of the model driver. The identification information may be any information that can be distinguished from one another, such as numbers or symbols.

[0026] (Driving characteristics database) The driving characteristics database 57 is a database that records data on the driving characteristics of the model driver who performed the driving behavior of each model driving data recorded in the experience information database 55. Here, "driving characteristics" refers to personal characteristics related to driving inclinations and driving behavior tendencies, such as driving style and perceived burden of driving. For example, multiple items are pre-set for driving style, such as "wanting to observe the speed limit," "wanting to maintain a sufficient distance from the vehicle ahead," "wanting to sufficiently slow down before entering a curve," "wanting to yield to the vehicle behind as much as possible," "wanting to move forward as much as possible even if it means changing lanes," and "wanting to keep the distance from the vehicle ahead as much as possible." In addition, multiple items are pre-set for perceived burden of driving, such as "roads with a lot of on-street parking," "driving late at night," "roads with many blind spots," "situations with many fast-moving vehicles," and "situations with heavy traffic," based on the assumption of what situations would be most burdensome.

[0027] FIG. 3 is an explanatory diagram showing an example of driving characteristic data recorded in the driving characteristic database 57. For each of Driver A, Driver B, Driver C, etc., who performed the driving behavior of the model driving data, evaluations of each item set as "driving style" and "burdensome situations" are recorded. The evaluations of each item of "driving style" and "burdensome situations" are self-evaluations on a five-point scale, with "5" indicating a high degree of applicability and "1" indicating a low degree of applicability. However, the number of levels (scales) for self-evaluation is not limited to five. The driving characteristic data shown in FIG. 3 is collected and recorded from the answers of each driver to questions presented on questionnaires such as the well-known Driving Style Questionnaire (DSQ) or Workload Sensitivity Questionnaire (WSQ), dedicated websites, or input devices 27, or from the results of driving aptitude tests conducted by public institutions.

[0028] Furthermore, the "driving characteristic type" is an evaluation result obtained by evaluating each of the items "driving style" and "situation where burden is felt" based on a preset evaluation, and is classified as either "impatient," "cautious," or "neither (-)." For example, the evaluation of the items "wanting to go as far ahead as possible even if it means changing lanes" and "wanting to close the distance between the vehicle in front as much as possible" are items that can positively evaluate an "impatient" driving characteristic. The evaluation of the other items are items that can negatively evaluate an "impatient" driving characteristic, in other words, items that can positively evaluate a "cautious" driving characteristic. Therefore, the driver's driving characteristic type can be classified according to the total value obtained by adding the evaluation of the items "wanting to go as far ahead as possible even if it means changing lanes" and "wanting to close the distance between the vehicle in front as much as possible" and subtracting the evaluation of the other items. Each item may be weighted and multiplied by a predetermined coefficient.

[0029] The classification of driving characteristic types may include more classifications than "impatient," "cautious," or "neither (-)." Furthermore, the means for collecting driving characteristic data is not limited to the above examples. For example, data on the driving behavior of each driver when they perform a simulated drive using the driving training device 1 or data on the driving behavior when they drive a real vehicle may be recorded, and the data on driving characteristics may be collected by automatically or manually determining whether each item applies or not in light of preset criteria, or by machine learning.

[0030] (1-2-3. Functional configuration of the processing unit) 2, the processing unit 51 of the control device 50 includes a driving characteristic determination unit 61, an experience information setting unit 62, a driving action detection unit 63, a re-experiencing condition setting unit 64, an output control unit 65, an action control unit 66, and a driving skill determination unit 67. Each of these units may have a function realized by execution of a computer program by a processor such as a CPU, or may be configured partially or entirely by analog circuits. Below, the function of each unit of the processing unit 51 will be briefly described, and then the processing operation of the processing unit 51 will be specifically described.

[0031] (Driving characteristics determination unit) The driving characteristic determination unit 61 acquires information about the driving characteristics of the trainee H, and executes a process of selecting a model driver whose driving characteristics are similar to those of the trainee H by referring to data on model drivers recorded in the driving characteristic database 57. The driving characteristic determination unit 61 acquires information about the driving characteristics of the trainee H based on data on the answers of each driver to questions presented on a questionnaire such as a publicly known Driving Style Check Sheet (DSQ) or Driving Stress Susceptibility Sheet (WSQ), a dedicated website, or via the input device 27. In this embodiment, the driving characteristic determination unit 61 evaluates each item shown in FIG. 3 based on a preset evaluation formula, and classifies the driving characteristics of the trainee H as "impatient," "cautious," or "neither." Then, the driving characteristic determination unit 61 selects a driver whose driving characteristics correspond to those of the trainee H as a model driver from among the drivers recorded in the driving characteristic database 57.

[0032] (Experience Information Setting Section) The experience information setting unit 62 executes a process of selecting model driving data to be experienced by the trainee H from the model driving data recorded in the experience information database 55. The experience information setting unit 62 selects data of model driving performed by the model driver selected by the driving characteristics determination unit 61 as model driving data to be experienced by the trainee H. The experience information setting unit 62 may randomly select a training course, or may select a training course that allows the trainee H to experience driving scenes that are difficult for the trainee H or driving scenes that the trainee H has little experience driving. The driving scenes that the trainee H has little experience driving may be selected by the trainee H from options displayed on the monitor device 13, for example. Alternatively, a course containing various driving scenes may be displayed, and the trainee H may be made to perform simulated driving actions to collect driving action data, and the driving scenes that the trainee H has little experience driving may be determined to be driving scenes that the trainee H has little experience driving.

