JP2007292908A - Driving ability determination apparatus, driving ability determination method and driving ability determination program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a driving ability determination apparatus, a method and a program, capable of appropriately determining deterioration of driving ability caused by dementia. <P>SOLUTION: When the driving ability is determined by outputting an image of a scenery which is viewed by a driver based on input of a driving operation by a driving operation input section, an image of a phenomenon in which an accident rate of an aged person is higher than those of other age groups, is output in a driving difficulty order, and the driving ability is determined, based on the image which is output to the image output section and the driving operation which is input by the driving operation input section. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a driving capability determination device, a driving capability determination method, and a driving capability determination program.

A driving simulator is known as a technique for determining whether or not safe driving can be performed (see, for example, Patent Document 1). In Patent Document 1, a tendency for safe driving is determined based on a value serving as a criterion for determination. At this time, the determination criteria are changed based on the sex, age, and vehicle type, and the determination material is optimized.
JP 2000-47569 A

In the conventional driving simulator described above, it has not been possible to appropriately determine a decrease in driving ability due to dementia.
That is, the recent road traffic law (Article 1031-2) stipulates that a driver's license can be canceled or stopped when it is found that the person has dementia. Therefore, it will be important to determine whether or not the patient has dementia. However, even if the judgment criteria are changed according to age as in Patent Document 1 described above, if the same judgment material is applied to all ages, it is appropriately judged that the elderly person or a decrease in driving ability peculiar to dementia Can not do it.
In addition, when it is determined that there is a risk of dementia based on medical knowledge, even if it is determined that the symptoms are the same level, there is a large difference in the driving ability of people with the same symptoms . Therefore, in order to cancel or stop the driving license, it is necessary to accurately determine whether or not the driving ability is reduced in addition to the progress of dementia.
The present invention has been made in view of the above problems, and an object of the present invention is to provide a driving ability determination device, method, and program capable of appropriately determining a reduction in driving ability caused by dementia.

  In order to achieve the above object, in the present invention, images of events in which the accident rate of elderly people is higher than those of other age groups are output in the order of difficulty of driving, and driving ability is determined based on the images and driving operations at this time. judge. In other words, when human ability declines with age, dementia occurs depending on the degree, but the type of accident caused by such a decline in ability is specific to the elderly (for example, the 2005 White Paper on Police, Chapter 1, Section 3 (1)). Therefore, in order to determine the driving ability of an elderly person who leads to dementia, it is not sufficient to simply adjust the parameters in the same determination material as other age groups.

  Therefore, the present invention outputs an image of an event in which the accident rate of the elderly is higher than that of other age groups, and determines driving ability based on the driving operation performed by the driver in this image. As a result, it is possible to make a determination specialized in the reduction of the driving ability of the elderly, and to appropriately determine the reduction of the driving ability due to dementia.

  Furthermore, in the present invention, images of each event are output in the order of difficulty of driving. Therefore, the driving ability can be determined very easily based on the degree of achievement of each event (for example, whether or not an accident has occurred). Moreover, since the image output here is an image of an event in which the accident rate of the elderly is higher than that of other age groups, the degree of progression of dementia is evaluated by outputting the images of this event in order of difficulty. It is also possible.

  Furthermore, according to the determination result by the driving capability determination device, the driver can grasp the difficulty level of the event achieved without causing an accident, so the driver can determine the degree of decrease in driving capability based on the determination result. Can be objectively recognized. Therefore, mild dementia can be detected early. In addition, if it is mild dementia, it is said that the patient's own awareness that the cognitive ability is reduced can delay the progression of symptoms, and the driving ability determination device according to the present invention is recognized. It can also be used to prevent illness. Furthermore, by repeatedly using the driving ability determination device, training for delaying the progression of dementia can be performed.

  Here, the driving image output means only needs to be able to output an image imitating the scenery visually recognized by the driver. Therefore, for example, it can be configured by a display device or the like that displays a landscape on a screen arranged in the direction of the driver's line of sight while the driver is seated in the vehicle. The image only needs to be able to reproduce various driving scenes simulating the scenery during driving of the vehicle. Therefore, data for reproducing various driving scenes, for example, image data and polygon data for reproducing a moving object on a building, road, road, etc. are recorded in a predetermined storage medium in advance. Based on the above, it is possible to adopt a configuration in which images are continuously changed to simulate driving of a vehicle.

  The driving operation input means may be provided with a device for inputting the driving operation so that the driver can perform the driving operation in a simulated manner. For example, it can be configured by an input unit that imitates a handle, an accelerator, a brake, a shift gear, and the like and a data acquisition unit that acquires the input operation and acquires data indicating the content of the input operation.

  In the image control means, the content of the output image in the driving image output means is controlled. For this purpose, for example, it is possible to adopt a configuration in which driving scene reproduction data stored in a predetermined storage medium in advance is acquired in accordance with switching of driving scenes and output to driving image output means. Of course, the switching order of the driving scenes may follow a predetermined order, or various driving scenes may be switched at random, and various configurations can be adopted.

  However, the event represented by the image is controlled so that the accident rate of the elderly is higher than that of other age groups as described above, and the events are output in the order of the difficulty of driving. Here, the events expressed by the images are various events that occur during driving, and include all events that should be dealt with in some way by driving operation. For example, the above events include signal switching, the appearance of vehicles on the road, the appearance of left and right turns on straight roads, the crossing of moving bodies (pedestrians, bicycles, etc.), the appearance of oncoming vehicles, etc. Can do. Moreover, what is necessary is just to determine the difficulty of driving for every event beforehand.

