JP2018126190A - Driver diagnostic device and driver diagnostic system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a driver diagnostic device and a driver diagnostic system capable of properly diagnosing a driver using a vehicle.SOLUTION: A driver diagnostic device includes: communication means for executing communication; calculation means for calculating a behavior evaluation index, which is an index for evaluating a driving behavior of a vehicle driver using received sensor information acquired from a sensor in the vehicle for each driver, and which calculates the behavior evaluation index based on the frequency of error behaviors, which are errors in the driving behaviors of the driver; first storage means for storing the history of the behavior evaluation indexes for a plurality of years for each driver; and first diagnostic means for diagnosing mild cognitive impairment of the driver based on the history of the behavior evaluation indexes for a plurality of years.SELECTED DRAWING: Figure 6

Description

  The present invention relates to a technique for diagnosing a driver of a vehicle.

  As a test method for cognitive dysfunction, a mini-mental state test (hereinafter also referred to as “MMSE”) and a clinical dementia severity determination scale (hereinafter also referred to as “CDR”) are known. ing.

  And the technique of analyzing the fluctuation | variation of an accelerator pedal operation opening is disclosed as a technique which test | inspects a driver's cognitive impairment using a vehicle (nonpatent literature 1). According to this method, the activity of the driver's brain is estimated by analyzing the fluctuation of the accelerator pedal operation opening degree.

  Further, Patent Document 1 describes a driving advice device that evaluates a dangerous driving degree based on travel time and travel record data. Patent Document 2 describes a technique for preventing accidents by sharing characteristic values between vehicles in a driving characteristic diagnosis system.

JP 2008-077502 A JP 2014-016875 A

Okutani, 3 others, “Measurement of driving characteristics of elderly drivers by pedal operation”, Automobile Engineering Society Proceedings, Vol. 43 (4), p. 929-934 (2012)

  MMSE is a kind of intelligence test, which can test cognitive impairment in a relatively short time. Moreover, in the examination using CDR, since the severity of cognitive dysfunction is evaluated by objective judgment from the viewpoint of a third party, the tendency to be able to examine cognitive dysfunction more accurately than MMSE. It is in. However, since these test the cognitive function in a period of several tens of minutes to several hours during the test, the symptoms do not always develop in daily life (hereinafter referred to as “MCI”). Is sometimes difficult to detect by these tests. In addition, when hearing with a third party (observer) is not obtained, inspection using CDR cannot be performed.

  On the other hand, in the prior art in which a driver is used to check a driver's cognitive dysfunction, the vehicle can replace a third party (observer). However, even in the prior art, it is difficult to detect MCI as in the above-described inspection, and a technique for diagnosing a driver's MCI using a vehicle has not been clarified so far.

  Conventionally, it is known to share the driving characteristics of a vehicle driver among vehicles. Here, if the MCI that is difficult to detect as described above can be diagnosed by using the vehicle, it is possible to suitably diagnose the driving characteristics of the driver of the vehicle by applying it.

The present invention has been made in view of the above-described problems, and an object of the present invention is to provide a driver diagnosis apparatus and a driver diagnosis system that can suitably diagnose a driver using a vehicle.

  The present invention relates to a driver diagnostic apparatus. In the first embodiment of the present invention (hereinafter sometimes referred to as “first invention”), the driver diagnosis device diagnoses the MCI of the driver of the vehicle.

  As described above, since symptoms of MCI do not always develop in daily life, MCI diagnosis involves observation of behavior related to advanced cognitive ability over a long period of time in daily life. is necessary. Here, in order to drive a vehicle safely, advanced cognitive abilities such as grasping a driving operation, recognition of the behavior of the vehicle, recognition / judgment of a situation around the vehicle, and behavior prediction of the surrounding vehicle are required. Therefore, the driving behavior of the driver can be regarded as behavior related to the above-described advanced cognitive ability. The driver's MCI can be diagnosed by observing the driving behavior over a relatively long period of time.

  Therefore, the driver diagnosis apparatus according to the first invention evaluates the driving behavior of the driver of the vehicle using the communication means for performing communication and the sensor information obtained from the sensor in the vehicle received by the communication means. A calculation means for calculating an action evaluation index that is an index for each driver, a calculation means for calculating the behavior evaluation index based on the number of error actions that are errors in the driving behavior of the driver, and for each driver First storage means for storing the history of the behavior evaluation index for a plurality of years, and first diagnosis means for diagnosing a driver's mild cognitive impairment based on the history of the behavior evaluation index for a plurality of years.

  In such a driver diagnostic apparatus, sensor information acquired by a measuring apparatus such as a triaxial acceleration sensor, a speed sensor, a yaw rate sensor, and a steering angle sensor, an image analysis apparatus including a camera, a distance measuring apparatus, a GNNS receiving apparatus, and the like. Is transmitted to the communication means. Then, the calculation means calculates an action evaluation index based on the number of error actions using the sensor information received by the communication means. Here, the error behavior includes, for example, lane departure, shortening of the inter-vehicle distance, sudden acceleration / deceleration, collision, traffic violation (excessive speed, neglect of signal, etc.), increase in time required for right turn, and the like. The number of such error actions tends to correlate with the driver's cognitive ability. Specifically, the number of error actions is likely to increase as the driver's cognitive ability decreases. For example, the behavior evaluation index can be calculated so that the evaluation of the driving behavior with respect to the driver becomes worse as the number of error behaviors increases. The calculating means may calculate an action evaluation index based on the number of error actions of one of the error actions exemplified above, or calculate an action evaluation index based on the number of types of error actions. May be.

  And a 1st memory | storage means memorize | stores the log | history of the behavior evaluation index calculated based on the frequency | count of an error action for several years. And a 1st diagnostic means diagnoses a driver | operator's MCI based on the log | history of the several years of action evaluation parameter | index. As described above, the driver diagnosis apparatus according to the first invention evaluates the driving behavior of the driver of the vehicle over a relatively long period using the history of a plurality of years of the behavior evaluation index, and recognizes the driver. By observing this change, the driver's MCI can be detected early. In addition, when MCI is discovered, it is notified to a driver | operator using a known means.

  As described above, the driver diagnosis apparatus according to the first invention enables early detection of MCI that is difficult to find. That is, the driver can be suitably diagnosed using the vehicle.

  Further, the calculation unit according to the first invention may calculate the behavior evaluation index based on a weighted sum of the number of times of the plurality of types of error behaviors.

  Such a calculation means can calculate an action evaluation index by changing the weighting according to the error degree of each error action among a plurality of types of error actions. For example, for an error action with a relatively large degree of error such as a collision, the influence of the number of actions on the action evaluation index can be increased.

  In addition, the first diagnosis means according to the first invention may cause a mild cognitive impairment in the driver when the value of the behavior evaluation index is greater than a predetermined threshold at a predetermined frequency or more in a predetermined first period. You may be diagnosed as having In this case, the behavior evaluation index is defined to mean that the larger the value, the worse the evaluation of the driving behavior of the driver.

