JP6241850B2 - Driving aptitude diagnosis device - Google Patents

Description

  The present invention relates to a driving aptitude diagnosis device that can easily and reliably diagnose the driving aptitude of a subject.

  In order to prevent traffic accidents in advance, it is necessary to take preventive safety measures. It is said that the cause of traffic accidents is 95% of recognition / judgment / operation in driving behavior. Since it is important to measure driving risk in advance, various driving aptitude tests have been developed. A driving aptitude test is an attempt to measure driving aptitude by distributing attention and giving restrictions to actions.

In order to identify the cause of human error in complicated and complicated traffic, attempts have been made to determine the risk of accidental functions and accidents. The attention function can be classified into the following three types.
(B) Maintenance: Sustainability of attention to keep responding to the stimulus for a certain period of time.
(B) Selection: The ability to focus attention and select a specific stimulus from many stimuli.
(C) Control: The capacity of attention that enables it to flexibly allocate attention to multiple stimuli simultaneously.

  As an attention function measurement developed so far, a fill-type attention function test (a test in which an answer is written on paper) is widely used. In recent years, a perceptual stimulus task (generally called a button press task) using a personal computer has been developed to measure moving visual acuity and reaction speed.

  The test for measuring the driving ability is particularly called driving aptitude test. The motion recognition measurement operation (Dynamic Vigilance Checker DVC) mainly measures the reaction speed and the operation accuracy by a combination of the dynamic visual acuity test and the Go / No-go task. The CRT driving aptitude test was developed by the National Police Agency and is currently deployed at the driving license center of each prefectural police. The MK-type safe driving advice test is composed of a questionable questionnaire type test and an operation type test using a personal computer, and tests for a urgent tendency from the psychological and exercise aspects.

  However, in order to perform a driving aptitude test in such a conventional technique, a dedicated device is necessary, and the subject needs to go to a facility where a device such as a police station or a driving school is located. In particular, tests such as CRT and DVC require a long inspection time (15 minutes at the shortest to 30 minutes for all inspections), require special devices, and cannot be easily and continuously inspected by the subject. In addition, the MK expression is designed to create a situation that makes you feel bored by slow movement in the virtual space and inadvertently induce and detect mistakes, so in situations such as intersections where advanced information processing is required It does not detect misjudgment.

  On the other hand, in the following Patent Document 1, the present inventors use the reaction to a moving object, or the measurement result of the simple reaction time and the discrimination reaction time, and the same method for measuring the simple reaction time and the measurement of the discrimination reaction time. We propose a driving aptitude diagnostic device to be executed in

JP 2012-120823 A

The technology disclosed in Patent Document 1 can diagnose the subject's driving suitability with high reliability, simplify the configuration of the apparatus, and reduce the burden on the subject by making the subject's operation easy. It was very good in that it was possible.
However, since the device disclosed in Patent Document 1 cannot measure the attention functions (a) to (c) at the same time with high accuracy, it is possible to make a judgment error in an intersection or the like where advanced information processing is required. It was not possible to diagnose the sex with high reliability.

  The present invention has been made to solve the above-described problems of the prior art, and requires three attention functions, namely, maintenance, selection, and control, which are necessary in complicated driving situations such as intersections. It is an object of the present invention to provide a driving aptitude diagnostic device that can accurately measure the attention function simultaneously with a simple test in a short time and can easily and reliably diagnose the driving aptitude of a subject.

  The invention according to claim 1 includes a monitor visually recognized by the subject, a switch operated by the subject, a storage unit that stores a predetermined program, and a control unit that executes the program. As means realized by executing the program, a plurality of types of moving objects that can be visually discerned, a display unit that displays on the monitor the boundary lines through which the moving objects pass, and a plurality of types of moving objects The setting means for setting the moving object to be selected by the subject from the switch, the movement control means for controlling the movement of a plurality of moving objects displayed simultaneously on the monitor, and the moving object approaching the boundary line The first judgment means for judging whether or not the kind of the moving object for which the subject presses the switch matches, and the first judgment means. A first determination unit that determines an incorrect answer when it is determined that the moving object is different from the selection target, and a second determination unit that determines whether the moving object is on the boundary line when the subject presses the switch; In the second determination means, when it is determined that the moving object is on the boundary line, it is determined to be correct, and when it is determined not to be on the boundary line, it is determined to be incorrect and when it is determined to be incorrect, When the object has not reached the boundary line, it is determined as the first incorrect answer, and when the moving object has passed the boundary line, the second determination means for determining as the second incorrect answer; When the determination is made, the third determination is performed as an intermediate reaction when the position of the boundary line with respect to the moving object at the time of pressing the switch is the front area of the moving object, and as a slow reaction when the position is the rear area of the moving object. hand And inspection results based on the determinations of the first determination means, the second determination means, and the third determination means, and data indicating a correlation between an existing inspection result and a traffic accident stored in the storage means The present invention relates to a driving aptitude diagnostic device characterized by having a diagnostic means for collating and diagnosing the possibility of a subject's traffic accident.

The invention according to claim 2 is characterized in that the first determination means counts the number of incorrect answers as the number of determination mistakes, and the second determination means determines the number of first incorrect answers as the number of fast responses, The number of incorrect answers is counted as the number of recognition errors, respectively, the third determination means counts the number of intermediate reactions and slow reactions, the storage means is the first determination means, the second determination means The number of misjudgments, fast reactions, recognition mistakes, intermediate reactions and slow reactions counted by the third judging means are stored as count numbers, respectively, and the traffic accident data depends on the type of traffic accident. Classify and store the presence or absence of accidents for each classification, the existing test results store the number of judgment errors, fast responses, cognitive errors, intermediate responses, slow responses counted for each past subject, For each classification The relationship between the presence or absence of an accident and the number of judgment errors, fast responses, recognition errors, intermediate responses, and slow responses counted for each past subject stored in the storage means is stored as correlation information, and the diagnosis means 2. The driving aptitude diagnosis apparatus according to claim 1, wherein a possibility of a subject's traffic accident is diagnosed based on the count number and the correlation information stored in the storage means.

