JP6977543B2 - Driving support method and driving support device - Google Patents

Description

The present invention relates to a driving support method and a driving support device.

Patent Document 1 describes a driving support system that evaluates a driver's driving skill by comparing a driving model used as an index for driving operation with the driving operation of the driver and provides driving support based on the evaluation result of the driving skill. Has been proposed.

Japanese Unexamined Patent Publication No. 2013-149154

In driving support based on driving skill, the higher the driving skill, the less the driving support. On the other hand, a driver with high driving skill can drive appropriately according to the characteristics of the vehicle even if the characteristics of the vehicle are difficult to understand, so that the evaluation result of the driving skill is not affected and the driving support is reduced. There is. For this reason, even though the characteristics of the vehicle are difficult to understand and the preparation for the driving operation is delayed, the driving support is reduced, and it may be difficult for the driver to feel a sense of unity with the vehicle.
An object of the present invention is to improve the sense of unity between the driver and the vehicle and the pleasure of driving.

In the driving support method according to one aspect of the present invention, the driving skill of a specific driver is evaluated, the brain wave of the driver is measured, and the driving operation of the specific vehicle by the driver obtained by the measurement of the brain wave is started. The driver's predictability with respect to the response characteristics of the vehicle responding to the driving operation is evaluated based on the degree of change in the brain wave signal from the time point before a predetermined time, and the vehicle is based on the evaluated driving skill and predictability. Provide driving support.

According to the present invention, it is possible to improve the sense of unity between the driver and the vehicle and the pleasure of driving.

It is a figure which shows the structural example of the example of the driving support device which concerns on embodiment. It is a schematic diagram of the waveform of the action preparation potential when the driver's predictability to the response characteristic of the vehicle corresponding to the driving operation is high. It is a schematic diagram of the waveform of the action preparation potential when the predictability is low. It is a block diagram which shows the 1st example of the functional structure of the controller shown in FIG. It is explanatory drawing of the 1st example of the driving support based on the driving skill and predictability. It is a flowchart of the driving support method of 1st Embodiment. It is a block diagram which shows the 2nd example of the functional structure of the controller shown in FIG. It is explanatory drawing of the 2nd example of the driving support based on the driving skill and predictability. It is a flowchart of the driving support method of 2nd Embodiment.

Hereinafter, the first and second embodiments of the present invention will be described with reference to the drawings. It should be noted that each drawing is a schematic one and may differ from the actual one. Further, the first and second embodiments of the present invention shown below exemplify devices and methods for embodying the technical idea of the present invention, and the technical idea of the present invention is a component component. The structure, arrangement, etc. of the above are not specified as follows. The technical idea of the present invention may be modified in various ways within the technical scope specified by the claims described in the claims.

(First Embodiment)
(composition)
See FIG. The driving support device 1 of the embodiment includes a controller 2, an ambient environment sensor group 3, a vehicle sensor group 4, a vehicle control actuator group 5, and a brain activity sensor 6.
The controller 2 provides driving control for a vehicle on which the driving support device 1 is mounted (hereinafter, may be referred to as "own vehicle") and driving support for driving a specific vehicle (that is, own vehicle) by a specific driver. It is an electronic control unit to perform.

The controller 2 includes a processor 20 and peripheral components such as a storage device 21. The processor 20 may be, for example, a CPU (Central Processing Unit) or an MPU (Micro-Processing Unit).
The storage device 21 may include any of a semiconductor storage device, a magnetic storage device, and an optical storage device. The storage device 21 may include a memory such as a register, a cache memory, a ROM (Read Only Memory) and a RAM (Random Access Memory) used as the main storage device.

The controller 2 may be realized by a functional logic circuit set in a general-purpose semiconductor integrated circuit. For example, the controller 2 may have a programmable logic device (PLD: Programmable Logic Device) such as a field-programmable gate array (FPGA).

The surrounding environment sensor group 3 is a sensor group that detects the surrounding environment of the own vehicle, and is used to acquire sensors such as a camera 30 and a radar 31 for detecting an object around the own vehicle, and map information of the surroundings. It is equipped with a map database (DB) 32 and a GPS (Global Positioning System) receiver 33.
The camera 30 is an image sensor that captures a predetermined range around the own vehicle and acquires image data.

The camera 30 is composed of, for example, a wide-angle CCD camera provided in the upper part of the front window in the vehicle interior. The camera 30 may be a stereo camera or an omnidirectional camera, and may include a plurality of image sensors. From the acquired image data, the camera 30 detects the road around the vehicle and structures around the road, road markings, signs, other vehicles, pedestrians, etc. as the surrounding conditions of the vehicle, and the detected vehicle itself. Information on the surrounding environment of the vehicle is output to the controller 2.

As the radar 31, a laser range finder (LRF) or a range finder using millimeter waves or ultrasonic waves can be adopted, and the radar 31 is composed of, for example, a millimeter wave radar provided in a front grill. The radar 31 scans a predetermined range around the own vehicle and detects obstacles such as other vehicles existing around the own vehicle. The radar 31 detects, for example, the relative position (direction) between the obstacle and the own vehicle, the relative speed of the obstacle, the distance from the own vehicle to the obstacle, and the like as the surrounding conditions of the own vehicle. The radar 31 outputs the detected information on the surrounding environment of the own vehicle to the controller 2.

As the map database 32, a storage medium such as a semiconductor memory or a disk medium can be used. The map database 32 stores map information including road type, road alignment, lane width, vehicle traffic direction, speed limit, and the like. The map information database may be managed by the server, and only the updated map information difference data may be acquired through, for example, a telematics service, and the map information stored in the map database 32 may be updated. The map database 32 may be built in the storage device 21 of the controller 2.