[0033] (Driving behavior detection unit) The driving action detection unit 63 executes a process for detecting the driving action of the trainee H. The driving action detection unit 63 acquires sensor signals transmitted from the steering angle sensor 19a, accelerator sensor 21a, and brake sensor 23a, detects the left and right steering angles of the steering wheel 17, and the operation amounts of the accelerator pedal 21 and the brake pedal 23, and records these in the storage unit 53 as time-series data associated with the travel distance of the training course. The driving action detection unit 63 also acquires sensor signals transmitted from the angular velocity sensor 29a and gaze detection sensor 29c, detects the gaze position of the trainee H on the training course displayed on the HMD 29, and records these in the storage unit 53 as time-series data.

[0034] (Re-experience condition setting section) The re-experiencing condition setting unit 64 executes processing for setting items to be re-experienced by the trainee H from the model driving data selected by the experience information setting unit 62. In this embodiment, the re-experiencing condition setting unit 64 is configured to have the trainee H drive a simulated driving course set in advance, compare the model driving behavior of a model driver whose driving characteristics are similar to those of the trainee H with the driving behavior of the trainee H, and preferentially set items with a large degree of discrepancy as re-experience items. Furthermore, in this embodiment, the re-experiencing condition setting unit 64 is configured to set more re-experience items the higher the driving skill of the trainee H.

[0035] Here, "re-experiencing" refers to having the trainee H perform simulated driving actions in accordance with the training course while presenting at least one of the operation of the control pedals, the operation of the steering wheel, or the eye position in the model driving actions to the trainee H, thereby allowing the trainee H to experience the model driving actions. For example, with the trainee H's hands and feet fixed to the steering wheel, accelerator pedal, and brake pedal, the steering drive unit 19b, the accelerator pedal drive unit 21b, and the brake pedal drive unit 23b are controlled to present the steering operation, accelerator operation, and braking operation of the model driving to the trainee H, and the trainee H moves his hands and feet in accordance with the controlled pedal operations, thereby allowing the trainee H to experience the steering operation, accelerator operation, and braking operation of the model driving. Alternatively, the HMD drive unit 29b is controlled to visually present the steering operation amount, accelerator operation amount, and braking operation amount of the model driving to the trainee H, and the trainee H moves his hands and feet in accordance with the presented pedal operations, thereby allowing the trainee H to experience the steering operation, accelerator operation, and braking operation of the model driving. In addition, the HMD driving unit 29b is controlled to visually present the gaze position of exemplary driving to the trainee H, and the trainee H moves his / her gaze to match the presented gaze position, allowing the trainee H to experience the gaze position of exemplary driving.

[0036] In certain technical fields, such as the medical field, a method is used in which a trainee relives the physical movements of an expert using VR technology or a vibration-generating motor. Conventional re-experiencing methods allow a trainee to relive a skill that is assumed to involve repeating a series of fixed actions. On the other hand, in the case of vehicle driving skills, which require a trainee to perform multiple actions in succession depending on the conditions of the vehicle and the surrounding environment, re-experiencing multiple pieces of information at once may prevent the trainee H from grasping all the information, and may not be effective in improving the trainee's driving skills. For this reason, in this embodiment, items that show a large discrepancy between the trainee H's driving behavior and the model driver's model driving behavior are prioritized as re-experiment items, and the higher the trainee H's driving skill, the more re-experiment items are set, thereby enhancing the effect of improving the trainee's driving skills.

[0037] (Output control section) The output control unit 65 controls the driving of the monitor device 13, the speaker system 15, and the HMD 29, and executes processing to control the output of images and sounds. In this embodiment, the output control unit 65 transmits model driving data selected by the experience information setting unit 62 to the HMD driving unit 29b, and causes the HMD 29 to display an image of the field of view of the trainee H while driving on the training course. The HMD driving unit 29b changes the image of the field of view according to the orientation and position of the HMD 29 detected by the angular velocity sensor 29a. In addition, the output control unit 65 causes the speaker system 15 to output ambient sounds, such as the sounds of other vehicles traveling, present in the training course data as stereophonic sound.

[0038] Furthermore, when the trainee H is to be visually presented with the amount of operation of any of the steering wheel 17, accelerator pedal 21, and brake pedal 23 by the model driver in the model driving action, the output control unit 65 transmits data on the amount of operation of the steering wheel 17, accelerator pedal 21, and brake pedal 23 by the model driver to the HMD driving unit 29b, causing the amount of operation of the steering wheel 17, accelerator pedal 21, and brake pedal 23 to be presented in the image in the field of view. Furthermore, when the trainee H is to be allowed to relive the gaze position of the model driver in the model driving action, the output control unit 65 transmits data on the gaze position of the model driver to the HMD driving unit 29b, causing the HMD driving unit 29b to present the gaze position in the image in the field of view. In this case, the output control unit 65 functions as a part of the re-experiencing control unit. At the same time, the output control unit 65 acquires the output data of the image from the HMD driving unit 29b and drives the monitor device 13 to display the image displayed on the HMD 29 on the monitor device 13.

[0039] (Motion control unit) The action control unit 66 controls the steering drive unit 19b, the accelerator pedal drive unit 21b, and the brake pedal drive unit 23b based on data of the model driving actions of the model driver who simulates driving the training course, and executes processing to drive at least one of the steering wheel 17, the accelerator pedal 21, and the brake pedal 23. In this case, the action control unit 66 functions as part of the re-experiencing control unit. This allows the trainee H to re-experience the model driving actions of the model driver. In this embodiment, the action control unit 66 causes the trainee H to re-experience the actions of the re-experience items set by the re-experience condition setting unit 64.

[0040] (Driving Skill Assessment Department) The driving skill determination unit 67 executes a process of determining the driving skill of the trainee H based on the driving behavior using the driving training device 1. In this embodiment, the steering angle of the steering wheel 17, the operation amount of the accelerator pedal 21 and the brake pedal 23, and the eye position when the trainee H performs a simulated driving behavior are compared with data on the steering angle of the steering wheel 17, the operation amount of the accelerator pedal 21 and the brake pedal 23, and the eye position of a model driver, and the degree of deviation is found to determine the driving skill of the trainee H. However, the method of evaluating driving skill is not limited to the above example.

