JP7727606B2 - Driving ability assessment system and driving ability assessment method - Google Patents

Description

The present invention relates to a driving ability assessment system and a driving ability assessment method for assessing the driving ability of a vehicle driver.

A known example of this type of device is one that measures a driver's safe driving ability (see, for example, Patent Document 1). This device intermittently applies a load to the driver through audio output to distract them, calculates steering entropy values that represent steering error under both a loaded and unloaded condition, and evaluates the driver's safe driving ability based on the difference between the error evaluation values calculated under the loaded and unloaded conditions.

By assessing the driving ability of elderly drivers and providing them with an opportunity to consider surrendering their driver's license or adopting driving assistance features as necessary, we can improve road safety and contribute to the development of sustainable transportation systems.

JP 2014-174848 A

However, the device described in Patent Document 1 above requires imposing a load on the driver in order to evaluate their safe driving ability, which interferes with driving.

One aspect of the present invention, a driving ability assessment system, includes an information acquisition unit that acquires steering angle data indicating changes in the vehicle's steering angle over time, along with information on whether a driving assistance function that assists the driver in at least one of acceleration/deceleration and braking among driving operations performed by the driver of the vehicle is activated, and an evaluation value calculation unit that calculates an evaluation value representing the driver's steering characteristics based on the steering angle data acquired by the information acquisition unit. The evaluation value calculation unit calculates the evaluation value by weighting, among the steering angle data acquired by the information acquisition unit, first steering angle data during a period when the driving assistance function is activated more heavily than second steering angle data during a period when the driving assistance function is not activated.

A driving ability assessment method according to another aspect of the present invention includes an information acquisition step executed by a computer to acquire steering angle data indicating changes in the vehicle's steering angle over time, along with information on whether a driving assistance function that assists the driver in at least one of acceleration, deceleration, and braking among driving operations performed by the driver of the vehicle is active, and an evaluation value calculation step to calculate an evaluation value representing the driver's steering characteristics based on the steering angle data acquired in the information acquisition step. In the evaluation value calculation step, the evaluation value is calculated by weighting, among the steering angle data acquired in the information acquisition step, first steering angle data during a period when the driving assistance function is active greater than the weighting of second steering angle data during a period when the driving assistance function is not active.

This invention makes it possible to assess driving ability without interfering with driving.

FIG. 1 is a diagram for explaining a driving section and a driving load. 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; FIG. 3 is a diagram for explaining fluctuations in the steering angle of a vehicle. FIG. 10 is a diagram illustrating an example of a degree display of the degree of steering shake. 3 is a time chart for explaining the state of the driving assistance function taken into account by the evaluation value calculation unit of FIG. 2 ; 3 is a flowchart showing an example of an evaluation process executed by the calculation unit of FIG. 2 . 3 is a flowchart showing an example of a weighting process executed by the calculation unit of FIG. 2 . 10 is a flowchart showing another example of weighting processing executed by the calculation unit of FIG. 2 .

Embodiments of the present invention will now be described with reference to Figures 1 to 8. 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. Declining cognitive function makes it difficult to drive a vehicle safely. The driving ability related to cognitive function can be determined based on driving data when the driver drives a vehicle, allowing the driver and their family to understand trends in cognitive function decline and support safe driving.

Figure 1 is a diagram explaining driving sections and driving load. As shown in Figure 1, the driving load imposed on a driver due to driving behavior changes 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, driving load increases on 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, high driving skills are required, as steering is required in conjunction with accelerator and brake operations, and vehicle sense is also required. In such driving sections (high-load sections), the driver's driving skills have a significant impact on driving stability.

On the other hand, there is almost no driving load when driving on straight roads. In other words, there is almost no steering required from the driver per unit of vehicle movement, and the vehicle's driving trajectory is extremely simple, so there is almost no driving load. In such driving sections (no-load sections), the driver's driving skill has almost no effect on driving stability.

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

However, even in no-load or low-load sections, for example, when driving on a single-lane highway (two-way traffic section) with no median strip, the driver needs to be aware of the conditions in their own lane and the oncoming lane. In this case, shifting their gaze between their own lane and the oncoming lane increases the driver's mental activity and increases the cognitive load (hereinafter referred to as "cognitive load"). Gaze shifts are also required in sections with multiple lanes with heavy traffic, sections with many signs and traffic signals, sections with many pedestrians such as in busy areas, sections with many blind spots such as curved roads with poor visibility or intersections, and sections where multiple roads intersect, thereby increasing the cognitive load.

