JP7697333B2 - Driving assistance method and driving assistance device - Google Patents

Description

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

Patent Document 1 proposes a driving control device that controls the driving state of the vehicle so that the vehicle travels in a position where the driver of the vehicle experiences a low level of psychological pressure.

Patent No. 6441610 specification

However, if driving control that suppresses the driver's sense of anxiety continues, there is a risk that a driver who is accustomed to the driving environment will feel that the driving of the vehicle is monotonous and will feel uncomfortable.
The present invention has an object to suppress the above-mentioned discomfort felt by the driver due to driving assistance control that controls the driving of the vehicle so as to reduce the driver's sense of anxiety.

In one aspect of the vehicle control method of the present invention, the surrounding environment of the vehicle is detected, a control value of the driving assistance control for controlling the traveling of the vehicle is calculated based on the detected surrounding environment, the vehicle is controlled based on the calculated control value, the degree of influence of the detected surrounding environment on the driver's anxiety is estimated, the control value is restricted if the estimated degree of influence is equal to or greater than a threshold, and the restriction on the control value is released based on the elapsed time since the control value restriction was started if the estimated degree of influence remains equal to or greater than the threshold.

According to the present invention, driving assistance control that controls the driving of the vehicle to reduce the driver's sense of anxiety can be prevented from causing discomfort to the driver.

1 is a diagram showing an example of a schematic configuration of a vehicle equipped with a driving assistance device according to an embodiment; FIG. 2 is a block diagram illustrating an example of a functional configuration of a controller according to the first embodiment. 6A and 6B are schematic diagrams showing examples of limiting and removing the limit on a control value. 4 is a flowchart of an example of a driving assistance method according to an embodiment. 13 is a flowchart of an example of a target trajectory generating process. FIG. 11 is a block diagram illustrating an example of a functional configuration of a controller according to a second embodiment. 5A is a schematic diagram showing an example of calculation of a driver's anxiety level, and FIG. 5B is a schematic diagram showing an example of limiting a control value based on the anxiety level. 5A is a schematic diagram showing an example of calculation of a driver's anxiety level, and FIG. 5B is a schematic diagram showing an example of limiting a control value based on the anxiety level.

Below, an embodiment of the present invention will be described with reference to the drawings. In the description of the drawings, identical or similar parts are given the same or similar reference numerals, and duplicate explanations will be omitted. Each drawing is schematic and may differ from the actual product. The embodiments shown below are examples of devices and methods for embodying the technical idea of the present invention, and the technical idea of the present invention is not limited to the devices and methods exemplified in the following embodiments. The technical idea of the present invention can be modified in various ways within the technical scope described in the claims.

First Embodiment
1 is a diagram showing an example of a schematic configuration of a vehicle equipped with a driving assistance device according to an embodiment. The vehicle 1 is equipped with a driving assistance device 10 that assists in driving the vehicle 1. The driving assistance device 10 detects a surrounding environment, which is a driving environment around the vehicle 1, and automatically controls the driving of the vehicle 1 based on the detected surrounding environment, thereby assisting an occupant (e.g., a driver) of the vehicle 1 in driving the vehicle 1.
Driving assistance of the host vehicle 1 by the driving assistance device 10 may include, for example, automobile speed control that automatically controls the vehicle speed of the host vehicle. For example, automobile speed control may include vehicle distance control and constant speed driving control. Furthermore, for example, driving assistance by the driving assistance device 10 may include steering assistance control that automatically controls the steering angle. For example, the steering assistance control may be lane departure prevention assistance. Furthermore, for example, driving assistance by the driving assistance device 10 may include autonomous driving control that automatically drives the host vehicle 1 without the involvement of an occupant.

The driving assistance device 10 includes an object sensor 11, a vehicle sensor 12, a positioning device 13, a map database 14, a navigation device 15, a communication device 16, a controller 17, and an actuator 18. In the drawings, the map database is referred to as a "map DB."
The object sensor 11 detects objects within a predetermined distance range (e.g., a detection area of the object sensor 11) from the host vehicle 1. The object sensor 11 detects the surrounding environment of the host vehicle 1, such as the relative position between the host vehicle 1 and objects present around the host vehicle 1, the distance between the host vehicle 1 and the objects, and the direction in which the objects are present. The object sensor 11 includes a plurality of different types of object detection sensors mounted on the host vehicle 1, such as a laser radar, a millimeter wave radar, a camera, and a LIDAR (Light Detection and Ranging, Laser Imaging Detection and Ranging), that detect objects around the host vehicle 1.

