Characteristic change detection system, characteristic change detection device, on-vehicle device, characteristic change detection method, and computer program

Abstract

This characteristic change detection system includes a characteristic change detection device that uses vehicle information pertaining to the travel of a vehicle to detect characteristic changes of a driver driving the vehicle over time. The characteristic change detection device includes a determination unit that references a first distribution indicating normal characteristics and a second distribution indicating danger characteristics and that determines whether the vehicle information indicates normal characteristics or danger characteristics.

Description

Characteristic change detection system, characteristic change detection device, in-vehicle device, characteristic change detection method, and computer program

[0001] The present disclosure relates to a characteristic change detection system, a characteristic change detection device, an in-vehicle device, a characteristic change detection method, and a computer program. This application claims priority to Japanese Application No. 2024-020754, filed February 15, 2024, the entire contents of which are incorporated herein by reference.

[0002] As society ages, the proportion of elderly drivers is increasing, and so is the proportion of traffic accidents caused by elderly drivers. As elderly people age, their physical functions change, such as a decline in dynamic vision, difficulty processing multiple pieces of information simultaneously, and a decline in the ability to make instantaneous decisions. Due to these changes in physical functions, elderly people may experience characteristics such as delays in steering or braking when driving.

[0003] However, because human functions tend to gradually decline, it is difficult for drivers to notice when they are making slow decisions when steering or braking during their daily driving.

[0004] As a technology related to driver driving, there is conventionally known a technology that warns a driver when the driver's condition is estimated to be abnormal. Such a technology is disclosed in Patent Document 1, which will be described later. The technology disclosed in Patent Document 1 defines a probability distribution of normal driving conditions and abnormal driving conditions, and estimates the driver's driving fitness based on this probability distribution. Patent Document 1 further displays a warning message on a display device according to the estimated driver's driving fitness.

[0005] JP 2009-145951 A

[0006] According to an aspect of the present disclosure, there is provided a characteristic change detection system including a characteristic change detection device that detects a change in characteristics over time of a driver who drives a vehicle using vehicle information related to the vehicle's travel. The characteristic change detection device includes a determination unit that determines whether the vehicle information indicates a normal characteristic or a dangerous characteristic by referring to a first distribution indicating a normal characteristic and a second distribution indicating a dangerous characteristic, both of which are prepared in advance.

[0007] The present disclosure can be realized not only as a characteristic change detection system, a characteristic change detection device, an in-vehicle device, a characteristic change detection method, and a computer program that include such characteristic configurations, but also as other systems, devices, methods, or computer programs that include a characteristic change detection system, a characteristic change detection device, an in-vehicle device, a characteristic change detection method, or a computer program.

[0008] FIG. 1 is a diagram for explaining an example of the configuration of a characteristic change detection system according to a first embodiment. FIG. 2 is a block diagram showing an example of the hardware configuration of an in-vehicle device shown in FIG. 1. FIG. 3 is a block diagram showing an example of the hardware configuration of a server shown in FIG. 1. FIG. 4 is a block diagram showing an example of the functional configuration of an in-vehicle system including the in-vehicle device shown in FIG. 1. FIG. 5 is a block diagram showing an example of the functional configuration of the server shown in FIG. 1. FIG. 6 is a diagram for explaining a characteristic change detection method. FIG. 7 is a diagram showing an example of driving data of a driver having normal characteristics. FIG. 8 is a diagram showing an example of driving data of a driver who may have dangerous characteristics. FIG. 9 is a diagram showing a frequency distribution of accelerator opening difference per unit time during cruising. FIG. 10 is a diagram showing a frequency distribution of accelerator opening difference per unit time during acceleration. FIG. 11 is a flowchart showing an example of the control structure of a program executed in the server according to the first embodiment. FIG. 12 is a flowchart showing an example of the control structure of a program executed in the server according to the first embodiment. FIG. 13 is a flowchart showing an example of the control structure of a program executed in the server according to the first embodiment. FIG. 14 is a flowchart showing an example of the control structure of a program executed in the server according to the first embodiment. FIG. 15 is a diagram showing an example of each group when driver characteristics are grouped. FIG. 16 is a diagram for explaining a process for updating a risk accumulation value. FIG. 17 is a flowchart showing an example of a control structure of a program executed in a server according to the first embodiment. FIG. 18 is a flowchart showing an example of a control structure of a program executed in a server according to a first modified example. FIG. 19 is a diagram for explaining an example of the configuration of a characteristic change detection system according to a second embodiment. FIG. 20 is a diagram for explaining an example of the configuration of a system according to a third embodiment. FIG. 21 is a block diagram showing an example of the functional configuration of the in-vehicle device shown in FIG. 20.

[0009] [Problem to be Solved by the Present Disclosure] The technology disclosed in Patent Document 1 estimates a driver's state that is different from normal, such as a state of drowsiness, a state of drinking, or a state of absentmindedness, as a driver's fitness for driving. That is, the technology disclosed in Patent Document 1 estimates the driver's state while driving. Therefore, even if the technology disclosed in Patent Document 1 is used, it is difficult to detect changes in the driver's characteristics due to aging.

[0010] The present disclosure has been made to solve such problems, and one object of the present disclosure is to provide a characteristic change detection system, a characteristic change detection device, an in-vehicle device, a characteristic change detection method, and a computer program that are capable of detecting changes in a driver's characteristics.

[0011] Effect of the Present Disclosure According to the present disclosure, it is possible to provide a characteristic change detection system, a characteristic change detection device, an in-vehicle device, a characteristic change detection method, and a computer program that are capable of detecting a change in a driver's characteristic.

[0012] [Description of Embodiments of the Present Disclosure] Preferred embodiments of the present disclosure will be listed and described below. At least some of the embodiments described below may be combined in any combination.

[0013] (1) A characteristic change detection system according to a first aspect of the present disclosure includes a characteristic change detection device that detects a change in a characteristic of a driver of a vehicle over time using vehicle information related to the vehicle's travel. The characteristic change detection device includes a determination unit that determines whether the vehicle information indicates a normal characteristic or a dangerous characteristic by referring to a first distribution indicating a normal characteristic and a second distribution indicating a dangerous characteristic, both of which are prepared in advance.

[0014] The determination unit determines whether the vehicle information indicates a normal characteristic or a dangerous characteristic by referring to the first distribution indicating a normal characteristic and the second distribution indicating a dangerous characteristic, thereby detecting a change in the driver's characteristics even if the driver's characteristics change over time.

[0015] (2) In (1) above, the characteristic change detection device includes an accumulation unit that maintains the risk count value for vehicle information that the judgment unit judges to indicate normal characteristics, and increases the risk count value for vehicle information that the judgment unit judges to indicate risk characteristics, thereby accumulating the risk, and a characteristic change detection unit that detects that the driver's characteristics have transitioned to risk characteristics depending on whether the risk count value accumulated by the accumulation unit is greater than or equal to a predetermined threshold value.

[0016] The accumulation unit maintains the risk count value for vehicle information that the determination unit determines to indicate normal characteristics, and increases the risk count value for vehicle information that the determination unit determines to indicate risk characteristics, thereby accumulating the risk levels.The characteristic change detection unit detects that the driver's characteristics have transitioned to risk characteristics depending on whether the risk count value is equal to or greater than a predetermined threshold.This makes it possible to detect changes in the driver's characteristics even if the driver's characteristics change over time.

[0017] (3) In the above (1) or (2), the characteristic change detection device may further include a notification unit that notifies at least one of the driver and a manager who manages the driver that the driver's characteristic has transitioned to a dangerous characteristic. This makes it possible to make the driver aware of the change in the driver's characteristic.

[0018] (4) In any one of (1) to (3) above, the vehicle information may include time-series information of at least one of an accelerator opening, a brake pressure, and a steering angle, thereby enabling changes in the driver's driving characteristics to be detected with high accuracy.

[0019] (5) In the above (4), the determination unit may be configured to calculate a difference between data per unit time in the time-series information and previous data, which is data per unit time immediately before that, and determine whether the calculated difference value indicates a normal characteristic or a dangerous characteristic by referring to the first distribution and the second distribution. This makes it possible to more accurately detect changes in the driver's driving characteristics.

[0020] (6) In any one of (1) to (5) above, the characteristic change detection device may further include an information acquisition unit that acquires at least one of biometric information of the driver, vehicle driving information, and environmental information outside the vehicle while the vehicle is driving, and a characteristic estimation unit that estimates the driver's characteristics based on the information acquired by the information acquisition unit. This makes it possible to estimate what characteristics have changed when the driver's characteristics have changed.