  Here, the predetermined threshold is a threshold for determining whether or not MCI has occurred in the driver. However, as described above, since the symptoms of MCI do not always develop in daily life, even if the driver is afflicted with MCI, the value of the behavioral evaluation index is limited in a certain period. Sometimes it becomes larger than the predetermined threshold value, and sometimes it becomes smaller than the predetermined threshold value. In addition, the diagnosis of MCI requires observation of behaviors related to the long-term advanced cognitive ability of subjects in daily life. Therefore, the first diagnosis means diagnoses that the driver has a mild cognitive impairment when the value of the behavior evaluation index is greater than a predetermined threshold at a predetermined frequency or more in a predetermined first period. Here, the predetermined first period and the predetermined frequency are a period and a frequency at which the symptoms of MCI can be detected. For drivers with relatively high driving frequency of the vehicle, for example, when the value of the action evaluation index is greater than a predetermined threshold at a frequency that can detect the symptoms of MCI in several days to several tens of days, The driver may be diagnosed as having MCI. For a driver with a relatively low driving frequency of the vehicle, for example, when the value of the behavior evaluation index is larger than a predetermined threshold at a frequency that can detect the symptoms of MCI in a few months, driving A person may be diagnosed with MCI. Note that the predetermined first period, the predetermined frequency, and the predetermined threshold can be determined in advance by experiments or the like.

  The predetermined threshold may be constant regardless of the age of the driver. As described above, the number of error actions tends to have a correlation with the driver's cognitive ability, and an action evaluation index is calculated based on the number of error actions. And the value of an action evaluation index tends to become large, so that a driver's cognitive ability falls. Here, if the predetermined threshold is constant regardless of the driver's age, the driver's cognitive ability can be diagnosed based on a unique standard.

  Further, the predetermined threshold value may change according to the age of the driver. When the driver is elderly, error behavior tends to occur according to aging. This is because, in elderly people, cognitive ability tends to decrease with aging even in a healthy state. Therefore, as described above, if the predetermined threshold is constant regardless of the driver's age, that is, if the diagnosis of MCI is performed based on an absolute evaluation criterion independent of the age, the older the driver is It becomes easier to diagnose that MCI has occurred. On the other hand, if the predetermined threshold is changed according to the age of the driver, for example, the predetermined threshold can be increased as the driver is older. Therefore, diagnosis of MCI based on the ease of occurrence of error behavior according to aging becomes possible.

  When the predetermined threshold varies depending on the age of the driver, the predetermined threshold is an average and variance of the values of the behavior evaluation indices of other driver groups of the same age as the driver. It may be determined based on. In this case, the diagnosis of MCI can be performed based on the relative evaluation criteria of other driver groups of the same age.

When the driver is elderly, the value of the behavior evaluation index tends to increase relatively slowly over a plurality of years. On the other hand, if the driver develops MCI in the middle of evaluating the driver's driving behavior for a plurality of years using the behavior evaluation index, the value of the behavior evaluation index after the onset of MCI is greater than that before the onset. Increases relatively steeply. In other words, when the driver develops MCI, the rate of change of the value of the behavior evaluation index becomes larger than before. Therefore, the diagnosis of MCI can be performed based on the rate of change of the value of the behavior evaluation index and the differential value of the rate of change.

  Therefore, the first diagnosis means according to the first invention has a mild cognitive impairment in the driver when the rate of change of the value of the behavior evaluation index in the predetermined second period is larger than the predetermined rate of change. May be diagnosed. In this case, the behavior evaluation index is defined to mean that the larger the value, the worse the evaluation of the driving behavior of the driver.

  Here, the predetermined change rate is a threshold value for determining whether or not MCI occurs in the driver. Further, the predetermined second period is a period during which the symptoms of MCI can be detected. Note that the predetermined change rate during the predetermined second period can be determined in advance by experiments or the like.

  The predetermined rate of change may be constant regardless of the age of the driver. Thus, if the predetermined rate of change is constant regardless of the driver's age, the driver's cognitive ability can be diagnosed based on a unique standard.

  The predetermined change rate may change according to the age of the driver. As described above, when the predetermined change rate changes according to the age of the driver, for example, the predetermined change rate can be increased as the driver is older. Therefore, diagnosis of MCI based on the ease of occurrence of error behavior according to aging becomes possible.

  The driver diagnosis apparatus according to the first invention is capable of diagnosing MCI based on a change rate of a behavior evaluation index value, a change rate of a behavior evaluation index value, or a change rate of a behavior evaluation index value. it can. In addition, the driver diagnosis apparatus according to the first invention may perform MCI diagnosis based on a combination of these appropriately.

  Further, the calculation means according to the first invention may calculate the behavior evaluation index using the sensor information at a predetermined position based on the vehicle position information received by the communication means.

  There may be a route that is easy to drive or a route that is difficult to drive in the travel route of the vehicle. And, an error behavior hardly occurs on a route that is easy to drive, and an error behavior tends to easily occur on a route that is difficult to drive. Therefore, the calculation means calculates the behavior evaluation index at a predetermined position where error behavior is relatively likely to occur. Thereby, the calculation processing burden can be reduced.

  In the second mode of the present invention (hereinafter sometimes referred to as “second invention”), the driver diagnosis device diagnoses the safe driving characteristics of the driver of the vehicle. Specifically, the driver diagnosis apparatus according to the second invention evaluates the driving behavior of the driver of the vehicle by using communication means for communication and sensor information obtained from the sensor in the vehicle received by the communication means. Calculating means for calculating an action evaluation index for each driver, and calculating means for calculating the action evaluation index based on the number of error actions that are errors in the driving behavior of the driver; A first storage means for storing a history of the behavior evaluation index for each, a second diagnosis means for diagnosing a driver's safe driving characteristics based on the history of the behavior evaluation index, and the safe driving characteristics for each driver Second storage means for storing the information, wherein the communication means notifies the driver of safe driving characteristics of a driver of a vehicle surrounding the vehicle driven by the driver.

As described above, the first diagnosis means of the driver diagnosis apparatus according to the first invention diagnoses the driver's MCI based on the history of the behavior evaluation index for a plurality of years. In other words, it can be said that the driving characteristics of the driver are diagnosed based on a history of evaluating the driving behavior of the driver over a relatively long period of time. As a result, the driver diagnosis apparatus according to the first aspect of the invention makes it possible to detect MCI that is relatively difficult to detect at an early stage. Here, in the driver diagnosis apparatus according to the second invention, the second diagnosis means diagnoses the safe driving characteristics of the driver based on the history of the behavior evaluation index. As described above, the second diagnosis means evaluates the driving behavior of the driver over a relatively long period of time, like the first diagnosis means that enables early detection of MCI that is relatively difficult to detect. Since the safe driving characteristics of the driver are diagnosed based on the history, the safe driving characteristics can be suitably diagnosed.