The vehicle sensor 12 is mounted on the host vehicle 1 and detects various information (vehicle signals) obtained from the host vehicle 1. The vehicle sensor 12 includes, for example, a vehicle speed sensor that detects the vehicle speed Vh of the host vehicle 1, a wheel speed sensor that detects the rotational speed of each tire equipped on the host vehicle 1, a three-axis acceleration sensor (G sensor) that detects the acceleration (including deceleration) in three axial directions of the host vehicle 1, a steering angle sensor that detects the steering angle (including the steering angle of the steering wheel or the steering angle of the steered wheels), a gyro sensor that detects the angular velocity generated in the host vehicle 1, a yaw rate sensor that detects the yaw rate, an accelerator sensor that detects the accelerator opening of the host vehicle, and a brake sensor that detects the amount of brake operation by the driver.

The positioning device 13 includes a Global Navigation System (GNSS) receiver and receives radio waves from a plurality of navigation satellites to measure the current position of the vehicle 1. The GNSS receiver may be, for example, a Global Positioning System (GPS) receiver. The positioning device 13 may be, for example, an inertial navigation system.
The map database 14 may store high-precision map information (hereinafter simply referred to as a "high-precision map") suitable as a map for automated driving. A high-precision map is map data with higher precision than map data for navigation (hereinafter simply referred to as a "navigation map"), and includes lane-by-lane information that is more detailed than road-by-road information. Hereinafter, the map represented by the map information in the map database 14 may be referred to simply as a "map."

For example, a high-precision map includes, as information for each lane, information on lane nodes that indicate reference points on lane reference lines (e.g., the center line within a lane) and information on lane links that indicate the section configuration of the lane between the lane nodes.
The lane node information includes the lane node's identification number, position coordinates, the number of connected lane links, and the identification numbers of the connected lane links. The lane link information includes the lane link's identification number, lane type, lane width, lane shape, lane boundary line shape and type, and lane reference line shape.

The navigation device 15 recognizes the current position of the vehicle by the positioning device 13, and obtains map information for the current position from the map database 14. The navigation device 15 sets a route to the destination input by the occupant on a road-by-road basis, and provides road guidance to the occupant according to the set route. The controller 17 may automatically drive the vehicle so that the vehicle travels along the route set by the navigation device 15 during autonomous driving control.
The communication device 16 performs wireless communication with a communication device outside the vehicle 1. The communication method used by the communication device 16 may be, for example, wireless communication via a public mobile communication network, vehicle-to-vehicle communication, road-to-vehicle communication, or satellite communication.

Actuator 18 operates the steering wheel, accelerator opening, and brake device of the host vehicle in response to control signals from controller 17 to generate vehicle behavior of the host vehicle. Actuator 18 includes a steering actuator, an accelerator opening actuator, and a brake control actuator. The steering actuator controls the steering angle of the steering of host vehicle 1. In other words, the steering actuator controls the steering mechanism. The accelerator opening actuator controls the accelerator opening of the host vehicle. The brake control actuator controls the braking operation of the brake device of host vehicle 1.

The controller 17 is an electronic control unit that performs driving assistance control of the host vehicle 1. The controller 17 performs automatic driving control that controls the traveling of the host vehicle 1 by controlling the actuator 18 based on the surrounding environment of the host vehicle 1 detected by the object sensor 11 and the map information of the map database 14.
The controller 17 includes a processor 17a and peripheral components such as a storage device 17b. The processor 17a may be, for example, a CPU or an MPU. The storage device 17b may include a semiconductor storage device, a magnetic storage device, an optical storage device, etc. The storage device 17b may include a register, a cache memory, and memories such as a ROM and a RAM used as a main storage device.

The functions of the controller 17 described below are realized, for example, by the processor 17a executing a computer program stored in the storage device 17b.
The controller 17 may be formed of dedicated hardware for executing each information processing described below. For example, the controller 17 may include a functional logic circuit set in a general-purpose semiconductor integrated circuit. For example, the controller 17 may include a programmable logic device such as a field programmable gate array (FPGA).