Furthermore, when making a turn at an intersection to change the vehicle's direction of travel and cross the oncoming lane (a right turn in countries and regions where vehicles drive on the left, or a left turn in countries and regions where vehicles drive on the right; hereafter referred to simply as a "right turn"), the driver must grasp the situation of the oncoming lane ahead while also grasping the situation of the lane ahead after the right turn in order to recognize the vehicle's target trajectory. In this case, too, the driver's gaze must shift between the oncoming lane ahead and the lane ahead, increasing the cognitive load.

In driving sections where cognitive load is high (hereinafter referred to as "specific sections"), the state of the driver's cognitive function affects driving stability. By acquiring driving data for specific 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 their cognitive function. For example, it is possible to acquire time-series location information for the vehicle and, based on the acquired location information, identify driving data for specific sections that have been preset in map information.

However, driving load and cognitive load change depending on whether the driver is receiving assistance from the vehicle's driving assistance functions, even when driving the same section. Therefore, in this embodiment, the driving ability assessment system is configured as follows to appropriately assess driving ability related to cognitive function by changing the way driving data is handled depending on the status of the vehicle's driving assistance functions.

Figure 2 is a block diagram showing an example of the main components of a driving ability assessment system (hereinafter referred to as the system) 10. As shown in Figure 2, the system 10 includes a computer having a calculation unit 11 such as a CPU, a storage unit 12 such as ROM and RAM, and peripheral circuits. The calculation unit 11 functions as an information acquisition unit 13, a load determination unit 14, an evaluation value calculation unit 15, a cognitive function assessment unit 16, and an information output unit 17. 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 installed in a vehicle, as a server device or the like provided external to the vehicle, or as a combination of an on-board device and an external server device or the like.

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 on a daily basis. The driving data includes at least time-series information on the status of the vehicle's driving assistance functions and time-series steering angle data that indicates changes in the steering angle of the steering wheel (steering wheel, handle) by the driver. The driving data also includes time-series position information of the vehicle.

Driving data is transmitted from the vehicle to the system 10 at a predetermined interval, for example, via a TCU (telematics control unit) installed in 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.

The load determination unit 14 determines, for each unit of time, whether a predetermined cognitive load is acting on the driver based on the driving data stored in the memory unit 12. More specifically, the load determination unit 14 determines, based on the vehicle's time-series position information, whether the driving section for each unit of time is a no-load section or low-load section (no-load/low-load section) or a high-load section as shown in Figure 1. Furthermore, the load determination unit 14 determines whether the driving section for each unit of time is a specific section among the no-load/low-load sections that is preset in the map information.

The evaluation value calculation unit 15 calculates an α value, which represents the average steering characteristics of an individual driver, and an Hp value, which represents the steering characteristics of the driver when cognitive load increases, based on the steering angle data stored in the memory unit 12. More specifically, the evaluation value calculation unit 15 calculates the α value based on steering angle data for a period determined by the load determination unit 14 to be traveling in a no-load/low-load section. The evaluation value calculation unit 15 also calculates the Hp value based on the calculated α value and steering angle data for a period determined by the load determination unit 14 to be traveling in a specific section.

Figure 3 is a diagram explaining fluctuations in the steering angle θ of a vehicle. When the vehicle is being driven stably, steering is smooth and the fluctuations in the steering angle θ are small. On the other hand, when the vehicle is being driven unstable, steering becomes shaky and the fluctuations in the steering angle θ become large.

More specifically, as shown in Figure 3, a predicted steering angle θp(n) at time n is calculated by second-order Taylor expansion centered on time (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 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)

Figure 4 is a diagram illustrating an example of a frequency display of the degree of steering error, showing an example of a frequency display of the prediction error e(n). The evaluation value calculation unit 15 calculates the predicted steering angle θp(n) and prediction error e(n) at each time point n based on the steering angle data for 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 steering error there is, 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 steering error there is, the broader the frequency distribution of the prediction error e(n) will be and the larger the α value will be.

