Driving ability assessment system and driving ability assessment method

Abstract

This driving capability determination system (10) comprises: an information acquisition unit (13) that acquires time-series travel data of a vehicle; an evaluation value calculation unit (16) that calculates an evaluation value representing the steering characteristics of the driver of the vehicle, on the basis of the travel data; and an event occurrence determination unit (15) that determines whether or not a predetermined event in which a predetermined load acts on the driver of the vehicle has occurred, on the basis of the travel data. The evaluation value calculation unit (16) specifies, as specific travel data, travel data acquired after it is determined that a predetermined event has occurred, from among the travel data, and calculates the evaluation value, on the basis of the travel data excluding the specific travel data from the travel data, or on the basis of the travel data corrected so that the weight for the evaluation value of the specific travel data is lower than the weight for the evaluation value of the other travel data.

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 assessment system according to one aspect of the present invention includes an information acquisition unit that acquires time-series driving data of a vehicle, and an evaluation value calculation unit that calculates an evaluation value that represents the steering characteristics of a driver of the vehicle based on the driving data acquired by the information acquisition unit. a driving data extraction unit that extracts first driving data of a no-load / low-load section other than a high-load section where a driving load imposed on a vehicle driver due to driving behavior is high, and extracts second driving data of a right-turn section, based on the driving data acquired by the information acquisition unit;and an event occurrence determination unit that determines whether a predetermined event that applies a predetermined load to a driver of the vehicle has occurred based on the driving data acquired by the information acquisition unit. The evaluation value calculation unit identifies, from the driving data acquired by the information acquisition unit, driving data acquired by the information acquisition unit after the time when the event occurrence determination unit determines that the predetermined event has occurred, as specific driving data. , th A first evaluation value representing the steering characteristics of the vehicle driver is calculated based on the driving data obtained by excluding the specific driving data from the first driving data, or based on the driving data corrected so that the weight of the evaluation value of the specific driving data included in the first driving data is lower than the weight of the evaluation value of the other first driving data, and the calculated first evaluation value and , th Based on the second travel data, a second evaluation value is calculated that represents the steering characteristics of the driver when a predetermined load is applied to the driver of the vehicle.

[0006] Another aspect of the present invention is a driving ability assessment method including: an information acquisition step of acquiring time-series driving data of a vehicle; and an evaluation value calculation step of calculating an evaluation value representing steering characteristics of a driver of the vehicle based on the driving data acquired in the information acquisition step. a driving data extraction unit that extracts first driving data of a no-load / low-load section other than a high-load section where a driving load imposed on a vehicle driver due to a driving behavior is high, and extracts second driving data of a right-turn section, based on the driving data acquired in the information acquisition step; and an event occurrence determination step of determining whether or not a predetermined event that applies a predetermined load to a driver of the vehicle has occurred based on the travel data acquired in the information acquisition step. In the evaluation value calculation step, of the travel data acquired in the information acquisition step, travel data acquired in the information acquisition step after the time when it is determined in the event occurrence determination step that the predetermined event has occurred is identified as specific travel data. , th A first evaluation value representing the steering characteristics of the vehicle driver is calculated based on the driving data obtained by excluding the specific driving data from the first driving data, or based on the driving data corrected so that the weight of the evaluation value of the specific driving data included in the first driving data is lower than the weight of the evaluation value of the other first driving data, and the calculated first evaluation value and , th Based on the second travel data, a second evaluation value is calculated that represents the steering characteristics of the driver when a predetermined load is applied to the driver of the vehicle. [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 5] 3 is a flowchart showing an example of processing executed by the calculation unit of FIG. 2; DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an embodiment of the present invention will be described with reference to FIGS. 1 to 5. 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. The driving ability related to cognitive function is determined based on driving data when the driver drives a vehicle, and safe driving can be supported by the driver and their family members by understanding the tendency of cognitive function decline.

[0010] Figure 1 is a diagram illustrating the relationship between driving sections and driving load. As shown in Figure 1, the driving load imposed on a driver due to driving behavior varies depending on the driving section, such as road shape. For example, driving load increases when driving on S-curves or winding roads, or when parking in a parking space. That is, the operational load due to driving operations increases in driving sections where the driver is required to make many steering maneuvers per unit of vehicle movement and the vehicle's driving trajectory has a complex shape. In this case, in addition to the high frequency of steering, the steering must be operated in coordination with accelerator and brake operations, and vehicle sense is also required, requiring high driving skills. In such driving sections (high-load sections), the driver's driving skills have a significant impact on driving stability.