According to a third aspect of the present invention, the diagnostic means includes the respective count numbers, judgment errors, fast responses, recognition errors, intermediate responses, and slow responses counted for each past subject stored in the storage means. 3. The method according to claim 2, wherein a comparison is made with each median of the numbers, and when the count number of the determination error is equal to or greater than the median value of the determination error, the subject is diagnosed as likely to cause a traffic accident. It relates to the driving aptitude diagnostic device described.

  According to a fourth aspect of the present invention, the diagnostic means is configured such that the subject at the intersection when the recognition error, the determination error, and the quick response count are equal to or greater than the median of the recognition error, the determination error, and the quick response, respectively. The driving suitability diagnosis apparatus according to claim 3, wherein diagnosis is made that an accident is likely to occur.

  The invention according to claim 5 comprises a white matter lesion inspection means for examining the degree of a cerebral white matter lesion of a subject, the white matter lesion inspection means comprises an evaluation means for evaluating the degree of a cerebral white matter lesion, and the diagnostic means comprises: 3. The driving aptitude diagnosis apparatus according to claim 2, wherein the possibility of a traffic accident of the subject is diagnosed based on an evaluation result by the evaluation unit, the count number, and correlation information stored in the storage unit. .

According to the first aspect of the present invention, the first determination means for determining whether or not the type of the moving object that has reached the boundary line matches the type of the moving object that the subject has pressed the switch; In one determination means, when it is determined that the moving object is different from the selection target moving object, the first determination means that determines that the answer is incorrect is provided, whereby the maintenance (discrimination and sustainability) attention function can be measured. Further, the movement control means simultaneously displays a plurality of moving objects on the monitor and controls their movements, whereby the attention function of selection (a task of tracking a plurality of simplified moving objects) can be measured. Further, when the subject presses the switch, the second determination means for determining whether or not the moving object is on the boundary line, and when the second determination means determines that the moving object is on the boundary line, the correct answer is determined. If it is determined that it is not on the boundary line, it is determined as an incorrect answer. If the moving object has not reached the boundary line, it is determined as the first incorrect answer. If it has passed, the attention function of control (behavior suppression by setting a boundary line) can be measured by having the second determination means for determining the second incorrect answer.
In addition, when the second determination means determines that the answer is correct, the position of the boundary line with respect to the moving object at the time of pressing the switch is the rear area of the moving object as an intermediate reaction when the position is the front area of the moving object. In some cases, since the third determination means for determining as a slow reaction is provided, the attention function of control (behavior suppression by setting a boundary line) can be measured more accurately.
Then, the test result based on the determination of the first determination means, the second determination means, and the third determination means is collated with the data indicating the correlation between the existing inspection result and the traffic accident stored in the storage means, and the subject Because it has a diagnostic means for diagnosing the possibility of traffic accidents, the three attention functions of maintenance, selection, and control can be measured simultaneously in a short time with a simple test, which makes it easy for the subject to drive In addition, it is possible to provide a driving aptitude diagnosis device that can make a diagnosis with high reliability.

According to the second aspect of the present invention, the storage means counts the first determination means, the second determination means, the third determination means, the number of determination errors, fast responses, recognition errors, intermediate responses, and slow responses. Are stored as counts, the traffic accident data is classified according to the type of traffic accident, the presence or absence of an accident for each classification is stored, and the existing test results are counted for each past subject. Stores the number of mistakes, fast responses, cognitive mistakes, intermediate responses, and slow responses, and the presence or absence of accidents for each category, judgment errors counted for each past subject stored in the storage means, and fast responses A correlation error, the number of intermediate responses, and the number of slow responses are stored as correlation information, and the diagnostic means determines whether the subject's traffic is based on the count number and the correlation information stored in the storage means. From diagnosing the possibility of late, it is possible to diagnose the type of easy traffic accident caused the subject.

According to the invention of claim 3, the diagnostic means includes the respective counts and judgment errors, fast responses, cognitive errors, intermediate responses, and slow responses counted for each past subject stored in the storage means. Compared with the median of the number of reactions, and when the number of misjudgment counts is greater than or equal to the median of the misjudgment, it is diagnosed that the subject is likely to cause a traffic accident. This possibility can be diagnosed with higher reliability.

  According to the invention according to claim 4, the diagnostic means is configured such that when the recognition error, the determination error, and the intermediate reaction count are equal to or more than the median of the recognition error, the discrimination error, and the fast response, respectively, Since it is diagnosed that an accident is likely to occur at the intersection, it is possible to diagnose with high reliability the ease of the accident at the intersection of the subject.

  According to the fifth aspect of the present invention, the diagnosis means is capable of subjecting the subject to a traffic accident based on the evaluation result of the white matter lesion of the subject by the evaluation means, and the count number and the correlation information stored in the storage means. Since the sex is diagnosed, the driving suitability of the subject can be diagnosed more accurately.