  Conventionally, a technique for preventing an accident from occurring by sharing a driver's driving characteristics between vehicles is known. And if the safe driving characteristic of the driver | operator of each vehicle shared between vehicles is exact, an accident can be prevented suitably. Here, as described above, the second diagnosis unit can preferably diagnose the driver's safe driving characteristics. In the driver diagnostic apparatus according to the second invention, the communication means notifies the safe driving characteristics of the driver of the surrounding vehicle, so that the driver can grasp the safe driving characteristics of the driver of the surrounding vehicle. . That is, the safe driving characteristics of the drivers of the respective vehicles are diagnosed relatively accurately, and such safe driving characteristics are shared between the vehicles. Therefore, according to the driver diagnostic apparatus according to the second invention, an accident can be suitably prevented.

  As described above, the driver diagnosis apparatus according to the second invention can preferably diagnose the driver using the vehicle.

  Further, the calculating means according to the second invention may calculate the behavior evaluation index based on a weighted sum of the number of times of the plurality of types of error actions, similarly to the calculating means according to the first invention. Alternatively, the behavior evaluation index may be calculated using the sensor information at a predetermined position based on the vehicle position information received by the communication unit.

  The present invention can be specified as a driver diagnostic system including the driver diagnostic device according to the first invention or the driver diagnostic device according to the second invention. Further, it can be specified as a driver diagnosis method performed by the driver diagnosis apparatus according to the first invention or the driver diagnosis apparatus according to the second invention. The above processes and means can be freely combined and implemented as long as no technical contradiction occurs.

  According to the present invention, it is possible to provide a driver diagnosis apparatus and a driver diagnosis system that can suitably diagnose a driver using a vehicle.

It is a system configuration figure concerning a first embodiment. It is a 1st flowchart figure of the process which an error action detection part performs. It is a 2nd flowchart figure of the process which an error action detection part performs. It is a 3rd flowchart figure of the process which an error action detection part performs. It is a figure which shows the list of error number parameters. In 1st embodiment, it is a figure which shows the log | history for several years of the action evaluation index value in case a driver | operator is a healthy person. In 1st embodiment, it is a figure which shows the log | history for several years of the action evaluation index value in case a driver is an MCI sufferer. In the modification 1 of 1st embodiment, it is a 1st figure which shows the log | history for several years of the action evaluation index value in case a driver | operator is a MCI sufferer. In the modification 1 of 1st embodiment, it is a 2nd figure which shows the log | history of the behavioral evaluation index value in case the driver is an MCI sufferer for several years. It is a system configuration figure concerning modification 2 of a first embodiment. It is a figure which shows the correlation with an action evaluation index value and a safe driving characteristic. It is a figure which shows the relationship between an action evaluation index value and the rank of a safe driving characteristic. It is the figure which displayed the rank of the safe driving characteristic of the driver | operator of the surrounding vehicle by HUD (Head-Up Display).

(First embodiment)
<Configuration>
The outline of the first embodiment will be described. The first embodiment is a system including a vehicle and a server. The server diagnoses the driver's MCI, thereby functioning as a driver diagnosis apparatus according to the first invention.

  FIG. 1 is a configuration diagram of a system according to the present embodiment. The system includes an in-vehicle terminal 100 and a server device 200. The in-vehicle terminal 100 and the server device 200 can be configured by a CPU, a main storage device, an auxiliary storage device, and an input / output device. Each unit shown in FIG. 1 functions by loading a program stored in the auxiliary storage device into the main storage device and executing it by the CPU. Note that all or part of the in-vehicle terminal 100 and the server device 200 may be executed using a circuit designed for exclusive use.

  First, the configuration of the in-vehicle terminal 100 will be described. The measurement unit 101 is a means for processing electrical signals output from various sensors (not shown) connected thereto. Various sensors are mounted on the vehicle, and measure the behavior of the vehicle and detect the driving operation of the driver. For example, a triaxial acceleration sensor, a speed sensor, a yaw rate sensor, a steering angle sensor, and a direction indicator switch sensor correspond to various sensors. The measurement unit 101 acquires sensor information based on output values of various sensors. For example, the measurement unit 101 acquires the acceleration in the three-axis direction of the vehicle as sensor information based on the output value of the three-axis acceleration sensor. For example, the steering wheel steering angle as sensor information based on the output value of the steering angle sensor. To get.

  The image analysis unit 102 is means for analyzing an image captured by a connected in-vehicle camera (not shown). The in-vehicle camera images the surroundings of the vehicle, for example, the left and right lanes of the vehicle, preceding vehicles, road signs, traffic signals, and pedestrians. The image analysis unit 102 acquires sensor information based on an image captured by the in-vehicle camera. For example, the image analysis unit 102 acquires the travel position of the vehicle with respect to the left and right lanes as sensor information based on the images of the left and right lanes of the vehicle imaged by the in-vehicle camera.

  The inter-vehicle distance calculation unit 103 is a means for calculating the inter-vehicle distance from the preceding vehicle based on an output from a connected distance measuring device (not shown). In other words, it is means for acquiring the inter-vehicle distance from the preceding vehicle as sensor information. The distance measurement is performed by, for example, an infrared laser method, a millimeter wave radar method, an ultrasonic method, or a lidar method. The inter-vehicle distance calculation unit 103 can also calculate the inter-vehicle distance based on the output from the image analysis unit 102.

  The GNNS receiving unit 104 is a unit that receives a signal from a navigation satellite via a connected antenna (not shown) and measures the position of the vehicle. In other words, it is means for acquiring vehicle position information as sensor information.

  Sensor information is acquired by the measurement unit 101, the image analysis unit 102, the inter-vehicle distance calculation unit 103, and the GNNS reception unit 104 described above.

  The in-vehicle terminal control unit 105 is a unit that controls the entire in-vehicle terminal 100. Specifically, it is means for controlling acquisition of sensor information, transmission of the sensor information to the server device 200, and the like by the measurement unit 101, the image analysis unit 102, the inter-vehicle distance calculation unit 103, and the GNNS reception unit 104.

  The server communication unit 106 is means for communicating with the server device 200 connected via a network. For example, the server communication unit 106 transmits sensor information to the server device 200 and transmits input from the driver to the server device 200 via the input / output unit 107 described later. Further, information stored in the server device 200 is received from the server device 200. In this embodiment, communication with the server apparatus 200 is performed by communication using a wireless LAN, but known techniques such as packet communication using a mobile phone network and WiMAX (Worldwide Interoperability for Microwave Access) can also be used.

  The input / output unit 107 is a means for presenting information to the driver and receiving input from the driver. It is composed of a liquid crystal display and a keyboard or a touch panel. The driver of the vehicle can input information for identifying the driver of the vehicle to the in-vehicle terminal 100 using the input / output unit 107. Or the information for specifying the driver of a vehicle may be inputted into in-vehicle terminal 100 using a known technique.

  Next, the configuration of the server device 200 will be described. The vehicle communication unit 201 is means for communicating with the in-vehicle terminal 100. For example, the vehicle communication unit 201 receives sensor information from the server communication unit 106 or receives input from the driver via the input / output unit 107 from the server communication unit 106. Moreover, the information memorize | stored in the server apparatus 200 is transmitted to the vehicle-mounted terminal 100. FIG. In the present embodiment, the vehicle communication unit 201 performs communication using a wireless LAN, similarly to the server communication unit 106. In the present embodiment, the vehicle communication unit 201 corresponds to the communication means in the present invention.