[0021] (7) In the above (6), a seat sensor may be provided in the seat where the driver sits, and the in-vehicle device may further transmit sensor data from the seat sensor to the characteristic change detection device as biological information. This allows, for example, a change in the characteristics related to the driver's driving posture to be estimated.

[0022] (8) In the above (6) or (7), the in-vehicle device may further be configured to transmit vehicle position information as driving information to the characteristic change detection device, thereby enabling estimation of changes in characteristics related to, for example, dynamic visual acuity or information processing ability.

[0023] (9) In any one of (6) to (8) above, the characteristic change detection device may be configured to communicate with an external server, and the information acquisition unit may be configured to acquire, as environmental information, weather information of a driving area when the vehicle is driving from the external server. This allows for estimation of a change in characteristics related to, for example, eyesight or information processing ability.

[0024] (10) In the above (2), the characteristic change detection device may further include a setting receiving unit that receives a setting for a period for accumulating the risk level, and the accumulating unit may be configured to accumulate the risk level for the period received by the setting receiving unit. This makes it possible to prevent the risk level from accumulating without an upper limit.

[0025] (11) In the above (10), the accumulation unit may shift the period when the period accepted by the setting acceptance unit is exceeded to accumulate the risk level, and update the count value of the risk level by deleting data that falls outside the period due to the shift, and the characteristic change detection device may further include a normal characteristic detection unit that detects that the driver's characteristic has transitioned from a risky characteristic to a normal characteristic depending on whether the updated count value of the risk level is less than a predetermined threshold, and a normal characteristic transition notification unit that notifies at least one of the driver and an administrator who manages the driver that the driver's characteristic has transitioned to the normal characteristic. This makes it possible to detect that the driver's characteristic has returned to the normal characteristic.

[0026] (12) An in-vehicle device according to a second aspect of the present disclosure is mounted on a vehicle and transmits vehicle information to the characteristic change detection device of the characteristic change detection system described in (1) or (2) above, thereby making it possible to detect changes in the driver's characteristics.

[0027] (13) A characteristic change detection device according to a third aspect of the present disclosure includes an acquisition unit that acquires vehicle information related to vehicle travel, and a processing unit that executes processing to detect a change in characteristics over time of a driver who drives the vehicle using the vehicle information acquired by the acquisition unit, wherein the processing unit includes a determination unit that determines whether the vehicle information indicates a normal characteristic or a dangerous characteristic by referring to a first distribution indicating a normal characteristic and a second distribution indicating a dangerous characteristic, which are prepared in advance. This makes it possible to detect a change in the driver's characteristics.

[0028] (14) In the above (13), the processing unit further includes an accumulation unit that maintains the count value of the risk level for vehicle information that the judgment unit judges to indicate normal characteristics and increases the count value of the risk level for vehicle information that the judgment unit judges to indicate dangerous characteristics, thereby accumulating the risk levels, and a characteristic change detection unit that detects that the driver's characteristics have transitioned to dangerous characteristics depending on whether the count value of the risk level accumulated by the accumulation unit is equal to or greater than a predetermined threshold value. This makes it possible to detect changes in the driver's characteristics.

[0029] (15) In the above (13) or (14), the characteristic change detection device may be a server device that communicates with the vehicle, thereby making it possible to easily detect changes in the driver's characteristics.

[0030] (16) In the above (13) or (14), the characteristic change detection device may be an on-board device mounted in a vehicle, thereby making it possible to easily detect changes in the driver's characteristics.

[0031] (17) A characteristic change detection method according to a fourth aspect of the present disclosure is a characteristic change detection method for detecting a change in a characteristic of a driver of a vehicle over time, the method including: an acquisition step in which a computer acquires vehicle information related to the traveling of the vehicle; and a detection step in which the computer detects a change in a characteristic of the driver driving the vehicle using the vehicle information acquired in the acquisition step. The step of detecting a change in a characteristic includes a determination step in which the vehicle information indicates a normal characteristic or a dangerous characteristic by referring to a first distribution indicating a normal characteristic and a second distribution indicating a dangerous characteristic, which are prepared in advance. This makes it possible to detect a change in the driver's characteristic.

[0032] (18) In the above (17), the detection step includes an accumulation step of maintaining the count value of the risk level for vehicle information determined in the determination step to exhibit normal characteristics and increasing the count value of the risk level for vehicle information determined in the determination step to exhibit dangerous characteristics, thereby accumulating the risk levels, and a step of detecting that the driver's characteristics have transitioned to dangerous characteristics depending on whether the count value of the accumulated risk level in the accumulation step is equal to or greater than a predetermined threshold value. This makes it possible to detect a change in the driver's characteristics.

[0033] (19) A computer program according to a fifth aspect of the present disclosure is a computer program that causes a computer to detect a change in characteristics of a driver who drives a vehicle over time, the computer program executing an acquisition step of acquiring vehicle information related to vehicle travel and a detection step of detecting a change in characteristics of the driver who drives the vehicle using the vehicle information acquired in the acquisition step. The detection step includes a determination step of determining whether the vehicle information indicates a normal characteristic or a dangerous characteristic by referring to a first distribution indicating a normal characteristic and a second distribution indicating a dangerous characteristic, which are prepared in advance. This makes it possible to detect a change in the driver's characteristics.

[0034] (20) In the above (19), the detection step includes an accumulation step of maintaining the count value of the risk level for vehicle information determined in the determination step to exhibit normal characteristics and increasing the count value of the risk level for vehicle information determined in the determination step to exhibit dangerous characteristics, thereby accumulating the risk levels, and a step of detecting that the driver's characteristics have transitioned to dangerous characteristics depending on whether the count value of the accumulated risk level in the accumulation step is equal to or greater than a predetermined threshold value. This makes it possible to detect a change in the driver's characteristics.

[0035] [Details of the embodiments of the present disclosure] Specific examples of a characteristic change detection system, a characteristic change detection device, an in-vehicle device, a characteristic change detection method, and a computer program according to embodiments of the present disclosure will be described below with reference to the drawings. Note that in the following embodiments, identical components are assigned the same reference numerals. Their functions and names are also identical. Therefore, detailed descriptions thereof will not be repeated.

[0036] (First embodiment) [Overall configuration] Referring to FIG. 1 , a characteristic change detection system 50 according to this embodiment includes an in-vehicle device 100 mounted on a vehicle 60 and a server 200 that communicates with the in-vehicle device 100. This characteristic change detection system 50 detects changes in the characteristics of the driver of the vehicle 60 over time and notifies the driver or the like. By notifying the driver or the like of a change in the driver's characteristics, the characteristic change detection system 50 makes the driver aware that, for example, a change in physical function has occurred. If there is an administrator 70 who manages the driver, the notification to the driver may be made via the administrator 70. Furthermore, a configuration may be adopted in which the change in characteristics is notified to the driver's relatives 80 or the like, and the relatives 80 or the like notify the driver.

[0037] In this embodiment, the server 200 notifies the driver, the driver's manager 70, the driver's relatives 80, or the like of a change in the driver's characteristics. To this end, the server 200 communicates with the terminal device 72 used by the manager 70 and the mobile terminal 82 carried by the driver's relatives 80, or the like, in addition to the in-vehicle device 100. However, the present disclosure is not limited to this configuration. For example, the server 200 may be configured to notify only the manager 70 of a change in the driver's characteristics. In this case, the manager 70 may be configured to notify the driver or the driver's relatives 80, or the like, of the change in the driver's characteristics. The server 200 may further be configured to notify only the driver's relatives 80, or the like, of a change in the driver's characteristics. In this case, the driver's relatives 80, or the like, may be configured to notify the driver of the change in the driver's characteristics.

[0038] The vehicle 60 may be, for example, a delivery vehicle such as a truck, and the driver of the vehicle 60 may be, for example, an employee of a delivery company. In this case, the administrator 70 manages, for example, the vehicle 60 that is the vehicle to be monitored and the driver of the vehicle 60. The administrator 70 may have a role of registering the vehicle and driver to be monitored in the system. In this embodiment, such a configuration will be described as an example.

[0039] The server 200 uses the data transmitted from the in-vehicle device 100 to detect changes in the characteristics of the driver of the vehicle 60 over time. Therefore, the server 200 can also be called a characteristic change detection device. The server 200 is, for example, a cloud server. However, the server 200 is not limited to this and may be another server device such as an on-premise server or an edge server.

[0040] The in-vehicle device 100 transmits to the outside vehicle data and other auxiliary data related to the traveling of the vehicle 60 on which the in-vehicle device 100 is installed. Specifically, the in-vehicle device 100 communicates with the server 200 and transmits the vehicle data and auxiliary data to the server 200.