The GPS receiver 33 acquires the latitude, longitude and altitude of the own vehicle as the current position of the own vehicle based on the radio waves received from the artificial satellite. The controller 2 collates the current position of the own vehicle acquired by the GPS receiver 33 with the map stored in the map DB 43, and acquires the current position of the own vehicle on the map stored in the map DB 43. Further, the controller 2 acquires the map information around the current position of the own vehicle from the map database 32.

The controller 2 sets a travel route from the current position of the own vehicle on the map to the destination set by the driver or the like, and presents the set travel route to the driver by the display of the display or the voice of the speaker. , It is possible to control the running of the own vehicle along the set running route.
Hereinafter, the information on the surrounding environment detected or acquired by the surrounding environment sensor group 3, the current position information of the own vehicle, and the map information around the own vehicle may be referred to as "surrounding environment information".

The vehicle sensor group 4 is a sensor group that detects the traveling state of the own vehicle. The vehicle sensor group 4 includes a vehicle speed sensor 40, an acceleration sensor 41, a yaw rate sensor 42, a steering sensor 43, an accelerator sensor 44, and a brake sensor 45.
The vehicle speed sensor 40 detects the vehicle speed from the wheel speed of the own vehicle and outputs the detected vehicle speed to the controller 2.
The acceleration sensor 41 detects the acceleration in the front-rear direction and the acceleration in the vehicle width direction of the own vehicle. The yaw rate sensor 42 detects the yaw rate generated in the own vehicle.

The steering sensor 43 detects the amount of operation of the steering wheel as a driving operation of the own vehicle. The steering sensor 43 may detect, for example, the current steering angle, which is the current rotation angle (steering operation amount) of the steering wheel, as the operation amount of the steering wheel. The steering sensor 43 outputs information on the amount of operation of the steering wheel to the controller 2.
The accelerator sensor 44 detects the amount of operation of the accelerator pedal (that is, the amount of depression) by the driver as a driving operation of the own vehicle. The accelerator sensor 44 outputs information on the amount of operation of the accelerator pedal to the controller 2.

The brake sensor 45 detects the operation amount (that is, the depression amount) of the brake pedal by the driver as the driving operation of the own vehicle. The brake sensor 45 outputs information on the amount of operation of the brake pedal to the controller 2.
Information on the vehicle speed, acceleration, yaw rate, steering wheel operation amount, accelerator pedal operation amount, and brake pedal operation amount detected by the vehicle sensor group 4 may be referred to as "vehicle information".

The controller 2 controls the running of the own vehicle based on the surrounding environment information and the vehicle information. The controller 2 is a control command (control) that controls the running of the own vehicle according to the driving operation of the driver based on the vehicle information of the operation amount of the steering wheel, the accelerator pedal, and the brake pedal output from the vehicle sensor group 4. Signal) is output. The control command may include, for example, a control command for a steering amount, an accelerator opening degree, and a brake braking amount.
The vehicle control actuator group 5 drives the steering mechanism, power source, power transmission device, braking device, and the like of the own vehicle based on the control command from the controller 2, and thereby the own vehicle according to the driving operation of the driver. Realize driving.

The vehicle control actuator group 5 may include, for example, a steering actuator 50, an accelerator actuator 51, and a brake actuator 52.
The steering actuator 50 includes, for example, an electric motor for steering the steering shaft and steering wheels of the own vehicle, and controls the steering direction and steering amount of the own vehicle based on a control command from the controller 2.
The accelerator actuator 51 includes, for example, an electronically controlled throttle valve, and controls the accelerator opening degree of the own vehicle based on a control command from the controller 2.
The brake actuator 52 includes a hydraulic circuit used for, for example, a VDC (Vehicle Dynamics Control), and controls the braking operation of the brake of the own vehicle based on a control command from the controller 2.

Further, the controller 2 drives the own vehicle by driving the steering mechanism, the power source, the power transmission device, the braking device, etc. of the own vehicle based on the ambient environment information detected or acquired by the ambient environment sensor group 3. Provide driving support to the driver.
For example, the controller 2 is a reference for the vehicle behavior of the own vehicle such as running speed, acceleration, traveling angle, and turning speed in a specific driving scene, and the operation mode of the own vehicle such as the accelerator pedal, the brake pedal, the steering operation model, and the operation timing. Based on the driving model, the operation intervention is performed so that the driving of the own vehicle follows the driving model.

For example, the driving model may include a steering profile representing a steering angle and a steering speed when traveling in a specific driving scene such as a curved road, and a vehicle behavior profile such as a yaw rate and a turning speed. The controller 2 drives the steering actuator 50 to apply steering torque so that the steering angle, steering speed, yaw rate, and turning speed of the own vehicle follow the driving model when traveling on a similar curved road. The driver may be assisted in driving.

Further, for example, the driving model may include a deceleration profile representing a braking operation amount, an operation speed, and a deceleration of a vehicle in a driving scene such as during emergency braking. The controller 2 may assist the driver in driving by driving the brake actuator 52 so as to follow the driving model during emergency braking.
Similarly, the driving model may include an acceleration profile representing the accelerator operating amount, operating speed, and vehicle acceleration in a particular driving scene, and the controller 2 drives the accelerator actuator 51 to follow the driving model in the driving scene. By doing so, the driver may be assisted in driving.

Such automatic driving support by the controller 2 may be reduced when the driver's driving skill is high. This is because there is little need for operation intervention by the controller 2 for an appropriate driving operation by a driver with high driving skill.
However, the response characteristics of the vehicle differ depending on the individual vehicle, and the ability to grasp the response characteristics also differs depending on the individual driver. For this reason, it is difficult for a specific driver to understand the response characteristics of a specific vehicle, which may delay preparation for a driving operation.