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

Furthermore, the evaluation value calculation unit 15 calculates the Hp value based on the calculated α value and the steering angle data for the specific section. More specifically, the evaluation value calculation unit 15 calculates the predicted steering angle θp(n) and prediction error e(n) for each time point n based on the steering angle data for the specific 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 the eight reference values -5α, -2.5α, -α, -0.5α, 0.5α, α, 2.5α, and 5α, the steering entropy value (Hp value) is divided into nine ranges: 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 each range P1 to P9, the steering entropy value (Hp value) is calculated using the following formula (ii):
Hp=-Σpi・log ₉ pi...(ii)

The Hp value represents the smoothness of steering, and decreases as steering variance decreases and the frequency distribution of prediction error e(n) becomes sharper. It also increases as steering variance increases and the frequency distribution of prediction error e(n) becomes broader. By using steering angle data from specific sections where there is a lot of eye movement and cognitive function is significantly affected by steering variance, it is possible to appropriately calculate the Hp value, which represents the driver's steering variance when cognitive load is higher than normal.

Figure 5 is a time chart for explaining the state of the driving assistance functions considered by the evaluation value calculation unit 15. The first driving assistance function is a driving assistance function that assists the driver of the vehicle in at least one of acceleration/deceleration and braking, and the second driving assistance function is a driving assistance function that assists the driver in steering.

First driving assistance functions include a function that assists with acceleration and deceleration so that the vehicle travels at a set speed, and a function that detects a preceding vehicle using a camera or other device and assists with acceleration and deceleration so that the vehicle follows the preceding vehicle while maintaining a set distance. They also include a function that assists with braking so that the vehicle stops when the preceding vehicle stops. Second driving assistance functions include a function that detects the driving lane using a camera or other device and issues a warning with steering vibration if the vehicle is in danger of deviating from the driving lane, and a function that assists with steering (applies steering force) so that the vehicle travels near the center of the driving lane. Even when activated, these driving assistance functions may not operate normally if the preceding vehicle or driving lane cannot be recognized by a camera or other device due to weather conditions, etc.

The time-series information regarding the status of the vehicle's driving assistance functions acquired by the information acquisition unit 13 includes information on whether the first driving assistance function is activated, information on whether the first driving assistance function is operating normally after activation, and information on whether the second driving assistance function is operating.

As shown in Figure 5, of the steering angle data acquired by the information acquisition unit 13 and stored in the memory unit 12, steering angle data during the period (t1 to t2, t3 onwards) when the first driving assistance function is activated, or more specifically, during the period after activation when the function is operating normally, will be referred to below as "first steering angle data." Furthermore, steering angle data other than the first steering angle data will be referred to below as "second steering angle data." Note that, of the first steering angle data, steering angle data during the period (t4 to t5) when the second driving assistance function is activated is excluded from the first steering angle data and treated as second steering angle data.

(Weighting of evaluation values)
The evaluation value calculation unit 15 performs weighting so that the weight of the first steering angle data is greater than the weight of the second steering angle data, and then calculates the Hp value, or the α value and the Hp value. More specifically, the evaluation value calculation unit 15 calculates the α value without weighting, and calculates the Hp value after weighting. Alternatively, the evaluation value calculation unit 15 performs weighting and then calculates the α value, and then calculates the Hp value after weighting.

(Weighting of alpha value - weighting of frequency)
When calculating the α value after weighting, the evaluation value calculation unit 15 first calculates a predicted steering angle θp1(n) and a prediction error e1(n) based on the first steering angle data for the period in which the load determination unit 14 determines that the vehicle is traveling in a no-load/low-load zone. The evaluation value calculation unit 15 also calculates a predicted steering angle θp2(n) and a prediction error e2(n) based on the second steering angle data for the same period. Next, the evaluation value calculation unit 15 multiplies the frequency of the prediction error e1(n) (frequency distribution shown by the solid line in FIG. 4 ) by a weighting coefficient W1 (0<W1, e.g., W1=1), multiplies the frequency of the prediction error e2(n) by a weighting coefficient W2 (0≦W2<W1, e.g., W2=0), and sums these. The evaluation value calculation unit 15 calculates the α value as the 90th percentile value in the frequency distribution of the weighted and summed prediction errors (W1e1(n) + W2e2(n)).

