Driving ability assessment system and driving ability assessment method

Abstract

To determine driving ability without hindering driving.SOLUTION: A driving ability determination system 10 includes: an information acquisition unit 13 for acquiring time-series traveling data of a vehicle; an identification unit 14 for identifying a driver of the vehicle; and an evaluation value calculation unit 16 for calculating an evaluation value representing steering characteristics of the driver identified by the identification unit 14 based on one cycle of traveling data from a start point to an end point of the vehicle among the traveling data acquired by the information acquisition unit 13.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a driving ability determination system and a driving ability determination method for determining the driving ability of a vehicle driver. [Background technology]

[0002] As this type of device, a device that measures a driver's safe driving ability is known (see, for example, Patent Document 1). The device described in Patent Document 1 intermittently applies a load to the driver by audio output to distribute attention, calculates steering entropy values ​​that represent steering deviations under load and no load conditions, and evaluates the driver's safe driving ability based on the difference between the deviation evaluation values ​​calculated under load and no load conditions. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2014-174848 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the device described in Patent Document 1 requires imposing a load on the driver in order to evaluate the driver's safe driving ability, which impedes driving. [Means for solving the problem]

[0005] The driving ability determination system according to one aspect of the present invention is Includes steering angle information The system includes an information acquisition unit that acquires time-series driving data, an identification unit that identifies the driver of the vehicle, and an evaluation value calculation unit that calculates an evaluation value that represents the steering characteristics of the driver identified by the identification unit based on one cycle of driving data from the start to end of the vehicle out of the driving data acquired by the information acquisition unit. The evaluation value calculation unit determines, based on the change in steering angle over time, whether the driving section per unit time is a high-load section where the driving load placed on the driver due to driving behavior is high, or a no-load / low-load section other than the high-load section, and determines whether the no-load / low-load section is a specified section where the cognitive load placed on the driver is high, calculates a first evaluation value that represents the driver's steering error based on the driving data in the no-load / low-load section, and calculates a second evaluation value that represents the driver's steering error when the cognitive load is high based on the driving data in the specified section.

[0006] Another aspect of the present invention is a method for determining driving ability, Includes steering angle information Acquire time-series driving data Information acquisition The method includes a step of identifying the driver of the vehicle, an identification step of identifying the driver of the vehicle, and an evaluation value calculation step of calculating an evaluation value representing the steering characteristics of the driver identified in the identification step based on one cycle of driving data from the start to the end of the vehicle, among the driving data acquired in the information acquisition step. In the evaluation value calculation step, based on the change in steering angle over time, it is determined whether the driving section per unit time is a high-load section where the driving load placed on the driver due to driving behavior is high, or a no-load / low-load section other than the high-load section, and it is also determined whether the no-load / low-load section is a specified section where the cognitive load placed on the driver is high. Based on the driving data in the no-load / low-load section, a first evaluation value representing the driver's steering error is calculated, and based on the driving data in the specified section, a second evaluation value representing the driver's steering error when the cognitive load is high is calculated. [Effects of the Invention]

[0007] According to the present invention, driving ability can be determined without interfering with driving. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram for explaining a driving section and a driving load. [Figure 2] 1 is a block diagram showing an example of a configuration of a main part of a driving ability determination system according to an embodiment of the present invention; [Figure 3] FIG. 3 is a diagram for explaining fluctuations in the steering angle of a vehicle. [Figure 4] FIG. 10 is a diagram illustrating an example of a degree display of the degree of steering shake. [Figure 5A] 3 is a flowchart showing an example of processing executed by the calculation unit of FIG. 2; [Figure 5B] 5B is a flowchart showing a modification of FIG. 5A in which driving data after the driver's door is opened is excluded. [Figure 5C] 5B is a flowchart showing a modified example of FIG. 5A in which driving data after the driver's door is opened and the driver leaves the seat is excluded. [Figure 5D] 5B is a flowchart showing a modification of FIG. 5A in which the driving data after the driver's door is opened and the other door(s) other than the driver's door is opened is excluded. [Figure 5E]5B is a flowchart showing a modified example of FIG. 5A in which driving data after the driver's door is opened and the driver's seat belt is unfastened is excluded. [Figure 5F] 5B is a flowchart showing a modified example of FIG. 5A in which driving data after the driver's door is opened and the driver is changed is excluded. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an embodiment of the present invention will be described with reference to Figs. 1 to 5F. A driving ability determination system according to an embodiment of the present invention determines the driving ability of a vehicle driver. Generally, a driver's driving behavior is composed of three elements: cognition, judgment, and operation. Of these elements, abilities related to "cognitive function," which is a person's intellectual function related to cognition and judgment, are known to gradually decline with age. When cognitive function declines, it becomes difficult to drive a vehicle safely.