If the driving support is small despite the delay in preparation for the driving operation, the driver may not feel the sense of unity with the vehicle and the pleasure and enjoyment of the driving operation.
Therefore, the driving support device 1 evaluates the ease of understanding of the response characteristics of a specific vehicle (that is, the own vehicle) for a specific driver as the predictability of the driver with respect to the response characteristics. Then, the driving support of the own vehicle is controlled based on both the driving skill and the predictability of the driver. For example, the start timing of driving support and the amount of driving support are changed based on the driving skill and predictability. Hereinafter, the predictability of the driver with respect to the response characteristics of the vehicle in response to the driving operation may be simply referred to as "predictability".

The controller 2 receives the vehicle information output from the vehicle sensor group 4 and evaluates the driving skill of the driver based on the vehicle information. For example, the controller 2 may determine the actual vehicle state based on the vehicle information and evaluate the driving skill of the driver based on the difference between the actual vehicle state and the driving model.
Further, the controller 2 evaluates the driver's predictability for the response characteristics of a specific vehicle (own vehicle) that responds to the driving operation based on the measurement of the driver's brain wave by the brain activity sensor 6.

The brain activity sensor 6 is a sensor that measures a driver's brain wave, and has a plurality of electrodes attached to the driver's head.
The brain activity sensor 6 outputs the driver's brain wave signal detected by these plurality of electrodes to the controller 2. The method of attaching these plurality of electrodes to the head is not particularly limited, but for example, the brain activity sensor 6 may be provided with a wearable electrode cap so as to be easily placed on the driver's head.

The controller 2 evaluates the predictability of the driver with respect to the response characteristics of the own vehicle in response to the driving operation for each driver based on the brain wave signal acquired by the brain activity sensor 6 within a predetermined period before the start of the driving operation. ..
The controller 2 may evaluate predictability based on, for example, the driver's behavioral readiness potential (sometimes referred to as "exercise preparation potential").
FIG. 2A schematically shows the waveform of the action preparation potential when the predictability is high (that is, when the response characteristic is easy for the driver to understand). FIG. 2B schematically shows the waveform of the action preparation potential when the predictability is low (that is, when the response characteristic is difficult for the driver to understand).

The solid line 70 in FIGS. 2A and 2B shows the waveform of the action preparation potential. The time point t2 is the time point at which the driving operation is started detected from the driving operation information, and the time point t1 is the time point before the first predetermined time T1 from the time point t2 at which the driving operation is started.
The first predetermined time T1 may be set so that the time point at which the action preparation potential starts to change before the start of the action of the driving operation becomes the time point t1, for example, 2 seconds.

Attention is paid to the slope (that is, the degree of change) α in which the action preparation potential decreases in the period from the time point t1 to the time point t2 when the operation is started. As can be seen from FIGS. 2A and 2B, the slope α when the predictability is high is larger than the slope α when the predictability is low.
Therefore, for example, the controller 2 evaluates the predictability based on the slope α at which the action preparation potential decreases from the time point t1. For example, the controller 6 may evaluate the predictability based on the slope α at which the action preparation potential decreases for a certain period starting from the time point t1. For example, the controller 6 may evaluate the predictability based on the slope α in which the action preparation potential decreases in the period from the time point t1 to the operation start time point t2. For example, if the absolute value of the slope α is larger than the threshold value TH, it is determined that the predictability is high, and if the absolute value of the slope α is equal to or less than the threshold value TH, it is evaluated that the predictability is low.

Further, the controller 6 may evaluate the predictability based on the amount of decrease in the action preparation potential from the time point t1. For example, the controller 6 may evaluate the predictability based on the amount of decrease in the action preparation potential for a certain period starting from the time point t1. For example, the controller 6 may evaluate the predictability based on the amount of decrease in the action preparation potential in the period from the time point t1 to the operation start time point t2.
The dotted line 71 in FIGS. 2A and 2B indicates the baseline of the action preparation potential. The controller 2 may set the baseline 71 of the action preparation potential based on the value of the action preparation potential before the time point t0 before the second predetermined time T2 before the operation start time t2.

The second predetermined time T2 may be set so that the time point t0 is earlier than the period when the action start of the driving operation starts to affect the action preparation potential, for example. That is, the second predetermined time T2 is longer than the first predetermined time T1, and the time point t0 is earlier than the time point t1. The second predetermined time T2 may be, for example, 4 seconds.
Attention is paid to the difference β of the action preparation potential at the baseline 71 and the operation start time t2 (that is, the amount of decrease in the action preparation potential from the baseline 71) β. The difference β when the predictability is high is larger than the difference β when the predictability is low.

Therefore, the controller 6 may evaluate the predictability based on the difference β between the baseline 81 and the action preparation potential after the time point t1. For example, the predictability may be evaluated based on the difference β between the baseline 81 and the action preparation potential after a certain period starting from the time point t1. For example, the predictability may be evaluated based on the difference β between the action preparation potential at the baseline 81 and the operation start time t2.
For example, if the difference β is larger than the threshold value TH2, it may be judged that the predictability is high, and if the difference β is equal to or less than the threshold value TH2, it may be evaluated as low predictability. Further, for example, the controller 2 may determine that the predictability is high when the difference between the baseline 71 and the action preparation potential exceeds the threshold value TH2 between the time point t1 and the operation start time point t2.

See FIG. The controller 2 controls the driving support of the own vehicle based on the evaluated driving skill and predictability. For example, the controller 2 changes the start timing of driving support and the amount of driving support based on the driving skill and predictability.
In this way, by controlling the driving support based not only on the driving skill but also on the predictability (easy to understand the response characteristics) evaluated for each individual driver, the driver is prepared for the driving operation. Driving support can be provided, and the sense of unity with the vehicle and the pleasure and enjoyment of driving operations can be improved.