1 is a block diagram showing an embodiment of a driving suitability diagnosis apparatus according to the present invention. A database of test results of subjects stored in the storage means and a database of existing data. It is a schematic diagram of a driving suitability diagnostic apparatus. It is explanatory drawing about the determination which a 1st determination means, a 2nd determination means, and a 3rd determination means perform. It is a flowchart of the processing operation which diagnoses the possibility of a traffic accident from a test subject's test result. It is a flowchart which shows the processing operation of the driving aptitude diagnosis using a driving aptitude diagnostic apparatus. It is a flowchart which shows the analysis method of the correlation with the existing test result and the data of a traffic accident. It is a block diagram which shows a white matter lesion inspection means. It is a photograph which shows the grade (G0-G4) of a white matter lesion. It is a figure which shows the frequency of premature reaction in the first half part and second half part of inspection time, (a) is a figure which shows the inspection result of a group without an accident history, and (b). It is a figure which shows the frequency | count of occurrence of the recognition mistake in the first half part of inspection time, and the latter half part, (a) is a figure which shows the test result of a group without an accident history, (b) is a group with an accident history. It is a figure which shows the frequency | count of occurrence of the judgment mistake in the first half part of inspection time, and the latter half part, (a) is a figure which shows the test result of a group without an accident history, (b) is a group with an accident history.

  The driving aptitude diagnostic device according to the present invention has three attention functions: discrimination and sustainability (maintenance), a task of tracking a plurality of simplified moving objects (selection), and behavior suppression (suppression) by setting a boundary line Can be tested at the same time. Each attention function will be described below.

<Maintenance (discrimination and sustainability)>
It is a discrimination reaction task that is frequently used in experimental psychology in conjunction with the persistence of attention for a certain period of time. Discrimination response task is a task to evaluate whether it reacts as quickly as possible without making a mistake in two or more types of response to the presented visual and auditory stimuli with two or more attributes. . In the present invention, as will be described later, this can be handled by a method in which the subject presses the switch corresponding to the type (color, etc.) of the moving object.

<Select (simplified task of tracking multiple moving objects)>
Visual attention is easily confused with single vision (gaze). In individual viewing, characteristics can be known by measuring eye movement by directing the viewpoint to some visual object, but it cannot be said that attention function can be measured from eye movement. This is clearly separated in cognitive psychology. In the present invention, as will be described later, it is evaluated whether a plurality of moving objects can be simultaneously displayed on a monitor and allowed to be tracked by a subject in order to prevent individual viewing, so that attention can be dealt with flexibly and flexibly.

<Suppression (behavior suppression by setting a boundary)>
In the present invention, the boundary line displayed on the monitor corresponds to a boundary for distinguishing whether the subject is safe or dangerous. By providing a limit of a safety zone, a moving object exists on the monitor and moves. Since there is always a need to be aware of the boundary line and the positional relationship, the work of timing the switch from the suppression to the action of pressing the switch automatically occurs, which is a kind of Go / No-go problem. Can be considered.

  Hereinafter, a preferred embodiment of a driving suitability diagnosis apparatus according to the present invention will be described with reference to the drawings.

  FIG. 1 is a block diagram showing an embodiment of a driving suitability diagnosis apparatus according to the present invention. The driving aptitude diagnosis apparatus according to the present invention includes a monitor 114 visually recognized by a subject, a switch 115 operated by the subject, a storage unit 100 that stores a predetermined program 101, and a control unit 104 that executes the program. Yes.

  The storage unit 100 is a memory including a RAM, a ROM, and the like, and stores a predetermined program 101 for executing the control unit 104 and an OS (operation system). As the memory constituting the storage unit 100, not only the internal memory of the computer provided with the control unit 104 but also an external memory connected so as to be accessible to the computer may be used as necessary.

  The control unit 104 includes a central processing unit (CPU), and reads a predetermined program 101 stored in the ROM of the storage unit 100 into the RAM and executes it. The control means 104 is a means realized by executing the predetermined program. The display means 105, the setting means 106, the motion control means 107, the first judgment means 108, the second judgment means 109, the first judgment means 110, A second determination unit 111, a third determination unit 112, a diagnosis unit 113, and each unit to be described later are provided. Each means will be described below.

The display means 105 displays on the monitor 114 a plurality of types of moving objects 42 that can be discriminated visually and a boundary line 41 through which the moving objects pass (see FIG. 3).
A plurality of types of moving objects can be identified as different types of visual subjects by different colors, shapes, sizes, and the like. For example, red and blue are set for different colors, and circles and squares are set for different shapes.
In the present invention, it is preferable that discrimination is possible by different colors. This is because it is easy to set a moving object to be selected and easy for the subject to understand.
The boundary line may be one or a plurality of straight lines, or a line that forms a figure such as a circle or a rectangle. In the example described later, the boundary line 41 is a line forming a quadrangle (see FIG. 4).

  The monitor 114 is a liquid crystal display or the like, and displays a moving object, a boundary line, and the like displayed by the display unit 105.

  The switches 115a and 115b provided in the operation unit 115 are turned on (transmit an input signal) when pressed by the subject, and are turned off (do not transmit an input signal) when not pressed. A dedicated push button switch may be used, a mouse or a keyboard may be used, and a switch when using a tablet terminal may be displayed on a display and operated with a touch panel. . An input signal when the switch is operated is transmitted to the control means 104.

  FIG. 3 shows an example of the driving suitability diagnosis apparatus according to the present invention. As shown in FIG. 3, it includes a monitor 114 and an operation unit 115. On the monitor 114, the boundary line 41 and the moving object 42 set by the setting means 106 are shown. The operation unit 115 includes switches 115a and 115b. Since there are a plurality of moving objects to be selected, the number of switches is not limited to two as long as it is a plurality.