[0010] Therefore, in this embodiment, the driving ability assessment system is configured as follows to assess driving ability, particularly driving ability related to cognitive function, based on driving data when the driver drives the vehicle, so that the driver and their family can understand the tendency of cognitive function to decline and thereby support safe driving.

[0011] Figure 1 is a diagram illustrating driving sections and driving load. As shown in Figure 1, driving sections on which a vehicle travels can be classified into no-load sections, such as straight lines, where the driver experiences almost no driving load due to driving behavior; high-load sections, such as S-curves, cranks, and parking, where the driver experiences a high driving load; and low-load sections, which are intermediate sections. More specifically, no-load sections are sections where the driver is required to make few steering movements per unit of vehicle travel and the vehicle's driving trajectory is simple. High-load sections are sections where the driver is required to make many steering movements per unit of vehicle travel and the vehicle's driving trajectory is complex. In such high-load sections, high driving skills are required, as steering is frequently required in conjunction with accelerator and brake operations, and vehicle sense is also required. In other words, the driver's driving skills have a significant impact on driving stability in high-load sections.

[0012] Low-stress sections include driving sections such as right curves, left curves, lane changes, right turns, and left turns. Among these low-stress sections, in sections where the vehicle changes direction by crossing the oncoming lane at an intersection (right-turn sections in countries and regions where vehicles drive on the left side, and left-turn sections in countries and regions where vehicles drive on the right side; hereinafter, simply referred to as "right-turn sections"), in order for the driver to recognize the target trajectory of the vehicle, it is necessary to grasp the situation of the oncoming lane ahead while also grasping the situation of the lane ahead after the right turn. In this case, the driver's line of sight shifts between the oncoming lane ahead and the lane ahead after the right turn, increasing the driver's mental activity and increasing the driving load, particularly the cognitive load related to cognition, compared to other low-stress sections. For this reason, the driver's cognitive function has a significant impact on driving stability in right-turn sections. By evaluating driving stability based on driving data from such right-turn sections, it is possible to determine the driver's driving ability related to cognitive function.

[0013] In addition to right-turn sections, driving sections in which a driver's cognitive function affects driving stability include, for example, sections with many signs and on-coming traffic, sections where eye movement is set to be more frequent than a predetermined amount, sections with traffic lights, sections where pedestrians are set to be more frequent than a predetermined amount, such as in busy areas, sections where blind spots during driving are set to be more frequent than a predetermined amount, such as intersections with poor visibility, and sections where multiple roads intersect. Therefore, by acquiring driving data for such sections in a manner that allows them to be distinguished from other sections and evaluating driving stability based on that driving data, it is also possible to determine the driving ability related to the driver's cognitive function. When time-series location information is acquired in addition to vehicle driving data, a predetermined specific driving section can be identified based on the location information.

[0014] FIG. 2 is a block diagram showing an example of the configuration of the main parts of a driving ability assessment system (hereinafter referred to as the system) 10. As shown in FIG. 2, the system 10 is configured to include a computer having a calculation unit 11 such as a CPU, a storage unit 12 such as a ROM and a RAM, and peripheral circuits thereof. The calculation unit 11 has, as functional components, an information acquisition unit 13, an identification unit 14, a determination unit 15, an evaluation value calculation unit 16, a cognitive function assessment unit 17, and an information output unit 18. The storage unit 12 stores information such as programs executed by the calculation unit 11 and setting values. The system 10 may be configured as an on-board device mounted on a vehicle, or as a server device provided outside the vehicle.

[0015] The information acquisition unit 13 acquires time-series vehicle driving data for each pre-registered driver. For example, it acquires driving data measured in a pre-registered vehicle that each driver drives daily. The driving data includes information on the steering angle of the steering wheel detected by a sensor mounted on each vehicle, information on the opening and closing of each seat side door including the driver's seat side door, information on the seating status of the driver's seat, information on whether the driver's seat belt is fastened or unfastened, and information on the biometrically authenticated driver.