(Weighting of Hp values - weighting of frequency)
When calculating the Hp value after weighting, the evaluation value calculation unit 15 first calculates a predicted steering angle θp1(n) and a prediction error e1(n) based on the first steering angle data for the period during which the load determination unit 14 determines that the vehicle is traveling through a specific section. The evaluation value calculation unit 15 also calculates a predicted steering angle θp2(n) and a prediction error e2(n) based on the second steering angle data for the same period. Next, the evaluation value calculation unit 15 multiplies the frequency of the prediction error e1(n) (frequency distribution indicated by the dashed line in FIG. 4 ) by a weighting coefficient W1 (0<W1, for example, W1=1), multiplies the frequency of the prediction error e2(n) by a weighting coefficient W2 (0≦W2<W1, for example, W2=0), and adds these results together. The evaluation value calculation unit 15 divides the frequency distribution of the weighted and summed prediction errors (W1e1(n) + W2e2(n)) into ranges P1 to P9 based on the α value, and calculates the Hp value using formula (ii) based on the proportions p1 to p9 of each range P1 to P9.

(Weighting of Hp values - direct weighting)
When calculating the Hp value after weighting, the evaluation value calculation unit 15 may calculate an Hp1 value based on the first steering angle data and an Hp2 value based on the second steering angle data, and multiply the calculated Hp1 value and Hp2 value by weighting coefficients W1 and W2. More specifically, the evaluation value calculation unit 15 calculates the predicted steering angle θp1(n), the prediction error e1(n), and the Hp1 value based on the first steering angle data for a period during which the load determination unit 14 determines that the vehicle is traveling in a specific section. In addition, the evaluation value calculation unit 15 calculates the predicted steering angle θp2(n), the prediction error e2(n), and the Hp2 value based on the second steering angle data for the same period. The evaluation value calculation unit 15 multiplies the Hp1 value by a weighting coefficient W1 (0<W1, for example, W1=1) and multiplies the Hp2 value by a weighting coefficient W2 (0≦W2<W1, for example, W2=0), and calculates the sum of these (W1Hp1+W2Hp2) as the Hp value.

The cognitive function assessment unit 16 assesses the driver's cognitive function based on the Hp value calculated by the assessment value calculation unit 15. That is, by continuously monitoring the Hp value, which represents steering fluctuations when cognitive load increases, it is possible to assess a trend toward a decline in the driver's cognitive function. For example, if the Hp value, calculated periodically (e.g., monthly) based on driving data from daily driving, is on the rise, it is assessed that the driver's cognitive function is on the decline.

The information output unit 17 transmits the evaluation results from the cognitive function evaluation unit 16 to a user device such as 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, it is easier for the driver to accept the current state of their cognitive function and consider appropriate measures early on.

Figure 6 is a flowchart showing an example of the evaluation process executed by the calculation unit 11 of the system 10. The process shown in this flowchart is executed, for example, periodically. First, in step S1, the driving data stored in the memory unit 12 is read. Next, in step S2, based on the driving data read in step S1, it is determined whether the driving section per unit time is a no-load/low-load section or a high-load section. Furthermore, based on the driving data for the no-load/low-load section, it is determined whether the driving section per unit time is a specific section.

Next, in step S3, the α value is calculated based on the steering angle data for the period determined in step S2 to be traveling in a no-load/low-load section. Next, in step S4, the Hp value is calculated based on the α value calculated in step S3 and the steering angle data for the period determined in step S2 to be traveling in a specific section. The latest Hp value calculated in step S4 is stored and accumulated in memory unit 12. Next, in step S5, 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 S6, the evaluation result of step S5 is sent to a pre-registered email address, and the process ends.

In this way, the α value and Hp value, which are indicators for assessing a driver's driving ability, can be calculated based solely on daily driving data, making it possible to assess driving ability without interfering with driving (steps S1-S4). Furthermore, the driver's cognitive function is automatically assessed based solely on daily driving data, and the assessment results are notified to the driver and their family, reducing the burden on family members living far away from the elderly driver (steps S1-S6).

Figure 7 is a flowchart showing an example of weighting processing executed by the calculation unit 11 of the system 10, and shows an example of weighting processing in which the frequency of prediction error is multiplied by a weighting coefficient in the process of calculating evaluation values (α value, Hp value). The processing shown in this flowchart is executed in steps S3 and/or S4 of Figure 6.