Next, the functional configuration of the controller 2 will be described. See FIG. The controller 2 includes a driving skill evaluation unit 80, a driving operation detection unit 81, an action preparation potential detection unit 82, a predictability evaluation unit 83, and a driving support control unit 84.
For example, the controller 2 executes the computer program stored in the storage device 21 of FIG. 1 by the processor 20, driving skill evaluation unit 80, driving operation detection unit 81, action preparation potential detection unit 82, and predictability evaluation unit 83. , And the function of the driving support control unit 84 may be realized.

The driving skill evaluation unit 80 receives the vehicle information output from the vehicle sensor group 4 and evaluates the driving skill of the driver based on the vehicle information. The driving skill evaluation unit 80 outputs the evaluation result of the driving skill to the driving support control unit 84.
The driving operation detection unit 81 receives vehicle information of the operating amount of the steering wheel, the accelerator pedal, and the brake pedal output from the vehicle sensor group 4. The driving operation detection unit 81 determines whether or not the driving operation has been performed by the driver based on the vehicle information, and if it is determined that the driving operation has been performed, detects the driving operation start time point t2. The operation detection unit 81 outputs the information of the operation start time point t2 to the predictability evaluation unit 83.

The action preparation potential detection unit 82 receives the electroencephalogram signal acquired by the brain activity sensor 6. The action preparation potential detection unit 82 detects the action preparation potential generated in the driver's brain based on the received electroencephalogram signal. For example, the action preparation potential detection unit 82 may detect the action preparation potential based on frequency analysis, pattern analysis, or the like of an electroencephalogram signal. The action preparation potential detection unit 82 outputs information on the action preparation potential to the predictability evaluation unit 83.

The predictability evaluation unit 83 determines the time point t1 before the first predetermined time T1 from the operation start time point t2. The predictability evaluation unit 83 calculates the slope α at which the action preparation potential decreases in the period from the time point t1 to the operation start time point t2.
For example, the predictability evaluation unit 83 may calculate the slope α by performing statistical processing such as the least squares method on the waveform of the action preparation potential.

The predictability evaluation unit 83 may calculate the difference β between the action preparation potential at the baseline 71 and the operation start time t2. The predictability evaluation unit 83 may set the baseline 71 of the action preparation potential based on the value of the action preparation potential before the time point t0 before the second predetermined time T2 before the operation start time t2.

The predictability evaluation unit 83 may calculate the slope α, the difference β, and the baseline 71 based on the waveform of the action preparation potential detected in one driving operation, and the behavior detected in a plurality of driving operations. The slope α, the difference β, and the baseline 71 may be calculated based on the average waveform obtained by averaging the waveforms of the preparation potentials.

Then, the predictability evaluation unit 83 evaluates the predictability of the driver based on the slope α and the difference β. For example, if the absolute value of the slope α is larger than the threshold value TH, it is determined that the predictability is high, and if the absolute value of the slope α is equal to or less than the threshold value TH, it is evaluated that the predictability is low. If the absolute value of the difference β is larger than the threshold value TH2, it is judged that the predictability is high, and if the absolute value of the difference β is equal to or less than the threshold value TH2, it is evaluated that the predictability is low.
The predictability evaluation unit 83 outputs the predictability evaluation result to the driving support control unit 84.

The driving support control unit 84 executes driving support for the driver who drives the own vehicle based on the vehicle information output from the vehicle sensor group 4 and the surrounding environment information output from the surrounding environment sensor group 3.
For example, the driving support control unit 84 identifies the current driving scene of the own vehicle based on the surrounding environment information. The driving support control unit 84 identifies a driving model corresponding to the current driving scene of the own vehicle.

Further, the driving support control unit 84 determines the current vehicle state of the own vehicle based on the vehicle information. The vehicle state may be, for example, the vehicle behavior of the own vehicle such as traveling speed, acceleration, traveling angle, and turning speed, or the operation mode of the own vehicle such as the accelerator pedal, the brake pedal, the steering operation model, and the operation timing.
The driving support control unit 84 provides driving support to the driver who drives the own vehicle by performing an operation intervention so that the current vehicle state of the own vehicle follows the driving model.

At this time, the driving support control unit 84 controls the driving support of the driver who drives the own vehicle based on the evaluation result of the driving skill and the evaluation result of the predictability. For example, the driving support control unit 84 may include a timing changing unit 85 and a support amount changing unit 86.
The timing changing unit 85 changes the start timing of the driving support based on the evaluation result of the driving skill and the evaluation result of the predictability.

For example, when the driving skill is high, the start of the driving support may be delayed in order to reduce the opportunity of the operation intervention based on the driving support and give priority to the driving operation by the driver. Therefore, when the driving skill is low, the timing changing unit 85 may set the start timing earlier than when the driving skill is high.
Further, if the predictability is low and the response characteristics of the own vehicle are difficult to understand, the driver is not ready for the driving operation and the driving operation may be delayed, so that the start of the driving support may be accelerated.

On the other hand, if the predictability is high and the driver can predict the response characteristics of the vehicle well, by reducing the chances of intervention for the driver's driving operation as much as possible, the sense of unity with the vehicle and the driving operation can be achieved. The feeling of pleasure can be improved.
Therefore, the timing changing unit 85 may delay the start timing when the predictability is high as compared with the case where the predictability is low. That is, when the predictability is low, the start timing may be earlier than when the predictability is high.