The following describes the automatic driving control by the controller 17. The controller 17 controls the actuator 18 based on the detection result of the surrounding environment of the host vehicle 1 by the object sensor 11, thereby executing driving assistance control for controlling the traveling of the host vehicle 1.
At this time, the controller 17 calculates a control value of driving assistance control that controls the traveling of the host vehicle 1 based on the surrounding environment of the host vehicle 1. For example, the control value of the driving assistance control may be a target vehicle speed that is a target value of the vehicle speed Vh of the host vehicle 1. In addition, for example, the control value of the driving assistance control may be a sequence of trajectory points that form a target traveling trajectory of the host vehicle 1. In the following description, the control value of the driving assistance control may be simply referred to as a "control value." The controller 17 controls the traveling of the host vehicle 1 by controlling the actuator 18 based on the calculated control value.

When such driving assistance control is executed, the driver may feel uneasy depending on the surrounding environment of the vehicle 1. For example, when the distance between the vehicle 1 and the preceding vehicle is short, when the weather at the current location of the vehicle 1 is bad (for example, when it is raining, snowing, or foggy), or when traveling on a curved road or in a tunnel, the driver may feel uneasy if the vehicle speed Vh is high.
In addition, for example, a feeling of uneasiness may be felt when the distance in the lane width direction between the vehicle and objects around the vehicle 1 (e.g., other tall vehicles, tall walls, tunnel walls) is short, or when the distance in the lane width direction between the road boundary and the vehicle 1 when driving on a curved road is short.
For this reason, the controller 17 estimates the degree of influence of the detected surrounding environment on the driver's sense of anxiety, and limits the control value when the estimated degree of influence is equal to or greater than a threshold value.

For example, the upper limit of vehicle speed Vh is reduced. This can reduce the driver's sense of anxiety when the distance between the preceding vehicle and vehicle 1 is short, when the weather at the current location of vehicle 1 is bad (for example, when it is raining, snowing, or foggy), or when driving on a curved road or in a tunnel. In addition, for example, the lower limit of the lane width direction distance between vehicle 1 and objects around vehicle 1, or the lane width direction distance between vehicle 1 and a road boundary when driving on a curved road, is increased. This can reduce the driver's sense of anxiety caused by approaching objects around vehicle 1 or a road boundary when driving on a curved road.

However, in general, a driver adapts to the surrounding environment over time (i.e., gets used to the surrounding environment). Therefore, it is considered that the degree of anxiety actually felt by the driver gradually deviates from the degree of influence estimated based on the surrounding environment over time. Therefore, if the control value is continuously limited, a driver who has become accustomed to the surrounding environment may feel that the driving of the vehicle under the driving assistance control is not sharp and may feel uncomfortable.
Therefore, when the state in which the estimated degree of influence remains equal to or greater than the threshold continues, the controller 17 releases the restriction on the control value based on the elapsed time since the restriction on the control value was started.
For example, the controller 17 may release the restriction on the control value when the estimated degree of influence continues to be equal to or greater than the threshold value and a predetermined time has elapsed since the restriction on the control value was started.
This makes it possible to prevent a driver who is accustomed to the surrounding environment from feeling uncomfortable due to the feeling that driving under driving assistance control is lacking in sharpness.

2 is a block diagram showing an example of a functional configuration of the controller 17 according to the first embodiment. The controller 17 includes an object detection unit 20, a vehicle position estimation unit 21, a map acquisition unit 22, a detection integration unit 23, an object tracking unit 24, an in-map position calculation unit 25, a trajectory generation unit 26, and a vehicle control unit 27.
The object detection unit 20 detects the positions, postures, sizes, speeds, etc. of objects around the vehicle 1, such as vehicles, motorcycles, pedestrians, obstacles, etc., based on the detection signals from the object sensor 11. The object detection unit 20 outputs detection results that express the two-dimensional positions, postures, sizes, speeds, etc. of objects in, for example, a zenith diagram (also called a plan view) of the vehicle 1 viewed from the air.

The vehicle position estimation unit 21 measures the absolute position of the vehicle 1, i.e., the position, attitude and speed of the vehicle 1 relative to a predetermined reference point, based on odometry using measurement results from the positioning device 13 and detection results from the vehicle sensor 12.
The map acquisition unit 22 acquires map information indicating the structure of the road on which the vehicle 1 is traveling from the map database 14. The map acquisition unit 22 may acquire the map information from an external map data server via the communication device 16.