(Weighting of alpha value - weighting of frequency)
When the frequency of the prediction error is multiplied by a weighting factor in the process of calculating the α value, the process shown in this flowchart is executed in step S3 of FIG. 6. As shown in FIG. 7, first, in step S10, a predicted steering angle θp1(n) is calculated based on first steering angle data for the period determined in step S2 to be traveling in a no-load/low-load zone, and a prediction error e1(n) is calculated. Also, a predicted steering angle θp2(n) is calculated based on second steering angle data for the same period, and a prediction error e2(n) is calculated. Next, in step S11, the frequency of the prediction error e1(n) calculated in step S10 is multiplied by a weighting factor W1, and the frequency of the prediction error e2(n) calculated in step S10 is multiplied by a weighting factor W2. Next, in step S12, the weighted frequencies W1e1(n) and W2e2(n) calculated in step S11 are added together. In this case, in step S3 of FIG. 6, the 90th percentile value in the frequency distribution of the weighted and summed prediction errors (W1e1(n)+W2e2(n)) calculated in step S12 is calculated as the α value.

(Weighting of Hp values - weighting of frequency)
When the frequency of the prediction error is multiplied by a weighting factor in the process of calculating the Hp value, the process shown in this flowchart is executed in step S4 of FIG. 6. As shown in FIG. 7, first, in step S10, a predicted steering angle θp1(n) is calculated based on first steering angle data for the period determined in step S2 to be traveling through a specific section, and a prediction error e1(n) is calculated. Also, a predicted steering angle θp2(n) is calculated based on second steering angle data for the same period, and a prediction error e2(n) is calculated. Next, in step S11, the frequency of the prediction error e1(n) calculated in step S10 is multiplied by a weighting factor W1, and the frequency of the prediction error e2(n) calculated in step S10 is multiplied by a weighting factor W2. Next, in step S12, the weighted frequencies W1e1(n) and W2e2(n) calculated in step S11 are added together. In this case, in step S4 of FIG. 6, the Hp value is calculated based on the frequency distribution of the weighted and summed prediction errors (W1e1(n) + W2e2(n)) summed in step S12 and the α value calculated in step S3 of FIG. 6.

(Weighting of Hp values - direct weighting)
FIG. 8 is a flowchart showing another example of the weighting process executed by the calculation unit 11 of the system 10. This is a variation of the flowchart of FIG. 6 in which the evaluation value (Hp value) is directly multiplied by a weighting factor. Steps S1 to S3, S5, and S6 in FIG. 8 are similar to steps S1 to S3, S5, and S6 in FIG. 6, and therefore will not be described here. As shown in FIG. 8, in step S4A, a predicted steering angle θp1(n), a predicted error e1(n), and an Hp1 value are calculated based on the α value calculated in step S3 and the first steering angle data for the period determined in step S2 to be traveling through the specific section. Furthermore, a predicted steering angle θp2(n), a predicted error e2(n), and an Hp2 value are calculated based on the second steering angle data for the same period. Next, in step S4B, the Hp1 value calculated in step S4A is multiplied by a weighting factor W1, and the Hp2 value calculated in step S4A is multiplied by a weighting factor W2. Next, in step S4C, the weighted Hp1 and Hp2 values calculated in step S4B are added together to calculate the Hp value (W1Hp1+W2Hp2).

It is believed that the driver can concentrate on steering while the first driving assistance function, which assists the vehicle in constant speed driving and following a preceding vehicle, is activated. In the evaluation, the first steering angle data from such a period is actively used, i.e., the weight of the first steering angle data is increased compared to the weight of the second steering angle data, thereby calculating the evaluation value, thereby enabling an accurate assessment of driving ability (steps S11, S4B).

For example, if the first steering angle data is actively used to calculate the Hp value, it is possible to evaluate the driver's steering characteristics when the cognitive load increases while the driver is receiving driving assistance and concentrating on steering. In this case, the accuracy of the Hp value, which is calculated and accumulated periodically, is improved, allowing for a more accurate evaluation of the driver's cognitive function. Furthermore, if the first steering angle data is also actively used to calculate the α value, it is possible to evaluate the driver's steering characteristics when the driver is receiving driving assistance and concentrating on steering, and the accuracy of the α value is improved, allowing for an even more accurate evaluation of the driver's cognitive function.