[0041] <1-3. Operation of the control device> Next, an example of the processing operation by the processing unit 51 of the control device 50 of the driving training device 1 according to the first embodiment will be specifically described.

[0042] FIG. 4 is a flowchart showing the main routine of the processing executed by the processing unit 51. First, the driving characteristic determination unit 61 of the processing unit 51 acquires information on the driving characteristics of the trainee H (step S11). Specifically, the driving characteristic determination unit 61 acquires information on the driving characteristics of the trainee H based on data on the answers of each driver to questions presented on a questionnaire such as the DSQ or WSQ that the trainee H has filled out in advance, a dedicated website, or the input device 27. In this embodiment, the driving characteristic determination unit 61 acquires data on self-evaluation on a five-point scale for each of the items such as those exemplified in Fig. 3. Note that the number of levels (stages) for self-evaluation is not limited to five.

[0043] Next, the experience information setting unit 62 of the processing unit 51 sets a training course for the trainee H to perform simulated driving actions (step S13). The experience information setting unit 62 may select a training course randomly, or may select a training course that allows the trainee H to experience driving scenes that the trainee H is not good at or that the trainee H has little experience driving. The driving scenes that the trainee H is not good at or that the trainee H has little experience driving may be selected by the trainee H from options displayed on the monitor device 13, for example. Alternatively, a training course including driving scenes that the trainee H is likely to feel burdened by may be selected based on the acquired information on the driving characteristics of the trainee H. Furthermore, a course including various driving scenes may be displayed, the trainee H is made to perform simulated driving actions, data on driving actions may be collected, and the driving scenes that the trainee H is not good at or that the trainee H has little experience driving may be determined by comparing the data with preset reference data. If only one training course is pre-stored in the experience information database 55, the step of setting the training course may be omitted.

[0044] Next, the driving characteristic determination unit 61 selects an exemplary driver for the trainee H (step S15). FIG. 5 is a flowchart showing an example of the process of selecting an exemplary driver. The driving characteristic determination unit 61 calculates a rating X for "impatient driving characteristic" based on the acquired information on the driving characteristics of the trainee H (step S31). For example, the driving characteristic question items are classified in advance into points that can be added and points that can be subtracted according to their contents, and the driving characteristic determination unit 61 calculates the rating X by adding or subtracting the evaluation of each question item. At this time, weighting may be performed according to the content of the question, and the evaluation of each question item may be multiplied by a predetermined coefficient before adding or subtracting.

[0045] Next, the driving characteristic determination unit 61 determines whether the calculated score X is less than a preset first threshold value α (step S33). The first threshold value α is a threshold value for determining that the type of driving characteristic of the trainee H is "careful", and is set to a value equivalent to 30 (relative value) when the score Xmin is 0 (relative value) when all question items are evaluated as "1" and the score Xmax is 100 (relative value) when all question items are evaluated as "5". Note that the value of the first threshold value α is not limited to the above example.

[0046] If the score X is less than the first threshold value α (S33 / Yes), the driving characteristic determination unit 61 determines that the type of driving characteristic of the trainee H is "careful" (step S35). Next, the driving characteristic determination unit 61 selects a driver whose driving characteristic type is "careful" as an exemplary driver from among the exemplary drivers recorded in the driving characteristic database 57 (step S37).

[0047] On the other hand, if the score X is equal to or greater than the first threshold value α (S33 / No), the driving characteristic determination unit 61 determines whether the score X exceeds a preset second threshold value β (step S39). The second threshold value β is a threshold value for determining that the type of driving characteristic of the trainee H is "impatient", and is set to a value equivalent to 70 (relative value) when, for example, the score Xmin is 0 (relative value) when the score for all question items is "1", and the score Xmax is 100 (relative value) when the score for all question items is "5". Note that the value of the second threshold value β is not limited to the above example.

[0048] If the score X exceeds the second threshold value β (S39 / Yes), the driving characteristic determination unit 61 determines that the type of driving characteristic of the trainee H is "impatient" (step S41). Next, the driving characteristic determination unit 61 selects a driver whose driving characteristic type is "impatient" as an exemplary driver from among the exemplary drivers recorded in the driving characteristic database 57 (step S43).

[0049] On the other hand, if the score X is equal to or less than the second threshold value β (S39 / No), that is, if the score X is equal to or greater than the first threshold value α and equal to or less than the second threshold value β, the driving characteristic determination unit 61 determines that the type of driving characteristic of the trainee H is "neither" (step S45). Next, the driving characteristic determination unit 61 selects a driver whose driving characteristic type is "neither" as an exemplary driver from among the exemplary drivers recorded in the driving characteristic database 57 (step S47).

[0050] Returning to FIG. 4, next, the experience information setting unit 62 selects, from the model driving data recorded in the experience information database 55, the driving behavior data of the model driver selected in step S15 when he or she simulated driving the training course set in step S13 as model driving data (step S17).

[0051] Next, the processing unit 51 causes the trainee H to perform a test drive using the training course data set in step S13 (step S19). Specifically, the output control unit 65 of the processing unit 51 transmits the training course data to the HMD driving unit 29b and causes the HMD 29 to display an image of the training course. While the test drive is being performed, the HMD driving unit 29b of the HMD 29 changes the image in the field of view according to the orientation and position of the HMD 29 detected by the angular velocity sensor 29a. In addition, the output control unit 65 causes the speaker system 15 to output ambient sounds, such as the sounds of other vehicles traveling, present in the training course data as stereophonic sound.