The setting unit 106 sets a moving object to be selected by the subject from a plurality of types of moving objects according to a predetermined program 101. Here, the “selection target” is a target that the subject should react at the time of examination (should press the switch).
For example, when multiple types of moving objects are red and blue spheres, if the red sphere is set as the selection target, the subject should press the switch when the red sphere overlaps the boundary line. The switch must not be pressed when the sphere overlaps the boundary line.

  The setting means 106 also sets the number of moving objects by a predetermined program 101. The number of moving objects is the number of moving objects set in one inspection, and is set to 80, 100, 120, etc., for example. A plurality of moving objects are displayed on the monitor 114 at the same time.

  The movement control unit 107 controls the movement of the moving object displayed by the display unit 105. Specifically, the moving direction, speed, distance, and position (initial position) of the moving object are controlled according to a predetermined program 101. In the example of FIG. 3, the moving object moves from the top, bottom, left, and right of the screen toward the center. Therefore, the movement control means 107 controls the movement and movement speed of the moving object in the vertical and horizontal directions. The moving speed in this case can be set, for example, in three stages. Therefore, in the driving aptitude diagnosis device according to the present invention, the motion control means 107 displays a plurality of moving objects on the monitor at the same time and controls their movements, thereby selecting (the task of tracking a plurality of simplified moving objects). Attention function can be measured.

  The first determination means 108 determines whether or not the type (color or the like) of the moving object that has approached the boundary line matches the type of moving object that the subject has pressed the switch. This determination can be made by comparing the type of the moving object set by the setting unit 106 with the type (color or the like) of the moving object that has reached the boundary line when the subject presses the switch.

  FIG. 4 is an explanatory diagram of the determination performed by the first determination unit, the second determination unit, and the third determination unit.

  When the first determination unit 108 determines that the moving object on which the subject has pressed the switch is different from the moving object 42 to be selected, the first determination unit 110 determines that it is an incorrect answer. Count as. For example, when the color of the moving object 42 displayed on the monitor is set to red and blue and the color of the moving object 42 to be selected is set to red by the first determination unit 108 and the first determination unit 110, the red color is not mistaken. The attention function of the subject's maintenance (discrimination and persistence), such as whether the switch can be pressed, can be measured.

  The second determination unit 109 determines whether the moving object is on the boundary line 41 when the subject presses the switch. In the second determination unit 109, the second determination unit and the third determination unit make more detailed determinations when the moving object is on the boundary line 41 and when it is not.

  The second determination means 111 determines that the answer is correct when it is determined that the moving object 42 is on the boundary line 41, and determines that the answer is incorrect when it is determined that the moving object 42 is on the boundary line 41. When the object has not reached the boundary line 41, it is determined as the first incorrect answer, and when the moving object 41 has passed the boundary line, it is determined as the second incorrect answer. This determination is performed by comparing the position coordinates of the moving object 42 with the position coordinates of the boundary line. By this determination, the attention function of control (behavior suppression by setting a boundary line) can be accurately measured.

  When the second determination unit 109 determines that the moving object is on the boundary line, the third determination unit 112 performs three types of determination based on the position of the boundary line with respect to the moving object when the switch is pressed. When the position of the boundary line 41 with respect to the moving object at the time of pressing the switch is the area A (front area) of the moving object, it is an intermediate reaction, and when it is the area B (center area) of the moving object, When it is the area C (rear area) of the moving object, it is determined as a slow reaction (see FIG. 4). This determination is made by comparing the position coordinates of the moving object and the position coordinates of the boundary line, or comparing the area of the moving object and the area where the moving object overlaps the boundary line. The size of each area in the third determination unit 112 can be set by the setting unit 106. The setting of the size of each area is not particularly limited. For example, the area A is 20%, the area B is 60%, the area C is 20%, the area A is 30%, the area B is 40%, and the area C is 30%. % Can be set.

  The diagnosis unit 113 is a data indicating the correlation between the inspection result based on the determination of the first determination unit 110, the second determination unit 111, and the third determination unit 112, and the existing inspection result stored in the storage unit 100 and the traffic accident. And examine the possibility of the subject's traffic accident. Details will be described with reference to FIGS.

FIG. 2 shows a test result database 102 and existing data database 103 stored in the storage unit 100.
The test result database 201 includes a judgment error, a fast response, a recognition error, an intermediate response, an optimal response, and a slow response determined by the first determination unit 110, the second determination unit 111, and the third determination unit 112. Each count number obtained by counting the number and the data of the number of correct answers and the number of incorrect answers are stored.
The existing data database 103 includes data relating to past traffic accidents, past test results (test results obtained by thousands to tens of thousands of subjects using the apparatus according to the present invention), and their correlation. Consists of correlation information indicating the relationship. Data relating to past traffic accidents is data on the presence or absence of an accident for each subject category, classifying the traffic accidents experienced by thousands to tens of thousands of past subjects according to the type of traffic accident. The past test results are the number of judgment errors, fast responses, cognitive errors, intermediate responses, and slow responses counted for each past subject. The types of traffic accidents include parking accidents, intersection accidents, rear-end collisions, accidents immediately after leaving the parking lot, single accidents, etc.
Further, the existing data database 103 may include data (white matter lesion occurrence site and grade) regarding the white matter lesion of the subject, which is a test result by the white matter lesion inspection means described later, in the past test result.
Based on the data related to past traffic accidents and the past inspection results as described above, the analysis results and the relationship between the inspection results obtained as the analysis results and the traffic accidents (for example, subjects with many judgment errors are traffic Corresponding information is stored as correlation information. This analysis method will be described later with reference to FIG.