The support amount changing unit 86 changes the driving support amount of the driving support based on the evaluation result of the driving skill and the evaluation result of the predictability. The driving support amount changed by the support amount changing unit 86 may be, for example, an operation amount added to the operation amount of the accelerator pedal, the brake pedal, the steering, etc. operated by the driver by the operation intervention by the driving support.
Further, the driving support amount may be, for example, a vehicle behavior amount added by the operation intervention by the driving support to the vehicle behavior caused by the driving operation of the driver. These driving support amounts may be added in a direction that increases the driving operation by the driver and the resulting vehicle behavior (that is, the same direction as the direction of the driving operation by the driver), and may be added in a direction that decreases (that is, the direction of the driving operation by the driver). , The direction opposite to the direction of the driving operation by the driver).

For example, when the driving skill is high, the driving support amount may be reduced in order to give priority to the driving operation by the driver. Therefore, the support amount changing unit 86 may increase the driving support amount when the driving skill is low as compared with the case where the driving skill is high.
For example, the support amount changing unit 86 may increase or decrease the driving support amount by increasing or decreasing the gain that determines the driving support amount. For example, the support amount changing unit 86 may increase the driving support amount by increasing the upper limit of the driving support amount added by the driving support by the driving support control unit 84, and may increase the driving support amount by decreasing the upper limit of the driving support amount. The amount may be reduced.

Further, when the predictability is low, the amount of operation may be reduced because the driver is not ready for the driving operation, so that the amount of driving support may be increased.
On the other hand, if the predictability is high and the driver can predict the response characteristics of the vehicle well, by reducing the intervention of the driver in the driving operation, the sense of unity with the vehicle and the pleasure of the driving operation are improved. Can be made to.
Therefore, the support amount changing unit 86 may increase the driving support amount when the predictability is low as compared with the case where the predictability is high.

FIG. 4 shows a first example of driving support based on driving skill and predictability. For example, when predictability is high, the start timing of driving support is delayed as much as possible in order to reduce the chances of operation intervention for the driver's driving operation and improve the sense of unity with the vehicle and the pleasure of driving operation. Driving support may be started immediately before the timing when driving operation is required.
On the other hand, when the predictability is low, the start timing of the driving support may be earlier than when the predictability is high in order to support the driver earlier.

In addition, when the predictability is high and the driving skill is high, the driving support amount is reduced in order to improve the sense of unity with the vehicle and the pleasure of the driving operation by giving priority to the driving operation of the driver. You can do it.
On the other hand, when the predictability is low and the driving skill is also low, the need for driving support is large, so that the amount of driving support may be increased.
In addition, when the predictability is high and the driving skill is low, or when the predictability is low and the driving skill is high, the driving support amount may be medium. For example, it may be more than in the case of high predictability and high driving skill, and less than in the case of low predictability and low driving skill.

The support amount changing unit 86 may change the response characteristics of the own vehicle in response to the driving operation based on the evaluation result of the driving skill and the evaluation result of the predictability. That is, the driving support by the driving support control unit 84 may include control of the response characteristic of the own vehicle (hereinafter, may be simply referred to as “response characteristic”) in response to the driving operation.
For example, drivers with high driving skills tend to prefer vehicles with high responsiveness. Therefore, for example, the support amount changing unit 86 increases the responsiveness of the own vehicle when it is evaluated that the driving skill is high, and lowers the responsiveness of the own vehicle when it is evaluated that the driving skill is low. May be changed.

When the driver's predictability is high, that is, when the response characteristics of the own vehicle are easy for the driver to understand, the higher the response of the own vehicle, the better the sense of unity with the vehicle and the pleasure of the driving operation. Therefore, for example, the support amount changing unit 86 may change the response characteristics so as to improve the responsiveness of the own vehicle when it is evaluated that the driver's predictability is high.
On the other hand, if the driver's predictability is low, that is, if the driver's response characteristics are difficult to understand, increasing the response may make it difficult to understand the response characteristics, resulting in a sense of unity with the vehicle and driving. There is a risk of impairing the pleasure of operation. Therefore, for example, the support amount changing unit 86 may change the response characteristics so as to lower the responsiveness of the own vehicle when it is evaluated that the predictability of the driver is low.

For example, the support amount changing unit 86 may change the response characteristic in response to the steering operation.
When the target steering angle δ is set by the steering angle θ of the steering wheel and the following equation (1), the response characteristics to the steering operation can be changed by changing the coefficient K multiplied by the steering angular velocity Δθ.
δ = K0 × θ + K × Δθ… (1)
K0 indicates a coefficient.
For example, the support amount changing unit 86 may increase the coefficient K to increase the responsiveness to the steering operation, and decrease the coefficient K to decrease the responsiveness to the steering operation.

The support amount changing unit 86 may change the response characteristic to the acceleration / deceleration operation by the same method.
Further, for example, the support amount changing unit 86 may change the response characteristics by changing the response delay to the driving operation, the rigidity of the spension or the like, and the tire pressure. For example, the responsiveness may be increased by increasing the rigidity of the spension, and the responsiveness may be decreased by decreasing the rigidity of the spension. The responsiveness may be increased by reducing the tire pressure, and the responsiveness may be increased by increasing the tire pressure.

(motion)
Next, an example of the driving support method of the first embodiment will be described with reference to FIG.
In step S1, the brain activity sensor 6 measures the driver's brain waves. The action preparation potential detection unit 82 (FIG. 3) of the controller 2 detects the action preparation potential generated in the driver's brain based on the electroencephalogram signal acquired by the brain activity sensor 6.

In step S2, the driving skill evaluation unit 80 acquires the vehicle information output from the vehicle sensor group 4.
In step S3, the predictability evaluation unit 83 evaluates the predictability of the driver based on the action preparation potential detected by the action preparation potential detection unit 82.
In step S4, the driving skill evaluation unit 80 evaluates the driving skill of the driver based on the vehicle information acquired from the vehicle sensor group 4.