[0052] Next, the driving action detection unit 63 of the processing unit 51 collects measurement data of the driving actions of the trainee H during the test drive (step S21). Specifically, the driving action detection unit 63 acquires sensor signals transmitted from the steering angle sensor 19a, accelerator sensor 21a, and brake sensor 23a, detects the left and right steering angles of the steering wheel 17 and the operation amounts of the accelerator pedal 21 and brake pedal 23, and records these as time-series data associated with the travel distance of the training course in the storage unit 53. The driving action detection unit 63 also acquires sensor signals transmitted from the angular velocity sensor 29a and gaze detection sensor 29c, detects the gaze position of the trainee H on the training course displayed on the HMD 29, and records these as time-series data in the storage unit 53.

[0053] Next, the processing unit 51 sets items of the exemplary driving actions that the trainee H is to relive (step S23). FIG. 6 is a flowchart showing an example of the process of setting the re-experiencing items. First, the driving skill assessment unit 67 of the processing unit 51 compares the measurement data of the driving actions of the trainee H during the test drive collected in step S21 with the exemplary driving data, and calculates the degree of deviation for each item (step S51). Specifically, the driving skill assessment unit 67 compares the measurement data of the left and right steering angles of the steering wheel 17 and the operation amounts of the accelerator pedal 21 and the brake pedal 23 by the trainee H during the test drive collected in step S21 with the left and right steering angles of the steering wheel and the operation amounts of the accelerator pedal and the brake pedal by the exemplary driver, and calculates the degree of deviation for each item. In addition, the driving skill assessment unit 67 compares the measurement data of the eye gaze position by the trainee H during the test drive collected in step S21 with the eye gaze position data of the exemplary driver, and calculates the degree of deviation for the eye gaze position.

[0054] For example, the driving skill assessment unit 67 calculates, as the degree of deviation for each item, the proportion of a period during which the left and right steering angle of the steering wheel 17 and the operation amount of the accelerator pedal 21 and the brake pedal 23 by the trainee H and the left and right steering angle of the steering wheel and the operation amount of the accelerator pedal and the brake pedal by the model driver exceed a predetermined operation amount error. Similarly, the driving skill assessment unit 67 calculates, as the degree of deviation, the proportion of a period during which the gaze position of the trainee H and the gaze position of the model driver exceed a predetermined distance error. The distance error in the gaze position is calculated as the distance between the coordinates of the gaze positions. At this time, the driving skill assessment unit 67 sets the priority of the re-experiencing to a higher order in descending order of the degree of deviation.

[0055] The items to be compared between the measured data of the trainee H's driving behavior and the model driving data are not limited to the measured data of the left and right steering angles of the steering wheel 17, the amount of operation of the accelerator pedal 21 and the brake pedal 23, and the eye gaze position, but may also include other data related to driving behavior.

[0056] Next, the driving skill assessment unit 67 calculates the average value μ of the deviations of the measurement data for all items (step S53). The driving skill assessment unit 67 determines whether the calculated average value μ of the deviations is less than 30% (step S55). If the average value μ of the deviations is less than 30% (S55 / Yes), the driving skill assessment unit 67 determines the driving skill level of trainee H to be "3" (step S57). If the driving skill level is "3", the re-experiencing condition setting unit 64 sets the top three items in terms of priority for re-experiencing as the items that trainee H is to re-experience (step S59).

[0057] On the other hand, if the average value μ of the deviation degrees is 30% or more (S55 / No), the driving skill assessment unit 67 determines whether the average value μ of the deviation degrees of the measurement data for all items is less than 70% (step S61). If the average value μ of the deviation degrees is less than 70% (S61 / Yes), the driving skill assessment unit 67 determines the driving skill level of trainee H to be "2" (step S63). If the driving skill level is "2", the re-experiencing condition setting unit 64 sets the top two items in the priority order of re-experiencing as the items that trainee H is to re-experience (step S65).

[0058] On the other hand, if the average deviation μ is 70% or more (S61 / No), the driving skill determination unit 67 determines that the driving skill level of the trainee H is "1" (step S67). If the driving skill level is "1", the re-experiencing condition setting unit 64 sets the item with the highest priority for re-experiencing as the item that the trainee H is to re-experience (step S69).

[0059] Figure 7 shows data on the calculation results of the deviation degree and driving skill level. The driving skill assessment unit 67 calculates the deviation degree from the model driving data for each piece of measurement data, such as the steering angle, accelerator position, brake position, and eye gaze position coordinates. The driving skill assessment unit 67 then sets the priority of the re-experiencing in descending order of the deviation degree. In the example shown in Figure 7, the deviation degree of the steering angle is 70%, the deviation degree of the eye gaze position coordinates is 65%, the deviation degree of the accelerator position is 40%, and the deviation degree of the brake position is 30%, so the priority of the re-experiencing is set in the order of steering angle, eye gaze position coordinates, accelerator position, and brake position.

[0060] Furthermore, the driving skill assessment unit 67 calculates the average value μ of the deviation degree for each item and assesses the driving skill level according to the average value μ. In the example shown in Figure 7, the average value μ of the deviation degree is 50%, so the driving skill level of trainee H is assessed as "2". The driving skill assessment unit 67 records the calculated data in the memory unit 53.

[0061] In this way, in this embodiment, the higher the level of the driving skill of the trainee H, the more re-experience items are set, and the trainee H is allowed to experience model driving actions for multiple items at the same time. As a result, the higher the level of the driving skill of the trainee H, the more efficiently the driving skill can be improved. Furthermore, since the re-experience items are set with priority given to items with a large degree of discrepancy between the driving action data of the trainee H and the driving action data of a model driver, the trainee H can be efficiently trained in driving actions that he or she is not good at.