[0016] The driving data of each vehicle, more specifically, the driving data for each cycle from the start to the end of each vehicle, together with a vehicle ID previously assigned to each vehicle, is transmitted to the system 10, for example, at a predetermined interval, via a TCU (telematics control unit) mounted on the vehicle. The information acquisition unit 13 acquires the driving data transmitted from pre-registered vehicles as time-series driving data for each pre-registered driver. The time-series driving data for each driver acquired by the information acquisition unit 13 is stored in the memory unit 12.

[0017] Here, one cycle refers to the period of time during which the vehicle is used to travel from a starting point to a destination point, from when the vehicle is started to when it is finished. The starting point of the vehicle may be, for example, when the ignition is turned on, and the finishing point may be, for example, when the ignition is turned off.

[0018] Each vehicle is provided near the driver's seat with an input / output unit including an output unit such as a display and a speaker and an input unit such as a touch panel and a microphone. When the vehicle is started, the driver currently driving the vehicle is identified via the input / output unit. For example, the output unit notifies the user of the pre-registered driver's user ID (user name), prompting the user to confirm or enter a password. The user then inputs the confirmation result via the input unit, and the current driver is identified based on the confirmation result. Instead of or in addition to these, the identification unit may include a camera for facial recognition or iris recognition, or a sensor for fingerprint recognition, palm print recognition, or finger vein recognition. It may also be capable of performing voice recognition via a microphone. Information about the driver identified by the identification unit when the vehicle is started is also transmitted to system 10 as driving data.

[0019] The identification unit 14 acquires driving data based on user information pre-registered for use of the driving ability assessment service, and identifies a driver whose driving stability is to be evaluated. More specifically, the identification unit 14 identifies driver information such as the vehicle ID of a pre-registered vehicle that the driver to be evaluated regularly drives and the user ID of the driver. The driver information identified by the identification unit 14 is stored in the storage unit 12. Based on this driver information, the driving data acquired from each vehicle by the information acquisition unit 13 is associated with each driver, and is accumulated in the storage unit 12 as driving data for each driver.

[0020] The determination unit 15 determines whether the driver's door was opened during one cycle from the start to the end of the vehicle based on the driving data for each driver stored in the memory unit 12. That is, the determination unit 15 determines whether the driver's door was opened during one cycle and there is a possibility that the driver has been changed. If the determination unit 15 determines that there is a possibility that the driver has been changed during one cycle, the driving data thereafter may be excluded from the driving data for each driver stored in the memory unit 12.

[0021] The determination unit 15 may determine whether or not the driver may have changed during one cycle by determining whether the driver's door is opened and the driver has left the seat.The determination unit 15 may determine whether or not the driver may have changed during one cycle by determining whether the driver's door is opened and a seat side door other than the driver's door is opened.The determination unit 15 may determine whether or not the driver may have changed during one cycle by determining whether the driver's door is opened and the driver's seat belt is unfastened.

[0022] When the driver is identified by the input / output unit on the vehicle side at all times or in conjunction with the opening and closing of the driver's door, the determination unit 15 can determine whether the driver has been changed directly based on the driver identification result. In this case, the determination unit 15 may determine whether the driver's door has been opened and the driver has been changed during one cycle.

[0023] The evaluation value calculation unit 16 calculates an α value and an Hp value that represent the steering characteristics of an individual driver based on the driving data for each driver stored in the storage unit 12. More specifically, the evaluation value calculation unit 16 determines the driving section per unit time based on the time change of the steering angle, and calculates an α value that represents the steering characteristics of the driver based on the driving data for the period in which it is determined that the vehicle is driving in a no-load section or a low-load section (no-load / low-load section).The evaluation value calculation unit 16 also calculates an Hp value that represents the steering characteristics of the driver when the cognitive load increases based on the driving data for the period in which it is determined that the vehicle is driving in a right-turn section.

[0024] 3 is a diagram for explaining fluctuations in the steering angle θ of a vehicle. When the vehicle is being driven stably, the steering is smooth and stable, resulting in small fluctuations in the steering angle θ. On the other hand, when the vehicle is being driven unstable, the steering becomes unstable and the fluctuations in the steering angle θ become large.