  The error behavior detection unit 202 uses the sensor information received by the vehicle communication unit 201 to detect the number of errors in the driving behavior of the driver (hereinafter also referred to as “error behavior”). Here, the error behavior includes, for example, lane departure, shortening of the inter-vehicle distance, sudden acceleration / deceleration, collision, traffic violation (excessive speed, neglect of signal, etc.), increase in time required for right turn, and the like. Then, the behavior evaluation index calculation unit 203 calculates a behavior evaluation index based on the number of error actions. Here, the behavior evaluation index is an index for evaluating the driving behavior of the driver. Details of processing performed by the error behavior detection unit 202 and the behavior evaluation index calculation unit 203 will be described later. In the present embodiment, the error behavior detection unit 202 and the behavior evaluation index calculation unit 203 correspond to the calculation unit in the present invention.

  The data storage unit 204 stores the behavior evaluation index calculated by the behavior evaluation index calculation unit 203. Here, the server apparatus 200 can be accessed from a plurality of vehicles. Therefore, the data storage unit 204 stores the behavior evaluation index for each driver in association with the information for identifying the vehicle driver received by the vehicle communication unit 201 and the behavior evaluation index. Further, the data storage unit 204 stores an action evaluation index for each driver over a plurality of years. That is, the data storage unit 204 stores a history of a plurality of years of behavior evaluation indexes for each driver. In the present embodiment, the data storage unit 204 corresponds to the first storage means in the first invention.

  The diagnosis unit 205 diagnoses the driver's MCI based on a history of a plurality of years of behavior evaluation indexes for each driver stored in the data storage unit 204. Details of the processing performed by the diagnosis unit 205 will be described later. In the present embodiment, the diagnosis unit 205 corresponds to the first diagnosis means in the first invention.

  The server control unit 206 is a unit that controls the entire server device 200. Specifically, the received sensor information is input to the error behavior detection unit 202. It also controls behavioral evaluation index calculation and storage, and MCI diagnosis. In addition, access between the vehicle communication unit 201 and the data storage unit 204 or the diagnosis unit 205 is controlled.

<Detection process of number of error actions>
Next, the error action number detection process will be described with reference to FIGS. 2A, 2B, and 3 which are flowcharts of the process performed by the error action detection unit 202. FIG. The error behavior includes, for example, lane departure, shortening of the distance between vehicles, sudden acceleration / deceleration, collision, traffic violation (excessive speed, neglect of signal, etc.), and increase of time required for a right turn. 2A, 2B, and 3 are repeatedly executed at a predetermined calculation cycle while the vehicle is traveling.

  2A and 2B show a flow for detecting sudden acceleration or sudden deceleration of the vehicle as an error action. In this flow, first, in S101, it is determined whether or not nflag, which is a flag for controlling the count of the number of data acquisitions by an acquisition counter n described later, is 1. If an affirmative determination is made in S101, the error behavior detection unit 202 proceeds to the process of S102, and if a negative determination is made in S101, the error behavior detection unit 202 proceeds to the process of S103.

  If an affirmative determination is made in S101, then in S102, an acquisition counter n that counts the number of data acquisitions is initialized to zero. The acquisition counter n is also initialized to 0 when the vehicle ignition is turned off.

  Next, in S103, 1 is added to the acquisition counter n. In step S <b> 104, the error behavior detection unit 202 acquires the acceleration Acc [n] and the travel distance D [n] received by the vehicle communication unit 201. Here, the acceleration Acc [n] is an array of acceleration values in the traveling direction of the vehicle, and the acceleration value is measured by the measurement unit 101 of the vehicle. The travel distance D [n] is an array of distance values traveled by the vehicle, and the distance value is measured by the measurement unit 101 of the vehicle. The acquired acceleration Acc [n] and travel distance D [n] are stored in the memory of the error behavior detection unit 202.

  Next, in S105, the accumulated travel distance Dis is calculated. In S105, the accumulated travel distance Dis is calculated by integrating the travel distance D [n]. In S106, it is determined whether or not the vehicle has traveled a unit distance. In S106, the above determination is performed based on the integrated travel distance Dis calculated in S105. Here, the unit distance is 1 km, for example. If an affirmative determination is made in S106, the error behavior detection unit 202 proceeds to the processing of S107, and if a negative determination is made in S106, the error behavior detection unit 202 proceeds to the processing of S109.

  If an affirmative determination is made in S106, then in S107, the flag nflag is set to 1, and in S108, the accumulated travel distance Dis is initialized to 0. Then, after the process of S108, the error behavior detecting unit 202 proceeds to the process of S111.

  On the other hand, if a negative determination is made in S106, the flag nflag is set to 0 in S109. Then, after the process of S109, the execution of this flow is terminated.

After S108, in S111, a reading counter i that counts the number of readings, and a rapid acceleration / deceleration number Nea that is the number of sudden accelerations or decelerations of the vehicle, which will be described later.
c is initialized to 0.

  Next, in S112, 1 is added to the reading counter i. In step S113, the acceleration Acc [i] stored in the memory of the error behavior detection unit 202 is read. In S114, it is determined whether or not the absolute value of the acceleration Acc [i] read in S113 is larger than a predetermined threshold value Accth. Here, the predetermined threshold value Accth is a threshold value for detecting sudden acceleration or deceleration of the vehicle, and when the absolute value of the acceleration Acc [i] is larger than the predetermined threshold value Accth, It can be determined that sudden deceleration has occurred. The predetermined threshold Accth is a predetermined value. In S114, determination may be performed so that each of rapid acceleration and rapid deceleration of the vehicle can be determined. For example, assuming that the acceleration Acc [i] becomes a positive value when the vehicle accelerates, it is determined in S114 whether or not the value of the acceleration Acc [i] is larger than a predetermined threshold value Accth ′ for sudden acceleration. May be. For example, assuming that the acceleration Acc [i] becomes a negative value when the vehicle decelerates, it is determined in S114 whether or not the value of the acceleration Acc [i] is smaller than a predetermined threshold value Accth ″ for sudden deceleration. May be. If an affirmative determination is made in S114, the error behavior detecting unit 202 proceeds to the process of S115, and if a negative determination is made in S114, the error behavior detecting unit 202 proceeds to the process of S116.

  If an affirmative determination is made in S114, then 1 is added to the rapid acceleration / deceleration number Neac in S115. That is, the number of sudden accelerations or decelerations of the vehicle is counted. If it is determined in S114 that it is possible to determine whether the vehicle is rapidly accelerating or decelerating, the number of times each of the vehicle is rapidly accelerating or decelerating may be counted in S115.

  Next, in S116, it is determined whether or not the value of the reading counter i is equal to the value of the acquisition counter n. If an affirmative determination is made in S116, the execution of this flow is terminated. If a negative determination is made in S116, the error behavior detection unit 202 returns to the process of S112.

  The error behavior detection unit 202 can detect the number of sudden accelerations or decelerations of the vehicle per unit distance by executing the processing described above.