[0062] Returning to FIG. 4, the processing unit 51 then causes the trainee H to perform a re-experience drive using the training course data set in step S13 (step S25). Specifically, the output control unit 65 of the processing unit 51 transmits the training course data to the HMD driving unit 29b and causes the HMD 29 to display an image of the training course. While the test drive is being performed, the HMD driving unit 29b of the HMD 29 changes the image in the field of view according to the orientation and position of the HMD 29 detected by the angular velocity sensor 29a. In addition, the output control unit 65 causes the speaker system 15 to output ambient sounds, such as the sounds of other vehicles traveling, present in the training course data as stereophonic sound.

[0063] During the re-experience driving, the operation control unit 66 controls the steering drive unit 19b, the accelerator pedal drive unit 21b, and the brake pedal drive unit 23b based on the data of the model driver's model driving behavior for the items set as re-experience items in step S23, to drive at least one of the steering wheel 17, the accelerator pedal 21, and the brake pedal 23. Furthermore, when having the trainee H re-experience the gaze position of the model driver, the output control unit 65 controls the HMD drive unit 29b to display the gaze position of the model driver in the image in the field of view. This allows the trainee H to drive the training course while experiencing one or more of the timing and amount of steering of the steering wheel 17, the timing and amount of change in the accelerator pedal or brake pedal, and the gaze position of the model driver while driving, and thus allows the trainee H to re-experience the model driving behavior of the model driver.

[0064] Alternatively, during the re-experience driving, the output control unit 65 may visually present to the trainee H the steering operation amount, accelerator operation amount, and brake operation amount for the items set as re-experience items in step S23 based on the data of the model driving behavior of the model driver. This allows the trainee H to drive the training course by aligning one or more of the timing and amount of steering of the steering wheel 17, the timing and amount of change in the accelerator pedal or brake pedal, and the eye position while driving to match the model driving, and allows the trainee H to re-experience the model driving behavior of the model driver.

[0065] After completing the re-experience driving, the driving skill assessment unit 67 notifies the trainee H of the assessment result of the driving skill by, for example, displaying the assessment result of the driving skill illustrated in Fig. 7 on at least one of the HMD 29 and the monitor device 13 (step S27). This allows the trainee H to know the driving actions and driving skill level that he or she is weak at, which can increase his or her motivation to improve his or her driving skill. At the same time, the trainee H can also know the driving actions that he or she is good at, which can be used to understand his or her own driving skill.

[0066] After the re-experience drive is performed, a test drive may be performed again and the driving skills may be judged to verify the effectiveness of the training. For example, the re-experience drive, test drive, driving skill judgment, and notification of driving skills may be repeatedly performed until the trainee H is satisfied and performs an operation to end the training.

[0067] <1-4. Effects> As described above, the driving training device 1 according to this embodiment selects a model driver whose driving characteristics are similar to those of the trainee H, and allows the trainee H to relive the driving actions of the model driver as model driving actions. This not only allows the trainee H to know the evaluation results of his or her own driving actions, but also allows the trainee H to train driving actions while experiencing model driving actions that match the driving characteristics of the trainee H. Therefore, driving skills can be improved efficiently.

[0068] Furthermore, the driving training device 1 according to this embodiment determines the type of driving characteristics of the trainee H based on the answers to a plurality of questions prepared in advance, and sets as the model driving behavior the driving behavior of a model driver with a similar type of driving characteristics from among a plurality of model drivers stored in advance in the driving characteristics database 57. Therefore, a model driver with driving inclinations and driving behavior tendencies similar to those of the trainee H is appropriately selected, and the trainee H can be trained in model driving behavior that matches his or her driving characteristics.

[0069] Furthermore, the driving training device 1 according to this embodiment compares the driving actions of the trainee H when he drives a training course with the driving actions of a model driver when he drives the same training course, and sets, from among a plurality of items set as driving actions, items that the trainee H is to relive based on the degree of discrepancy between the driving actions of the trainee H and the driving actions of the model driver. This allows the trainee H to preferentially relive driving actions that the trainee H is not good at, thereby efficiently improving his driving skills. On the other hand, the driving training device 1 according to this embodiment can also preferentially allow the trainee H to relive driving actions that the trainee H is good at. In this case, by starting training with driving actions that the trainee H is relatively good at, the trainee H is more likely to feel a sense of accomplishment in improving his driving skills, and his motivation for improving his driving skills can be increased.

[0070] Furthermore, the driving training device 1 according to this embodiment sets the number of driving action items that the trainee H is to relive based on the average value μ of the degree of discrepancy between the driving action of the trainee H and the driving action of the model driver for each item. As a result, the higher the driving skill level, the more driving actions can be relived at the same time, and driving skills can be improved efficiently.

[0071] <<2. Second Embodiment>> Next, a driving training device according to a second embodiment of the present disclosure will be described.

[0072] Generally, in order to achieve safe driving behavior while driving a vehicle, it is desirable for a driver to have a surplus of attention resources, which are the mental resource capacity for processing various information acquired through sight, hearing, etc. Similarly, even if trainee H is made to relive multiple driving actions simultaneously when he / she does not have a surplus of attention resources, there is a risk that trainee H will not be able to grasp all of the information, and the effect of improving driving skill cannot be expected. For this reason, in the second embodiment, if the trainee H's attention resource capacity decreases during the relive-driving, the relive-driving is terminated.

[0073] The following describes the driving training device according to the second embodiment, focusing mainly on the differences from the driving training device according to the first embodiment.

[0074] <2-1. Configuration of the driver training device> 8 is a block diagram showing an example of the configuration of the control device 50 of the driving training device 1 according to the second embodiment. The driving training device 1 according to this embodiment includes a biosensor 31 that detects bioinformation of the trainee H, and the control device 50 is configured to be able to acquire a sensor signal output from the biosensor 31.