[0025] More specifically, as shown in FIG. 3, a predicted steering angle θp(n) at time point n is calculated by second-order Taylor expansion centered on time point (n-1) based on the actual steering angles θ(n-3), θ(n-2), and θ(n-1) at time points n-3, n-2, and n-1 immediately preceding the specific time point n. Since the predicted steering angle θp(n) is an estimated value assuming smooth steering, if the actual steering is smooth, it will match the actual steering angle θ(n), but if the actual steering is unstable, it will deviate from the actual steering angle θ(n) depending on the degree of the unstable steering. The degree of such unstable steering can be expressed as a prediction error e(n) calculated by the following equation (i): e(n)=θ(n)-θp(n) (i)

[0026] FIG. 4 is a diagram illustrating an example of a frequency display of the degree of steering error, showing an example of the frequency display of the prediction error e(n). The evaluation value calculation unit 16 calculates the predicted steering angle θp(n) and prediction error e(n) at each time point n based on driving data from the no-load and low-load sections, and calculates the 90th percentile value (α value) of the frequency distribution of the prediction error e(n) as shown by the solid line. The smoother the steering and the less the steering error, the sharper the frequency distribution of the prediction error e(n) will be centered around "0°," where there is no steering error, and the smaller the α value will be. On the other hand, the more the steering error is, the broader the frequency distribution of the prediction error e(n) will be and the larger the α value will be.

[0027] By using driving data from no-load and low-load sections, excluding high-load sections where there is a lot of steering and where driving skill has a large effect on steering error, it is possible to appropriately calculate the α value, which represents the driver's steering error under normal conditions.

[0028] Furthermore, the evaluation value calculation unit 16 calculates an Hp value that represents the steering characteristics of the driver when the cognitive load increases, based on the calculated α value and the driving data of the right-turn section. More specifically, the evaluation value calculation unit 16 calculates a predicted steering angle θp(n) and a prediction error e(n) at each time point n based on the driving data of the right-turn section, and divides the frequency distribution of the prediction error e(n) as shown by the dashed line into nine ranges P1 to P9 based on the α value. That is, based on eight reference values ​​-5α, -2.5α, -α, -0.5α, 0.5α, α, 2.5α, and 5α, the system is divided into nine ranges: P1 (up to -5α), P2 (-5α to -2.5α), P3 (-2.5α to -α), P4 (-α to -0.5α), P5 (-0.5α to 0.5α), P6 (0.5α to α), P7 (α to 2.5α), P8 (2.5α to 5α), and P9 (5α or higher). Then, based on the proportions p1 to p9 of each range P1 to P9, the steering entropy value (Hp value) is calculated using the following formula (ii). Hp=-Σpi·log9pi ···(ii)

[0029] The Hp value represents the smoothness of steering, and the smaller the steering error and the sharper the frequency distribution of the prediction error e(n), the smaller the value, and the larger the steering error and the broader the frequency distribution of the prediction error e(n).By using driving data from right-turn sections where there is a lot of eye movement and the cognitive function is greatly affected by steering error, it is possible to appropriately calculate the Hp value, which represents the driver's steering error when the cognitive load is higher than usual.

[0030] The cognitive function evaluation unit 17 evaluates the cognitive function of the driver based on the Hp value calculated by the evaluation value calculation unit 16. That is, by continuously monitoring the Hp value, which indicates the steering error when the cognitive load increases, it is possible to evaluate the tendency of the cognitive function of the driver to decline. For example, if the Hp value calculated periodically (e.g., monthly) based on the driving data of daily driving tends to increase, it is evaluated that the cognitive function is declining.

[0031] The information output unit 18 transmits the evaluation results by the cognitive function evaluation unit 17 to a user terminal of the driver or a family member. For example, a notification can be sent to a pre-registered email address. In this case, the notification can prompt the driver or a family member to consider returning their driver's license or switching to a vehicle with enhanced driving assistance functions. Because objective information based on driving data is provided, the driver can easily accept the current state of their own cognitive function and consider appropriate measures early on.

[0032] FIG. 5A is a flowchart showing an example of processing executed by the calculation unit 11 of the system 10, and shows an example of processing for evaluating the driving ability of a pre-registered driver based on all driving data of a pre-registered vehicle. The processing shown in this flowchart is executed, for example, periodically. First, in step S1, the driver (user ID) to be evaluated is identified. Next, in step S2, the driving data associated with the user ID identified in step S1 is read from the storage unit 12. Next, in step S3, the driving section per unit time is determined based on the driving data read in step S2.