[0011] On the other hand, when driving on a straight road, there is almost no driving load. In other words, in driving sections where the driver is required to make almost no steering movements per unit of vehicle movement and the vehicle's driving trajectory is extremely simple, there is almost no operational load due to driving operations. In such driving sections (no-load sections), the driver's driving skills have almost no effect on driving stability.

[0012] Driving loads fall somewhere in between these two when driving on curved roads, changing lanes on multi-lane roads, turning right or left at intersections, etc. Even in these driving sections (low load sections), the driver's driving skill does not have much of an impact on driving stability.

[0013] However, even in a low-load section, when a turning maneuver is performed at an intersection to change the direction of travel of the vehicle across the oncoming lane (a right turn in countries and regions where vehicles drive on the left side, and a left turn in countries and regions where vehicles drive on the right side; hereinafter simply referred to as a "right turn"), in order for the driver to recognize the target trajectory of the vehicle, it becomes necessary to grasp the situation of the oncoming lane ahead while also grasping the situation of the lane ahead after the right turn, and therefore a shift of the driver's gaze occurs between the oncoming lane ahead and the lane ahead after the right turn. In such a right-turn section, the driver's mental activity increases, and the driving load, especially the cognitive load related to cognition, becomes high, so the state of the driver's cognitive function affects the stability of driving.

[0014] By acquiring driving data for such right-turn sections in a manner that allows them to be distinguished from other sections and evaluating driving stability based on that driving data, it is possible to determine the driver's driving ability related to cognitive function. The driving data for the right-turn section can be distinguished from other driving data based on information about the steering angle of the steering wheel.

[0015] However, even in a low-load or no-load section other than a right-turn section, if a predetermined event that imposes a psychological load on the driver occurs, the cognitive load may increase. For example, if some information is announced in the vehicle while driving, if a situation occurs in which the vehicle's safety device is activated, or if a situation occurs in which sudden braking or the horn is required, the cognitive load increases. Therefore, in this embodiment, the driving ability assessment system is configured as follows so that driving ability related to cognitive function can be appropriately assessed by handling driving data when a predetermined event occurs separately from other driving data.

[0016] 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 includes 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 its functional components, an information acquisition unit 13, a driving data extraction unit 14, an event occurrence 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 in a vehicle, or as a server device provided outside the vehicle.

[0017] The information acquisition unit 13 acquires time-series vehicle driving data for each pre-registered driver. For example, the information acquisition unit 13 acquires driving data measured for a pre-registered vehicle that each driver drives daily. The driving data includes time-series information on the steering angle of the steering wheel, as well as information on the illumination of warning lights and indicator lights, activation information on safety devices such as an anti-skid system and an anti-lock brake system, activation information on the horn, and information on vehicle deceleration. The driving data may also include information on the activation of warnings and functions provided by advanced driver assistance systems such as lane departure warnings. The information may also include information on whether or not an alarm is output via an in-vehicle speaker or display, whether or not a horn from a nearby vehicle is sounded via an in-vehicle microphone, and whether or not a voice input above a predetermined volume is made by an emergency vehicle, a public address vehicle, or the like. The information may also include images of the driver's face taken by an in-vehicle camera and the results of image processing of the images, and images of the external environment taken by an exterior camera and the results of image processing of the images.

[0018] The driving data 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 storage unit 12.

[0019] The traveling data extraction unit 14 extracts first traveling data when the vehicle travels in a no-load section or a low-load section (no-load / low-load section) and second traveling data when the vehicle travels in a right-turn section based on the time-series traveling data acquired by the information acquisition unit 13. More specifically, the traveling section is determined for each unit time based on the change in the steering angle over time, and traveling data for a period when it is determined that the vehicle is traveling in a no-load / low-load section is extracted as the first traveling data. In addition, traveling data for a period when it is determined that the vehicle is traveling in a right-turn section is extracted as the second traveling data.

[0020] The event occurrence determination unit 15 determines whether or not a predetermined event that increases cognitive load has occurred for each unit time based on the driving data acquired by the information acquisition unit 13, more specifically, the first driving data and the second driving data extracted by the driving data extraction unit 14. Here, the predetermined event may be a sudden event that occurs while the vehicle is driving and that is relatively difficult for the driver to predict.

[0021] The event occurrence determination unit 15 determines whether a warning light or an indicator light is illuminated, for example, based on illumination information of the warning light or indicator light. It also determines whether a safety device is activated based on activation information of the safety device, and determines whether a horn is activated based on activation information of the horn. It also determines whether the deceleration of the vehicle has increased to or above a predetermined value, i.e., whether the vehicle has been suddenly braked, based on deceleration information. It also determines whether a warning or function activation by an advanced driver assistance system has occurred, whether an in-vehicle notification has been output, or whether an audio input of a volume above a predetermined volume (for example, a horn from a nearby vehicle) has occurred.