The detection integration unit 23 integrates the multiple detection results obtained by the object detection unit 20 from each of the multiple object detection sensors, and outputs one detection result for each object. Specifically, based on the detection results (object position, orientation, size, and speed results) obtained from each object detection sensor, the most reasonable position that minimizes errors is calculated while taking into account the error characteristics of each object detection sensor, and outputs one two-dimensional position, orientation, size, speed, etc. for each object. The detection integration unit 23 uses known sensor fusion technology to comprehensively evaluate the detection results obtained by multiple types of sensors and obtain more accurate detection results.
The object tracking unit 24 tracks the object detected by the object detection unit 20. Specifically, based on the detection results integrated by the detection integration unit 23, the object tracking unit 24 verifies (associates) the identity of the object between different times from the behavior of the object output at different times, and predicts behavior such as the speed and attitude (e.g., yaw angle) of the object based on the association.

The map position calculation unit 25 estimates the position and attitude of the vehicle 1 on the map from the absolute position of the vehicle 1 obtained by the vehicle position estimation unit 21 and the map information acquired by the map acquisition unit 22. That is, it estimates the road on which the vehicle 1 is traveling, the road width direction position of the vehicle 1 within the road (for example, the road width direction distance between the road boundary and the vehicle 1), the lane on which the vehicle 1 is traveling, the lane width direction position of the vehicle 1 within the lane, etc.

The trajectory generating unit 26 generates a target driving trajectory, which is a target value of the driving trajectory along which the host vehicle 1 travels.
For example, the target driving trajectory may include a sequence of trajectory points, which is a sequence of points forming the target driving trajectory, and a target vehicle speed of the host vehicle 1 at each of the sequence of trajectory points. In the following description, the target speed at each of the sequence of trajectory points forming the target driving trajectory may be referred to as a "target vehicle speed profile." The target driving trajectory (i.e., the sequence of trajectory points, which is a sequence of points forming the target driving trajectory, and the target vehicle speed profile) is an example of a "control value of driving assistance control" described in the claims.

The trajectory generation unit 26 includes an influence degree estimation unit 30 , a familiarity degree determination unit 31 , driving history data 32 , and a control command value calculation unit 33 .
The influence degree estimation unit 30 estimates the influence degree of the surrounding environment of the vehicle 1 on the driver's anxiety based on the surrounding environment of the vehicle 1 acquired from the detection results of the sensors of the object sensor 11. Hereinafter, the influence degree estimated by the influence degree estimation unit 30 will be simply referred to as "influence degree".

For example, the influence estimator 30 may estimate the influence according to the characteristics of the surrounding environment acquired from the detection results of the object sensor 11 or the like. For example, the characteristic of the surrounding environment used to estimate the influence may be the distance between the preceding vehicle and the host vehicle 1. For example, the influence is estimated so that the influence is higher when the distance between the preceding vehicle and the host vehicle 1 is short compared to when it is long. For example, the influence may be set to 0 when the distance between the preceding vehicle and the host vehicle 1 is equal to or greater than a threshold, and may be set to a predetermined value greater than 0 when the distance between the preceding vehicle and the host vehicle 1 is less than the threshold. Also, the influence may be higher as the distance between the preceding vehicle and the host vehicle 1 is shorter.

Furthermore, for example, the feature of the surrounding environment used to estimate the degree of influence may be the weather at the current location of the vehicle 1. For example, the degree of influence is estimated so that the degree of influence is higher in bad weather than in good weather. For example, the degree of influence is estimated so that the degree of influence is higher in rain, snow, or fog than in sunny or cloudy weather. For example, the degree of influence may be set to 0 when the weather is sunny or cloudy, and the degree of influence may be set to a predetermined value greater than 0 when the weather is rain, snow, or fog.
The influence degree estimation unit 30 may recognize the weather at the current location of the vehicle 1 based on, for example, the detection results of a sensor such as the object sensor 11. For example, it may recognize whether the weather at the current location of the vehicle 1 is sunny, cloudy, rainy, snowy, or foggy by performing image recognition on an image captured by the camera of the object sensor 11. In addition, the communication device 16 may obtain weather data for the current location of the vehicle 1 from an external weather data server system.