  The definition of the elderly can be determined in advance, such as 60 years or older, 65 years or older. In other words, the events controlled by the image control means are statistically extracted from accidents that have a high incidence in the elderly when comparing the accident rate of the elderly with the accident rates of other age groups. You can define it by doing Therefore, it is only necessary to define the elderly by dividing the ages so that a statistical comparison can be made. According to recent statistics, the elderly have a higher incidence of accidents caused by inappropriate driving operation, temporary suspension, violation of traffic classification, and priority traffic obstruction in comparison with other age groups. In addition, the incidence of accidents due to maximum speed violations is low compared to other age groups. Therefore, in the present invention, an event associated with an accident caused by inappropriate driving operation, temporary suspension, violation of traffic classification, or obstructing priority traffic may be employed.

  The driving ability determination means only needs to be able to determine the driving ability based on the image and the driving operation. In other words, it is only necessary to be able to determine the driving ability based on the determination items such as whether an accident has occurred in the simulated driving, whether a dangerous situation has occurred, or whether the laws and regulations can be observed.

  Furthermore, as an example of an event output as an image by the image control means, pay attention to a plurality of abilities that decrease with aging, and adopt an event that should be dealt with using at least two of these abilities Is possible. In other words, the ability to decrease with age increases with the onset of dementia, so if you use these abilities to determine the driving ability for events that should be dealt with, it will correspond to the degree of progression of dementia. The driving ability can be determined.

  In addition, when assessing the degree of progression of dementia based on medical knowledge, one reason for the different driving ability of patients with the same evaluation is that driving ability is determined by the combined ability of multiple abilities. Can be mentioned. Therefore, it is possible to comprehensively determine a decrease in driving ability caused by dementia by simultaneously determining at least two of a plurality of abilities that decrease with aging.

  In addition, memory ability, cognitive ability, judgment ability, execution ability are mentioned as the ability to decline with aging. Memories include memory such as the journey to the intended destination and traffic rules, and cognitive powers include signal switching, lanes, distances to surrounding vehicles and moving objects, and relative speed. Is mentioned. In addition, as judgment power, speed necessary for straight ahead, right turn, left turn, etc., distance required between surrounding vehicles and moving bodies, whether or not lane change is possible, and without contacting with surrounding vehicles or moving bodies The judgment power, such as timing for moving, is mentioned. The execution ability includes an ability to operate the driving operation input means as intended.

  As described above, if the ability of the driver is broken down into individual abilities, it is possible to easily define the driving difficulty as described above. That is, it can be configured such that the difficulty level increases as the number of required abilities increases among the plurality of abilities. Of course, at this time, even if the ability is the same, the more the number of objects that should exhibit the ability, the higher the difficulty level. For example, the event of turning left at an intersection with a signal while avoiding a stopped vehicle is more difficult than the event of passing a straight road according to the signal.

  Furthermore, the driving capability determination means only needs to be able to determine the driving capability based on the image and the driving operation. For example, the driving capability may be determined based on whether or not an accident has occurred due to the driving operation. That is, in the present invention, the images of each event are output in the order of difficulty, so that when an accident occurs, the driving ability can be determined by the order of the events. Note that the conditions for determining an accident may be stored in a predetermined storage medium in advance and determined based on this condition. For example, it is possible to adopt a configuration in which an accident occurs when signal ignorance, contact of the own vehicle with respect to a moving body or an oncoming vehicle, etc. occur in a simulated manner.

  Furthermore, it is possible to comprehensively judge the driving ability in combination with another element at this time. In other words, even if an accident does not occur in each event, there is a difference in the risk level of the operation content. For example, even if the vehicle or the moving body is not touched, if the speed during driving is excessive or if the vehicle is meandering, the driving ability should be determined to be low. Therefore, a configuration may be adopted in which the driving ability is determined together with the driving risk generated by the driving operation.

  At this time, the degree of danger may be determined based on the image and the driving operation, and the vehicle running state such as detecting the distance and relative speed between the host vehicle and the other vehicle based on the image and the driving operation. And the determination may be made according to the conditions for determining the degree of risk stored in advance in a predetermined storage medium.

  As described above, various elements can be adopted as elements for determining the degree of risk. For example, it is possible to employ a configuration in which the degree of risk is determined based on how the vehicle meanders due to a driving operation. That is, according to the driving operation input means, the traveling direction and speed of the vehicle can be adjusted. Therefore, if it is determined whether or not the inside of the lane is meandered by this operation, it is determined whether or not the driving is dangerous. can do.

  Furthermore, as a risk determination element, a signal switching timing in the image and a braking timing of the vehicle by the driving operation may be employed. In other words, when a vehicle should be braked by a signal, the risk increases if it cannot be stopped with a margin, and the risk increases when sudden braking is performed, so when did the braking occur according to the switching of the signal? It is possible to determine whether or not the operation is dangerous. When detecting the timing, the time from when the signal image is switched to when the braking is started may be detected, or may be detected based on the distance between the point where the braking is started and the signal. Various configurations can be employed.