[0033] Next, in step S4, an α value is calculated based on the driving data read out in step S2 for the period determined to be a no-load / low-load section in step S3. Next, in step S5, an Hp value is calculated based on the driving data read out in step S2 for the period determined to be a right-turn section in step S3 and the α value calculated in step S4. The latest Hp value calculated in step S5 is stored and accumulated in memory unit 12. Next, in step S6, the latest Hp value stored in memory unit 12 is compared with past Hp values ​​to determine the driver's driving ability related to cognitive function. Next, in step S7, the evaluation result of step S6 is sent to a pre-registered email address, and the process ends.

[0034] In this way, the α value and Hp value, which are indicators for determining a driver's driving ability, can be calculated based only on daily driving data, so driving ability can be determined without interfering with driving (steps S1 to S5). Furthermore, the α value and Hp value are calculated based on one cycle of driving data from start to finish of a pre-registered vehicle, so the driving ability of each pre-registered driver can be appropriately determined (steps S2 to S6). Furthermore, the driver's cognitive function is automatically evaluated based only on daily driving data, and the evaluation results are notified to the driver or their family, reducing the burden on family members who live far away from the elderly driver who drives the vehicle (steps S1 to S7).

[0035] 5B to 5F are flowcharts showing modifications of FIG. 5A, and FIG. 5B is a flowchart showing an example of processing for evaluating the driving ability of each driver after excluding driving data after the driver's seat door is opened from the driving data of each cycle. In this case, after reading out the driving data for each driver stored in the memory unit in step S2, the process proceeds to step S8, where it is determined whether the driver's seat door was opened during each cycle based on the opening / closing information of each seat door included in the driving data.

[0036] If the answer in step S8 is YES, the process proceeds to step S9, where the driving data for each cycle after the driver's door is opened is excluded from the driving data for each driver stored in the memory unit, and the process proceeds to step S3. If the answer in step S8 is NO, the process proceeds directly to step S3. As a result, if the driver's door is opened during each cycle and there is a possibility that the driver has changed, the driving data thereafter is excluded from the driving data for each driver, allowing for a more appropriate determination of each driver's driving ability.

[0037] 5C is a flowchart showing an example of a process for evaluating the driving ability of each driver after excluding driving data after the driver's door is opened and the driver leaves the seat from the driving data of each cycle. In this case, after reading the driving data for each driver stored in the memory unit 12 in step S2, it is determined in step S8 whether the driver's door was opened during each cycle, and if the determination is affirmative, the process proceeds to step S10. In step S10, it is determined whether the driver left the seat when the driver's door was opened during each cycle based on the seating information of the driver included in the driving data.

[0038] If the result in step S10 is affirmative, the process proceeds to step S9, where the driving data for each cycle after the driver's door is opened is excluded from the driving data for each driver stored in the memory unit, and the process proceeds to step S3. If the result in step S8 or step S10 is negative, the process proceeds directly to step S3. As a result, only when the driver has left the seat during each cycle and there is a high possibility that the driver has been replaced, subsequent driving data is excluded from the driving data for each driver, allowing the driving data to be used effectively while more appropriately determining the driving ability of each driver.

[0039] 5D is a flowchart showing an example of a process for evaluating the driving ability of each driver after excluding driving data from the driving data of each cycle when the driver's door is opened and other door openings are performed. In this case, after reading the driving data for each driver stored in the memory unit 12 in step S2, it is determined in step S8 whether the driver's door was opened during each cycle. If the determination is affirmative, the process proceeds to step S11. In step S11, it is determined whether other door openings are performed when the driver's door is opened during each cycle, based on the door opening / closing information of each door included in the driving data.

[0040] If step S11 is positive, the process proceeds to step S9, where the driving data for each cycle after the driver's door is opened is excluded from the driving data for each driver stored in the memory, and the process proceeds to step S3. If step S8 or step S11 is negative, the process proceeds directly to step S3. As a result, only when the driver's door and another passenger door are simultaneously opened during each cycle and there is a high possibility that the driver and another passenger have switched places, the subsequent driving data is excluded from the driving data for each driver. This makes it possible to effectively utilize the driving data and more appropriately determine each driver's driving ability.

[0041] 5E is a flowchart showing an example of a process for evaluating the driving ability of each driver after excluding driving data after the driver's door is opened and the driver's seat belt is unfastened from the driving data of each cycle. In this case, after reading out the driving data for each driver stored in the memory unit 12 in step S2, it is determined in step S8 whether the driver's door was opened during each cycle, and if the determination is affirmative, the process proceeds to step S12. In step S12, it is determined whether the driver's seat belt was unfastened when the driver's door was opened during each cycle, based on the driver's seat belt fastening / unfastening information included in the driving data.