In step S5, the driving support control unit 84 determines whether or not the driver has high predictability. When the predictability is high (step S5: Y), the process proceeds to step S9. When the predictability is high or low (step S5: N), the process proceeds to step S6.
In step S6, the driving support control unit 84 determines whether or not the driver's driving skill is high. When the driving skill is high (step S6: Y), the process proceeds to step S8. When the driving skill is low (step S6: N), the process proceeds to step S7.

In step S7 (when the predictability is low and the driving skill is low), the timing changing unit 85 sets the start timing of the driving support to a relatively early timing. The support amount changing unit 86 increases the driving support amount so as to provide driving support with a larger driving support amount. After that, the process ends.
In step S8 (when the predictability is low and the driving skill is high), the timing changing unit 85 sets the start timing of the driving support to a relatively early timing. The support amount changing unit 86 adjusts the driving support amount so as to provide driving support with a medium driving support amount. After that, the process ends.

In step S9, the driving support control unit 84 determines whether or not the driver's driving skill is high. When the driving skill is high (step S9: Y), the process proceeds to step S11. When the driving skill is low (step S9: N), the process proceeds to step S10.
In step S10 (when the predictability is high and the driving skill is low), the timing changing unit 85 delays the start timing of the driving support as much as possible and sets it immediately before the timing when the driving operation is required. The support amount changing unit 86 adjusts the driving support amount so as to provide driving support with a medium driving support amount. After that, the process ends.
In step S11 (when the predictability is high and the driving skill is high), the timing changing unit 85 delays the start timing of the driving support as much as possible and sets it immediately before the timing when the driving operation is required. The support amount changing unit 86 reduces the driving support amount so as to provide driving support with a smaller driving support amount. After that, the process ends.

(Effect of the first embodiment)
(1) The driving skill evaluation unit 80 evaluates the driving skill of a specific driver. The brain activity sensor 6 measures the driver's brain waves. The predictability evaluation unit 83 responds to the driving operation based on the degree of change in the brain wave signal from a time point before a predetermined time before the start time of the driving operation for a specific vehicle by the driver obtained by measuring the brain wave. Evaluate the driver's predictability of the response characteristics. The driving support control unit 84 controls the driving support of the vehicle based on the evaluated driving skill and predictability.

As a result, by controlling the driving support based not only on the driving skill but also on the predictability (easiness of understanding of the response characteristic) evaluated for each individual driver, for example, the response of a specific vehicle having a high driving skill is high. The characteristics are difficult to understand, and as a result, appropriate driving support can be provided even when preparations for driving operations are delayed.
Similarly, even when the driving skill is low but the response characteristics of the vehicle are easy to understand, it is possible to provide appropriate driving support according to the driver.
For this reason, it is possible to improve the sense of unity with the vehicle and the pleasure and enjoyment of driving.

(2) The timing change unit 85 changes the start timing of driving support based on the driving skill and predictability. This makes it possible to match the start timing of driving support with the driving skill and predictability.
(3) The timing changing unit 85 advances the start timing when the driving skill is low as compared with the case where the driving skill is high. This makes it possible to match the start timing of driving support with the driving skill.

(4) The timing changing unit 85 advances the start timing when the predictability is low as compared with the case where the predictability is high. This makes it possible to match the start timing of driving support with predictability.
(5) The support amount changing unit 86 changes the driving support amount of the driving support based on the driving skill and the predictability. As a result, the driving support amount of the driving support can be matched with the driving skill and the predictability.

(6) The support amount changing unit 86 increases the driving support amount when the driving skill is low as compared with the case where the driving skill is high. This makes it possible to match the amount of driving support with the driving skill.
(7) The support amount changing unit 86 increases the driving support amount when the predictability is low as compared with the case where the predictability is high. This makes it possible to match the amount of driving support with the driving skill.

(8) The support amount changing unit 86 improves the responsiveness of the vehicle that responds to the driving operation of the driver when the driving skill is high as compared with the case where the driving skill is low. This makes it possible to match the responsiveness of the vehicle with the driving skill.
(9) When the predictability is high, the support amount changing unit 86 increases the responsiveness of the vehicle in response to the driver's driving operation as compared with the case where the predictability is low. This makes it possible to match the responsiveness of the vehicle with the predictability.

(Second Embodiment)
Subsequently, the driving support device 1 of the second embodiment will be described. The controller 2 of the second embodiment changes the start timing of driving support based on a predetermined driving model based on the driving skill and predictability of the driver.
For example, the controller 2 has data on the vehicle behavior of the own vehicle such as the traveling speed, acceleration, traveling angle, and turning speed during driving by each driver, and the own vehicle such as the accelerator pedal, the brake pedal, the steering operation model, and the operation timing. Data of the operation mode of the above may be accumulated, and the driving model of this driver may be generated as a predetermined driving model based on the accumulated data. The driving model generated based on the operation history of each individual driver may be hereinafter referred to as a "self-driving model".

Further, a standard driving model other than the self-driving model may be set and used for the driving operation. Such a standard operation model may be referred to as a "standard operation model". The standard driving model may accumulate data on vehicle behavior during driving by one or more standard drivers and data on the operation mode of the own vehicle, and may be set based on the accumulated data. The standard operation model may be determined by calculation based on a theoretical simulation.

Further, the controller 2 of the second embodiment changes the driving support amount of the driving support based on a predetermined driving model based on the driving skill and predictability of the driver.
For example, the controller 2 may change a predetermined driving model based on the driving skill and predictability of the driver, and provide driving support for the own vehicle according to the changed driving model.
Further, for example, the controller 2 may select one of a plurality of predetermined driving models based on the driving skill and predictability, and provide driving support for the own vehicle according to the selected driving model.