[0041] The vehicle data includes position data indicating the traveling position of the vehicle 60 and traveling data while the vehicle 60 is traveling. The position data is, for example, position coordinate data such as GPS (Global Positioning System) data, and includes time information while traveling. The position data can be, for example, data acquired by a car navigation device (hereinafter also referred to as "on-board navigation") installed in the vehicle 60. The traveling data includes, for example, time-series information of the accelerator opening, brake pressure, and steering angle. The traveling data may be time-series information of at least one of these. The traveling data can be, for example, CAN (Controller Area Network) information.

[0042] The incidental data includes biosensor data of the driver. The biosensor data is sensor data from a biosensor. The biosensor includes a seating sensor, which is a body pressure (pressure) sensor installed in the driver's seat in the vehicle 60. The biosensor may include a wearable device worn by the driver or a biodata measuring device such as a fitness tracker, instead of or in addition to the seating sensor. In this case, the measurement data used as the biosensor data may be the driver's heart rate data, the driver's stress data, the driver's physical condition data, etc. Furthermore, if the vehicle 60 is equipped with an on-board camera that captures images of the exterior of the vehicle 60, the image data (image data) captured by the on-board camera may be used as the incidental data. The image data captured by the on-board camera can provide environmental information about the vehicle 60 while it is traveling. For example, the image data can provide information such as whether the vehicle is traveling at night, whether there is traffic congestion, or whether visibility ahead is poor due to rain or other factors.

[0043] The in-vehicle device 100 receives vehicle data and additional data (including driver's biosensor data) of the vehicle 60 from the vehicle 60 and biosensor, respectively, at a predetermined sampling period, and transmits the received data to the server 200.

[0044] The server 200 receives the vehicle data and the additional data transmitted from the in-vehicle device 100. The server 200 further communicates with an external server 90, which is an external data source. The external server 90 may be a single server or multiple servers. The external server 90 provides environmental information external to the vehicle 60 while the vehicle 60 is traveling. In this case, the environmental information includes, for example, weather data and time data. Of the received data, the server 200 mainly uses the vehicle data to detect changes in the characteristics of the driver of the vehicle 60.

[0045] Changes in driver characteristics include changes from normal characteristics to dangerous characteristics. Normal characteristics are, for example, characteristics that allow a driver to drive normally. Dangerous characteristics are, for example, characteristics that may lead to dangerous driving situations due to a decline in the driver's physical functions. Such changes in characteristics often occur over time. Therefore, it is difficult for drivers to notice that their characteristics have changed into dangerous characteristics during daily driving.

[0046] For example, a delay in judgment of steering or braking, which falls under the category of risk characteristics, can occur even with normal characteristics. In other words, the delay in judgment may be a fluctuation within the range of normal characteristics. Therefore, even if a delay in judgment occurs, it is difficult to immediately determine that the driver's characteristics have changed from normal characteristics to risk characteristics. This also applies to the driver himself.

[0047] To address these inconveniences, server 200 according to this embodiment uses vehicle data transmitted from in-vehicle device 100 to detect subtle changes in the driver's usual accelerator work, braking operation, and the like of vehicle 60. Server 200 accumulates these changes to detect that the driver's characteristics have changed to dangerous characteristics. When characteristic change detection system 50 detects that the driver's characteristics have changed to dangerous characteristics, it notifies the driver of this change to make him / her aware of the change in characteristics.

[0048] Changes in characteristics are often accompanied by a decline in physical functions due to aging, and are therefore more likely to occur in elderly drivers. Therefore, in this paper, dangerous characteristics are treated as equivalent to characteristics seen in elderly drivers. Examples of characteristics of elderly drivers include the following:

[0049] Characteristics of elderly drivers: (1) Accurate driving becomes difficult due to an overall decline in physical strength, etc. It also becomes difficult to continue driving for long periods of time. (2) Deteriorating eyesight makes it difficult to obtain information about the surrounding situation, making it difficult to make appropriate judgments. (3) Slower reflexes, etc., can lead to delayed responses in sudden situations. (4) Driving tends to become self-centered, making it difficult to objectively grasp traffic conditions.

[0050] Generally, the dominant characteristic of elderly drivers is a delayed reaction. Therefore, it is important to detect the transition to the elderly driver's characteristic before the delayed reaction becomes significant (i.e., before the elderly driver's characteristic becomes apparent), and to provide a warning or guide the driver to a system that does not cause a delayed reaction.

[0051] As described above, the characteristics of elderly drivers include multiple types of characteristics. Therefore, if it is possible to understand what type of characteristics have changed, it will be more effective to alert the driver or to guide them to a system that does not cause a delayed reaction.

[0052] Taking these points into consideration, the characteristic change detection system 50 according to this embodiment estimates the changed characteristics of the elderly driver in more detail based on the vehicle position data and accompanying data received from the in-vehicle device 100, as well as environmental information from the external server 90, etc.

[0053] 2 , the in-vehicle device 100 includes a computer 102. The computer 102 includes a control unit 110 that controls the entire in-vehicle device 100, a memory 120 that stores various data, an in-vehicle communication unit 130 that communicates with the in-vehicle network, and a communication unit 140 that communicates with the external wireless device 62. The control unit 110, the memory 120, the in-vehicle communication unit 130, and the communication unit 140 are all connected to a communication bus 150, and data exchange between them is performed via the communication bus 150.

[0054] The control unit 110 includes an arithmetic unit 112, a read-only memory (ROM) 114 that stores a boot-up program for the computer 102, and a randomly writable and readable random access memory (RAM) 116. The arithmetic unit 112 includes, as a computing element (processor), a central processing unit (CPU) or a micro processing unit (MPU). The memory 120 includes, for example, a non-volatile memory such as a flash memory. The ROM 114 or the memory 120 stores software (computer programs) executed by the arithmetic unit 112 and various information (data).

[0055] The in-vehicle communication unit 130 provides an interface (IF) for communicating with an in-vehicle network. The in-vehicle communication unit 130 communicates with the in-vehicle network in accordance with a predetermined communication protocol. The in-vehicle network may be any of CAN, LIN (Local Interconnect Network), MOST (Media Oriented Systems Transport), FlexRay, 10BASE-T1S, 100BASE-T1, 1000BASE-T1, CXPI (Clock Extension Peripheral Interface), and ASRB (Automotive Safety Restraints Bus) CAN XL. In this embodiment, although not limited to this, a CAN is used as the in-vehicle network.

[0056] Under the control of the control unit 110, the in-vehicle device 100 (computer 102) receives data (vehicle data and additional data) for detecting changes in driver characteristics via an in-vehicle network or the like, and transmits the received data to an external server 200 (see FIG. 1 ). The communication unit 140 provides an IF for communication with an exterior wireless device 62. The exterior wireless device 62 is a wireless device for communicating with devices external to the vehicle 60. The communication unit 140 communicates with the server 200 via the exterior wireless device 62.

[0057] 3 , the server 200 includes a computer 202. The computer 202 includes a control unit 210, a storage device 220, and a communication unit 230. The control unit 210 includes a CPU 212, a GPU (Graphics Processing Unit) 214, a ROM 216, and a RAM 218. The control unit 210, the storage device 220, and the communication unit 230 are all connected to a communication bus 240, and data exchange between them is performed via the communication bus 240.

[0058] The storage device 220 includes a non-volatile storage device such as a flash memory or a hard disk drive. The storage device 220 stores various information and computer programs to be executed by the CPU 212. The communication unit 230 provides a connection to the network 64 that enables communication with other devices including the in-vehicle device 100.

[0059] The server 200 communicates with the in-vehicle device 100 (see FIG. 1) via the network 64 and the communication unit 230. The server 200 also communicates with external devices other than the in-vehicle device 100 via the network 64 and the communication unit 230.

[0060] The server 200 acquires data for detecting a change in the characteristics of the driver of the vehicle 60 in which the in-vehicle device 100 is installed from the in-vehicle device 100. The server 200 detects a change in the characteristics of the driver of the vehicle 60 based on the acquired data and feeds back the detection result to the driver. In other words, the server 200 notifies the driver that the driver's characteristics have changed to characteristics that may lead to a dangerous driving situation.

[0061] A computer program for causing server 200 to function as each functional unit of server 200 according to this embodiment is stored and distributed on a predetermined storage medium such as a DVD (Digital Versatile Disc) or a USB (Universal Serial Bus) memory, and is then transferred from this medium to storage device 220. Alternatively, the computer program may be transmitted to computer 202 from an external device via network 64 and stored in storage device 220.