  Furthermore, as a risk determination element, a relationship between the speed of the vehicle and the distance (inter-vehicle distance) from another vehicle may be employed. That is, when stopping a running vehicle, the vehicle stops after running the idle running distance and the braking distance, and therefore the vehicle speed and the distance required for the stop are in a specific relationship. Accordingly, if the distance between the vehicle speed and another vehicle is detected, it can be determined whether or not the driving is dangerous.

  Furthermore, as the risk determination element, the position of the vehicle and the distance between the vehicle and the moving body at the position may be adopted. In other words, if an event occurs when the moving body crosses a road surface such as a pedestrian crossing, if the vehicle crosses the moving path (pedestrian crossing) of the moving body, It is necessary to cross the movement route with a certain distance. Therefore, if the distance between the vehicle and the moving body is detected when the vehicle crosses the path of the moving body, it can be determined whether or not the driving is dangerous.

  Further, as the risk determination element, the position of the host vehicle and the distance between the host vehicle and the oncoming vehicle at the position may be employed. In other words, when a vehicle travels across the path of an oncoming vehicle, such as a right turn at an intersection in Japan, to avoid contact with the oncoming vehicle, the distance from the oncoming vehicle should be sufficient. It is necessary to cross the travel route. Therefore, if the position of the host vehicle in which the simulated driving is performed and the distance between the host vehicle and the oncoming vehicle at the position are detected, it can be determined whether the driving is dangerous. Of course, when the oncoming vehicle is behind the host vehicle, it is not necessary to determine the degree of risk.

  Although the case where the present invention is realized as an apparatus has been described above, the present invention can also be applied to a method for realizing such an apparatus. Of course, the substantial operation is the same as that of the apparatus described above. In addition, the driving ability determination device as described above may be realized alone, applied to a certain method, or used in a state where the method is incorporated in another device. The idea is not limited to this and includes various aspects. Therefore, it can be changed as appropriate, such as software or hardware.

  In the case of software for controlling the method as an embodiment of the idea of the invention, it naturally exists and is used also on the recording medium on which the software or software is recorded. The software recording medium may be a magnetic recording medium, a magneto-optical recording medium, or any recording medium that will be developed in the future. The same is true for the replication stage of primary replicas and secondary replicas. In addition, even when the communication apparatus is used as the supply device, the present invention is not used. Further, even when a part is software and a part is realized by hardware, the idea of the invention is not completely different, and a part is stored on a recording medium and is appropriately changed as necessary. It may be in a form that is read.

Here, embodiments of the present invention will be described in the following order.
(1) Configuration of the present invention:
(2) Driving ability judgment processing:
(2-1) Risk level determination:
(3) Other embodiments:

(1) Configuration of the present invention:
FIG. 1 is a diagram showing an outline of a driving ability determination device according to the present invention, and FIG. 2 is a schematic block diagram showing a configuration of the driving ability determination device. As shown in FIG. 1, the driving ability determination device 10 includes various parts simulating a driver's seat of an automobile, such as a driver's seat 11, a handle 12 a, and a display 13. That is, the display 13 is formed in the direction of the line of sight when the driver is seated on the driver's seat 11, and the handle 12a is attached to the front of the body when the driver is seated on the driver's seat 11. Can operate the steering wheel.

  A brake pedal 12b and an accelerator pedal 12c are provided below the handle 12a, and the brake pedal 12b and the accelerator pedal 12c can be operated while the driver is seated on the driver's seat 11. The driving ability determination device 10 also includes a shift gear (not shown) that can be operated while the driver is seated on the driver's seat 11. Further, in the present embodiment, a so-called automatic vehicle is imitated, but it goes without saying that a device imitating a manual vehicle may be provided with a clutch pedal.

  In the present embodiment, a speaker 14 and a meter 15 simulating a speedometer are further provided. The speaker 14 outputs an output simulating a sound during driving, and the speed during the simulated driving is displayed on the meter 15. It has come to be. In the present embodiment, the driving operation input unit 12 for inputting a driving operation is configured by the handle 12a, the brake pedal 12b, the accelerator pedal 12c, and a shift gear (not shown).

  With these configurations, a driver seated in the driver's seat 11 can experience simulated driving. For this reason, in the driving ability determination device 10, the driving operation input unit 12, the display 13, the speaker 14, and the like described above. The meter 15 is connected to the bus via an interface (I / F 12d, 13a, 14a, 15a). As shown in FIG. 2, the CPU 20, RAM 21, ROM 22, and HDD 23 are connected to a bus, and the CPU 20 controls each unit. In this configuration, the CPU 20 can control each unit using a program and data (not shown) recorded in the ROM 22 and the HDD 23, and can perform predetermined arithmetic processing.

  The CPU 20 can execute a driving simulator for performing simulated driving as this program, and includes an image control unit 20a and a driving ability determination unit 20b as part of its functions. In this driving simulator, the driving scene is reproduced on the display 13 by using the image data 23a and the driving scene data 23b recorded in the HDD 23 in advance. At this time, the image control unit 20a outputs images of events in which the accident rate of elderly people is higher than that of other age groups in the order of difficulty of driving.

  That is, the image data 23a is polygon data for constructing a driving scene by a road, a building, a moving body such as a person or a bicycle, and the image control unit 20a displays the driving scene described in the driving scene data 23b. Accordingly, an image showing a driving landscape is generated by appropriately referring to the image data 23a. Here, the driving scene data 23b includes data indicating an event to be reproduced as a driving scene and a driving difficulty level in the event, and the image control unit 20a includes polygon data matching the event and the difficulty level. Is extracted from the image data 23a to construct a landscape. With this configuration, images of events in which the accident rate of elderly people is higher than other age groups are output in the order of difficulty of driving.