  In the process of S114 described above, it may be determined whether or not the vehicle has collided with the preceding vehicle. In this case, for example, assuming that the acceleration Acc [i] becomes a negative value when the vehicle decelerates, in S114, whether or not the value of the acceleration Acc [i] is smaller than a predetermined threshold value Accth ″ ′ for the collision. Is determined. In S115, the number of vehicle collisions is counted. Thereby, the number of collisions per unit distance can be detected.

  2A and 2B, the sensor information received by the vehicle communication unit 201 is stored in the memory of the error behavior detection unit 202 until the vehicle travels a unit distance. When the vehicle travels a unit distance, the number of error actions is counted based on the sensor information stored in the memory of the error action detection unit 202. On the other hand, the error behavior detection unit 202 may count the number of error behaviors when the vehicle travels a unit distance. This will be described with reference to FIG.

FIG. 3 shows a flow for detecting overspeed as an error action. In this flow, first, in S201, the error behavior detection unit 202 acquires the vehicle speed value Vcar, the travel distance value D, and the speed limit value Vlimit received by the vehicle communication unit 201. Here, the vehicle speed value Vcar is a travel speed value of the vehicle, and is measured by the measurement unit 101 of the vehicle. The travel distance value D is a value of the distance traveled by the vehicle, and is measured by the measurement unit 101 of the vehicle. The speed limit value Vlimit is a speed limit value of a road on which the vehicle is traveling. The speed limit value Vlimit is acquired by the image analysis unit 102 analyzing the road sign image captured by the in-vehicle camera. Alternatively, the speed limit value Vlimit is acquired when the GNNS receiving unit 104 of the vehicle receives the speed limit value at the current location from the map database.

  Next, in S202, the accumulated travel distance Dis is calculated. In S202, the accumulated travel distance Dis is calculated by integrating the travel distance value D. In S203, it is determined whether or not the vehicle has traveled a unit distance. In S203, the above determination is performed based on the accumulated travel distance Dis calculated in S202. Here, the unit distance is 1 km, for example. If an affirmative determination is made in S203, the error behavior detection unit 202 proceeds to the process of S206, and if a negative determination is made in S203, the error behavior detection unit 202 proceeds to the process of S204.

  If a negative determination is made in S203, it is then determined in S204 whether or not the vehicle speed value Vcar acquired in S201 is greater than the speed limit value Vlimit acquired in S201. If an affirmative determination is made in S204, the error behavior detection unit 202 proceeds to the process of S205. If a negative determination is made in S204, the execution of this flow is terminated.

  If an affirmative determination is made in S204, then in S205, 1 is added to the speed limit excess number Nevc, which is the number of times the speed limit is exceeded. That is, the number of times the speed limit is exceeded is counted. Then, after the process of S205, the execution of this flow is terminated.

  If an affirmative determination is made in S203, then, in S206, the accumulated travel distance Dis and the speed limit excess number Nevc are initialized to zero. Then, after the process of S206, execution of this flow is terminated.

  The error behavior detection unit 202 can detect the number of times of exceeding the speed limit per unit distance by executing the processing described above.

  Then, the error action detection unit 202 includes, as error actions, lane departure, shortening of the inter-vehicle distance, increasing variation of the inter-vehicle distance, increasing time required for a right turn, sudden deceleration, sudden acceleration, collision, traffic violation, and direction indication It is possible to detect the unusedness of the vessel. As will be described later, the behavior evaluation index calculation unit 203 calculates a behavior evaluation index based on the error number parameter related to the number of error actions. Here, FIG. 4 shows a list of error number parameters in the present embodiment. The error behavior detection unit 202 can calculate these parameters based on the processing referring to the flow shown in FIGS. 2A, 2B, and 3 described above. For example, the number of lane departures is calculated using the steering angle value (or standard deviation), yaw rate value (or standard deviation), or acceleration value (or standard deviation) measured by the vehicle measurement unit 101. The Alternatively, the number of lane departures is calculated using information on the travel position of the vehicle with respect to the lane acquired by the image analysis unit 102 of the vehicle. In addition, for example, the number of detections for shortening the inter-vehicle distance is calculated using the inter-vehicle distance value with the preceding vehicle acquired by the inter-vehicle distance calculation unit 103 of the vehicle. Further, for example, the increase in the variation in the inter-vehicle distance is calculated using the inter-vehicle distance value (or standard deviation) from the preceding vehicle acquired by the inter-vehicle distance calculation unit 103.

ここで、ＤＥＲは行動評価指標の値である。また、Ｎｅｌｏｓ、Ｎｅｌｏｙ、Ｎｅｌｏａ、Ｎｅｌｏｐ、Ｎｅｄｓ、Ｎｅｄｖ、Ｎｅｒｔ、Ｎｅｓｄ、Ｎｅｓａ、Ｎｅｃｌ、Ｎｅｔｖ、およびＮｅｂｌは、上記の図４に示したエラー数パラメータである。また、Ｃ０からＣ１１は、エラー数パラメータに乗算される重み係数である。この重み係数は、実験等によって予め定められる。 <Behavior evaluation index calculation process>
Next, behavior evaluation index calculation processing performed by the behavior evaluation index calculation unit 203 will be described. The behavior evaluation index calculation unit 203 calculates a behavior evaluation index using the following formula 1.

Here, DER is a value of an action evaluation index. Further, Nelos, Neloy, Nealo, Nerop, Neds, Nedv, Nert, Nesd, Nesa, Necl, Netv, and Nebl are the error number parameters shown in FIG. Further, C0 to C11 are weighting coefficients that are multiplied by the error number parameter. This weighting factor is determined in advance by experiments or the like.

  Then, the behavior evaluation index value DER calculated as in the above equation 1 means that the evaluation of the driving behavior of the driver is worse as the value becomes larger. In Equation 1, each error number parameter is multiplied by a weighting factor. However, the behavioral evaluation index value DER need not be multiplied by a weighting factor. In calculating the behavior evaluation index value DER, all of the error number parameters shown in FIG. 4 may be used, some of them may be used, or one of them may be used. Good.

  Further, the behavior evaluation index calculation unit 203 may calculate the behavior evaluation index value DER at a predetermined position where error behavior is relatively likely to occur. In other words, it is not necessary to calculate the behavior evaluation index value DER at a position where error behavior is relatively difficult to occur. This is because, in a position where error behavior is relatively difficult to occur, error behavior may not occur even if the driver's cognitive ability is reduced. The determination of the predetermined position is performed based on information received from the map database by the GNNS receiving unit 104 of the vehicle.

<Memory processing of action evaluation index>
Next, a behavior evaluation index storage process performed by the data storage unit 204 will be described. As described above, the data storage unit 204 stores a history of a plurality of years of behavior evaluation indices for each driver. Here, the behavior evaluation index value DER calculated by the above formula 1 is a value based on the number of error behaviors per unit distance. Therefore, when the driver travels more than a unit distance throughout the day, the behavior evaluation index value DER is calculated for each unit distance. The action evaluation index value DER of the day can be calculated by dividing the sum of the action evaluation index values DER of the day by the travel distance of the day. The data storage unit 204 stores the daily behavior evaluation index value DER calculated in this way. Or the data storage part 204 memorize | stores the action evaluation index value DER for every several days calculated by dividing what integrated the action evaluation index value DER for every unit distance over the several days by the travel distance of the said several days. May be.