According to this embodiment, the following effects can be achieved.
(1) The system 10 includes an information acquisition unit 13 that acquires steering angle data indicating changes in the steering angle of the vehicle over time, along with information on whether a first driving assistance function that assists in at least one of acceleration/deceleration and braking among driving operations performed by a driver of the vehicle is activated, and an evaluation value calculation unit 15 that calculates an evaluation value representing the steering characteristics of the driver based on the steering angle data acquired by the information acquisition unit 13 ( FIG. 2 ). The evaluation value calculation unit 15 calculates the evaluation value by weighting, among the steering angle data acquired by the information acquisition unit 13, first steering angle data during a period when the first driving assistance function is activated more heavily than second steering angle data during a period when the first driving assistance function is not activated.

In this way, by calculating an evaluation value that serves as an index for determining a driver's driving ability based on daily driving data, the driver's driving ability can be determined without interfering with driving. Furthermore, while the first driving assistance function, which assists the vehicle in constant speed driving and following a preceding vehicle, is activated, the driver can concentrate on steering, and by actively utilizing steering angle data from such periods, driving ability can be determined with high accuracy.

(2) The first steering angle data is steering angle data during a period when the first driving assistance function is operating normally after activation. The second steering angle data further includes steering angle data during a period when the first driving assistance function is not operating normally after activation. For example, even if the first driving assistance function is activated, if a preceding vehicle cannot be recognized due to weather conditions or the like, the steering angle data during a period when the first driving assistance function is not actually operating is treated as second steering angle data. This enables the driver's driving ability to be determined more accurately.

(3) The information acquisition unit 13 further includes information on whether a second driving assistance function that assists the driver in steering is operating. The first steering angle data is steering angle data for a period when the first driving assistance function is activated and the second driving assistance function is not activated. The second steering angle data further includes steering angle data for a period when the first driving assistance function is activated and the second driving assistance function is activated. By reducing the weight of the steering angle data for a period when steering assistance is being performed, which is the subject of the evaluation of driving ability, the driver's driving ability can be determined more accurately.

(4) The system 10 further includes a load determination unit 14 that determines whether a predetermined cognitive load is acting on the driver based on the steering angle data acquired by the information acquisition unit 13 (Figure 2). The evaluation value calculation unit 15 calculates an α value that represents the driver's average steering characteristics based on the steering angle data acquired by the information acquisition unit 13, and calculates an Hp value that represents the driver's steering characteristics when a predetermined cognitive load is acting based on the calculated α value and the steering angle data acquired by the information acquisition unit 13 for a period during which the load determination unit 14 determines that a predetermined cognitive load is acting.

The evaluation value calculation unit 15 calculates the α value without weighting either the first steering angle data or the second steering angle data, while calculating the Hp value by weighting the first steering angle data more heavily than the second steering angle data. In this way, by actively using the first steering angle data obtained when the driver is receiving driving assistance and concentrating on steering, resulting in increased cognitive load, the Hp value can be accurately calculated, enabling an accurate evaluation of the driver's cognitive function. Furthermore, because the steering angle data used to calculate the α value is used regardless of the state of the driving assistance function, ensuring a sufficient amount of data allows the α value, which represents the driver's average steering characteristics, to be appropriately calculated.

(5) The evaluation value calculation unit 15 calculates the α value by weighting the first steering angle data more heavily than the second steering angle data, and calculates the Hp value by weighting the first steering angle data more heavily than the second steering angle data. For example, if a sufficient amount of data is available, the α value can be accurately calculated and the driver's cognitive function can be accurately evaluated by actively using the first steering angle data obtained when the driver is receiving driving assistance and concentrating on steering and cognitive load increases.

In the above embodiment, an example was described in which the α value was calculated based on steering angle data when traveling through a no-load/low-load section, and the Hp value was calculated based on steering angle data when traveling through a specific section, but 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 steering angle data for each driver.

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, executed by a computer, for acquiring steering angle data indicating changes in the vehicle's steering angle over time, along with information on whether a first driving assistance function, which assists the driver of the vehicle in at least one of acceleration, deceleration, and braking, is active, and evaluation value calculation steps S3, S4, S10-S12, and S4A-A4C for calculating an evaluation value representing the driver's steering characteristics based on the steering angle data acquired in the information acquisition step S1 (see FIGS. 6-8). In the evaluation value calculation steps S3, S4, S10-S12, and S4A-A4C, the evaluation value is calculated by weighting the first steering angle data acquired in the information acquisition step S1 during periods when the first driving assistance function is active more heavily than the second steering angle data acquired during periods when the first driving assistance function is not active.