  In addition, the driving simulator is based on the operation content input by the driving operation input unit 12, and the position of the own vehicle, the position of the other vehicle, the moving body (person or bicycle) in the landscape constructed by the image control unit 20a. Etc.), data indicating the objects in the landscape (signals and road positions), etc. are sequentially updated to grasp the relative relationship of the objects in the three-dimensional space. This data is used as drive data 23d. The data is sequentially recorded on the HDD 23.

  The HDD 23 records in advance condition data 23c including data for determining the risk of simulated driving, and the driving ability determination unit 20b determines the driving ability based on the drive data 23d and the condition data 23c. judge. That is, based on the drive data 23d, the distance between the host vehicle and another object (another vehicle, a person, a building, etc.) is acquired, and it is determined whether or not a contact accident has occurred during the simulated operation, leading to the accident. The driving ability is determined based on the difficulty level of the event at the stage. In the present embodiment, a score corresponding to the difficulty level of the event at the stage of the accident is determined in advance.

  Furthermore, in this embodiment, based on the drive data 23d, the distance between the own vehicle and another object (another vehicle, a person, a building, etc.), the relative speed between the own vehicle and another object, and the like are calculated. The driving risk is calculated based on the data 23c. Then, the score corresponding to the degree of risk is subtracted from the score, and the total score is set as the driving ability.

  As described above, in the present invention, when reproducing a driving scene, images of events in which the accident rate of the elderly is higher than that of other age groups are output in the order of difficulty of driving. The cause of the accident rate of the elderly being higher than other age groups is due to the ability to decrease with age. Also, this ability declines rapidly in the case of dementia. Therefore, it is possible to appropriately determine a decrease in driving ability due to dementia by outputting images of events in which the accident rate of elderly people is higher than that of other age groups in the order of difficulty of driving. In addition, the degree of progression of dementia can be determined. Furthermore, it is possible to slow down the progression of dementia by making the simulated driver aware of the decline in ability.

(2) Driving ability judgment processing:
Next, details of the driving ability determination process in the above-described configuration will be described. FIG. 3 is a flowchart showing the processing of the driving simulator, and mainly shows the processing in the image control unit 20a and the driving ability determination unit 20b. In the state where the driving simulator is being executed, the driving ability determination unit 20b appropriately acquires the input content in the driving operation input unit 12 and records it as drive data 23d.

  After the driving simulator is started, the image control unit 20a refers to the driving scene data 23b, generates events in the order of difficulty, and outputs the images. First, the image control unit 20a The task with difficulty level -1 is executed, and the drive data 23d at that time is recorded (step S100). In this embodiment, five levels of difficulty are prepared in advance. Here, a series of processes for outputting an image of each event is performed in the order of “difficulty-1 to difficulty-5” in descending order of difficulty. "Execute".

  FIG. 4 is a diagram schematically showing a driving scene at each difficulty level. In this example, a driving scene at an intersection is shown for each difficulty level. As shown in the upper left of FIG. 4, difficulty −1 is a driving scene in which the host vehicle Vs goes straight through the intersection, and the driver appropriately performs simulated driving according to the switching of the signal S. In the above-described driving scene data 23b, data indicating such a driving scene is data of difficulty level-1, and the image control unit 20a appropriately refers to the image data 23a based on this data, and displays the driving scene on the display 13. To reproduce. Of course, an image is output on the display 13 so that the scenery corresponds to the viewpoint of the driver. At this time, the image control unit 20a appropriately switches the image on the display 13 according to the operation input of the driving operation input unit 12, and simultaneously records the above-described drive data 23d.

  In addition, in the task of the difficulty level −1 in the present embodiment, the driver is based on the memory that stores the procedure that the driver should go straight through the intersection, the recognition that recognizes the display content of the signal S, and the display content of the signal S The ability to determine whether or not to enter an intersection and the ability to execute an entry to an intersection or stop before the intersection based on the contents of recognition are required. Since the above memory ability, cognitive ability, judgment ability, and execution ability decline with aging and decline rapidly with the progression of dementia, difficulty -1 is a task that uses these ability This makes it possible to determine the degree of progression of dementia.

  The driving ability determination unit 20b acquires the distance between the host vehicle and another object (another vehicle, a person, a building, etc.) based on the drive data 23d that is sequentially acquired while executing the task, and simulates it. It is determined whether or not the task has been achieved without causing a contact accident during driving (step S105). If it is determined in step S105 that the task has been achieved without causing a contact accident, a flag indicating that the task of difficulty level-1 has been achieved is turned on (step S110), and the task having the next difficulty level is set. Move. If it is not determined in step S105 that the task has been achieved without causing a contact accident, driving ability determination processing is performed (step S175).

  Next, the image control unit 20a and the driving ability determination unit 20b execute tasks from difficulty level −2 to difficulty level −5. That is, the image control unit 20a refers to the driving scene data 23b and the image data 23a, and reproduces the driving scene corresponding to the difficulty level on the display 13 (steps S115, S130, S145, and S160). Of course, at this time, the image control unit 20a appropriately switches the image on the display 13 in accordance with the operation input of the driving operation input unit 12, and simultaneously records the drive data 23d.