[0062] [Functional Configuration] (In-Vehicle System 300) Referring to Fig. 4, vehicle 60 includes in-vehicle system 300. In-vehicle system 300 includes in-vehicle camera 302, biometric sensor 310, in-vehicle navigation system 320, and in-vehicle device 100. In-vehicle camera 302, biometric sensor 310, in-vehicle navigation system 320, and in-vehicle device 100 are communicably connected to one another via in-vehicle network 330. In-vehicle device 100 is further communicably connected to various ECUs (Electronic Control Units) via in-vehicle network 330.

[0063] The on-board camera 302 captures images of the external situation of the vehicle 60. The on-board camera 302 may also include a camera that captures images of the internal situation of the vehicle 60. For example, this camera may be used for driver facial recognition. This makes it possible to determine whether the driver driving the vehicle 60 is a driver registered as a detection target, thereby improving the accuracy of detecting changes in the driver's characteristics.

[0064] The biological sensor 310 includes a seating sensor 312 and a biological data measurement device 314. The seating sensor 312 is a body pressure (pressure) sensor provided in the driver's seat 66 inside the vehicle. The seating sensor 312 periodically measures the distribution of the body pressure of the driver seated in the seat 66 and outputs the measured value as sensor data. The sensor data output by the seating sensor 312 includes seat pressure data. The biological data measurement device 314 is, for example, a heart rate sensor that measures the driver's heart rate. As described above, the biological data measurement device 314 may be a wearable device worn by the driver, a fitness tracker, or the like. In this case, the biological data from the biological data measurement device 314 may be transmitted to the in-vehicle device 100 without going through the in-vehicle network 330.

[0065] The in-vehicle navigation system 320 includes a location information acquisition unit 322, a route guidance unit 324, a display unit 326, and an audio output unit 328. The location information acquisition unit 322 acquires location data indicating the traveling location of the vehicle 60. The route guidance unit 324 searches for a route to a destination and displays it on the display unit 326, and receives traffic congestion information and displays the traffic situation on the display unit 326. The display unit 326 displays various information including map information. The audio output unit 328 notifies the driver of various information such as route guidance and traffic congestion information by voice. When the in-vehicle navigation system 320 receives a notification indicating a change in the driver's characteristics via the in-vehicle device 100, it issues a warning to the driver.

[0066] In addition to the communication unit 140, the in-vehicle device 100 includes, as functional units, a vehicle data receiving unit 160, an incidental data receiving unit 170, and a warning notification unit 180. Each of the vehicle data receiving unit 160, the incidental data receiving unit 170, and the warning notification unit 180 is connected to an in-vehicle network 330. Each of the vehicle data receiving unit 160, the incidental data receiving unit 170, and the warning notification unit 180 is also connected to the communication unit 140, and communicates with the server 200 (see FIG. 1 ) via the communication unit 140.

[0067] The vehicle data receiving unit 160 includes a traveling data receiving unit 162 and a position data receiving unit 164. The traveling data receiving unit 162 receives traveling data of the vehicle 60 while the vehicle is traveling from a vehicle control unit (not shown) such as an ECU. The traveling data receiving unit 162 receives, for example, CAN information as traveling data of the vehicle 60. The received CAN information includes at least one of information on the accelerator opening, brake pressure, and steering angle. The traveling data may also include speed data and acceleration data while the vehicle 60 is traveling. The position data receiving unit 164 receives, from the in-vehicle navigation system 320, position data acquired by the in-vehicle navigation system 320. The traveling data receiving unit 162 receives the traveling data of the vehicle 60 via the in-vehicle network 330 and transmits the received traveling data together with accompanying data to the server 200 via the communication unit 140.

[0068] The incidental data receiving unit 170 includes a biometric data receiving unit 172 and an image data receiving unit 174. The biometric data receiving unit 172 receives sensor data from the seating sensor 312 via the in-vehicle network 330 and transmits the received sensor data as incidental data to the server 200 via the communication unit 140. More specifically, the biometric data receiving unit 172 receives seat pressure data of the driver from the seating sensor 312 and calculates the driver's center of gravity position based on the received seat pressure data. That is, the biometric data receiving unit 172 extracts data on the driver's center of gravity position from the received seat pressure data. The biometric data receiving unit 172 transmits the seat pressure data or the extracted center of gravity position data to the server 200 as sensor data. The biometric data receiving unit 172 may be configured to receive the driver's biometric data measured by the biometric data measuring device 314 and transmit the data to the server 200 via the communication unit 140. The image data receiving unit 174 receives images captured by the in-vehicle camera 302 as image data, and transmits the received sensor data as additional data to the server 200 via the communication unit 140 .

[0069] The warning notification unit 180 receives a notification of a change in the driver's characteristics from the server 200 (see FIG. 1) via the communication unit 140, and transmits the received notification to, for example, the in-vehicle navigation system 320 via the in-vehicle network 330. Upon receiving the notification from the warning notification unit 180, the in-vehicle navigation system 320 issues a warning to the driver (for example, that the characteristics have changed and that this may pose a risk).

[0070] 5 , the control unit 210 of the server 200 includes, as functional units, an information acquisition unit 250, a detection processing unit 252, a setting acceptance unit 254, a notification unit 256, and a normal characteristic transition notification unit 258. The information acquisition unit 250 controls the communication unit 230 to acquire various data such as vehicle data and auxiliary data, and stores the acquired data in the storage device 220. The detection processing unit 252 uses the data stored in the storage device 220 to execute a process of detecting changes in the characteristics of the driver who drives the vehicle over time.

[0071] The detection processing unit 252 includes a determination unit 260, an accumulation unit 262, a characteristic change detection unit 264, a normal characteristic detection unit 266, and an estimation unit 268. The determination unit 260 determines whether the vehicle data (driving data) indicates a normal characteristic or a risky characteristic by referring to a first distribution indicating a normal characteristic and a second distribution indicating a risky characteristic. The first distribution indicating a normal characteristic and the second distribution indicating a risky characteristic are pre-stored in, for example, the storage device 220. The accumulation unit 262 has a risk level counter and maintains the risk level count value for vehicle data determined by the determination unit 260 to indicate a normal characteristic (i.e., does not count as a risk level), and increases the risk level count value for vehicle data determined by the determination unit 260 to indicate a risky characteristic, thereby performing a process of accumulating the risk level. The characteristic change detection unit 264 detects that the driver's characteristic has transitioned to a risky characteristic based on whether the risk level count value accumulated by the accumulation unit 262 is equal to or greater than a predetermined threshold. The normal characteristic detection unit 266 detects when the driver's characteristic has transitioned to a dangerous characteristic and then returned to a normal characteristic. The estimation unit 268 estimates the driver's characteristic in more detail based on the driver's biological data and data on the environment outside the vehicle 60 while the vehicle 60 is traveling (e.g., weather data).

[0072] The setting reception unit 254 receives a setting for a period for accumulating risk levels in the process of detecting a characteristic change. The accumulation unit 262 of the detection processing unit 252 accumulates risk levels for the period received by the setting reception unit 254. That is, the accumulation unit 262 calculates an accumulated value for the period received by the setting reception unit 254. When the period received by the setting reception unit 254 has elapsed, the period is shifted. The period can be set via the setting reception unit 254, for example, by the administrator 70 operating the terminal device 72 (see FIG. 1 ).

[0073] When the characteristic change detection unit 264 detects that the driver's characteristic has transitioned to a dangerous characteristic, the detection processing unit 252 notifies the notification unit 256. Upon receiving the notification from the detection processing unit 252, the notification unit 256 executes processing to notify the communication unit 230 that the driver's characteristic has changed.

[0074] When the normal characteristic detection unit 266 detects that the driver's characteristics have returned to the normal characteristics, the detection processing unit 252 notifies the normal characteristic transition notification unit 258. Upon receiving the notification from the detection processing unit 252, the normal characteristic transition notification unit 258 executes processing to notify via the communication unit 230 that the driver's characteristics have changed.

[0075] <<Characteristic Change Detection Method>> A method for detecting a change in a driver's characteristics will be described with reference to Fig. 6. This characteristic change detection method counts potentially dangerous driving states (also called "risk levels") over time, and determines that a transition to a dangerous characteristic has occurred when the count value (cumulative value) is equal to or exceeds a predetermined threshold value.

[0076] -Definition of Distribution- An example of a distribution is shown in Fig. 6A. The horizontal axis of the graph shown in Fig. 6A represents the analysis target value (x), and the vertical axis represents the distribution P(x) (for example, frequency or probability).

[0077] For an analysis target value (x), a distribution indicating normal characteristics is defined as p(x), and a distribution indicating dangerous characteristics (such as characteristics that cause delayed reaction) is defined as p'(x). The distribution p(x) indicating normal characteristics peaks when x = μ, and the distribution p'(x) indicating dangerous characteristics peaks when x = μ'. Driving data can be used for the analysis target value (x).