  FIG. 5 shows a flowchart of a processing operation for diagnosing the possibility of a traffic accident from the test result of the subject. As shown in FIG. 5, the diagnosis of the possibility of a traffic accident is made by collating the test results of the subject with the correlation information indicating the relationship between the existing test results and the accident.

  The inspection result based on the determination of the first determination unit 110, the second determination unit 111, and the third determination unit 112 is collated with the data indicating the correlation between the existing inspection result stored in the storage unit 100 and the traffic accident. Because it has the diagnostic means 113 for diagnosing the possibility of the subject's traffic accident, the three attention functions of maintenance, selection and control can be measured simultaneously with a simple test, and the driving suitability of the subject can be simplified. In addition, it is possible to make a diagnosis with high reliability.

FIG. 6 is a flowchart showing the processing operation of the driving aptitude diagnosis using the driving aptitude diagnosis apparatus according to the present invention. Hereinafter, the flow of the driving suitability diagnosis method will be described based on this flowchart.
The setting means 106 sets the number of all moving objects, the number of moving objects to be displayed at one time, and the selection target according to a predetermined program stored in the storage means 100.
Based on the position (initial position), speed, and direction of the moving object set according to a predetermined program, the moving control unit 107 sequentially moves the moving object displayed on the monitor together with the boundary line by the display unit 105.
The subject operates the switch 115.
The first determination means 108 determines whether or not the type of the moving object that has reached the boundary line matches the type of the moving object that the subject has pressed the switch. If it is determined that the selected object is different from the moving object to be selected, the first determination unit 110 determines that the answer is incorrect, and the incorrect answer is counted as a determination error. When the first determination unit 108 determines that the two match, the process proceeds to the determination by the second determination unit 109.
The second determination unit 109 determines whether the moving object is on the boundary line when the subject presses the switch. The second determination unit 111 determines that the moving object is on the boundary line, determines that the moving object is correct, and determines that the moving object is not on the boundary line. When the object has not reached the boundary line, it is determined as the first incorrect answer, and when the moving object has passed the boundary line, it is determined as the second incorrect answer. In this case, the first incorrect answer is counted as an early response, and the second incorrect answer is counted as a recognition error. If it is determined that the answer is correct, the process proceeds to determination by the third determination unit 112 in order to perform more detailed determination.
In the case where the position of the boundary line with respect to the moving object when the switch is pressed is the front area (area A) of the moving object, the third determination means is the central area (area B) of the moving object as an intermediate reaction Is determined as a slow reaction when it is the rear region (region C) of the moving object. In this case, the number of fast and slow reactions is counted (see FIG. 4).
After counting the number of mistakes, fast responses, cognitive mistakes, intermediate responses, slow responses and correct and incorrect answers, it is determined whether all moving objects have been processed, and if they are processed, check If the process is not completed or processed, the moving object setting process is performed again and the inspection continues.

  The diagnosis unit 113 performs a diagnosis based on the inspection result obtained by the processing operation shown in FIG. 6 and data (correlation information) indicating the correlation between the existing inspection result and a traffic accident.

  FIG. 7 is a flowchart showing a method for analyzing a correlation between an existing inspection result and traffic accident data. The correlation information will be described with reference to FIG.

Convert past test results into explanatory variables. First, ranks for all subjects are calculated from the counts of judgment errors, fast responses, cognitive errors, intermediate responses, and slow responses. When this rank is higher than the median value of all subjects, it is converted into an explanatory variable (1), and when it is lower, it is converted into an explanatory variable (0). The explanatory function is based on the test results of the subject, the explanatory function (1) based on the age (for example, elderly people over 65 years old and non-elderly), gender (male, female), and driving frequency (high or low). And may be converted into the explanatory function (2).

  In addition, the traffic accident data is converted into objective variables. First, classify traffic accidents by accident type. For example, in a parking lot, in an intersection, in a rear-end collision, immediately after leaving the parking lot, it is classified as single / other. Based on the accidents classified respectively, if there is an accident history, it is converted into the objective variable (1), and if there is no accident, it is converted into the objective variable (0).

（ＯＤＳ）値を求める。 About the converted explanatory variable (1), explanatory variable (0), objective variable (1) and objective variable (0),
The values corresponding to the explanatory variable (1) and the objective variable (1) are A,
The values corresponding to the explanatory variable (0) and the objective variable (1) are B,
The value corresponding to the explanatory variable (1) and the objective variable (0) is C,
The values corresponding to the explanatory variable (0) and the objective variable (0) are D,
And A to D, the following (Formula 1) and (Formula 2):

Determine the (ODS) value.

The A, B, C, and D values are calculated including factors such as the subject's sex, age, and driving frequency. Specifically, it is calculated by a method called logistic regression analysis. The (ODS) value can be calculated by multiplying a plurality of test results (for example, judgment error, cognitive error, fast response, etc.).
Regarding the correlation information, if the lower limit value in the 95% confidence interval of the (ODS) value is greater than 1, it is determined that there is a relationship between the objective function and the explanatory function, and the significance probability between the data related to the traffic accident and the inspection result Is 5% or less, it is judged that there is a significant difference. Those corresponding to the two are stored as having a high correlation between the accident and the diagnosis result.
For example, if the lower limit value in the 95% confidence interval of the (ODS) value obtained by Equation 1 is greater than 1 and the significance probability is 5% or less for the relationship between a single accident and premature reaction, the single accident and premature reaction It is determined that there is a correlation with the reaction, and is stored as correlation information. Then, in the test result by the method shown in FIG. 6, it is diagnosed that a subject with a large number of premature reaction counts is likely to cause a single accident.
As for the correlation information, results with high and low correlation are stored. Therefore, in the diagnosis result, the possibility of the subject's traffic accident can be displayed as a probability.