[0075] The biosensor 31 detects bioinformation such as the heartbeat, pulse, brain waves, respiration, blood pressure, and body temperature of the trainee H, and transmits a sensor signal to the control device 50. The biosensor 31 may be, for example, a radio wave Doppler sensor for detecting the heartbeat of the trainee H, or a non-wearable pulse sensor for detecting the pulse of the trainee H. The biosensor 31 may also be an electrode set embedded in the steering wheel 17 for measuring the heartbeat or electrocardiogram of the trainee H. The biosensor 31 may also be a thermography device for measuring the surface temperature of the skin of the trainee H. Furthermore, the biosensor 31 may be a wearable sensor that is worn by the trainee H to detect bioinformation of the trainee H. The wearable biosensor may be, for example, a wristwatch type or a wearable device that is worn on the head or arm.

[0076] The control device 50 of the driving training device 1 according to this embodiment also includes an attention resource amount estimation unit 68. The attention resource amount estimation unit 68 estimates the attention resource amount of the trainee H based on the movement of the trainee H's eyeballs, pupils, or eyelids detected based on a sensor signal from the gaze detection sensor 29c provided in the HMD 29, and on the trainee H's biological information detected based on a sensor signal from the biological sensor 31. The re-experience condition setting unit 64 changes the number of re-experience items based on the estimated attention resource amount of the trainee H while the re-experience driving is being performed.

[0077] The driving training device 1 of this embodiment is configured in the same way as the driving training device 1 of the first embodiment, except that it is equipped with a biosensor 31 and an attention resource amount estimation unit 68, and the functions of the re-experiencing condition setting unit 64 are changed or added.

[0078] <2-2. Operation of the control device> 9 is a flowchart showing an example of processing operations executed by the processing unit 51 of the control device 50 of the driving training device 1 according to this embodiment. The flowchart shown in FIG. 9 replaces steps S23 to S25 in the flowchart shown in FIG.

[0079] The processing unit 51 executes the processes of steps S11 to S21 in accordance with the above-described procedure. Furthermore, the driving skill assessment unit 67 compares the measurement data of the trainee H's driving behavior during the test drive collected in step S21 with the model driving data in the same procedure as in step S51 described above, and calculates the degree of deviation for each item (step S71). Here, as shown in Fig. 7, the degree of deviation for each item of driving behavior is calculated, and the priority of re-experiencing is set to a higher order in descending order of the degree of deviation.

[0080] Next, the re-experience condition setting unit 64 selects items to be re-experienced by the trainee H based on the driving skill assessment result (step S73). In this embodiment, the number and type of re-experience items at the start of the re-experience driving are not particularly limited, but for example, as described in the first embodiment, the number of re-experience items is set according to the level of the trainee H's driving skill, and a predetermined number of items ranked highest in the re-experience priority are set as items to be re-experienced by the trainee H. Next, the processing unit 51 starts executing the re-experience driving using the data of the training course set in step S13 (step S75).

[0081] Next, the attention resource amount estimation unit 68 acquires sensor signals transmitted from the gaze detection sensor 29c and the biometric sensor 31, and estimates the attention resource amount Y of the trainee H (step S77). In this embodiment, the attention resource amount estimation unit 68 calculates the attention resource amount Y by converting the measured information into a preset standard.

[0082] For example, the attention resource amount estimation unit 68 measures at least one of the pupil size, the number of blinks, and the variability in gaze position of the trainee H based on the sensor signal of the gaze detection sensor 29c. The attention resource amount estimation unit 68 also measures at least one of the heart rate, electroencephalogram (EEG), blood pressure, and body temperature of the trainee H based on the sensor signal of the biosensor 31. The attention resource amount estimation unit 68 then applies the measured information to a formula that converts it into a preset reference value to calculate the attention resource amount Y. The larger the pupil size, the more frequently the blinks, and the greater the variability in gaze position, the smaller the value of the attention resource amount Y, indicating a state in which the trainee H's information processing capacity is low. The higher the heart rate, blood pressure, or body temperature, or the smaller the amplitude of alpha waves or the larger the amplitude of beta waves extracted from the electroencephalogram, the smaller the value of the attention resource amount Y, indicating a state in which the trainee H's information processing capacity is low.

[0083] Next, the re-experiencing condition setting unit 64 determines whether the estimated attention resource amount Y is equal to or less than a preset threshold Y0 (step S79). If the attention resource amount Y exceeds the threshold Y0 (S79 / No), the trainee H's information processing amount is maintained at a high level, and the processing unit 51 returns to step S73 to continue the re-experiencing run. At this time, the re-experiencing condition setting unit 64 changes the number of re-experiencing items based on the attention resource amount Y (step S73). For example, if the attention resource amount Y exceeds an item number change threshold Y1 that is greater than the threshold Y0, the re-experiencing condition setting unit 64 increases the number of re-experiencing items. At this time, it is preferable to add items with a high priority for re-experiencing. On the other hand, if the attention resource amount Y exceeds the threshold Y0 and is equal to or less than the item number change threshold Y1, the re-experiencing condition setting unit 64 reduces the number of re-experiencing items. At this time, it is preferable to remove items with a low priority for re-experiencing.

[0084] On the other hand, if the attention resource amount Y is equal to or less than the threshold Y0 (S79 / Yes), the amount of information processing by the trainee H is low, so the processing unit 51 ends the re-experience running and proceeds to step S27.