  Furthermore, the driving ability determination unit 20b acquires the distance between the host vehicle and other objects (other vehicles, persons, buildings, etc.) based on the drive data 23d that is sequentially acquired when executing each task, and simulates it. It is determined whether the task has been achieved without causing a contact accident during driving (steps S120, S135, S150, S165). If it is determined in these steps that the task has been achieved without causing a contact accident, a flag indicating that the task of each difficulty level has been achieved is turned on (steps S125, S140, S155, S170). In the above tasks, when it is not determined that the task has been achieved without causing a contact accident, or when the task of difficulty -5 is achieved without causing a contact accident, a driving ability determination process is performed (step S175).

  In the example shown in FIG. 4, the difficulty level −2 is that when the host vehicle Vs goes straight through the intersection, a low-speed other vehicle Vo is traveling in front of the host vehicle Vs, and the other vehicle Vo goes straight or turns left at the intersection. It is a driving scene. In this driving scene, the driver appropriately performs a simulated driving in which the own vehicle Vs avoids the other vehicle Vo according to the switching of the signal S.

  Note that, in the task of difficulty level-2 in the present embodiment, the memory that stores the procedure that the driver should go straight through the intersection, the display content of the signal S, the traveling direction of the other vehicle Vo, and the distance from the other vehicle Vo Distance, relative speed, cognitive ability to recognize the presence of other vehicles in the adjacent lane, control of own vehicle Vs based on display content of signal S, whether to overtake other vehicle Vo, whether lane change can be done safely Judgment power for judging whether or not, and the ability to execute an approach to enter an intersection, decelerate before the intersection, or overtake based on the contents of recognition are required. Further, the ability required at difficulty level-2 is more complicated than difficulty level-1. Therefore, it becomes possible to determine the degree of progression of dementia.

Further, in the example shown in FIG. 4, difficulty level −3 is a driving scene in which the host vehicle Vs makes a left turn at an intersection, and pedestrians P ₁ and P ₂ pass through a pedestrian crossing C that crosses the road surface in the left turn direction. The driver makes a left turn with the own vehicle Vs as appropriate in accordance with the switching of the signal S and performs a simulated driving to avoid the pedestrians P ₁ and P ₂ .

In the task of difficulty level-3 in the present embodiment, the driver stores the memory that stores the procedure of turning left at the intersection, the display content of the signal S, and the pedestrian P ₁ existing on the road surface other than the traveling lane, The presence of P ₂ , the cognitive ability to recognize the distance and relative speed with pedestrians P ₁ and P ₂ , the control of the vehicle Vs based on the display content of the signal S, whether or not to enter the pedestrian crossing C, safely Judgment power for judging whether left turn is possible, etc., each ability of execution ability to enter the intersection or decelerate in front of the pedestrian crossing C based on the recognized contents, or accelerate after the left turn is required. Further, the number of objects to be recognized at the difficulty level −3 is larger than the difficulty level −2, and the required ability is more complicated than the difficulty level −2. Therefore, it becomes possible to determine the degree of progression of dementia.

Further, in the example shown in FIG. 4, difficulty level −4 is a driving scene in which the own vehicle Vs traveling from the upper side to the lower side of the drawing turns right at the intersection, and the other vehicle Vo traveling from the lower side to the upper side makes a left turn. The pedestrians P ₁ and P ₂ pass through the pedestrian crossing C crossing the road surface in the right turn direction of the host vehicle Vs. The driver appropriately enters the intersection with the own vehicle Vs according to the switching of the signal S, turns right after waiting for the passage of the other vehicle Vo, and performs a simulated driving that avoids the pedestrians P ₁ and P ₂ .

In the task of difficulty level -4 in the present embodiment, the driver stores the memory that stores the procedure for turning right at the intersection, the display contents of the signal S, and other vehicles Vo and walking that are present on the road surface other than the driving lane. the presence of person P _1, P _2, another vehicle Vo and pedestrians P _1, cognitive to recognize the distance and the relative speed of the P _2, control of the vehicle Vs based on the display content of the signal S, the crosswalk C Judgment to determine whether or not to enter, whether it is safe to make a right turn, etc., based on the contents of recognition, entering an intersection or starting a right turn, decelerating before crosswalk C, or accelerating after a right turn Each ability of the ability to perform is required. Further, the number of objects to be recognized at the difficulty level −4 is larger than the difficulty level −3, and the distance to the recognition object is also far. Therefore, the required ability is more complicated than difficulty level-3, and it becomes possible to judge the degree of progression of dementia.

Further, in the example shown in FIG. 4, difficulty level −5 is a driving scene in which the own vehicle Vs traveling upward from the lower side of the figure turns right at the intersection, and the other vehicle Vo ₁ traveling downward from the upper side makes a right turn or goes straight. The other vehicle Vo ₂ traveling downward from the upper side makes a left turn or goes straight, and further, pedestrians P ₁ and P ₂ pass through the crosswalk C crossing the road surface of the own vehicle Vs in the right turn direction. The driver appropriately enters the intersection with the own vehicle Vs according to the change of the signal S, turns right while maintaining safety in relation to the other vehicles Vo ₁ and Vo ₂ , and avoids the pedestrians P ₁ and P ₂ . Simulate driving.