<MCI diagnosis processing>
Next, the MCI diagnosis process performed by the diagnosis unit 205 will be described. Since the symptoms of MCI do not always develop in daily life, even if the driver suffers from MCI, the behavioral evaluation index value DER may be relatively large in a certain period. For example, it may be relatively small. In addition, the diagnosis of MCI requires observation of behaviors related to the long-term advanced cognitive ability of subjects in daily life. Therefore, the diagnosis unit 205 diagnoses that the driver has MCI when the behavior evaluation index value DER is greater than a predetermined threshold DERth at a predetermined frequency or more in a predetermined first period. This will be described in detail below.

  FIG. 5 and FIG. 6 show a comparison between a healthy person and an MCI-affected person with respect to the history of the behavioral evaluation index value DER over a plurality of years. FIG. 5 is a diagram illustrating a history of a plurality of years of the behavior evaluation index value DER when the driver is a healthy person. On the other hand, FIG. 6 is a diagram illustrating a history of a plurality of years of the behavior evaluation index value DER when the driver is a person suffering from MCI. It is assumed that the drivers in FIGS. 5 and 6 are of the same age and are relatively old. Here, lines L2 to L5 in FIGS. 5 and 6 represent the variance of the behavior evaluation index values DER of other driver groups of the same age as the driver, and line L2 represents the behavior of the other driver groups. The average value of the evaluation index value DER, lines L3 to L5 represent values obtained by adding a standard deviation to the average value. Specifically, the line L3 represents the average value + σ, the line L4 represents the average value + 2σ, and the line L5 represents the average value + 3σ. 5 and 6, the line L5 corresponds to the predetermined threshold value DERth.

  In FIG. 5, a line L1 indicates a time transition of the behavior evaluation index value DER when the driver is a healthy person. Here, the period from time t1 to time t2, and the period from time t2 to time t3 are, for example, one year. A period from time t2 after time t2 and before time t3 to time t3 is defined as a predetermined first period Δt1. As shown in FIG. 5, the behavior evaluation index value DER increases with time from line L1 to line L5. This is because in elderly people, even in a healthy state, cognitive ability tends to decrease with aging. Here, the increase rate of the action evaluation index value DER in the line L1 is substantially constant, and the line L1 changes between the line L2 and the line L3. That is, the behavior evaluation index value DER when the driver is a healthy person is smaller than the predetermined threshold DERth represented by the line L5 in the predetermined first period Δt1.

  On the other hand, in FIG. 6, a line L1 ′ indicates a time transition of the behavior evaluation index value DER when the driver is an MCI sufferer. The times t1, t2, t23, t3 and the predetermined first period Δt1 are as described in the description of FIG. The line L1 ′ changes in the same manner as the line L1 in FIG. 5 from time t1 to time t2. In the period from time t2 to time t3, the increase rate of the action evaluation index value DER on the line L1 ′ is larger than before. As a result, during the period from time t2 to time t3, the behavior evaluation index value DER represented by the line L1 ′ is larger than the predetermined threshold value DERth represented by the line L5. Specifically, focusing on the predetermined first period Δt1, the behavioral evaluation index value DER becomes larger than the predetermined threshold DERth in the period Δt11 in the predetermined first period Δt1, as shown by the hatched portion in FIG. ing.

  As described above, the diagnosis unit 205 diagnoses that the driver has MCI when the behavior evaluation index value DER is greater than the predetermined threshold DERth at a predetermined frequency or more in the predetermined first period. Here, the predetermined first period and the predetermined frequency are a period and a frequency at which the symptoms of MCI can be detected. As shown in FIG. 6, when the behavior evaluation index value DER becomes larger than the predetermined threshold DERth in the period Δt11 in the predetermined first period Δt1, the diagnosis unit 205 causes MCI in the driver. Can be diagnosed.

  When MCI is found, information is presented to the driver via the input / output unit 107. Information can also be presented using known means such as e-mail. Even when no MCI is found, it is preferable that information accumulated in the data storage unit 204 can be referred to by the driver. As a result, the driver can know his / her behavior evaluation index value DER. Further, for example, when the driver's behavior evaluation index value DER is increasing, the server device 200 can notify the driver of this even before MCI is discovered. Thus, a warning can be given to a driver who may develop MCI.

  When the diagnosis unit 205 performs the MCI diagnosis process as described above, early detection of MCI that is difficult to detect becomes possible. That is, the driver diagnosis apparatus according to the first invention can preferably diagnose the driver using the vehicle. In the present embodiment, the diagnosis unit 205 can make a diagnosis of MCI based on a relative evaluation standard of other driver groups of the same age.

(Modification 1 of the first embodiment)
Next, Modification 1 of the above-described first embodiment will be described. In the present modification, detailed description of substantially the same configuration and substantially the same processing as those of the above-described first embodiment will be omitted.

  FIG. 7 is a diagram showing a plurality of years of behavior evaluation index values DER when the driver is an MCI-affected person, as in FIG. 6 described above. The times t1, t2, t23, t3 and the predetermined first period Δt1 are as described in the description of FIG. In FIG. 7, a line L6 represents a predetermined threshold DERth. Here, as indicated by a line L6, the predetermined threshold DERth is constant. Thereby, a driver's cognitive ability can be diagnosed based on a unique standard.

  In FIG. 7, paying attention to the predetermined first period Δt1, the behavior evaluation index value DER is higher than the predetermined threshold DERth in the period Δt11 ′ of the predetermined first period Δt1, as indicated by the hatched portion in FIG. It is getting bigger. As described above, when the behavior evaluation index value DER becomes larger than the predetermined threshold DERth in the period Δt11 ′ of the predetermined first period Δt1, the diagnosis unit 205 diagnoses that the driver has MCI. Can do.

  When the driver is elderly, error behavior is likely to occur according to aging. Therefore, the predetermined threshold DERth may be changed according to the age of the driver. This makes it possible to diagnose MCI based on the ease of occurrence of error behavior according to aging.

  FIG. 8 is a diagram showing a plurality of years of behavior evaluation index values DER when the driver is an MCI-affected person, as in FIG. Times t1, t2, and t3 are as described in the description of FIG. Here, a period from time t23 ′ after time t2 and before time t3 to time t3 is defined as a predetermined second period Δt2. In this case, the change of the line L1 ′ in the predetermined second period Δt2 is represented by the line L7.

  In FIG. 8, the slope of the line L7 is represented by Rder. And the line L8 shown with a dashed-dotted line has shown the line whose inclination is Rderth. Here, the slope Rderth is a threshold value for determining whether or not MCI has occurred in the driver, and corresponds to a predetermined rate of change in the first invention. In FIG. 8, it can be seen that the rate of change of the behavior evaluation index value DER in the predetermined second period Δt2 is larger than the predetermined rate of change. In this case, the diagnosis unit 205 can diagnose that MCI has occurred in the driver. The predetermined rate of change may be constant regardless of the driver's age, or may change according to the driver's age. Here, the value of the behavior evaluation index value DER varies depending on the individual driving skill, whereas the rate of change of the behavior evaluation index value DER is based on a change amount of the behavior evaluation index value DER in a predetermined period. Therefore, when the diagnosis unit 205 performs the MCI diagnosis process as described above, it is possible to eliminate the influence of individual differences in driving skills and perform the diagnosis.