[0042] If the answer is YES in step S12, the process proceeds to step S9, where the driving data for each cycle after the driver's door is opened is excluded from the driving data for each driver stored in the memory unit, and the process proceeds to step S3. If the answer is NO in step S8 or step S12, the process proceeds directly to step S3. Only when the driver unfastens their seat belt during each cycle and there is a high possibility that the driver has been replaced is the subsequent driving data excluded from the driving data for each driver, so that the driving ability of each driver can be more appropriately determined while making effective use of the driving data.

[0043] 5F is a flowchart showing an example of a process for evaluating the driving ability of each driver after excluding driving data after the driver's door is opened and the driver is changed from the driving data of each cycle. In this case, after reading the driving data for each driver stored in the memory unit 12 in step S2, it is determined in step S8 whether the driver's door was opened during each cycle, and if the determination is affirmative, the process proceeds to step S13. In step S13, it is determined whether the driver was changed when the driver's door was opened during each cycle based on the driver identification result included in the driving data.

[0044] If step S13 is answered in the affirmative, the process proceeds to step S9, where the driving data for each cycle after the driver's door is opened is excluded from the driving data for each driver stored in the memory unit, and the process proceeds to step S3. If step S8 or step S13 is answered in the negative, the process proceeds directly to step S3. As a result, only when the driver is changed during each cycle, the driving data thereafter is excluded from the driving data for each driver, so that the driving ability of each driver can be more appropriately determined while effectively utilizing the driving data.

[0045] According to this embodiment, the following effects can be achieved. (1) The system 10 includes an information acquisition unit 13 that acquires time-series driving data of the vehicle, an identification unit 14 that identifies the driver of the vehicle, and an evaluation value calculation unit 16 that calculates an α value and an Hp value that represent the steering characteristics of the driver identified by the identification unit 14 based on one cycle of driving data from the start to the end of the vehicle among the driving data acquired by the information acquisition unit 13 (FIGS. 2 and 5A). This allows the α value and the Hp value, which serve as indicators for determining the driver's driving ability, to be calculated based on daily driving data, making it possible to determine the driver's driving ability without interfering with driving. Furthermore, because the α value and the Hp value are calculated based on one cycle of driving data from the start to the end of a pre-registered vehicle, the driving ability of each pre-registered driver can be appropriately determined.

[0046] (2) The evaluation value calculation unit 16 calculates the α value and Hp value representing the steering characteristics of the driver at the time of starting the vehicle identified by the identification unit 14 based on one cycle of driving data (FIGS. 5A to 5F). That is, the evaluation value calculation unit 16 calculates the α value and Hp value of the individual driver identified at the time of starting the vehicle based on one cycle of driving data from start to finish of a pre-registered vehicle that the driver to be evaluated drives on a daily basis. This makes it possible to more appropriately determine the driving ability of the individual driver to be evaluated.

[0047] (3) The system 10 further includes a memory unit 12 that stores the driving data acquired by the information acquisition unit 13 and the α value and Hp value calculated by the evaluation value calculation unit 16 in association with the driver at the time of starting the vehicle identified by the identification unit 14 (FIG. 2). With this configuration, the driving data acquired from each vehicle by the information acquisition unit 13 and the evaluation value calculated by the evaluation value calculation unit 16 are associated with each pre-registered driver and stored in the memory unit 12 as driving data for each driver to be evaluated.

[0048] (4) The driving data includes information about the vehicle's state detected by sensors mounted on the vehicle. The system 10 further includes a determination unit 15 that determines whether the driver has changed during one cycle based on the driving data acquired by the information acquisition unit 13 (FIG. 2). The evaluation value calculation unit 16 identifies, from the driving data for one cycle, the driving data from the time of startup to the time when the determination unit 15 determines that the driver has changed, and calculates the α value and Hp value based on the identified driving data (FIGS. 5B to 5F). As a result, if there is a possibility that the driver has changed, the driving data after that can be excluded from the pre-registered driving data of the individual driver, thereby enabling a more appropriate assessment of the individual driver's driving ability.