Further, for example, the features of the surrounding environment used to estimate the influence degree may be the structure of the road on which the vehicle 1 travels (hereinafter, sometimes referred to as the "vehicle road") or the lane shape of the lane on which the vehicle 1 travels (hereinafter, sometimes referred to as the "vehicle lane"). For example, the influence degree is estimated so that the influence degree is higher when traveling on a curved road than when traveling on a straight road. For example, the influence degree when traveling on a straight road may be set to 0, and the influence degree when traveling on a curved road may be set to a predetermined value greater than 0. The influence degree may be estimated according to the curvature of the curved road. For example, the influence degree may be set to 0 when the curvature is less than a threshold value, and the influence degree may be set to a predetermined value greater than 0 when the curvature is equal to or greater than the threshold value. For example, the influence degree is estimated so that the influence degree is higher when the curvature is large than when it is small. For example, the influence degree may be higher as the curvature becomes smaller.
Also, for example, the degree of influence is estimated so that the degree of influence is higher when traveling inside the tunnel than when traveling outside the tunnel. For example, the degree of influence when traveling outside the tunnel may be set to 0, and the degree of influence when traveling inside the tunnel may be set to a predetermined value greater than 0.

For example, the features of the surrounding environment used to estimate the degree of influence may be the lane width distance between the vehicle and objects present around the vehicle 1 (e.g., other tall vehicles, tall walls, tunnel walls) or the lane width distance between the vehicle 1 and the road boundary when traveling on a curved road.
For example, the influence degree is estimated so that the influence degree is higher when the lane width direction distance is short compared to when it is long. For example, the influence degree may be set to 0 when the lane width direction distance is equal to or greater than a threshold, and the influence degree may be set to a predetermined value greater than 0 when the lane width direction distance is less than the threshold. The influence degree may be set to a higher value as the lane width direction distance becomes shorter.

Of the above influence levels, the influence level estimated based on the distance between the preceding vehicle and the vehicle 1, the influence level estimated based on the weather at the current location of the vehicle 1, and the influence level estimated based on the road structure of the road and the lane shape of the vehicle's lane affect the vehicle speed Vh of the vehicle 1 at which the driver feels uneasy. In other words, when the distance between the preceding vehicle and the vehicle 1 is short, when the weather at the current location of the vehicle 1 is bad, or when driving on a curved road or in a tunnel, the vehicle speed Vh at which the driver can travel without feeling uneasy is lower.

On the other hand, the lane width direction distance between the vehicle 1 and objects around the vehicle 1 or the lane width direction distance between the vehicle 1 and a road boundary when the vehicle 1 is traveling on a curved road affects the traveling trajectory of the vehicle 1 that makes the driver feel uneasy. That is, when there are objects around the vehicle 1 or when the vehicle 1 is traveling on a curved road, the range in which a traveling trajectory that does not make the driver feel uneasy can be generated is narrowed.
Therefore, the influence estimator 30 may separately estimate the influence of the surrounding environment that affects the vehicle speed Vh of the host vehicle 1 and the influence of the surrounding environment that affects the traveling trajectory of the host vehicle 1. Hereinafter, the influence related to the vehicle speed Vh may be referred to as the "first influence," and the influence related to the traveling trajectory may be referred to as the "second influence."

The influence estimator 30 may estimate as the first influence degree one of the influence degree estimated based on the distance between the preceding vehicle and the vehicle 1, the influence degree estimated based on the weather at the current position of the vehicle 1, and the influence degree estimated based on the road structure of the road or the lane shape of the lane. The first influence degree may be estimated by combining a plurality of these influence degrees. For example, the average, median, maximum, or minimum value of the plurality of influence degrees may be obtained as the first influence degree, or a weighted sum of the plurality of influence degrees may be obtained as the first influence degree.

The influence degree estimation unit 30 may estimate, as the second influence degree, any one of the influence degree estimated based on the lane width direction distance between the host vehicle 1 and other vehicles with high vehicle heights present around the host vehicle 1, the influence degree estimated based on the lane width direction distance between the host vehicle 1 and a high wall present around the host vehicle 1, the influence degree estimated based on the lane width direction distance between the host vehicle 1 and a tunnel inner wall present around the host vehicle 1, and the influence degree estimated based on the lane width direction distance between the host vehicle 1 and a road boundary when traveling on a curved road. A combination of multiple influence degrees among these influence degrees may be used to estimate the second influence degree. For example, the average, median, maximum, or minimum value of multiple influence degrees may be obtained as the second influence degree, or a weighted sum of multiple influence degrees may be obtained as the second influence degree.