The above description is merely an example, and the present invention is not limited to the above-described embodiments and variations, as long as the features of the present invention are not impaired. It is also possible to combine one or more of the above embodiments and variations in any desired manner, and it is also possible to combine variations together.

10. Driving ability assessment system (system), 11. Calculation unit, 12. Memory unit, 13. Information acquisition unit, 14. Load assessment unit, 15. Evaluation value calculation unit, 16. Cognitive function assessment unit, 17. Information output unit

Claims (6)

an information acquisition unit that acquires steering angle data indicating a change in the steering angle of the vehicle over time, together with information on whether a driving assistance function that assists at least one of acceleration/deceleration and braking among driving operations performed by a driver of the vehicle is activated;
an evaluation value calculation unit that calculates an evaluation value representing a steering characteristic of a driver based on the steering angle data acquired by the information acquisition unit,
A driving ability assessment system characterized in that the evaluation value calculation unit calculates the evaluation value by giving a greater weight to first steering angle data during a period in which the driving assistance function is activated, among the steering angle data acquired by the information acquisition unit, than to second steering angle data during a period in which the driving assistance function is not activated. 2. The driving ability determination system according to claim 1,
the first steering angle data is steering angle data during a period in which the driving assistance function is operating normally after activation,
The driving ability determination system, wherein the second steering angle data further includes steering angle data for a period during which the driving assistance function is not operating normally after activation. 2. The driving ability determination system according to claim 1,
the driving assistance function is a first driving assistance function,
The information acquisition unit further includes information on whether a second driving assistance function that assists the driver in steering is operating,
the first steering angle data is steering angle data during a period when the first driving assistance function is activated and the second driving assistance function is not activated,
A driving ability determination system characterized in that the second steering angle data further includes steering angle data for a period during which the first driving assistance function is activated and the second driving assistance function is in operation. 2. The driving ability determination system according to claim 1,
a load determination unit that determines whether a predetermined load is acting on the driver based on the steering angle data acquired by the information acquisition unit,
the evaluation value calculation unit calculates a first evaluation value representing an average steering characteristic of the driver based on the steering angle data acquired by the information acquisition unit, and calculates a second evaluation value representing the steering characteristic of the driver when the predetermined load is applied based on the calculated first evaluation value and steering angle data acquired by the information acquisition unit for a period during which the load determination unit determines that the predetermined load is applied;
A driving ability assessment system characterized in that the evaluation value calculation unit calculates the first evaluation value without weighting either the first steering angle data or the second steering angle data, while calculating the second evaluation value by weighting the first steering angle data more than the weighting of the second steering angle data. 2. The driving ability determination system according to claim 1,
a load determination unit that determines whether a predetermined load is acting on the driver based on the steering angle data acquired by the information acquisition unit,
the evaluation value calculation unit calculates a first evaluation value representing an average steering characteristic of the driver based on the steering angle data acquired by the information acquisition unit, and calculates a second evaluation value representing the steering characteristic of the driver when the predetermined load is applied based on the calculated first evaluation value and steering angle data acquired by the information acquisition unit for a period during which the load determination unit determines that the predetermined load is applied;
The driving ability assessment system is characterized in that the evaluation value calculation unit calculates the first evaluation value by making the weight of the first steering angle data greater than the weight of the second steering angle data, and calculates the second evaluation value by making the weight of the first steering angle data greater than the weight of the second steering angle data. Each of the methods is executed by a computer.
an information acquisition step of acquiring steering angle data indicating a time-series change in the steering angle of the vehicle together with information on whether a driving assistance function that assists at least one of acceleration/deceleration and braking among driving operations performed by a driver of the vehicle is activated;
an evaluation value calculation step of calculating an evaluation value representing a steering characteristic of a driver based on the steering angle data acquired in the information acquisition step,
A driving ability assessment method characterized in that in the evaluation value calculation step, the evaluation value is calculated by increasing the weight of first steering angle data during a period in which the driving assistance function is activated, among the steering angle data acquired in the information acquisition step, compared to the weight of second steering angle data during a period in which the driving assistance function is not activated.