In the task of difficulty level-5 in the present embodiment, the driver stores the memory that stores the procedure for turning right at the intersection, the display contents of the signal S, and other vehicles Vo ₁ existing on the road surface other than the driving lane, the presence of Vo ₂ and pedestrians P _1, P _2, other vehicle Vo _1, Vo ₂ or pedestrians P _1, cognitive to recognize the distance and the relative speed of the P _2, the vehicle based on the display contents of the signal S Based on the control of Vs, whether to enter pedestrian crossing C, whether it is possible to make a right turn safely, whether to make a right turn with sufficient distance from other vehicles Vo ₂ , etc. Thus, each of the ability to execute the approach to the intersection or start of the right turn, the deceleration before the crosswalk C, or the acceleration after the right turn is required. Further, the number of objects to be recognized at the difficulty level −5 is larger than the difficulty level −4, and the required ability is more complicated than the difficulty level −4. Therefore, it becomes possible to determine the degree of progression of dementia.

  When the above processing is performed, the driving ability determination unit 20b determines the driving ability of the simulated driver based on the flag and the drive data 23d (step S175). In this embodiment, the difficulty level of a task that can be achieved without reaching an accident is grasped based on the flag, and a score is added according to the achieved difficulty level. Further, the risk level of driving is grasped based on the drive data 23d, and the score is reduced according to the risk level. That is, a scoring system is constructed so that the degree of progression of dementia is smaller when the score is higher, and the scoring system is constructed so that more detailed driving ability can be grasped by appropriately subtracting the score according to the degree of risk.

  Here, it is only necessary to be able to calculate a score that reflects the overall judgment result of driving ability. For example, 20 points are added for each stage depending on whether or not the tasks of difficulty-1 to difficulty-5 have been achieved. . As a result, the score is 0 points, 20 points, 40 points,... 100 points according to the degree of difficulty achieved. As for the risk level, a parameter is acquired based on the drive data 23d when the task of each difficulty level is executed, and a plurality of levels of risk level are assigned to this parameter.

  For example, if parameter values are assigned in advance such as safety, medium risk, high risk, etc., the safety value is 0 (no deduction), the risk is -5, and the risk is high. For example, -10 points may be set, and the score may be appropriately reduced. As described above, when the score corresponding to the driving ability is calculated, the driving ability determination unit 20b transmits data for displaying the score to the display 13. As a result, the display 13 outputs the score of the simulated driver.

(2-1) Risk level determination:
Next, an example of risk determination in the present embodiment will be described. Here, various parameters reflecting the driving risk can be used. For example, the risk may be determined based on the degree of meandering. That is, when the position of the host vehicle in the lane is not stable, the degree of danger increases, and thus it is assumed that the driving ability is low.

FIG. 5A is a diagram illustrating the degree of meandering and the degree of risk thereof. In the graph shown in FIG. 5A, the horizontal axis represents time, the vertical axis represents the position of the host vehicle, and the right end, center, and left end of the lane in which the host vehicle is traveling are illustrated on the vertical axis. Here, the center position of the host vehicle is set as the position of the host vehicle, and the manner in which the position of the host vehicle moves in the lane is shown with time. Further, threshold values T _{1 to} T ₄ are set in advance for the position of the host vehicle, and when the position of the host vehicle is within the range of the threshold values T _{1 to} T ₂ , the safety and the position of the host vehicle are set as threshold values. It is said that the risk level is high when it is within the range of T _{1 to} T ₃ and T _{2 to} T ₄ , and the risk level is high when the position of the host vehicle is not less than the threshold value T _{3 and not} more than T ₄ .

Note that data indicating these threshold values T _{1 to} T ₄ is recorded in the HDD 23 as the condition data 23c. Since the position of the own vehicle as described above can be acquired based on the drive data 23d, the drive data 23d when the task of each difficulty level is executed is referred to, and the own vehicle's position is excluded except when the lane is changed. The position is acquired and compared with a threshold value. As a result, it is possible to determine the degree of danger when executing the task of each difficulty level.

  Further, the degree of danger may be determined based on the timing at which the signal S switches and the brake timing. That is, when the signal S switches from blue to yellow, the simulated driver performs a braking operation, but the driving risk varies depending on the timing. Therefore, the driving ability is determined based on parameters corresponding to these timings.

  More specifically, the distance that can be safely stopped at the intersection can be estimated based on the timing at which the signal S switches, the timing at which the brake operation is performed, and the speed of the host vehicle at that time. For example, if the stop distance is defined as the distance from when the signal S switches from blue to yellow until it stops, the stop distance is the free running distance that travels from when the signal S switches from blue to yellow until the brake operation is started. And the braking distance from when the brake operation is performed until the vehicle stops.

Therefore, based on the drive data 23d, if the speed v of the host vehicle when the signal S is switched from blue to yellow and the time t from when the signal S is switched from blue to yellow until the brake operation is obtained, t The idling distance can be calculated by xv. Further, the braking distance can be calculated by v ² / (254 × friction coefficient of road surface). The constant “254” for calculating the braking distance is a value obtained by various studies, but is not limited to this value. Further, the constant and the friction coefficient of the road surface are defined in advance and recorded in the HDD 23 as the condition data 23c.