The driving suitability diagnosis apparatus according to the present invention includes a white matter lesion inspection means 116 for examining the degree of cerebral white matter lesion of a subject. FIG. 8 is a block diagram showing white matter lesion inspection means.
The white matter lesion inspecting means 116 is a device having a function capable of inspecting the degree and occurrence site of the subject's cerebral white matter lesion. The white matter lesion inspecting means 116 has image data obtaining means 117 for obtaining image data of the subject's brain, and image data. Image data processing means 118 for processing the image data acquired by the acquisition means 117, image display means (display) 119 for displaying an image obtained by the processing of the image data processing means 118, the image data and a predetermined program Storage means 120 composed of an internal memory or an external memory, and evaluation means 121 for evaluating the degree and occurrence site of a cerebral white matter lesion based on the image obtained by the processing of the image data processing means 118.

Specific examples of the white matter lesion inspection means 116 include an MRI (magnetic resonance imaging apparatus) and a CT (computer tomography apparatus). In particular, MRI is preferably used because it enables high-precision inspection.
The image data acquisition unit 117, the image data processing unit 118, the image display unit 119, the storage unit 120, and the evaluation unit 121 may be configured as an integrated device, or may be configured by connecting a plurality of devices. For example, when using MRI or CT, the storage unit 120 and the evaluation unit 121 for storing the image analysis program may be provided in a computer built in the MRI or CT, or may be provided in a computer connected to the outside. The driving suitability diagnosis apparatus according to the present invention may be provided.

  The evaluation means 121 of the white matter lesion inspection means 116 determines the grade of the test subject's cerebral white matter lesions in five grades of grades 0 to 4 based on the image obtained by the processing of the image data processing means 118 (the disease state progresses as the grade increases). And where the cerebral white matter lesion occurs (whether it is the frontal lobe including the prefrontal cortex or other regions).

FIG. 9 is a photograph showing the grade of cerebral white matter lesion. Specifically, it is a photograph of an image obtained by processing the cross-sectional image data of the subject's brain obtained using MRI, and the grade of cerebral white matter lesion is 0 to 4 (G0 to G4). Shows the case.
In FIG. 9, the white part indicated by the arrow is a white matter lesion occurrence site, and the white matter lesion occurrence site is not confirmed in G0, but as the grade increases to G1, G2, G3, and G4, the white matter lesion occurrence site You can see that it is getting bigger. It becomes a lesion (bilateral white matter lesion) spreading from the G2 or higher to the left and right cerebral white matter (both sides of the cerebral hemisphere).

The driving aptitude diagnosis apparatus according to the present invention includes the white matter lesion inspection means 116, so that the diagnosis means 113 performs the evaluation result by the evaluation means 121 and the test result of the driving aptitude diagnosis (judgment error, fast reaction, recognition error, intermediate Based on the response, the number of slow responses and the number of correct answers and incorrect answers) and the data (correlation information) indicating the correlation between the existing test result stored in the storage means 100 and the traffic accident (correlation information). Diagnose the possibility of an accident. Thus, by adding a factor related to the white matter lesion of the subject (the presence or absence of bilateral white matter lesion), it becomes possible to diagnose the subject's more accurate driving aptitude.

  EXAMPLES Hereinafter, the present invention will be described more specifically with reference to examples, but the present invention is not limited to these examples. In the embodiment, the on-text (ODS) value may be referred to as an odds ratio.

<Subject>
From July 2012 to December 2013, 3306 healthy middle-aged and elderly persons (1821 males, 1485 females, average age 53.48 years old) who underwent brain dock screening (MRI) at the Kochi Screening Clinic Brain Dock Center ), The driving aptitude test (DAT: driving ability test) using the driving aptitude diagnostic apparatus according to the present invention was conducted as part of the brain dock higher brain function test with sufficient informed consent and consent ( (See Table 1). Table 2 shows the number of subjects for each accident type by age.

<MRI inspection>
Diagnosis and grade evaluation of cerebral white matter lesions were carried out by a specialist using T1-weighted images, T2-weighted images, and flare images of MRI (manufactured by Hitachi Medical Corporation: ECHELON Vega 1.5T). The grades of white matter lesions were classified according to whether they were present on both sides of the cerebral hemisphere (presence or absence of bilateral white matter lesions) (see Table 1).

<Operating aptitude test (DAT)>
The test | inspection was performed as follows using the driving | operation aptitude diagnostic apparatus of this invention.
The apparatus is a computer comprising an apparatus body incorporating a monitor 114, a switch 115, a control means 104, and a storage means 100. As a setting for this inspection, the boundary line is a square, and a plurality of moving objects appear at the same time, and move inward from the top, bottom, left, and right toward the boundary line (see FIG. 3). The moving objects are blue and red, and the switches are of two types so that red and blue can be selected.
The evaluation contents of the driving aptitude diagnosis will be described with reference to FIG. As shown in FIG. 4, if the button is pressed when the moving object 42 reaches the boundary line 41, it is determined that the answer is correct (correct answer), and the position of the boundary line 41 with respect to the moving object when the switch is pressed is determined as the front area A ( When the reaction was in the front 20%), it was determined as an intermediate reaction, and when it was in the rear region C (20% portion), it was determined as a slow reaction. When the moving object 42 did not exist on the boundary line 41 or when the button corresponding to the color was mistakenly pressed, the answer was incorrect. When it was pressed before reaching the boundary line 41, it was regarded as a quick reaction, and when the boundary line 41 was exceeded, it was regarded as a recognition error. When the user presses the moving object 42 in the wrong color, it is determined as a determination error.
The test was performed for 3 minutes, with the number of moving objects being 100, and the moving speed was changed in three stages. The number of judgment errors, fast responses, recognition errors, intermediate responses, and slow responses was counted.