[0078] - Time series transition of the analysis target value (x) - Figure 6 (B) shows an example of time series transition of the analysis target value (x). The horizontal axis of the graph shown in Figure 6 (B) represents time (t), and the vertical axis represents the analysis target value (x).

[0079] The analysis target value (x) is monitored. When the driver's characteristics are normal, the analysis target value (x) takes a value near μ. When the driver's characteristics are dangerous, the analysis target value (x) takes a value near μ'.

[0080] - Time series transition of risk level (a) - Figure 6 (C) shows an example of time series transition of risk level (a). The horizontal axis of the graph shown in Figure 6 (C) represents time (t), and the vertical axis represents risk level (a).

[0081] The function a(x) represents the risk, and the risk a(x) is defined as in the following equation (1).

[0082] a(x)=ln(p'(x) / p(x))...(1)

[0083] If the value of a(x) is positive, it is determined that there is a high possibility that the characteristic is a dangerous characteristic, and if it is negative, it is determined that there is a high possibility that the characteristic is a normal characteristic.

[0084] - Time series transition of cumulative risk value (I) - Fig. 6(D) shows an example of time series transition of cumulative risk value (I). The horizontal axis of the graph shown in Fig. 6(D) represents time (t), and the vertical axis represents cumulative risk value (I).

[0085] Let function I(t) be the cumulative risk, and t ｎ = nΔt, x(t ｎ ) = x ｎ is defined by the following equation: where Δt is a unit of time (not limited to 1 second).

[0086] I(0) = 0 I(t ｎ ) = I(t ｎ－１ ) + η(a(x ｎ )) a(x ｎ ) η(a)=1(a≧0) η(a)=0(a<0)

[0087] When the cumulative risk value (I) exceeds a predetermined threshold value, it is determined that a transition has occurred from a normal characteristic to a risk characteristic.

[0088] <<Example of Data to be Analyzed>> Figure 7 shows an example of driving data of a driver with normal characteristics. Figure 8 shows an example of driving data of a driver who may have dangerous characteristics. The horizontal axis of Figures 7 and 8 represents time t, and the vertical axis represents speed v(t) and accelerator opening ac(t). A skilled driver in his 30s is assumed to be a driver with normal characteristics, and an elderly person is assumed to be a driver who may have dangerous characteristics.

[0089] As shown in Figure 7, the accelerator opening of a driver with normal characteristics is relatively smooth. In contrast, as shown in Figure 8, the accelerator opening of a driver who may have dangerous characteristics fluctuates greatly and is not very smooth. This is thought to be due to the fact that as people get older, their ankle joints become stiff and they are unable to fully grasp the situation, so they make decisions such as slowing down at the last moment, resulting in inconsistent accelerator work.

[0090] In this way, it is thought that the accelerator opening degree while driving is likely to reflect the characteristics of elderly drivers. Therefore, the time series data of the accelerator opening degree during acceleration and cruising (the difference in the accelerator opening degree per unit time) can be used as the data to be analyzed (the value to be analyzed (x)).

[0091] <Examples of distributions showing normal characteristics and dangerous characteristics> Figure 9 shows the frequency distribution of the difference in accelerator opening per unit time during cruising. Note that the "difference in accelerator opening per unit time" is the time series data x(t ｎ ) (hereinafter also referred to as "unit time data"), and the difference between adjacent data (i.e., x(t ｎ ) - x(t ｎ－１ 10 shows the frequency distribution of the accelerator opening difference per unit time during acceleration. The horizontal axis of FIGS. 9 and 10 represents the accelerator opening difference per unit time, and the vertical axis represents the frequency.

[0092] Referring to Figure 9, it can be seen that the accelerator opening differences for experienced drivers in their 30s are concentrated at 0 and 5, indicating uniform accelerator work. On the other hand, the frequency of accelerator opening differences of 0 and 5 for elderly drivers is lower than for experienced drivers in their 30s. Conversely, relatively large accelerator opening differences such as 20 and -30 are also observed. The distribution for elderly drivers tends to be more spread out than that for experienced drivers in their 30s. These two distributions do not have different peak positions (modes) as shown in Figure 6A, but rather have nearly identical peak positions and different variances. Even in such cases, the risk can be expressed using Equation (1). That is, assuming the total frequency is equal in the two distributions, p'(x) < p(x) and ln(p'(x) / p(x)) < 0 near the peak position, and p'(x) > p(x) and ln(p'(x) / p(x)) > 0 away from the peak position.

[0093] 10, the frequency distribution during acceleration shows a tendency similar to that during cruising (see FIG. 9). That is, the distribution of elderly drivers tends to be more spread out horizontally compared to the distribution of experienced drivers in their 30s.

[0094] Based on these trends, a distribution indicating a normal characteristic and a distribution indicating a dangerous characteristic are determined. Note that the driving data is not limited to the accelerator opening, and time series data such as brake pressure and steering angle may also be used.

[0095] [Software Configuration] In the following description, it is assumed that the storage device 220 of the server 200 stores vehicle data and auxiliary data transmitted from the in-vehicle device 100, as well as environmental data acquired from the external server 90, and the like.

[0096] (Server 200) <<Period Setting Process>> Referring to Fig. 11, a control structure of a computer program executed by server 200 (see Fig. 1) for setting a period for accumulating risk levels will be described. This program starts in response to input of a period for accumulating risk levels via an external terminal, for example.

[0097] This program includes step S1000, in which the input period is set as the period for accumulating the risk level and the program is terminated. The period for accumulating the risk level can be input by, for example, the administrator 70 operating the terminal device 72 (see FIG. 1). The period for accumulating the risk level is arbitrary, but can be set to, for example, 30 days.

[0098] <<Characteristic Change Detection Process>> Referring to Figure 12, a control structure of a computer program executed by server 200 (see Figure 1) to detect changes in driver characteristics based on cruising data will be described. This program is started, for example, according to a preset schedule. For example, this program is started at a certain time every day.

[0099] This program includes step S2000, which acquires driving data from storage device 220 (see FIG. 3), and step S2010, which is executed after step S2000 and extracts cruising data from the acquired driving data. This program further includes step S2020, which executes steps S2030 to S2060, described below, for each unit of data time, and repeats this process until all the data time has been processed. In step S2010, it is determined whether the vehicle is cruising based on, for example, whether the speed change is within a predetermined range.

[0100] In step S2020, the process (process for calculating and accumulating the risk) that is repeated until all the data for a unit time is processed includes step S2030, which calculates the difference from the previous data per unit time, and step S2040, which is executed after step S2030 and uses the calculated difference as the analysis target value (x) to calculate the risk (a) by referring to a distribution indicating a normal characteristic and a distribution indicating a risk characteristic. The "difference from the previous data per unit time" refers to the difference between adjacent data in the time series data for a unit time interval. The repeated process further includes step S2050, which is executed after step S2040 and accumulates the risk according to the calculated risk (a), and step S2060, which is executed after step S2050 and determines whether the accumulated value of the risk is equal to or greater than a predetermined threshold and branches the control flow according to the determination result. In step S2030, for example, the time series data for a unit time interval is calculated based on the calculated risk (a). ０ = 1, t １ = 3, t ２ If = -2, the difference (d) is d １ = 2, d ２ = -5. In step S2050, if the calculated risk (a) is positive, the risk is accumulated.

[0101] If it is determined in step S2060 that the cumulative value of the risk level is not equal to or greater than the predetermined threshold value, the process of step S2020 is repeated.

[0102] This program further includes step S2070, which is executed when it is determined in step S2060 that the cumulative value of the risk level is equal to or greater than a predetermined threshold, and which notifies an external device that the driver's characteristic has transitioned to a risky characteristic (changed from a normal characteristic to a risky characteristic). In step S2070, the system notifies, for example, the administrator 70, that the driver's characteristic has transitioned to a risky characteristic.

[0103] When step S2020 is repeated until all data for the unit time has been processed, or when the processing of step S2070 is completed, this program ends.

[0104] 13, the control structure of a computer program executed by server 200 (see FIG. 1) for detecting changes in driver characteristics based on acceleration data will be described. This program is started, for example, according to a preset schedule. For example, this program is started at a certain time every day.

[0105] This program includes step S2012 instead of step S2010 in the program of Fig. 12. The processes in steps S2000, S2020, and S2070 in Fig. 13 are the same as the processes in the respective steps shown in Fig. 12. The differences will be described below.

[0106] This program is executed after step S2000 and includes step S2012, in which acceleration data is extracted from the acquired driving data. The process in step S2020 (the process of calculating and accumulating the risk level) is performed on the acceleration data extracted in step S2012.