  Further, a threshold value for determining the degree of danger with respect to the speed v of the host vehicle is determined in advance. For example, since the stop distance is 17 m when the speed of the host vehicle is 40 km / h, 17 m and 25 m are set as threshold values. At this time, when the stop distance is 17 m or less, the degree of danger is high, and when the stop distance is 17 to 25 m, the risk is high, and when the stop distance is 25 m or more, it is safe. This threshold value is recorded in the HDD 23 as the condition data 23c. Further, the threshold value may be determined in advance for each speed, a threshold value of an arbitrary speed may be calculated by interpolation calculation from the threshold value set for the representative speed, or a specific value may be determined based on the drive data 23d. You may calculate by a rule.

  Furthermore, the degree of risk may be determined based on the inter-vehicle distance. That is, since the degree of danger changes according to the distance between the host vehicle and the other vehicle, the driving ability is determined based on the inter-vehicle distance. Note that the inter-vehicle distance required for safe driving varies depending on the speed of the host vehicle, and the greater the speed of the host vehicle, the larger the inter-vehicle distance is required. The driving ability is determined based on the parameter to be evaluated.

  In the present embodiment, this parameter is the speed of the host vehicle / the distance between the vehicles, and FIG. 5B is a diagram showing this parameter. In FIG. 5B, the horizontal axis represents time, and the vertical axis represents the speed of the host vehicle / inter-vehicle distance, and this parameter changes with time due to simulated driving. The speed / inter-vehicle distance of the host vehicle decreases as the inter-vehicle distance increases as the host vehicle speed is the same, and decreases as the host vehicle speed decreases as the inter-vehicle distance is the same.

Therefore, the smaller the value of the speed / distance between the subject vehicles, the higher the safety. Therefore, thresholds T _{5 to} T ₆ are set in advance for the speed / inter-vehicle distance of the own vehicle. When the own vehicle speed / inter-vehicle distance is equal to or less than the threshold T ₅ , safety, When the inter-vehicle distance is within the range of the threshold values T _{5 to} T ₆ , the risk level is high, and when the position of the host vehicle is equal to or higher than the threshold value T ₆ , the risk level is high.

The data indicating the threshold values T ₅ and T ₆ is recorded in the HDD 23 as the condition data 23c. Since the inter-vehicle distance can be acquired based on the position of the own vehicle and the position of the other vehicle, the above-described parameters can be acquired by referring to the drive data 23d. Therefore, referring to the drive data 23d when the task of each difficulty level is executed, the above parameters are acquired and compared with a threshold value. As a result, it is possible to determine the degree of danger when executing the task of each difficulty level.

  Furthermore, the degree of risk may be determined based on the distance from the pedestrian. For example, in a task where a pedestrian crosses a pedestrian crossing, if the own vehicle enters the pedestrian crossing, if the distance between the own vehicle and the pedestrian is acquired as a parameter, the risk of contact between the own vehicle and the pedestrian The degree can be evaluated. Therefore, in the present embodiment, the driving ability is determined based on this parameter.

Therefore, the position of the host vehicle and the position of the pedestrian when the host vehicle enters the pedestrian crossing are acquired from the drive data 23d, and the distance between the host vehicle and the pedestrian at this stage is acquired. For example, the distance between the host vehicle Vs and the pedestrians P ₁ and P ₂ is acquired when the host vehicle Vs enters the pedestrian crossing C at the difficulty level −3 in FIG. For the distance between the host vehicle and the pedestrian, a threshold value is determined in advance. For example, 5m and 10m are set as the threshold value and recorded in the HDD 23 as the condition data 23c. In this configuration, when the distance between the host vehicle and the pedestrian is 5 m or less, the degree of risk is high, and when the distance between the host vehicle and the pedestrian is 5 m to 10 m, the risk between the host vehicle and the pedestrian is high. It can be considered safe when the distance is 10 m or more.

  Furthermore, the degree of risk may be determined based on the distance from the oncoming vehicle when the host vehicle turns right. For example, in a task where the host vehicle turns to the right, if the host vehicle enters the oncoming lane of the oncoming vehicle, if the distance between the host vehicle and the oncoming vehicle is acquired as a parameter, the risk of the host vehicle and the oncoming vehicle contacting The degree can be evaluated. Therefore, in the present embodiment, the driving ability is determined based on this parameter.

Therefore, the position of the host vehicle and the position of the oncoming vehicle when the host vehicle enters the oncoming vehicle lane are acquired from the drive data 23d, and the distance between the host vehicle and the oncoming vehicle at this stage is acquired. . For example, when the host vehicle Vs enters the travel lane of the oncoming vehicle Vo ₂ at the difficulty level −5 of FIG. 4, the distance between the host vehicle Vs and the oncoming vehicle Vo ₂ is acquired. A threshold value is determined in advance for the distance between the host vehicle and the oncoming vehicle. For example, 30 m and 50 m are set as threshold values and recorded in the HDD 23 as the condition data 23c. According to this configuration, when the distance between the host vehicle and the oncoming vehicle is 30 m or less, the degree of risk is large, and when the distance between the host vehicle and the oncoming vehicle is 30 m to 50 m, the host vehicle and the oncoming vehicle are in the risk level. It can be said that it is safe when the distance to is 50 m or more. The degree of risk as described above can be evaluated for each task, and a deduction may be performed for each task.

(3) Other embodiments:
In the present invention, it is only necessary to appropriately determine the decrease in driving ability caused by dementia by outputting images of events in which the accident rate of elderly people is higher than that of other age groups in the order of difficulty of driving. Various configurations other than the above-described embodiment can be employed. For example, the structure of the driving ability determination device is not limited to the form shown in FIG. 1, and may be a vehicle body or a device having an external shape imitating the vehicle body. A device that varies the height or position or transmits vibrations may be provided, and various configurations may be employed.