The functional configuration of the controller 2 of the second embodiment will be described in detail with reference to FIG. The same components as those in the first embodiment are designated by the same reference numerals, and the description thereof will be omitted.
The controller 2 includes an operation model storage unit 91 for storing an operation model such as a self-operation model or a standard operation model.
Further, the driving support control unit 84 includes a driving model generation unit 90, a driving model selection unit 92, and a driving model changing unit 93.

The driving model generation unit 90 accumulates data on the vehicle behavior of the own vehicle and data on the operation mode of the own vehicle during driving by each driver for each driver, and based on these accumulated data, this A driver's self-driving model is generated for each driving scene. The driving model generation unit 90 stores the generated self-driving model in the driving model storage unit 91.
The driving model selection unit 92 identifies the current driving scene of the own vehicle based on the ambient environment information output from the ambient environment sensor group 3. The driving model selection unit 92 selects a self-driving model corresponding to the current driving scene of the own vehicle from the driving scenes stored in the driving model storage unit 91.

The timing changing unit 85 changes the start timing of driving support based on the self-driving model selected by the driving model selection unit 92 based on the evaluation result of the driving skill and the evaluation result of the predictability.
Further, the driving model changing unit 93 changes the self-driving model used for driving support based on the evaluation result of the driving skill and the evaluation result of the predictability. The driving support control unit 84 provides driving support for the own vehicle based on the self-driving model changed by the driving model changing unit 93.

With reference to FIG. 7, an example of changing the start timing and the driving model based on the driving skill and predictability will be described.
When the driving skill is high and the predictability is high, the start timing of the driving support is set in order to reduce the opportunity of the driver's operation intervention for the driving operation and to improve the sense of unity with the vehicle and the pleasure of the driving operation. Driving assistance may be started just before the timing when the driving operation is required, with a delay as much as possible.

Further, the driving model changing unit 93 changes the self-driving model generated by the driving model generation unit 90 to a driving model with a higher driving level, and uses it as a driving model used for driving support.
Here, the driving model with a higher driving level may be, for example, a driving model requiring a higher driving skill, a driving model by a driver with a higher driving skill, or a driving model with a higher degree of difficulty.

The driving model changing unit 93 may raise the driving level of the driving model by, for example, modifying the driving model so that the responsiveness of the driving operation in response to the change of the surrounding environment is improved. Also, for example, the operating level of the operating model may be increased by reducing the delay in responding to changes in the surrounding environment.
By experiencing driving at a higher level than one's own driving skill, the driver can feel the joy of driving.

Further, even when the driving skill is low and the predictability is high, the driving model changing unit 93 changes the self-driving model generated by the driving model generation unit 90 to a driving model with a higher driving level and uses it for driving support. Use as a driving model. However, the start timing of driving support is earlier than when the driving skill is high.
In this way, even if the driving skill is low, if the predictability is high and the driver can predict the response characteristics of the own vehicle well, the driver is ready for the driving operation and has a margin.

Therefore, by letting the driver experience a higher level of driving than his / her own driving skill, the driver who has a relatively low driving skill can feel the pleasure of the driving operation.
At this time, by accelerating the start of driving support and giving the driver a margin, it is possible to prevent drivers with relatively low driving skills from feeling fear or discomfort with driving support with a high driving level, and it is higher. You can experience level driving comfortably.

On the other hand, when the predictability is low, the driver is not ready for the driving operation because the response characteristics of the own vehicle are difficult to understand, and the driving operation may be delayed, so that the start of the driving support is accelerated.
If a high level of driving support is provided when the response characteristics of the own vehicle are difficult to understand and the driver is not ready for the driving operation, even a driver with a high driving skill may rather impair the pleasure of the driving operation. Therefore, the self-driving model generated by the driving model generation unit 90 is not changed, and is used as the driving model for driving support.
For drivers with low driving skills, changing the self-driving model to a driving model with a lower driving level and using it for driving support will cause anxiety and discomfort due to the difficulty in understanding the response characteristics of the own vehicle. Reduce.

The driving model changing unit 93 may change the standard driving model based on the evaluation result of the driving skill and the evaluation result of the predictability instead of the self-driving model. The driving support control unit 84 may provide driving support for the own vehicle based on the standard driving model changed by the driving model changing unit 93.
Further, instead of changing the operation level of the operation model, a plurality of standard operation models having different levels may be prepared in advance in the operation model storage unit 91. The driving model changing unit 93 selects the standard driving model of one of the plurality of standard driving models of different levels as the driving model to be used for driving support, so that the driving level of the driving model used for driving support is selected. May be changed.

(motion)
An example of the driving support method of the second embodiment will be described with reference to FIG.
The processing of steps S21 to S26 and step S29 is the same as the processing of steps S1 to S6 and step S9 of FIG.
In step S27 (when the predictability is low and the driving skill is low), the timing changing unit 85 sets the start timing of the driving support to a relatively early timing. The driving model changing unit 93 sets the driving model used for driving support to a driving model at a lower level than the self-driving model. After that, the process ends.

In step S28 (when the predictability is low and the driving skill is high), the timing changing unit 85 sets the start timing of the driving support to a relatively early timing. The driving model changing unit 93 sets the driving model used for driving support to a driving model at the same level as the self-driving model. After that, the process ends.

In step S30 (when the predictability is high and the driving skill is low), the timing changing unit 85 sets the start timing of the driving support to a relatively early timing. The driving model changing unit 93 sets the driving model used for driving support to a driving model having a higher level than the self-driving model. After that, the process ends.