[0107] 14, a control structure of a computer program executed by server 200 (see FIG. 1) for classifying and detailing driver characteristics will be described. This program starts, for example, in response to a determination in the characteristic change detection process that the cumulative value of the risk level is equal to or greater than a predetermined threshold value.

[0108] This program includes step S3000, which acquires vehicle data, incidental data, environmental data, etc., corresponding to data for which the risk level (a) is positive from storage device 220 (see FIG. 3), and step S3010, which is executed after step S3000, which classifies the risk characteristics based on the acquired incidental data and environmental data, and terminates this program. The classification result in step S3010 may be notified together with notifying manager 70, etc., that the driver's characteristics have transitioned to risk characteristics.

[0109] The program in FIG. 14 may be executed for the data when the risk level (a) is positive in step S2040 of the programs in FIGS.

[0110] Referring to FIG. 15, in step S3010 of the program in FIG. 14, classification into, for example, four characteristics (groups) is performed.

[0111] (1) First Group: Time information and weather information indicate whether it is night or day, and whether it is dark. If the risk level is counted in such a situation (i.e., if the value of risk level (a) is positive), it is assumed that the person has difficulty seeing objects in dark places due to a decrease in night vision. Therefore, the characteristics of this group can be said to be a decrease in night vision.

[0112] (2) Second group: Vehicle position information (location data) indicates whether the vehicle is traveling on a highway. If a risk level is detected while driving on a highway, it is assumed that the driver is unable to match the speed of other vehicles due to a decline in dynamic visual acuity. Therefore, the characteristics of this group can be said to be a decline in dynamic visual acuity and a decline in information processing ability.

[0113] (3) Third group: Continuous driving time (e.g., 30 minutes or more) and driving posture can be determined from time information and biological information (seat pressure / center of gravity position, etc.). For example, if the continuous driving time is long or the center of gravity position is shifted frequently, it is assumed that fatigue will accumulate early due to an inability to adopt a correct driving position due to decreased muscle strength and physical strength. Therefore, the characteristics of this group can be said to be decreased muscle strength and physical strength.

[0114] (4) Fourth Group: Vehicle position information (location data) and driving history information indicate whether the vehicle was driving in an unfamiliar location. If a risk level is counted while driving in an unfamiliar location, it is assumed that the driver needs to process a lot of information while driving in an unfamiliar location, but the narrowing of the field of view and the decline in dynamic visual acuity make it difficult for the brain to recognize peripheral vision. Therefore, the characteristics of this group can be said to be a decline in peripheral vision and a decline in information processing ability.

[0115] In this way, by further classifying risk characteristics into smaller categories, it becomes possible to more effectively warn the driver or guide the driver to a system that does not cause a delayed response. As will be described later, when characteristics of the first group are observed, the on-board navigation system is remotely controlled to select a route with as many streetlights as possible when driving at night. When characteristics of the second group are observed, the on-board navigation system is remotely controlled to select a route that mainly uses ordinary roads and, when using expressways, is remotely controlled to select a route that is as short as possible. When characteristics of the third group are observed, the on-board navigation system is remotely controlled to select a route that includes appropriate rest stops. When characteristics of the fourth group are observed, the on-board navigation system is remotely controlled to select a route that includes easy-to-follow roads even if it takes longer.

[0116] <<Updating Process of Accumulated Risk Value>> Referring to FIG. 16 , the risk accumulation is performed for the period set in the period setting process. However, the accumulation of the risk does not stop even after the set period has elapsed. In this case, the oldest data is excluded (subtracted) from the cumulative value, and the cumulative risk value is updated for the set period. That is, the set period is shifted to the right. The oldest data to be subtracted is referred to here as data outside the monitoring period. By updating the cumulative risk value while shifting the set period in this way, it becomes possible to determine whether a driver who has once changed to a risky characteristic has returned to a normal characteristic. For example, a driver's characteristic may improve to a normal characteristic by following improvement instructions from the system or advice from a relative. By detecting such a change, it is possible to prevent the driver from changing to a risky characteristic again.

[0117] 17, a control structure of a computer program executed by server 200 (see FIG. 1) for updating the cumulative risk value will be described. This program is started according to a preset schedule, for example.

[0118] This program includes step S4000, which determines whether data outside the monitoring period is available and branches the control flow in accordance with the determination result, and step S4010, which is executed if it is determined in step S4000 that data outside the monitoring period is available and acquires the data. This program further includes step S4020, which is executed after step S4010 and subtracts the value of the data from the accumulated risk (accumulated risk value), and step S4030, which is executed after step S4020 and determines whether the accumulated risk value is below a predetermined threshold and branches the control flow in accordance with the determination result. This program further includes step S4040, which is executed if it is determined in step S4030 that the accumulated risk value is below the predetermined threshold and notifies an external device that the driver's characteristics have returned from the dangerous characteristics to the normal characteristics.

[0119] The program ends when it is determined in step S4000 that there is no data outside the monitoring period, when it is determined in step S4030 that the cumulative value of the risk level is not below the predetermined threshold value, or when the processing of step S4040 is completed. In step S4040, for example, the administrator 70 or the like is notified that the driver's characteristics have returned to normal.

[0120] [Operation] The characteristic change detection system 50 according to this embodiment operates as follows. Referring to Fig. 1, the in-vehicle device 100 transmits vehicle data and additional data of the vehicle 60 to the server 200. The server 200 receives and stores the vehicle data and additional data transmitted from the in-vehicle device 100. The server 200 further obtains environmental data corresponding to the vehicle data, etc. from the external server 90, and stores the environmental data in the storage device 220 in association with the vehicle data and additional data.

[0121] Server 200 executes a process of detecting a change in the characteristics of the driver of vehicle 60 using vehicle data (driving data) stored in storage device 220 (step S2020 in FIGS. 12 and 13). When server 200 detects that the driver's characteristics have changed from normal to dangerous (YES in step S2060 in FIGS. 12 and 13), it notifies manager 70 or the like of this (step S2070 in FIGS. 12 and 13).

[0122] Server 200 continues to execute the process of detecting changes in the driver's characteristics and monitors whether the driver's characteristics are returning to normal. Specifically, if there is data outside the monitoring period (YES in step S4000 of FIG. 17 ), server 200 acquires the data and subtracts the data value from the cumulative risk value (steps S4010 and S4020). If the cumulative risk value is below a predetermined threshold (YES in step S4030), server 200 notifies administrator 70 or the like of this (step S4040).

[0123] As described above, in the characteristic change detection system 50 according to the present embodiment, the determination unit 260 determines whether the vehicle data (driving data) indicates a normal characteristic or a risky characteristic by referring to the first distribution indicating a normal characteristic and the second distribution indicating a risky characteristic. The accumulation unit 262 maintains the risk level count value for vehicle data determined by the determination unit 260 to indicate a normal characteristic, and increases the risk level count value for vehicle data determined by the determination unit 260 to indicate a risky characteristic, thereby accumulating the risk levels. The characteristic change detection unit 264 detects that the driver's characteristic has transitioned to a risky characteristic based on whether the risk level count value is equal to or greater than a predetermined threshold value. This makes it possible to detect a change in the driver's characteristic even if the driver's characteristic changes over time.

[0124] The server 200 as a characteristic change detection device further includes a notification unit 256 that notifies at least one of the driver and the manager 70 who manages the driver that the driver's characteristic has transitioned to a risky characteristic. This makes it possible to make the driver aware that the driver's characteristic has changed.

[0125] The vehicle data (driving data) may include time-series information on at least one of the accelerator opening, brake pressure, and steering angle, thereby enabling changes in the driver's driving characteristics to be detected with high accuracy.

[0126] The determination unit 260 may be configured to calculate a difference between data per unit time in the time-series data and previous data, which is data per unit time immediately before that, and determine whether the calculated difference value indicates a normal characteristic or a dangerous characteristic by referring to the first distribution and the second distribution. This makes it possible to more accurately detect changes in the driver's driving characteristics.

[0127] The server 200 may further include an information acquisition unit 250 that acquires at least any one of biometric data of the driver, vehicle driving data, and environmental data outside the vehicle while the vehicle is driving, and an estimation unit 268 that estimates the characteristics of the driver based on the information acquired by the information acquisition unit 250. This makes it possible to estimate what characteristics have changed when the driver's characteristics have changed.

[0128] The vehicle 60 may further include a seating sensor 312 provided in the seat where the driver sits, and the in-vehicle device 100 may further be configured to transmit sensor data from the seating sensor 312 as biological data to the server 200. This makes it possible to estimate, for example, changes in characteristics related to the driver's driving posture.