<Statistical analysis>
Logistic regression analysis of all accidents, parking accidents, intersection accidents, rear-end collisions, accidents immediately after leaving the parking lot, and accidents when changing lanes and DAT variables, taking into account gender and driving frequency Went.
The objective variable was set to (1) when each accident was encountered from all accident types in the past 10 years, and (0) when no accident was encountered. Gender was binarized with (0) for women, (1) for men, (0) for groups with low driving frequency, and (1) for groups with high driving frequency.
Each explanatory variable was divided into two medians. Specifically, the ranks for all subjects are calculated from the counts of judgment errors, fast responses, cognitive errors, intermediate responses, and slow responses. When this rank was higher than the median value of all subjects, it was converted into an explanatory variable (1), and when it was lower, it was converted into an explanatory variable (0).

<Creating DAT variables>
Table 3 shows the parameters used for the analysis. Assuming that not only one variable, but each variable has an influence on each other, the product of parameters 2 and 3 was also analyzed. Logistic regression analysis was performed using variables consisting of a total of 21 combinations as explanatory variables. Since “intermediate reaction” and “slow reaction” are contradictory variables, they were excluded from the analysis.

3306 subjects were divided into two groups, over 60 years old and under 60 years old. In addition, based on the presence or absence of bilateral white matter lesions for each age group, 1) bilateral white matter lesions under 60 years old, 2) bilateral white matter lesions under 60 years old, 3) bilateral white matter lesions under 60 years old, 4) Divided into 4 groups with bilateral white matter lesions over 60 years old.
The test results and traffic accident data were converted into objective variables and explanatory variables, respectively, and (ODS) values were calculated using (Equation 1) and (Equation 2).
From the calculation results, regarding the relationship between the objective variable and the explanatory variable, the lower limit value in the 95% confidence interval of the (ODS) value is greater than 1, or the significance of the traffic accident data and the inspection result is significant. It was examined whether the probability was 5% or less.

  Table 4 shows the results of all subjects.

<Accidents>
Until now, subjects who answered that they had any accidents such as intersections, rear-end collisions, and other accidents were determined as having an accident (ODS) value. Significant differences were seen at the 1% level in groups with many misjudgments.
<Accident at intersection>
Causes of accidents at intersections include oversight of motorcycles in the same direction, oversight of pedestrians and bicycles in the opposite direction and the same direction, and oversight of opposing four-wheeled and two-wheeled vehicles. In complicated traffic, higher attention function is required, and cognitive decline at the time of attention dispersion is thought to have affected the occurrence of accidents. Cognitive errors (oversight) × judgment errors × multiple results of intermediate responses Significance was seen in the group with a higher multiplier value.

  For groups under 60 years old, group 1) Table 5 shows results without bilateral white matter lesions under 60 years old, and 2) Table 6 shows results with bilateral white matter lesions under 60 years old.

As shown in Table 5, in Group 1), there was a significant difference in the odds ratio in the product of the intermediate reaction and cognitive error in the parking lot accident (odds ratio: 1.477, significance probability: 0.032). ). In addition, a significant difference was found in the odds ratio in the product of slow reaction and cognitive error even in a rear-end collision (odds ratio: 1.495, significance: 0.039). Furthermore, there was a significant difference in the odds ratio between the late reaction and the recognition error in the lane change accident (odds ratio: 2.469, significance: 0.033).
As shown in Table 6, in group 2), a significant difference was found in the odds ratio in the product of the quick response and the recognition error in the parking lot accident (odds ratio: 2.135, significance probability: 0.033).

  For groups over 60 years old, 3) Table 7 shows results without bilateral white matter lesions over 60 years old, and 4) Table 8 shows results with bilateral white matter lesions over 60 years old.

As shown in Table 7, in the group 3), a significant difference in odds ratio was found in the product of the intermediate response and judgment error in the rear-end collision (odds ratio: 6.092, significance probability: 0.021). In the intersection accident, a significant difference was found in the significant probability of the variable of the product of slow response, slow response and misjudgment, but no significant difference was found in the odds ratio.
As shown in Table 8, in group 4), a significant difference in odds ratio was found only in the product of slow reaction, cognitive error and judgment error in an intersection accident (odds ratio: 4.382, significance probability: 0.048). ).

Describe the relationship between DAT variables and accident classification.
In the intersection accident, a group with bilateral white matter lesions over 60 years old had a significant difference in odds ratio in the product of slow reaction, cognitive error and judgment error in the intersection accident.
In the parking accident, there was a significant difference in the odds ratio in the product of intermediate response and cognitive error in the group under 60 years old and without bilateral white matter lesions, and the product of fast response and cognitive error in the group with bilateral white matter lesions. There was a significant difference in the odds ratio among the variables.
In the rear-end collision, there was a significant difference in odds ratio in the variable of product of slow response and cognitive error in the group under 60 years old in the group without bilateral white matter lesions. In the group over 60 years old, a significant difference was found in the odds ratio in the variable of the product of intermediate response and misjudgment in the group without bilateral white matter lesions.
In the case of an accident immediately after leaving the parking lot, there is a significant difference in significance in the product of fast response and cognitive error, slow reaction and misjudgment, cognitive error and misjudgment only in the group without bilateral white matter lesions under 60 years old. Although there was no significant difference in the odds ratio.
In the accident at the time of lane change, there was a significant difference in odds ratio due to slow reaction and cognitive error only in the group without bilateral white matter lesions under 60 years old.