  Even when the diagnosis unit 205 performs the MCI diagnosis process as described above, the early detection of the MCI that is difficult to detect becomes possible.

(Modification 2 of the first embodiment)
Next, Modification 2 of the first embodiment described above will be described. This modification is a system including a vehicle, a server, and a smartphone on the vehicle side. FIG. 9 is a configuration diagram of a system according to the present modification. Note that the same means as those in the first embodiment are denoted by the same reference numerals, and description thereof is omitted.

  This system is configured to include a smartphone 300 and a server device 200 mounted on the vehicle. The smartphone 300 and the server device 200 can be configured by a CPU, a main storage device, an auxiliary storage device, and an input / output device. Each program shown in FIG. 9 functions by loading a program stored in the auxiliary storage device into the main storage device and executing it by the CPU. Note that all or part of the smartphone 300 and the server device 200 may be executed using a dedicated circuit.

  A smartphone 300 different from the configuration described in the description of the first embodiment will be described. The smartphone 300 is similar to the vehicle-mounted terminal 100 described in the description of the first embodiment, and includes a measurement unit 301, an image analysis unit 302, an inter-vehicle distance calculation unit 303, a GNNS reception unit 304, a terminal control unit 305, and a server communication unit 306. And an input / output unit 307.

  The measurement unit 301 is a means for processing electrical signals output from various sensors (not shown) connected in the same manner as in the first embodiment. The various sensors are, for example, a triaxial acceleration sensor and a triaxial gyro sensor. The image analysis unit 302 is a means for analyzing an image captured by a camera (not shown) connected as in the first embodiment. The inter-vehicle distance calculation unit 303 is a means for calculating the inter-vehicle distance from the preceding vehicle as in the first embodiment. The inter-vehicle distance calculation unit 303 can calculate the inter-vehicle distance based on the output from the image analysis unit 302. The GNNS receiving unit 304 is a unit that receives a signal from a navigation satellite via an antenna (not shown) connected in the same manner as in the first embodiment, and measures the position of the vehicle.

  Sensor information is acquired by the measurement unit 301, the image analysis unit 302, the inter-vehicle distance calculation unit 303, and the GNNS reception unit 304 described above.

  The terminal control unit 305 is a unit that controls the entire smartphone 300. The server communication unit 306 is means for communicating with the server device 200 connected via a network, as in the first embodiment. The input / output unit 307 is a means for presenting information to the driver and receiving input from the driver, as in the first embodiment.

  In the server device 200 that receives sensor information from the smartphone 300 described above, as in the first embodiment, the number of error behavior detection processing, behavior evaluation index calculation processing, behavior evaluation index storage processing, MCI A diagnostic process is performed. This enables early detection of MCI that is difficult to find.

(Second embodiment)
The second embodiment is a system including a vehicle and a server. The server functions as a driver diagnosis apparatus according to the second invention by diagnosing the driver's safe driving characteristics. The system configuration according to the second embodiment is represented by FIG. 1 as in the first embodiment. Hereinafter, in the configuration of the present system, a configuration different from the configuration described in the description of the first embodiment will be described.

  In the system configuration shown in FIG. 1, the diagnosis unit 205 diagnoses a driver's safe driving characteristics based on a history of a plurality of years of behavior evaluation indices for each driver stored in the data storage unit 204. Details of the processing performed by the diagnosis unit 205 will be described later. In the present embodiment, the diagnosis unit 205 corresponds to the second diagnosis means in the second invention.

  The data storage unit 204 stores the safe driving characteristics of the driver diagnosed by the diagnosis unit 205 for each driver. The vehicle communication unit 201 transmits to the server communication unit 106 of the vehicle the safe driving characteristics of the drivers of the vehicles around the vehicle driven by the driver stored in the data storage unit 204 to the vehicle driver. To do. In addition, the server apparatus 200 can specify the driver | operator of a surrounding vehicle by transmitting the positional information and driver | operator information of each vehicle to the server apparatus 200 by the server communication part 106 of each vehicle. Therefore, the vehicle communication unit 201 can correctly transmit the safe driving characteristics of the drivers of the surrounding vehicles. In the present embodiment, the data storage unit 204 corresponds to the first storage unit and the second storage unit in the second invention.

  The input / output unit 107 displays the safe driving characteristics of the driver of the surrounding vehicle received by the server communication unit 106. Details of this will be described later. In the present embodiment, the input / output unit 107 corresponds to the display means in the second invention.

  Next, the driver's safe driving characteristic diagnosis process performed by the diagnosis unit 205 will be described. In the present embodiment, the behavior evaluation index value DER is calculated using the above formula 1, as in the first embodiment described above. Therefore, the behavior evaluation index value DER means that the evaluation of the driving behavior of the driver is worse as the value becomes larger. And the safe driving characteristic and the action evaluation index value DER which the diagnosis part 205 of this embodiment diagnoses have a correlation as shown in FIG. FIG. 10 is a diagram showing the correlation between the behavior evaluation index value DER and the safe driving characteristics, and the safe driving characteristics become lower as the behavior evaluation index value DER increases. Here, the safe driving characteristic represents the magnitude of the tendency of the driver to perform safe driving. The larger the value, the easier the driver performs safe driving.

  The diagnosis unit 205 ranks the driver's safe driving characteristics according to the behavior evaluation index value DER. This will be described with reference to FIG. FIG. 11 is a diagram illustrating a relationship between the behavior evaluation index value DER and the rank of the safe driving characteristic. In FIG. 11, Sc1, Sc2, and Sc3 represent the ranks of the safe driving characteristics, and the safe driving characteristics increase in the order of Sc3, Sc2, and Sc1. That is, a driver with a safe driving characteristic rank of Sc1 is more likely to perform safe driving than a driver with a safe driving characteristic rank of Sc2 and Sc3.

  As shown in FIG. 11, a driver whose behavior evaluation index value DER is equal to or greater than D0 and less than D1 has a rank of the safe driving characteristic of Sc1. A driver whose behavioral evaluation index value DER is D1 or more and less than D2 has a safety driving characteristic rank of Sc2, and a driver of D2 or more has a safety driving characteristic rank of Sc3. Thus, D0, D1, and D2 serve as threshold values for determining the rank of safe driving characteristics. And the diagnostic part 205 can rank a driver | operator's safe driving characteristic based on the log | history of the action evaluation index value DER for every driver memorize | stored in the data memory | storage part 204, and these threshold values. . Here, the diagnosis unit 205 ranks the driver's safe driving characteristics with reference to the comparison process between the behavior evaluation index value DER and the predetermined threshold in the MCI diagnosis process of the first embodiment described above. Can do.