[0129] The in-vehicle device 100 may further be configured to transmit vehicle position data while the vehicle is traveling as vehicle data to the server 200. This makes it possible to estimate, for example, changes in characteristics related to dynamic visual acuity or information processing ability.

[0130] The server 200 may communicate with an external server 90, and the information acquisition unit 250 may acquire, as environmental data, weather information of the driving area when the vehicle 60 is driving from the external server 90. This makes it possible to estimate, for example, changes in characteristics related to eyesight or information processing ability.

[0131] The server 200 may further include a setting receiving unit 254 that receives a setting for a period for accumulating the risk level, and the accumulating unit 262 may be configured to accumulate the risk level for the period received by the setting receiving unit 254. This makes it possible to prevent the risk level from accumulating without an upper limit.

[0132] The accumulator 262 accumulates the risk level by shifting the period when the period accepted by the setting accepting unit 254 is exceeded, and updates the risk level count value by deleting data that falls outside the period due to the shift. The server 200 may further include a normal characteristic detector 266 that detects that the driver's characteristics have transitioned from risky characteristics to normal characteristics depending on whether the updated risk level count value is below a predetermined threshold, and a normal characteristic transition notifier 258 that notifies at least one of the driver and the administrator 70 who manages the driver that the driver's characteristics have transitioned to normal characteristics. This makes it possible to detect that the driver's characteristics have returned to normal characteristics and notify this fact.

[0133] (First Modification) In the first embodiment described above, an example has been shown in which the characteristic classification process is executed when the cumulative value of the risk level is determined to be equal to or greater than a predetermined threshold value, or when the risk level (a) is positive. However, the present disclosure is not limited to such an embodiment. For example, vehicle data (travel data) may be grouped in advance based on vehicle data, incidental data, environmental data, etc., and the characteristic classification process may be executed for each group. In the first modification, such a configuration example will be described.

[0134] The server according to the first modification executes the program shown in Fig. 18 instead of the program shown in Fig. 14. This program is started, for example, according to a preset schedule. For example, this program is started at a certain time every day.

[0135] Referring to FIG. 18, this program includes step S3100 of acquiring vehicle data, incidental data, and environmental data from storage device 220 (see FIG. 3), and step S3110, which is executed after step S3100, of dividing the driving data included in the vehicle data into a plurality of groups based on the vehicle data (such as position data), incidental data, and environmental data, and storing the data in storage device 220, and then terminating the program.

[0136] In the first modified example, in the characteristic change detection process, the programs shown in FIGS. 12 and 13 are executed for the driving data of each group.

[0137] 19 , a characteristic change detection system 50A according to this embodiment differs from the first embodiment in that it detects a change in the characteristics of a driver of a general vehicle over time and notifies the driver. A vehicle 60a is a general vehicle such as a private car. In this case, the manager is, for example, a relative of the driver.

[0138] The vehicle 60a is equipped with the same in-vehicle device 100 as in the first embodiment. The other configurations are the same as those in the first embodiment.

[0139] 20, a characteristic change detection device according to this embodiment differs from the first embodiment in that the characteristic change detection device is an on-vehicle device 400 mounted on a vehicle 60b. That is, in this embodiment, on-vehicle device 400 has the functions of server 200 (see FIG. 1) in the first embodiment.

[0140] The in-vehicle device 400 detects changes in the characteristics of the driver of the vehicle 60b over time and notifies the driver or the driver's relatives or the like.

[0141] 21 , the in-vehicle device 400 further includes, as functional units, a detection processing unit 410 and a setting acceptance unit 420. The vehicle data received by the vehicle data receiving unit 160 and the incidental data received by the incidental data receiving unit 170 are stored in a storage device 402. The in-vehicle device 400 communicates with an external server 90 (see FIG. 20 ), which is an external data source, via the communication unit 140. The in-vehicle device 400 acquires environmental information and the like from the external server 90 and stores the information in the storage device 402.

[0142] The detection processing unit 410 and the setting reception unit 420 have the same functions as the detection processing unit 252 and the setting reception unit 254 (see FIG. 5) of the server 200, respectively. The detection processing unit 410 executes a process of detecting a change in the characteristics of the driver of the vehicle 60b (see FIG. 20) over time using data stored in the storage device 402. The detection processing unit 410 includes a determination unit 430, an accumulation unit 432, a characteristic change detection unit 434, a normal characteristic detection unit 436, and an estimation unit 438. These functional units have the same functions as the functional units of the server 200 (see FIG. 5) in the first embodiment.

[0143] When the characteristic change detection unit 434 detects that the driver's characteristic has transitioned to a dangerous characteristic, the detection processing unit 410 notifies the warning notification unit 180 of this fact. Upon receiving the notification from the detection processing unit 410, the warning notification unit 180 notifies (warns) the driver of this fact, or executes processing such as notifying a terminal outside the vehicle 60b (for example, a mobile terminal carried by a relative of the driver) via the communication unit 140 that the driver's characteristic has changed.

[0144] When the normal characteristic detection unit 436 detects that the driver's characteristic has returned to the normal characteristic, the detection processing unit 410 notifies the warning notification unit 180 of this fact. Upon receiving the notification from the detection processing unit 410, the warning notification unit 180 notifies the driver of this fact, or executes processing such as notifying a terminal outside the vehicle 60b (for example, a mobile terminal carried by a relative of the driver) via the communication unit 140 that the driver's characteristic has returned to the normal characteristic.

[0145] This configuration also makes it possible to detect changes in the driver's characteristics.

[0146] The other configurations and effects are the same as those of the first embodiment.

[0147] While the above embodiment illustrates an example in which a single threshold value is used as the threshold value for the cumulative risk value, the present disclosure is not limited to such an embodiment. For example, multiple threshold values ​​may be used to issue notifications (warnings) in stages as the cumulative risk value increases.

[0148] In the above embodiment, four groups are shown for grouping driver characteristics, but the present disclosure is not limited to such an embodiment. The number of groups for grouping may be two, three, or five or more. The four groups (characteristics) may be other than those shown in the above embodiment. Furthermore, other groups (characteristics) may be added in addition to the four groups.

[0149] In the above embodiment, an example has been shown in which CAN information is used as vehicle data. However, the present disclosure is not limited to such an embodiment. The vehicle data may be information other than CAN information.

[0150] In the above embodiment, an example has been shown in which time-series data of at least one of the accelerator opening, brake pressure, and steering angle is used as the vehicle data (driving data), but the present disclosure is not limited to such an embodiment. The vehicle data (driving data) may be data other than these as long as it is data that can detect changes in the driver's characteristics over time.

[0151] Each process (each function) in the above-described embodiments may be realized by a processing circuit (circuitry) including one or more processors. The processing circuit may be configured by an integrated circuit or the like that combines one or more memories, various analog circuits, and various digital circuits in addition to the one or more processors. The one or more memories store programs (instructions) that cause the one or more processors to execute each of the processes. The one or more processors may execute each of the processes according to the program read from the one or more memories, or according to a logic circuit designed in advance to execute each of the processes. The processor may be a CPU, GPU, DSP (Digital Signal Processor), FPGA (Field Programmable Gate Array), ASIC (Application Specific Integrated Circuit), or any other processor suitable for computer control. The plurality of physically separated processors may cooperate with each other to execute the respective processes. For example, the processors mounted on the respective physically separated computers may cooperate with each other via a network such as a LAN (Local Area Network), a WAN (Wide Area Network), or the Internet to execute the respective processes.

[0152] Furthermore, a recording medium can be provided that stores a program that causes a computer to execute the processes executed by server 200 (e.g., the processes shown in FIG. 12 or 13). The recording medium is, for example, an optical disc (such as a DVD (Digital Versatile Disc)) or a removable semiconductor memory (such as a USB (Universal Serial Bus) memory). Although the computer program can be transmitted via a communication line, the recording medium is a non-transitory recording medium. By loading the program stored in the recording medium into a computer, the computer can detect changes in the characteristics of the driver of the vehicle over time, as described above.

[0153] (Additional Note) In other words, the computer-readable non-transitory recording medium stores a computer program that causes a computer to detect changes in the characteristics of a driver who drives a vehicle over time, the computer program causing the computer to execute an acquisition step of acquiring vehicle information related to the vehicle's traveling, and a detection step of detecting changes in the characteristics of the driver who drives the vehicle using the vehicle information acquired in the acquisition step, the detection step including a judgment step of determining whether the vehicle information indicates normal characteristics or dangerous characteristics by referring to a first distribution indicating normal characteristics and a second distribution indicating dangerous characteristics, which are prepared in advance.