  The relationship between the test result of the subject and the traffic accident analyzed as described above is stored in the storage unit 100 as correlation information and used in the subsequent test. From these results, the driving aptitude diagnosis device according to the present invention can be used to identify a dangerous driver who is likely to cause a traffic accident, and is useful for predicting dangerous driving by accident classification. It can be said that it has. Furthermore, since a sufficient relationship was observed between white matter lesions and traffic accidents, especially intersection accidents in the group over 60 years of age, more accurate examination of the subjects can be made by diagnosing by adding the test results on the subject's cerebral white matter lesions. It becomes possible to diagnose driving aptitude.

Further, FIGS. 10 to 12 illustrate the number of occurrences of quick reaction, recognition error, and judgment error for the group with no accident history and the group with an accident example, and is divided into the first half and the second half of the examination time. Yes.
Comparing the number of mistakes in the first half and the second half, groups with no history of accidents tended to reduce the number of mistakes in the second half. This is considered to be a learning effect by habituation. On the other hand, the number of mistakes in the latter half of the group with a history of accidents tended to increase.

  The present invention is suitably used as a driving aptitude diagnostic device for diagnosing the possibility of a traffic accident when a subject is driving a car.

100-storage means 101-program 102-subject data 103-existing data 104-control means 105-display means 106-setting means 107-motion control means 108-first judgment means 109-second judgment means 110-first Determination means 111-second determination means 112-third determination means 113-diagnosis means 114-monitor 115-switch 116-white matter lesion inspection means 121-evaluation means

Claims (5)

A monitor visually recognized by the subject, a switch operated by the subject, a storage means for storing a predetermined program, and a control means for executing the program,
As means for realizing the control means by executing the program,
Display means for displaying on the monitor a plurality of types of moving objects that can be visually discerned, and boundary lines through which the moving objects pass;
Setting means for setting a moving object to be selected by the subject to be selected by the switch from the plurality of types of moving objects;
Movement control means for controlling the movement of a plurality of moving objects displayed simultaneously on the monitor;
A first determination means for determining whether or not the type of the moving object that has reached the boundary line and the type of the moving object that the subject has pressed the switch match,
In the first determining means, if it is determined that the moving object is different from the selection target moving object, the first determining means that determines that the answer is incorrect and the first determining means whether the moving object is on the boundary line when the subject presses the switch. Two judgment means;
In the second determination means, when it is determined that the moving object is on the boundary line, it is determined as a correct answer,
When it is determined that it is not on the boundary line, it is determined as an incorrect answer. When it is determined that the answer is incorrect, if the moving object has not reached the boundary line, it is determined as the first incorrect answer. A second determination means for determining a second incorrect answer if it has passed,
When the second determination means determines that the answer is correct, the position of the boundary line with respect to the moving object when the switch is pressed is an intermediate reaction when the position is in the front area of the moving object, and is in the rear area of the moving object. A third determination means for determining as a slow reaction;
The inspection results based on the determinations of the first determination means, the second determination means, and the third determination means are collated with the data indicating the correlation between the existing inspection results and the traffic accident stored in the storage means. Having a diagnostic means for diagnosing the possibility of the subject's traffic accident,
A driving aptitude diagnostic device characterized by that. The first determination means counts the number of incorrect answers as the number of determination errors,
The second determination means counts the number of first incorrect answers as the number of fast responses and the number of second incorrect answers as the number of recognition errors, respectively.
The third determination means counts the number of intermediate reactions and slow reactions,
The storage means
The number of misjudgments, fast responses, recognition errors, intermediate responses, and slow responses counted by the first determination unit, the second determination unit, and the third determination unit are stored as count numbers, respectively.
The traffic accident data is classified according to the type of traffic accident, and stores the presence or absence of an accident for each classification.
The existing test results store the number of judgment errors, fast responses, cognitive errors, intermediate responses, and slow responses counted for each past subject,
Furthermore, the relationship between the presence / absence of an accident for each category and the number of judgment errors, fast responses, recognition errors, intermediate responses, and slow responses counted for each past subject stored in the storage means is stored as correlation information. And
The diagnosis means diagnoses the possibility of a subject's traffic accident based on the count number and the correlation information stored in the storage means.
The driving suitability diagnosis apparatus according to claim 1. The diagnostic means includes
Compare the respective counts with the median of the number of judgment errors, fast responses, cognitive errors, intermediate responses, slow responses counted for each past subject stored in the storage means,
The driving suitability diagnosis apparatus according to claim 2, wherein when the count number of the determination mistake is equal to or greater than a median value of the determination mistake, the subject is diagnosed as likely to cause a traffic accident. The diagnostic means includes
The subject is diagnosed as likely to cause an accident at an intersection when the number of recognition errors, determination errors, and fast responses is equal to or greater than the median of the recognition errors, determination errors, and fast responses, respectively. Item 4. The driving aptitude diagnostic device according to item 3. Provided with a white matter lesion examination means for examining the degree of cerebral white matter lesion of the subject,
The white matter lesion inspection means comprises an evaluation means for evaluating the degree of cerebral white matter lesions,
The diagnostic means diagnoses the possibility of a subject's traffic accident based on the evaluation result by the evaluation means, the count number, and the correlation information stored in the storage means.
The driving suitability diagnosis apparatus according to claim 2.

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