[0085] <2-3. Effects> As described above, the driving training device 1 according to this embodiment ends the re-experience driving when the amount of attentional resources Y of the trainee H becomes low. This provides the same effect as the driving training device 1 according to the first embodiment, and also prevents the training of driving actions from being continued in a state where improvement in driving skills is not expected. Furthermore, the driving training device 1 according to this embodiment changes the number of re-experience items according to the amount of attentional resources Y of the trainee H. This makes it possible to have the trainee H re-experience many driving actions at the same time when the amount of information processing by the trainee H is high, thereby enabling efficient improvement of driving skills.

[0086] <<3. Other Embodiments>> The driving training device 1 according to each of the embodiments described above can be modified in various ways. Some modified examples of the driving training device 1 according to the above embodiments will be described below.

[0087] For example, in the driving training device 1 according to each of the above-described embodiments, the trainee H is given priority in reliving items with a high priority for reliving, but the technology of the present disclosure is not limited to such an example. For example, in the driving training device 1 according to each of the above-described embodiments, the trainee H is given priority in reliving items that have a large deviation from an experienced driver and are difficult for the trainee H, but it is also considered that items with a small deviation from an experienced driver are easier to improve driving skills. For this reason, the trainee H may be given priority in reliving items with a low priority for reliving.

[0088] 10 is a flowchart showing an example of a process for changing the re-experience items depending on whether or not the re-experience driving has had an effect of improving driving skills. The flowchart shown in FIG. 10 is inserted between step S25 and step S27 in the flowchart shown in FIG.

[0089] After executing the processes of steps S11 to S25 in accordance with the above-described procedure, the processing unit 51 causes the trainee H to perform a test drive using the data of the training course set in step S13 again (step S91). Furthermore, the driving performance detection unit 63 collects measurement data of the driving performance of the trainee H during the test drive (step S93) and judges the driving skill (step S95). The process of judging the driving skill is executed in accordance with the process of calculating the deviation degree shown in the flowchart of FIG. 6.

[0090] Next, the driving skill determination unit 67 determines whether the degree of deviation of the items that the trainee H was made to relive during the re-experiencing drive has decreased (step S97). If the degree of deviation of the items that the trainee H was made to re-live has decreased (S97 / Yes), the processing unit 51 determines that the driving skill has been improved through the re-experiencing and proceeds to step S27. On the other hand, if the degree of deviation of the items that the trainee H was made to re-live has not decreased (S97 / No), the re-experiencing condition setting unit 64 preferentially sets items with a low priority for re-experiencing as the re-experiencing items (step S99).

[0091] Then, the processing unit 51 causes the trainee H to perform a re-experience drive again using the data of the training course set in step S13 (step S101). At this time, for the re-experience items set in step S99, the processing unit 51 controls the steering drive unit 19b, the accelerator pedal drive unit 21b, and the brake pedal drive unit 23b based on the data of the model driving behavior of the model driver, and drives at least one of the steering wheel 17, the accelerator pedal 21, and the brake pedal 23.

[0092] In this way, if the re-experience driving does not have any effect on improving driving skills, the re-experience items can be changed and the re-experience driving can be carried out, allowing trainee H to preferentially re-experience driving actions that are likely to be improved, thereby efficiently improving driving skills.

[0093] Furthermore, in the driving training device 1 according to each of the above-described embodiments, the driving characteristic types are classified by the score X calculated based on the data of the trainee H's answers to the questions about his driving characteristics, and a driver with driving characteristics that match the type of driving characteristic of the trainee H is selected as the model driver, but the technology of the present disclosure is not limited to such an example. For example, the driving characteristic determination unit 61 may compare the data of the answers to each question about the driving characteristics of the model driver recorded in the driving characteristic database 57 with the data of the answers to each question about the driving characteristics of the trainee H, and select the driver with the largest number of matches as the model driver. In this case, weighting may be assigned to one or more specific items.

[0094] Furthermore, in the driving training device 1 according to each of the above-described embodiments, a model driver is selected based on the data of the trainee H's answers to the questions about his / her driving characteristics, but the technology of the present disclosure is not limited to such an example. For example, the degree of deviation (see FIG. 8) of each item of driving behavior when driving the same training course may be determined, and the driver with the smallest degree of deviation may be selected as the model driver.

[0095] Furthermore, in the above embodiment, the HMD 29 displays the image of the training course and the gaze position of the model driver, but the technology of the present disclosure is not limited to this example. For example, a head-mounted wearable device equipped with a gaze detection sensor and an angular velocity sensor may be used instead of the HMD, and the monitor device 13 may display the image of the training course and the gaze position of the model driver, so that the trainee H performs simulated driving actions while looking at the monitor device 13. In this case, the gaze position of the model driver may be displayed on the monitor device 13 as image data together with the image of the training course, or the gaze position of the model driver may be presented on the monitor device 13 using a light projection device such as a laser pointer.

[0096] Although the preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, the present disclosure is not limited to such examples. It is clear that a person skilled in the art to which the present disclosure pertains can conceive of various modifications or alterations within the scope of the technical ideas set forth in the claims, and it is understood that these also naturally fall within the technical scope of the present disclosure.