[0154] Embodiments obtained by appropriately combining the techniques disclosed above are also included within the technical scope of the present disclosure.

[0155] The embodiments disclosed herein are merely examples, and the present disclosure is not limited to the above-described embodiments. The scope of the present disclosure is defined by the claims in the scope of the claims, taking into consideration the description of the detailed description of the invention, and includes all modifications within the meaning and scope equivalent to the wordings described therein.

[0156] 50, 50A Characteristic change detection system 60, 60a, 60b Vehicle 62 Exterior wireless device 64 Network 66 Seat 70 Manager 72 Terminal device 80 Relative 82 Portable terminal 90 External server 100, 400 In-vehicle device 102, 202 Computer 110, 210 Control unit 112 Calculation unit 114, 216 ROM 116, 218 RAM 120 Memory 130 In-vehicle communication unit 140, 230 Communication unit 150, 240 Communication bus 160 Vehicle data receiving unit 162 Traveling data receiving unit 164 Position data receiving unit 170 Accessory data receiving unit 172 Biometric data receiving unit 174 Image data receiving unit 180 Warning notification unit 200 Server 212 CPU 214 GPU 220, 402 Storage device 250 Information acquisition unit 252, 410 Detection processing unit 254, 420 Setting reception unit 256 Notification unit 258 Normal characteristic transition notification unit 260, 430 Determination unit 262, 432 Accumulation unit 264, 434 Characteristic change detection unit 266, 436 Normal characteristic detection unit 268, 438 Estimation unit 300 In-vehicle system 302 In-vehicle camera 310 Biometric sensor 312 Seat occupancy sensor 314 Biometric data measurement device 320 In-vehicle navigation 322 Position information acquisition unit 324 Route guidance unit 326 Display unit 328 Audio output unit 330 In-vehicle network

Claims

1. A characteristic change detection system including a characteristic change detection device that detects changes in the characteristics of a driver driving a vehicle over time using vehicle information related to the vehicle's travel, wherein the characteristic change detection device includes a judgment unit that judges whether the vehicle information indicates a normal characteristic or a dangerous characteristic by referring to a first distribution indicating a normal characteristic and a second distribution indicating a dangerous characteristic, both of which are prepared in advance.

2. The characteristic change detection device of claim 1 includes: an accumulation unit that maintains the danger level count value for the vehicle information that the judgment unit judges to indicate a normal characteristic, and increases the danger level count value for the vehicle information that the judgment unit judges to indicate a dangerous characteristic, thereby executing a process to accumulate the danger level; and a characteristic change detection unit that detects that the driver's characteristic has transitioned to a dangerous characteristic depending on whether the danger level count value accumulated by the accumulation unit is equal to or greater than a predetermined threshold value.

3. A characteristic change detection system as described in claim 1 or claim 2, wherein the characteristic change detection device further includes a notification unit that notifies at least one of the driver and an administrator who manages the driver that the driver's characteristic has transitioned to a dangerous characteristic.

4. A characteristic change detection system according to any one of claims 1 to 3, wherein the vehicle information includes time-series information of at least one of accelerator opening, brake pressure, and steering angle.

5. The characteristic change detection system of claim 4, wherein the judgment unit calculates a difference value between data per unit time in the time series information and previous data, which is data per unit time immediately before that, and determines whether the calculated difference value indicates a normal characteristic or a dangerous characteristic by referring to the first distribution and the second distribution.

6. A characteristic change detection system as described in any one of claims 1 to 5, wherein the characteristic change detection device further includes: an information acquisition unit that acquires at least one of biometric information of the driver, driving information of the vehicle, and environmental information outside the vehicle while the vehicle is driving; and a characteristic estimation unit that estimates the characteristics of the driver based on the information acquired by the information acquisition unit.

7. A characteristic change detection system as described in claim 6, further comprising: an on-board device mounted in a vehicle and transmitting the vehicle information to a characteristic change detection device; and a seating sensor provided in a seat where the driver is seated, wherein the on-board device further transmits sensor data from the seating sensor as the biological information to the characteristic change detection device.

8. The characteristic change detection system according to claim 6, wherein the in-vehicle device further transmits vehicle position information while the vehicle is traveling as the traveling information to the characteristic change detection device.

9. A characteristic change detection system as described in any one of claims 6 to 8, wherein the characteristic change detection device communicates with an external server, and the information acquisition unit acquires weather information of the driving area when the vehicle is driving from the external server as the environmental information.

10. The characteristic change detection system of claim 2, wherein the characteristic change detection device further includes a setting reception unit that receives the setting of the period for accumulating the risk level, and the accumulation unit accumulates the risk level for the period received by the setting reception unit.

11. The accumulation unit accumulates the risk level by shifting the period accepted by the setting acceptance unit when the period exceeds the period accepted by the setting acceptance unit, and updates the count value of the risk level by deleting data that falls outside the period due to the shift, and the characteristic change detection device further includes: a normal characteristic detection unit that detects that the driver's characteristic has transitioned from a risky characteristic to a normal characteristic depending on whether the updated count value of the risk level is less than a predetermined threshold value; and a normal characteristic transition notification unit that notifies at least one of the driver and an administrator who manages the driver that the driver's characteristic has transitioned to a normal characteristic. A characteristic change detection system as described in claim 10.

12. An in-vehicle device that is mounted on a vehicle and transmits the vehicle information to the characteristic change detection device of the characteristic change detection system according to claim 1 or 2.

13. A characteristic change detection device comprising: an acquisition unit that acquires vehicle information related to the running of a vehicle; and a processing unit that executes processing to detect changes in characteristics over time of a driver who drives the vehicle using the vehicle information acquired by the acquisition unit, wherein the processing unit includes a judgment unit that judges whether the vehicle information indicates a normal characteristic or a dangerous characteristic by referring to a first distribution that indicates a normal characteristic and a second distribution that indicates a dangerous characteristic, both of which are prepared in advance.

14. The processing unit further includes an accumulation unit that maintains the danger level count value for the vehicle information that the judgment unit judges to indicate a normal characteristic, and increases the danger level count value for the vehicle information that the judgment unit judges to indicate a dangerous characteristic, thereby executing a process to accumulate the danger level, and a characteristic change detection unit that detects that the driver's characteristic has transitioned to a dangerous characteristic depending on whether the danger level count value accumulated by the accumulation unit is equal to or greater than a predetermined threshold value. The characteristic change detection device described in claim 13.

15. The characteristic change detection device according to claim 13 or 14, wherein the characteristic change detection device is a server device that communicates with the vehicle.

16. The characteristic change detection device according to claim 13 or 14, wherein the characteristic change detection device is an on-board device mounted on the vehicle.

17. A characteristic change detection method for detecting changes in the characteristics of a driver who drives a vehicle over time, comprising: an acquisition step in which a computer acquires vehicle information related to the running of the vehicle; and a detection step in which the computer detects changes in the characteristics of the driver who drives the vehicle using the vehicle information acquired in the acquisition step, wherein the detection step includes a determination step in which the computer determines whether the vehicle information indicates a normal characteristic or a dangerous characteristic by referring to a first distribution indicating a normal characteristic and a second distribution indicating a dangerous characteristic, which are prepared in advance.

18. The characteristic change detection method described in claim 17, wherein the detection step further includes an accumulation step of maintaining the danger level count value for the vehicle information determined in the judgment step to exhibit normal characteristics, and increasing the danger level count value for the vehicle information determined in the judgment step to exhibit dangerous characteristics, thereby executing a process to accumulate the danger levels, and a step of detecting that the driver's characteristics have transitioned to dangerous characteristics depending on whether the accumulated danger level count value in the accumulation step is equal to or greater than a predetermined threshold value.

19. A computer program that causes a computer to detect changes in the characteristics of a driver who drives a vehicle over time, the computer executing an acquisition step of acquiring vehicle information related to the vehicle's travel, and a detection step of detecting changes in the characteristics of the driver who drives the vehicle using the vehicle information acquired in the acquisition step, the detection step including a determination step of determining whether the vehicle information indicates normal characteristics or dangerous characteristics by referring to a first distribution indicating normal characteristics and a second distribution indicating dangerous characteristics, both of which are prepared in advance.

20. The computer program of claim 19, wherein the detection step further includes an accumulation step of maintaining the danger level count value for the vehicle information determined in the judgment step to exhibit normal characteristics, and increasing the danger level count value for the vehicle information determined in the judgment step to exhibit dangerous characteristics, thereby executing a process to accumulate the danger levels; and a step of detecting that the driver's characteristics have transitioned to dangerous characteristics depending on whether the accumulated danger level count value in the accumulation step is equal to or greater than a predetermined threshold value.

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