[0022] In addition to or instead of these, the event occurrence determination unit 15 may determine whether a traffic participant is within a predetermined distance from the vehicle and whether the driver's line of sight is directed toward the traffic participant, based on images from an in-vehicle camera or an outside-vehicle camera and the image processing results thereof. Also, based on images from an in-vehicle camera and the image processing results thereof, the event occurrence determination unit 15 may estimate the driver's emotion by matching the driver's facial expression to one of human emotion patterns, and determine whether the estimated emotion is surprise.

[0023] The evaluation value calculation unit 16 calculates an α value (first evaluation value) that represents the steering characteristics of the driver based on the first driving data extracted by the driving data extraction unit 14, and calculates an Hp value (second evaluation value) that represents the steering characteristics of the driver when the cognitive load increases based on the second driving data. At this time, the evaluation value calculation unit 16 calculates the α value and the Hp value after excluding or correcting, as described below, driving data from the time when the event occurrence determination unit 15 determines that a predetermined event has occurred until a predetermined time has elapsed. In other words, since the occurrence of the predetermined event is a special situation in which the cognitive load increases abnormally, the driving data during that time is excluded or corrected. The predetermined time may be a fixed time (for example, about 30 seconds) or may be changed depending on the content of the event that has occurred.

[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] When correcting the driving data for use, the evaluation value calculation unit 16 corrects the driving data from the time when it is determined that a predetermined event has occurred until a predetermined time has elapsed so as to reduce the prediction error e(n), which is the degree of blur. When a notification accompanied by steering wheel vibration is given, the driving data during the notification is excluded, and the driving data for a predetermined period of time after the notification accompanied by vibration has ended is corrected.

[0027] 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 the prediction error e(n) at each time point n based on the first driving data obtained by excluding and correcting driving data after the occurrence of a predetermined event, 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 at "0°" where there is no steering error, and the smaller the α value will be. On the other hand, the more the steering error, the broader the frequency distribution of the prediction error e(n) will be and the larger the α value will be.

[0028] By using the first driving data from the no-load / low-load section, which excludes the high-load section where there is a lot of steering and where driving skill has a large effect on steering error, it is possible to properly calculate the α value, which represents the driver's steering error under normal conditions. Furthermore, by excluding or correcting the driving data from the first section after the occurrence of a specified event, it is possible to more appropriately calculate the α value.

[0029] 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 second driving data.

[0030] More specifically, the predicted steering angle θp(n) and prediction error e(n) for each time point n are calculated based on second driving data, which excludes and corrects driving data after the occurrence of a predetermined event. The frequency distribution of the prediction error e(n), as shown by the dashed line, is divided 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 nine ranges are P1 (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 the ranges P1 to P9, the steering entropy value (Hp value) is calculated by the following formula (ii). Hp=-Σpi·log9pi ···(ii)

[0031] 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 larger the value. By using the second driving data from the right-turn section, where there is a lot of eye movement and the cognitive function is significantly affected by steering error, the Hp value, which represents the driver's steering error when cognitive load is higher than normal, can be calculated appropriately. Furthermore, by excluding or correcting the driving data from the second section after a specific event has occurred, the Hp value can be calculated more appropriately.

[0032] 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.

[0033] 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.

[0034] FIG. 5 is a flowchart showing an example of processing executed by the calculation unit 11 of the system 10. The processing shown in this flowchart is executed, for example, periodically. First, in step S1, all time-series driving data stored in the memory unit 12 is read. Next, in step S2, the driving section for each unit time is determined. Next, in step S3, first driving data for a period determined in step S2 to be a no-load / low-load section and second driving data for a period determined to be a right-turn section are extracted from all the driving data read in step S1. Next, in step S4, it is determined whether a predetermined event has occurred for each unit time based on the first driving data and second driving data extracted in step S3.

[0035] Next, in step S5, the driving data from the time when it is determined in step S4 that the predetermined event has occurred until a predetermined time has elapsed is excluded or corrected, and an α value is calculated based on the first driving data extracted in step S3. Next, in step S6, the driving data from the time when it is determined in step S4 that the predetermined event has occurred until a predetermined time has elapsed is excluded or corrected, and an Hp value is calculated based on the second driving data extracted in step S3 and the α value calculated in step S5. The latest Hp value calculated in step S6 is stored and accumulated in memory unit 12. Next, in step S7, 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 S8, the evaluation result of step S7 is sent to a pre-registered email address, and the process ends.