[0049] (5) The driving data includes information about the state of the vehicle, such as information about whether the driver's door is open or closed. The determination unit 15 determines whether the driver has changed during one cycle based on the driving data acquired by the information acquisition unit 13 (FIG. 5B). If the driver's door is opened between the start and end of the vehicle, indicating a possibility of a driver change, the subsequent driving data is excluded from the pre-registered driving data of the individual driver, thereby enabling a more appropriate determination of the individual driver's driving ability.

[0050] (3) The driving data includes information about the vehicle state, such as information about whether the driver's door is open or closed and information about the driver's seat occupancy. Based on the driving data acquired by the information acquisition unit 13, the determination unit 15 determines whether the driver has changed during one cycle by determining whether the driver's door has been opened and the driver has left the seat during one cycle (FIG. 5C). Only when there is a high possibility that the driver's door has been opened, the driver has left the seat, and the driver has changed between the start and end of the vehicle, the subsequent driving data is excluded. This makes it possible to appropriately determine the driving ability of each individual driver while effectively utilizing the driving data.

[0051] (4) The driving data includes information about the vehicle state, such as information about whether the driver's door is open or closed, and information about whether other passenger doors are open or closed, based on the driving data acquired by the information acquisition unit 13. The determination unit 15 determines whether the driver has changed during one cycle by determining whether the driver's door is opened and whether other passenger doors are opened during one cycle (FIG. 5D). Only when the driver's door and other passenger doors are opened simultaneously between the start and end of the vehicle, indicating a high possibility that the driver has changed places with another passenger, is the subsequent driving data excluded. This makes it possible to appropriately determine the driving ability of each individual driver while effectively utilizing the driving data.

[0052] (5) The driving data includes information about the vehicle state, such as information about whether the driver's door is open or closed and information about whether the driver's seat belt is fastened or unfastened. The determination unit 15 determines whether the driver has changed during one cycle by determining whether the driver's door is opened and the driver's seat belt is unfastened during one cycle based on the driving data acquired by the information acquisition unit 13 (FIG. 5E). Only when the driver's door is opened, the seat belt is unfastened, and there is a high possibility that the driver has changed between the start and end of the vehicle, subsequent driving data is excluded. This makes it possible to appropriately determine the driving ability of each individual driver while effectively utilizing the driving data.

[0053] (6) The driving data includes information about the vehicle state, such as information about whether the driver's door is open or closed and information about the biometrically authenticated driver. The determination unit 15 determines whether the driver has changed during one cycle by determining whether the driver's door is opened and the driver has changed during one cycle based on the driving data acquired by the information acquisition unit 13 (FIG. 5F). Only when the driver's door is opened and the driver has changed between the start and end of the vehicle is the subsequent driving data excluded. This makes it possible to appropriately determine the driving ability of each individual driver while effectively utilizing the driving data.

[0054] In the above embodiment, an example has been described in which the α value is calculated based on the driving data when the vehicle travels in a no-load / low-load section, and the Hp value is calculated based on the driving data when the vehicle travels in a right-turn section, as shown in Fig. 1 etc. However, the evaluation value calculation unit is not limited to this. The evaluation value calculation unit may be any unit that calculates an evaluation value that represents the steering characteristics of each driver based on the driving data for each driver.

[0055] Although the present invention has been described above as a driving ability determination system, it can also be used as a driving ability determination method. That is, the driving ability determination method includes an information acquisition step S2 of acquiring time-series driving data of the vehicle, an identification step S1 of identifying the driver of the vehicle, and evaluation value calculation steps S4 and S5 of calculating an evaluation value representing the steering characteristics of the driver identified in the identification step S1 based on one cycle of driving data from the start to the end of the vehicle out of the driving data acquired in the information acquisition step S2 (FIG. 5A).

[0056] The above description is merely an example, and the present invention is not limited to the above-described embodiment and modifications as long as the features of the present invention are not impaired. One or more of the above-described embodiment and modifications can be arbitrarily combined, and modifications can also be combined with each other. [Explanation of symbols]

[0057] 10 driving ability assessment system (system), 11 calculation unit, 12 memory unit, 13 information acquisition unit, 14 identification unit, 15 assessment unit, 16 evaluation value calculation unit, 17 cognitive function assessment unit, 18 information output unit

Claims

1. an information acquisition unit that acquires time-series driving data including information on the steering angle of the vehicle; an identification unit that identifies a driver of the vehicle; an evaluation value calculation unit that calculates an evaluation value that represents the steering characteristics of the driver identified by the identification unit based on one cycle of driving data from a start point to an end point of the vehicle, among the driving data acquired by the information acquisition unit; The evaluation value calculation unit determines, based on the change in the steering angle over time, whether the driving section per unit time is a high-load section in which the driver's driving behavior places a large driving load, or a no-load / low-load section other than the high-load section, and determines whether the no-load / low-load section is a specified section in which the cognitive load placed on the driver is increased, calculates a first evaluation value representing the driver's steering error based on the driving data in the no-load / low-load section, and calculates a second evaluation value representing the driver's steering error when the cognitive load is increased based on the driving data in the specified section.