  The configuration shown in FIG. 2 is also an example, and it is not essential to use a general-purpose processor as shown in FIG. 2, and a configuration using a dedicated IC may be used. In the former case, it is easy to modify or upgrade a program that operates under the control of a general-purpose OS. In the latter case, it is relatively easy to increase the processing speed, and it is easy to easily reproduce a complicated driving scene.

  Furthermore, in the above-described example, the difficulty level is set in five levels, but it is needless to say that the difficulty level may be plural, and may be five levels or more, or may be five levels or less. In addition, when an accident is reached at a certain difficulty level, the subtask may be executed in more detailed stages, and the driving ability may be judged using the subtask, and various processing procedures are adopted. Is possible.

  Furthermore, the driving scene for each difficulty level may be expressed using 3DCG based on polygon data or the like as described above, or the planar image shown in FIG. 4 may be simply displayed. It can be configured to reproduce various other scenes. In addition to the above-described example, various configurations can be adopted as a risk determination method. For example, it may be determined whether or not a brake operation or a steering wheel operation has been performed suddenly, or a response to an oncoming vehicle interruption may be evaluated.

It is a figure which shows the external shape of a driving capability determination apparatus. It is a schematic block diagram which shows the structure of a driving capability determination apparatus. It is a flowchart which shows the process of a driving simulator. It is a figure which shows typically the driving scene for every difficulty. It is explanatory drawing explaining the example of a risk degree.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 ... Driving capability determination apparatus 11 ... Driver's seat 12 ... Driving operation input part 12a ... Handle 12b ... Brake pedal 12c ... Accelerator pedal 13 ... Display 14 ... Speaker 15 ... Meter 20 ... CPU
20a ... Image control unit 20b ... Driving ability determination unit 21 ... RAM
22 ... ROM
23a ... Image data 23b ... Driving scene data 23c ... Condition data 23d ... Drive data

Claims (13)

Driving image output means for outputting an image imitating the scenery visually recognized by the driver;
Driving operation input means for inputting driving operation;
Image control means for controlling the image according to the input driving operation, and outputting an image of an event in which the accident rate of the elderly is higher than that of other age groups in the order of difficulty of driving,
A driving capability determination device comprising: a driving capability determination unit that determines a driving capability based on an image output by the image control unit and a driving operation input by the driving operation input unit.   The driving ability determination device according to claim 1, wherein the event is an event to be dealt with using at least two or more of a plurality of abilities that decrease with aging.   The driving ability determination device according to claim 2, wherein the plurality of abilities that decrease with aging are any combination of memory ability, cognitive ability, judgment ability, and execution ability.   The driving ability determination device according to claim 2, wherein the degree of difficulty increases as the number of required abilities among the plurality of abilities increases.   The driving ability determination means determines whether an accident has occurred during driving based on the image output by the image control means and the driving operation input by the driving operation input means, and an accident has occurred. The driving ability determination device according to any one of claims 1 to 4, wherein the driving ability is determined based on a difficulty level of an event that has ended without being performed.   6. The driving ability determination unit according to claim 5, wherein the driving ability determination means determines the driving ability based on a difficulty level of an event that has ended without the occurrence of an accident and a driving risk level generated by the driving operation. Driving ability judgment device.   The driving capability determination device according to any one of claims 1 to 6, wherein the driving capability determination means determines the driving capability based on a meandering condition of the vehicle by the driving operation.   The image includes an image of a signal that switches at a predetermined timing, and the driving capability determination means determines the driving capability based on a braking timing of the vehicle by the driving operation and a timing at which the signal switches. The driving ability determination device according to any one of claims 1 to 7.   The driving capability determination according to any one of claims 1 to 8, wherein the driving capability determination means determines the driving capability based on a speed of the vehicle by the driving operation and a distance between the vehicle and another vehicle. apparatus.   The image includes a moving object that crosses a road, and the driving ability determination means determines the driving ability based on a distance between the position of the vehicle by the driving operation and the moving object. The driving ability determination device according to any one of claims 1 to 9.   The image includes an image of an oncoming vehicle traveling in an opposite lane, and the driving ability determining means determines the driving ability based on a distance between the position of the vehicle by the driving operation and the oncoming vehicle. The driving ability determination device according to any one of claims 1 to 10. A driving ability determination method for outputting an image imitating a landscape visually recognized by a driver based on an input of a driving operation by a driving operation input section to an image output section, and determining driving ability,
In the image output unit, an image control step for outputting an image of an event in which the accident rate of elderly people is higher than that of other age groups in order of difficulty of driving;
A driving capability determination method comprising: a driving capability determination step of determining a driving capability based on an image output to the image output unit and a driving operation input by the driving operation input unit. A driving ability determination program that outputs an image imitating a landscape visually recognized by the driver based on an input of a driving operation by the driving operation input unit to the image output unit, and determines driving ability,
The image is controlled in accordance with the driving operation input by the driving operation input unit, and in the image output, images of events in which the accident rate of the elderly is higher than other age groups are output in order of difficulty of driving. Image control function,
A driving capability determination program for causing a computer to realize a driving capability determination function for determining driving capability based on the image output to the image output unit and the driving operation input by the driving operation input unit. .

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