[0097] The following aspects also fall within the technical scope of the present disclosure. (1) A control device for a driving training device, wherein the processor of the control device for the driving training device further estimates the amount of attentional resources of the trainee during training and sets the number of items to be experienced by the trainee based on the information on the amount of attentional resources. (2) A control device of a driving training device has a processor that has the trainee experience either an item with a large or small degree of discrepancy between the trainee and the model driver, and then compares the driving behavior of the trainee when driving a specified driving scene with the driving behavior of the model driver with a small degree of discrepancy. If the degree of discrepancy in the item that the trainee has experienced does not decrease, the control device has the trainee experience an item with the opposite degree of discrepancy between the item that the trainee has experienced and either a small or large item. (3) A control device for a driving training device, the processor of which performs at least one of the following processes to present the operation of the control pedal or steering wheel in accordance with the model driving behavior: driving the control pedal or steering wheel provided on the driving training device; or displaying the operation amount of the control pedal or steering wheel on a display device. (4) A driving training device including a display device, an operation pedal, a steering wheel, and one or more control devices that control at least one of the display device, the operation pedal, and the steering wheel, The control device a re-experiencing condition setting unit that acquires information on driving characteristics related to the trainee's driving inclination or driving behavior tendency, selects a model driver whose driving characteristics are similar to those of the trainee from a plurality of model drivers stored in advance, and sets the driving behavior of the model driver as a model driving behavior; a re-experiencing control unit that displays an image of a driving scene on a display device while presenting at least one of the operation of the operation pedals, the operation of the steering wheel, or the eye position in the model driving behavior to the trainee, thereby allowing the trainee to experience the model driving behavior; A driving training device comprising: (5) A computer program applied to a driving training device that allows a trainee to drive a vehicle while displaying an image of a driving scene on a display device, one or more processors, Acquiring driving characteristic information related to the trainee's driving inclination or driving behavior tendency; selecting a model driver whose driving characteristics are similar to those of the trainee from among a plurality of model drivers stored in advance; Setting the driving behavior of the exemplary driver as an exemplary driving behavior; Displaying an image of a driving scene on a display device and presenting at least one of the operation of the control pedals, the operation of the steering wheel, or the eye position in the model driving behavior to the trainee, so that the trainee can experience the model driving behavior; A computer program that causes a process including the steps of: (6) A recording medium on which the computer program described in (5) above is recorded. [Explanation of symbols]

[0098] 1: driving training device, 11: seat, 13: monitor device, 15: speaker system, 17: steering wheel, 17a: steering angle sensor, 17b: steering wheel drive unit, 19: steering wheel, 19a: steering angle sensor, 19b: steering drive unit, 21: accelerator pedal, 21a: accelerator sensor, 21b: accelerator pedal drive unit, 21c: line of sight detection sensor, 23: brake pedal, 23a: brake sensor, 23b: brake pedal driver, 27: input device, 29: HMD, 29a: angular velocity sensor, 29b: HMD driver, 29c: gaze detection sensor, 31: biosensor, 50: control device, 51: processing unit (processor), 53: storage unit (memory), 55: experience information database, 57: driving characteristics database, 61: driving characteristics determination unit, 62: experience information setting unit, 63: driving action detection unit, 64: re-experience condition setting unit, 65: output control unit, 66: action control unit, 67: driving skill determination unit, 68: attention resource amount estimation unit

Claims

1. A control device applied to a driving training device that allows a trainee to drive a vehicle while displaying an image of a driving scene on a display device, one or more processors; and one or more memories communicatively coupled to the one or more processors; the one or more processors: a re-experiencing condition setting process for acquiring information on the driving style that the trainee aims to achieve, selecting a model driver who is similar to the driving style that the trainee aims to achieve from a plurality of model drivers stored in advance, and setting the driving behavior of the model driver as a model driving behavior; Next, a video of a driving scene in which a predetermined training course is driven is displayed on the display device, and at least one of the operation amount of the control pedal, the operation amount of the steering wheel, and the eye position in the model driving behavior is presented to the trainee in accordance with the driving scene, thereby allowing the trainee to experience the model driving behavior.

2. the one or more processors:

2. The control device of the driving training device according to claim 1, wherein in the re-experiencing condition setting process, the driving style that the trainee aims for is classified into a plurality of types, and the driving actions of a model driver that has a driving style similar to the driving style that the trainee aims for from among a plurality of model drivers stored in advance are set as the model driving actions.

3. the one or more processors:

2. The control device of the driving training device according to claim 1, wherein in the re-experiencing condition setting process, a degree of deviation between the driving behavior of the trainee when driving the predetermined training course and the driving behavior of a plurality of model drivers stored in advance is determined, and the driving behavior of the model driver with the smallest degree of deviation is set as the model driving behavior.

4. the one or more processors: In the re-experiencing condition setting process, a driving action of the trainee when driving the predetermined training course is compared with a driving action of the model driver when driving the predetermined training course; 2. The control device of the driving training device according to claim 1, wherein, of the plurality of items set as the driving behaviors, an item to be experienced by the trainee is set based on a degree of deviation between the driving behaviors of the trainee and the driving behaviors of the model driver.

5. the one or more processors:

5. The control device of the driving training device according to claim 4, wherein in the re-experience condition setting process, the number of items to be experienced by the trainee is set based on the degree of discrepancy between the driving behavior of the trainee and the driving behavior of the model driver.

6. The one or more processors:

2. The control device of a driving training device according to claim 1, wherein the re-experiencing condition setting process compares the driving behavior of the trainee when driving the predetermined training course with the driving behavior of the model driver when driving the predetermined training course, and sets one of the plurality of items set as the driving behavior, which has a large or small degree of deviation between the driving behavior of the trainee and the driving behavior of the model driver, as the item to be experienced by the trainee, and then executes the re-experiencing control process.

3. The control device of a driving training device according to claim 1, wherein the re-experiencing condition setting process compares the driving behavior of the trainee when driving the predetermined training course with the driving behavior of the model driver when driving the predetermined training course, and when the degree of deviation between the item to be experienced by the trainee does not decrease, sets the item to be experienced by the trainee with the small degree of deviation if the item to be experienced by the trainee has the large degree of deviation, and when the item to be experienced by the trainee has the small degree of deviation, sets the item to be experienced by the trainee with the large degree of deviation if the item to be experienced by the trainee has the small degree of deviation.

7. The one or more processors: The control device for a driving training device according to claim 1 , wherein the re-experiencing control process allows the trainee to experience the model driving behavior by operating the operation pedals and the steering wheel based on the data of the model driving behavior.

Images