In step S31 (when the predictability is high and the driving skill is high), the timing changing unit 85 delays the start timing of the driving support as much as possible and sets it immediately before the timing when the driving operation is required. The driving model changing unit 93 sets the driving model used for driving support to a driving model having a higher level than the self-driving model. After that, the process ends.

(Effect of the second embodiment)
(1) The timing changing unit 85 changes the start timing of driving support based on a predetermined driving model based on the driving skill and predictability. This makes it possible to start driving support using a driving model generated based on, for example, the driving history of the driver, at a timing that matches the driving skill and predictability of the driver.
(2) The driving model changing unit 93 changes the driving support amount of the driving support based on the predetermined driving model based on the driving skill and the predictability. Thereby, for example, the driving support amount of the driving support using the driving model generated based on the driving history of the driver can be matched with the driving skill and the predictability of the driver.

(3) The driving model changing unit 93 changes a predetermined driving model based on the driving skill and predictability. The driving support control unit 84 provides driving support for the own vehicle according to the changed driving model. Thereby, for example, the driving model generated based on the driving history of the driver can be corrected based on the driving skill and the predictability, and can be used for driving support.
(4) The driving model changing unit 93 selects one of a plurality of predetermined driving models based on the driving skill and predictability. The driving support control unit 84 provides driving support for the own vehicle according to the selected driving model. Thereby, for example, driving support can be provided by a driving model selected according to the driving skill and predictability.

(Modification example)
The driving support control unit 84 may perform automatic driving control of the own vehicle by using, for example, a driving model stored in the driving model storage unit 91. For example, the lane change may be performed by automatically changing the lane of the own vehicle by using the driving model stored in the driving model storage unit 91.
At this time, the action preparation potential detection unit 82 may detect the intention of the driver's driving operation by detecting the action preparation potential at the time of the driving operation.

The driving support control unit 84 may start automatic driving control using the driving model when the intention of the driving operation is detected.
At that time, the timing changing unit 85 may change the start timing of the automatic driving control based on the driving skill and the predictability.
The driving model changing unit 93 may change the driving level of the driving model used for the automatic driving control based on the driving skill and predictability.

1 ... Driving support device, 2 ... Controller, 3 ... Surrounding environment sensor group, 4 ... Vehicle sensor group, 5 ... Vehicle control actuator group, 6 ... Brain activity sensor, 20 ... Processor, 21 ... Storage device, 30 ... Camera, 31 ... radar, 32 ... map database, 33 ... GPS receiver, 40 ... vehicle speed sensor, 41 ... acceleration sensor, 42 ... yaw rate sensor, 43 ... steering sensor, 44 ... accelerator sensor, 45 ... brake sensor, 50 ... steering actuator, 51. ... Accelerator actuator, 52 ... Brake actuator, 80 ... Driving skill evaluation unit, 81 ... Driving operation detection unit, 82 ... Action preparation potential detection unit, 83 ... Predictability evaluation unit, 84 ... Driving support control unit, 85 ... Timing change unit , 86 ... Support amount changing unit, 90 ... Driving model generation unit, 91 ... Driving model storage unit, 92 ... Driving model selection unit, 93 ... Driving model changing unit

Claims (14)

Evaluate the driving skills of a particular driver and
The driver's brain wave was measured and
The above-mentioned response characteristics of the vehicle in response to the driving operation based on the degree of change of the brain wave signal from a time point before a predetermined time of the driving operation start time for the specific vehicle by the driver obtained by the measurement of the brain wave. Evaluate the predictability of the driver and
A driving support method comprising controlling the driving support of the vehicle based on the evaluated driving skill and the predictability. The driving support method according to claim 1, wherein the starting timing of the driving support is changed based on the driving skill and the predictability. The driving support method according to claim 2, wherein when the driving skill is low, the start timing is earlier than when the driving skill is high. The driving support method according to claim 2 or 3, wherein when the predictability is low, the start timing is earlier than when the predictability is high. The driving support method according to any one of claims 2 to 4, wherein the start timing of driving support based on a predetermined driving model is changed based on the driving skill and the predictability. The driving support method according to claim 1, wherein the driving support amount of the driving support is changed based on the driving skill and the predictability. The driving support method according to claim 6, wherein when the driving skill is low, the driving support amount is increased as compared with the case where the driving skill is high. The driving support method according to claim 6 or 7, wherein when the predictability is low, the driving support amount is increased as compared with the case where the predictability is high. The driving support method according to claim 6, wherein the driving support amount of the driving support based on a predetermined driving model is changed based on the driving skill and the predictability. The driving support method according to claim 6, wherein a predetermined driving model is changed based on the driving skill and the predictability, and the driving support of the vehicle is performed according to the changed driving model. The driving support according to claim 6, wherein any one of a plurality of predetermined driving models is selected based on the driving skill and the predictability, and the driving support of the vehicle is performed according to the selected driving model. Method. The invention according to any one of claims 6 to 11, wherein when the driving skill is high, the responsiveness of the vehicle responding to the driving operation of the driver is improved as compared with the case where the driving skill is low. Driving support method. The invention according to any one of claims 6 to 12, wherein when the predictability is high, the responsiveness of the vehicle responding to the driving operation of the driver is improved as compared with the case where the predictability is low. Driving support method. A brain wave sensor that measures the driver's brain wave,
The driver who evaluates the driving skill of a specific driver and obtains the driver by the electroencephalogram sensor during the period from a predetermined time before the start time of the driving operation for the specific vehicle by the driver to the start time of the driving operation. The driver's predictability with respect to the response characteristics of the vehicle in response to the driving operation is evaluated based on the electroencephalogram signal of the above, and the driving support of the vehicle is controlled based on the evaluated driving skill and the predictability. With the controller
A driving support device characterized by being equipped with.

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