2. 2. The driving ability determination system according to claim 1, A driving ability determination system characterized in that the evaluation value calculation unit calculates the evaluation value representing the steering characteristics of the driver at the start-up point identified by the identification unit based on the driving data of the one cycle.

3. 3. The driving ability determination system according to claim 2, A driving ability assessment system characterized by further comprising a memory unit that stores the driving data acquired by the information acquisition unit and the evaluation value calculated by the evaluation value calculation unit in association with the driver at the time of startup identified by the identification unit.

4. In the driving ability determination system according to any one of claims 1 to 3, The driving data includes information about the state of the vehicle detected by a sensor mounted on the vehicle, a determination unit that determines whether the driver has changed during the one cycle based on the traveling data acquired by the information acquisition unit, A driving ability determination system characterized in that the evaluation value calculation unit identifies the driving data of the one cycle from the time of startup to the time when the determination unit determines that the driver has changed, and calculates the evaluation value based on the identified driving data.

5. 5. The driving ability determination system according to claim 4, The driving data includes information on the state of the vehicle, such as opening and closing information of the driver's seat side door, A driving ability determination system characterized in that the determination unit determines whether the driver has changed during one cycle by determining whether the driver's side door has been opened during the one cycle based on the driving data acquired by the information acquisition unit.

6. 5. The driving ability determination system according to claim 4, The driving data includes information on the state of the vehicle, such as information on whether the driver's seat side door is open or closed, and information on whether the driver is seated in the driver's seat. The driving ability determination system is characterized in that the determination unit determines whether the driver has changed during one cycle by determining whether the driver's side door has been opened and the driver has left the seat during the one cycle based on the driving data acquired by the information acquisition unit.

7. 5. The driving ability determination system according to claim 4, The travel data includes, as information about the state of the vehicle, information about whether a driver's seat side door is open or closed, and information about whether a seat side door other than the driver's seat side door is open or closed, The driving ability determination system is characterized in that the determination unit determines whether the driver has changed during one cycle by determining whether the driver's side door has been opened and whether a seat side door other than the driver's side door has been opened during one cycle based on the driving data acquired by the information acquisition unit.

8. 5. The driving ability determination system according to claim 4, The driving data includes information on the state of the vehicle, such as information on whether the driver's door is open or closed, and information on whether the driver's seat belt is fastened or unfastened, A driving ability determination system characterized in that the determination unit determines whether the driver has changed during one cycle by determining whether the driver's side door has been opened and the driver's seat belt has been unfastened during the one cycle based on the driving data acquired by the information acquisition unit.

9. 5. The driving ability determination system according to claim 4, The driving data includes, as information about the state of the vehicle, information about whether the driver's door is open or closed and information about the driver who has been biometrically authenticated; The driving ability determination system is characterized in that the determination unit determines whether the driver has changed during one cycle by determining whether the driver's side door has been opened and the driver has changed during the one cycle based on the driving data acquired by the information acquisition unit.

10. an information acquisition step of acquiring time-series driving data including information on the steering angle of the vehicle; an identifying step of identifying a driver of the vehicle; an evaluation value calculation step of calculating an evaluation value representing the steering characteristics of the driver identified in the identification step, based on the driving data of one cycle from the start to the end of the vehicle, among the driving data acquired in the information acquisition step, In the evaluation value calculation step, based on the change in the steering angle over time, it is determined whether the driving section per unit time is a high-load section in which the driving load placed on the driver due to the driving behavior is high, or a no-load / low-load section other than the high-load section, and it is determined whether the no-load / low-load section is a specified section in which the cognitive load placed on the driver is high, and a first evaluation value representing the driver's steering error is calculated based on the driving data in the no-load / low-load section, and a second evaluation value representing the driver's steering error when the cognitive load is high is calculated based on the driving data in the specified section.

Images