[0036] In this way, the α value and Hp value, which are indices for determining a driver's driving ability, can be calculated based only on daily driving data, making it possible to determine driving ability without interfering with driving (steps S1 to S6). Furthermore, by excluding or correcting driving data after the occurrence of a predetermined event as a special situation in which cognitive load is abnormally high, the α value and Hp value can be calculated appropriately (steps S2 to S6). Furthermore, the cognitive function of the driver 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 (steps S1 to S8).

[0037] 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 evaluation value calculation unit 16 that calculates an evaluation value of the driving ability that represents the steering characteristics of the driver based on the driving data, and an event occurrence determination unit 15 that determines whether a predetermined event that increases cognitive load has occurred based on the driving data (Figure 2).

[0038] The evaluation value calculation unit 16 identifies, from the driving data, driving data subsequent to the time when it is determined that a predetermined event has occurred as specific driving data, and calculates an evaluation value of driving ability based on driving data from which the specific driving data has been excluded, or based on driving data that has been corrected so that the weight of the evaluation value of driving ability for the specific driving data is lower than the weight of the evaluation value of driving ability for the other driving data. This makes it possible to calculate the α value and Hp value, which are indices for determining the driver's driving ability, based on everyday driving data, and therefore to determine driving ability without interfering with driving.

[0039] Furthermore, by treating the driving data after the occurrence of a predetermined event separately from other driving data, it is possible to appropriately determine driving ability related to cognitive function. For example, by excluding the driving data during a special situation in which cognitive load is abnormally high, it is possible to appropriately calculate the α value and Hp value.

[0040] (2) The evaluation value calculation unit 16 identifies, from among the driving data, driving data from the time when it is determined that a predetermined event has occurred until a predetermined time has elapsed as specific driving data. In other words, when a predetermined event that imposes a psychological burden on the driver occurs, the cognitive load increases for a certain period of time thereafter, so by handling the driving data during that period separately from other driving data, it is possible to appropriately assess driving ability related to cognitive function.

[0041] (3) The evaluation value calculation unit 16 calculates an α value representing the driver's steering characteristics based on the driving data obtained by excluding specific driving data from the first driving data, or based on driving data corrected so that the weight of the specific driving data included in the first driving data is lowered compared to the weight of the driving ability evaluation value of the remaining first driving data, and calculates an Hp value representing the driver's steering characteristics when a predetermined load is applied to the driver based on the calculated α value and the second driving data. In other words, after the occurrence of a predetermined event, the driving data during that time is considered to be a special situation in which cognitive load is abnormally increased, and is corrected to reduce the prediction error e(n), which represents the degree of deviation, and then used to calculate the α value and the Hp value. This allows the α value and the Hp value to be calculated appropriately.

[0042] (4) The evaluation value calculation unit 16 calculates the Hp value based on the α value and the driving data obtained by excluding the specific driving data from the second driving data, or based on the α value and driving data corrected so that the weighting of the driving ability evaluation value of the specific driving data included in the second driving data is lower than the weighting of the driving ability evaluation value of the other second driving data.

[0043] (5) The predetermined events are the activation of an alarm device that notifies the driver of information, the activation of a safety device installed in the vehicle, an increase in the vehicle's deceleration to a predetermined value or more, and the sound of a warning horn around the vehicle. In other words, if some information is announced in the vehicle while driving, if a situation occurs in which the vehicle's safety device is activated, or if a sudden braking or warning horn occurs, the driver will be subjected to psychological stress and cognitive load will increase. By handling the driving data when such predetermined events occur separately from other driving data, it is possible to appropriately assess driving ability related to cognitive function.

[0044] In the above embodiment, an example has been described in which the traveling data extraction unit 14 extracts the first traveling data and the second traveling data by determining the traveling section per unit time based on the time change of the steering angle, but the traveling data extraction unit is not limited to this. For example, the traveling section per unit time may be determined based on the time change of the vehicle's position information, or the traveling section may be identified based on the position information and map information.

[0045] In the above embodiment, an example has been described in which the α value is calculated based on the first driving data when traveling in a no-load / low-load section, and the Hp value is calculated based on the second driving data when traveling in a right-turn section, as shown in Fig. 1 etc., but the first section and the second section are not limited to such sections. The first section and the second section may have a relationship such that the second section imposes a higher cognitive load than the first section. For example, the first section may be any no-load / low-load section excluding the second section.