Next, the safe driving characteristics of the driver of the surrounding vehicle displayed by the input / output unit 107 will be described. FIG. 12 is an example in which the rank of the safe driving characteristics of the driver of the surrounding vehicle is displayed by HUD (Head-Up Display). In FIG. 12, the rank of the safe driving characteristic of the driver of the surrounding vehicle and the forward view are superimposed and displayed by AR (Augmented Reality) technology. And in the rank of the safe driving characteristic shown in FIG. 12, the single square of the solid line represents rank Sc1, the single square of the broken line represents rank Sc2, and the double square of the solid line represents rank Sc3. .

According to FIG. 12, it can be seen that there is a vehicle driven by a driver having a relatively low rank of safe driving characteristics in the lane on the side of the median strip ahead of the vehicle. In this case, the driver can take measures to prevent accidents such as traveling away from such a vehicle.

  The means for displaying the rank of the safe driving characteristics of the driver of the surrounding vehicle is not limited to the HUD, and for example, the rank can be displayed on an electronic room mirror or an electronic side mirror. Alternatively, the rank can be displayed on the map of the navigation screen.

DESCRIPTION OF SYMBOLS 100 In-vehicle terminal 101 Measurement part 102 Image analysis part 103 Inter-vehicle distance calculation part 104 GNNS receiving part 105 In-vehicle terminal control part 106 Server communication part 107 Input / output part 200 Server apparatus 201 Vehicle communication part 202 Error action detection part 203 Action evaluation index calculation part 204 Data storage unit 205 Diagnosis unit 206 Server control unit

Claims (18)

A communication means for performing communication;
A calculation means for calculating for each driver an action evaluation index, which is an index for evaluating the driving action of the driver of the vehicle, using sensor information obtained from the sensor in the vehicle received by the communication means. Calculating means for calculating the behavior evaluation index based on the number of error behaviors that are errors in the driving behavior of
First storage means for storing a history of a plurality of years of the behavior evaluation index for each driver;
A first diagnostic means for diagnosing a driver's mild cognitive impairment based on a history of the behavior evaluation index for a plurality of years;
A driver diagnostic device comprising: The calculation means calculates the behavior evaluation index based on a weighted sum of the number of types of error behaviors of a plurality of types.
The driver diagnostic apparatus according to claim 1. The behavior evaluation index is an index that is defined to mean that the evaluation of the driving behavior of the driver is worse as the value increases,
The first diagnosis means diagnoses that the driver has a mild cognitive impairment when the value of the behavior evaluation index is greater than a predetermined threshold at a predetermined frequency or more in a predetermined first period.
The driver diagnostic apparatus according to claim 1 or 2. The predetermined threshold value is constant regardless of a driver's age,
The driver diagnostic apparatus according to claim 3. The predetermined threshold varies according to the age of the driver,
The driver diagnostic apparatus according to claim 3. The predetermined threshold is determined based on an average and variance of values of the behavior evaluation index of other driver groups of the same age as the driver.
The driver diagnosis apparatus according to claim 5. The behavior evaluation index is an index that is defined to mean that the evaluation of the driving behavior of the driver is worse as the value increases,
The first diagnosis means diagnoses that the driver has mild cognitive impairment when the change rate of the value of the behavior evaluation index in a predetermined second period is larger than the predetermined change rate,
The driver diagnostic apparatus according to claim 1 or 2. The predetermined rate of change is constant regardless of the age of the driver,
The driver diagnostic apparatus according to claim 7. The predetermined change rate changes according to the age of the driver,
The driver diagnostic apparatus according to claim 7. The calculation means calculates the behavior evaluation index using the sensor information at a predetermined position based on the vehicle position information received by the communication means.
The driver diagnostic apparatus according to any one of claims 1 to 9. A communication means for performing communication;
A calculation means for calculating for each driver an action evaluation index, which is an index for evaluating the driving action of the driver of the vehicle, using sensor information obtained from the sensor in the vehicle received by the communication means. Calculating means for calculating the behavior evaluation index based on the number of error behaviors that are errors in the driving behavior of
First storage means for storing a history of the behavior evaluation index for each driver;
A second diagnostic means for diagnosing a driver's safe driving characteristics based on the history of the behavior evaluation index;
Second storage means for storing the safe driving characteristics for each driver;
With
The driver diagnosis apparatus characterized in that the communication means notifies the driver of safe driving characteristics of a driver of a vehicle surrounding the vehicle driven by the driver. The calculation means calculates the behavior evaluation index based on a weighted sum of the number of types of error behaviors of a plurality of types.
The driver diagnostic apparatus according to claim 11. The calculation means calculates the behavior evaluation index using the sensor information at a predetermined position based on the vehicle position information received by the communication means.
The driver diagnostic apparatus according to claim 11 or 12. A driver diagnosis system including a server device and a vehicle,
The server device is
A communication means for performing communication;
A calculation means for calculating for each driver an action evaluation index, which is an index for evaluating the driving action of the driver of the vehicle, using sensor information obtained from a sensor in the vehicle received by the communication means. Calculating means for calculating the behavior evaluation index based on the number of error behaviors that are errors in the driving behavior of the person,
First storage means for storing a history of the behavior evaluation index for each driver;
A second diagnostic means for diagnosing a driver's safe driving characteristics based on the history of the behavior evaluation index;
Second storage means for storing the safe driving characteristics for each driver;
Have
The vehicle
Server communication means for communicating with the server device;
Display means for displaying information of surrounding vehicles of the vehicle;
Have
The communication means transmits to the vehicle the safe driving characteristics of a driver of a vehicle surrounding the vehicle,
The driver diagnostic system characterized in that the display means displays a safe driving characteristic of the driver of the surrounding vehicle received by the server communication means. The calculation means calculates the behavior evaluation index based on a weighted sum of the number of types of error behaviors of a plurality of types.
The driver diagnosis system according to claim 14. The calculation means calculates the behavior evaluation index using the sensor information at a predetermined position based on the position information of the vehicle received by the communication means.
The driver diagnosis system according to claim 14 or 15. Communicating, and
A step of calculating for each driver an action evaluation index, which is an index for evaluating the driving behavior of the driver of the vehicle, using sensor information obtained from the sensor in the received vehicle, and an error in the driving behavior of the driver Calculating the behavior evaluation index based on the number of error behaviors that are:
Storing a multi-year history of the behavior evaluation index for each driver;
Diagnosing a driver's mild cognitive impairment based on a plurality of years of history of the behavior evaluation index;
A driver diagnostic method including: Communicating, and
A step of calculating for each driver an action evaluation index, which is an index for evaluating the driving behavior of the driver of the vehicle, using sensor information obtained from the sensor in the received vehicle, and an error in the driving behavior of the driver Calculating the behavior evaluation index based on the number of error behaviors that are:
Storing a history of the behavior evaluation index for each driver;
Diagnosing the driver's safe driving characteristics based on the history of the behavior evaluation index;
Storing the safe driving characteristics for each driver;
Notifying the driver of the safe driving characteristics of the driver of the surrounding vehicle of the vehicle that the driver drives;
A driver diagnostic method including:

Images