[0046] While the present invention has been described above as a driving ability assessment system, it can also be used as a driving ability assessment method. That is, the driving ability assessment method includes an information acquisition step S1 for acquiring time-series driving data of a vehicle, evaluation value calculation steps S5 and S6 for calculating an evaluation value of driving ability that represents the steering characteristics of the driver based on the driving data, and an event occurrence determination step S4 for determining whether a predetermined event that increases cognitive load has occurred based on the driving data (FIG. 5). In the evaluation value calculation steps S5 and S6, driving data after the time when it is determined that the predetermined event has occurred is identified as specific driving data, and a driving ability evaluation value is calculated based on driving data from which the specific driving data has been excluded, or based on driving data that has been corrected so that the weight of the evaluation value of driving ability for the specific driving data is lower than the weight of the evaluation value of driving ability for the remaining driving data.

[0047] 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]

[0048] 10 driving ability determination system (system), 11 calculation unit, 12 memory unit, 13 information acquisition unit, 14 driving data extraction unit, 15 event occurrence determination unit, 16 evaluation value calculation unit, 17 cognitive function evaluation unit, 18 information output unit

Claims

1. an information acquisition unit that acquires time-series driving data of the vehicle; an evaluation value calculation unit that calculates an evaluation value representing the steering characteristics of the driver of the vehicle based on the driving data acquired by the information acquisition unit; a travel data extraction unit that extracts first travel data of a no-load / low-load section other than a high-load section where a driving load imposed on a driver of the vehicle due to a driving behavior is large, and extracts second travel data of a right-turn section, based on the travel data acquired by the information acquisition unit; an event occurrence determination unit that determines whether a predetermined event that imposes a predetermined load on a driver of the vehicle has occurred based on the traveling data acquired by the information acquisition unit, The evaluation value calculation unit identifies, from the driving data acquired by the information acquisition unit, driving data acquired by the information acquisition unit after the point at which the event occurrence determination unit determines that the specified event has occurred as specific driving data, and calculates a first evaluation value representing the steering characteristics of the driver of the vehicle based on driving data obtained by excluding the specific driving data from the first driving data, or based on driving data corrected so that the weight for the evaluation value of the specific driving data included in the first driving data is lower than the weight for the evaluation value of the other first driving data, and calculates a second evaluation value representing the steering characteristics of the driver when the specified load is applied to the driver of the vehicle based on the calculated first evaluation value and the second driving data.

2. 2. The driving ability determination system according to claim 1, A driving ability assessment system characterized in that the evaluation value calculation unit identifies, from the driving data acquired by the information acquisition unit, driving data from the time when the event occurrence determination unit determines that the specified event has occurred to the time when a specified time has elapsed as the specified driving data.

3. 3. The driving ability determination system according to claim 1, A driving ability assessment system characterized in that the evaluation value calculation unit calculates the second evaluation value based on the first evaluation value and driving data in which the specific driving data is excluded from the second driving data, or based on the first evaluation value and driving data corrected so that the weight of the specific driving data included in the second driving data for the evaluation value is lower than the weight of the other second driving data for the evaluation value.

4. In the driving ability determination system according to any one of claims 1 to 3, A driving ability assessment system characterized in that the specified event is one of the following: the activation of an alarm device that notifies the driver of information, the activation of a safety device installed in the vehicle, an increase in the deceleration of the vehicle to a specified value or more, and the sound of a warning horn around the vehicle.

5. an information acquisition step of acquiring time-series driving data of the vehicle; an evaluation value calculation step of calculating an evaluation value representing the steering characteristics of the driver of the vehicle based on the traveling data acquired in the information acquisition step; a travel data extraction unit that extracts first travel data of a no-load / low-load section other than a high-load section where a driving load imposed on a driver of the vehicle due to a driving behavior is large, and extracts second travel data of a right-turn section, based on the travel data acquired in the information acquisition step; an event occurrence determination step of determining whether or not a predetermined event that imposes a predetermined load on the driver of the vehicle has occurred based on the traveling data acquired in the information acquisition step, In the evaluation value calculation step, of the driving data acquired in the information acquisition step, driving data acquired in the information acquisition step after the point at which it is determined that the specified event has occurred in the event occurrence determination step is identified as specific driving data, and a first evaluation value representing the steering characteristics of the driver of the vehicle is calculated based on driving data obtained by excluding the specific driving data from the first driving data, or based on driving data corrected so that the weight for the evaluation value of the specific driving data included in the first driving data is lower than the weight for the evaluation value of the other first driving data, and a second evaluation value representing the steering characteristics of the driver when the specified load is applied to the driver of the vehicle is calculated based on the calculated first evaluation value and the second driving data.

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