The familiarity determination unit 31 determines whether the influence estimated by the influence estimation unit 30 is equal to or greater than a predetermined threshold. When the influence is equal to or greater than the predetermined threshold, the familiarity determination unit 31 limits the control value of the driving assistance control by the driving assistance device 10.
For example, the familiarity determination unit 31 may limit the control value of the driving assistance control by narrowing the allowable range of the control value of the driving assistance control. For example, when the first influence degree estimated by the influence degree estimation unit 30 is equal to or greater than a predetermined threshold, the familiarity determination unit 31 may limit the target vehicle speed profile (i.e., the target vehicle speed) of the vehicle 1 by setting the allowable maximum speed of the target vehicle speed profile generated by the trajectory generation unit 26 lower. Also, when the second influence degree estimated by the influence degree estimation unit 30 is equal to or greater than a predetermined threshold, the familiarity determination unit 31 may limit the trajectory point sequence of the target driving trajectory of the vehicle 1 by setting the allowable lower limit value of the lane width direction distance between the position of the trajectory point sequence of the target driving trajectory generated by the trajectory generation unit 26 and an object around the vehicle 1 longer. Also, the trajectory point sequence may be limited by setting the allowable lower limit value of the lane width direction distance between the road boundary when driving on a curved road and the position of the trajectory point sequence longer.

Next, the familiarity determination unit 31 determines the familiarity, which is the degree to which the driver has become accustomed to the surrounding environment. For example, if the influence level continues to be equal to or above a threshold, the familiarity of the driver is determined based on the elapsed time from the point in time when the control value restriction began (for example, the point in time when the influence level became equal to or above a predetermined threshold). For example, the familiarity determination unit 31 in the first embodiment determines that the driver has become sufficiently accustomed to the surrounding environment if the influence level continues to be equal to or above a threshold and a predetermined time T has elapsed since the control value restriction began.

The familiarity determination unit 31 releases the restriction on the control value according to the familiarity of the driver. The familiarity determination unit 31 of the first embodiment releases the restriction on the control value when it determines that the driver is sufficiently familiar with the surrounding environment (i.e., when the state in which the influence degree is equal to or greater than the threshold continues and a predetermined time T has elapsed since the restriction on the control value was started).
3A is a schematic diagram showing an example of limiting and releasing the limit of the control value. When the familiarity determination unit 31 determines that the influence degree is equal to or greater than a predetermined threshold value at time t0, the familiarity determination unit 31 starts limiting the control value. In the example of FIG. 3A, the allowable maximum speed of the target vehicle speed profile is limited from V0 to VL, which is lower than V0.
When limiting the control value, the familiarity determination unit 31 may gradually change the control value. In the example of Fig. 3A, the maximum allowable speed is gradually decreased from V0 to VL during the period from time t0 to time t1.

The familiarity degree determination unit 31 releases the restriction on the control value at time t2, which is a predetermined time T later than time t0 when the restriction on the control value was started. Note that the restriction on the control value may be released not at time t0 but at a time later than time t1, which is a predetermined time T. In the example of Fig. 3(a) , the limit on the control value is released by setting the maximum allowable speed of the target vehicle speed profile to V0 again.
The familiarity degree determination unit 31 may also gradually change the control value when releasing the restriction on the control value. In the example of Fig. 3A, the familiarity degree determination unit 31 gradually increases the maximum allowable speed from VL to V0 during the period from time t2 to time t3.
The familiarity level determination unit 31 may set the rate of change of the control value (i.e., the amount of change of the control value per unit time) when the restriction of the control value is released after a predetermined time T has elapsed since the start of the restriction of the control value to be lower than the rate of change of the control value when the control value is restricted. In other words, the time from time t2 to time t3 may be longer than the time from time t0 to time t1.

On the other hand, if the influence degree becomes less than the threshold before the predetermined time T has elapsed after the start of the restriction of the control value, it is not necessary to continue the restriction of the control value. Therefore, the familiarity level determination unit 31 releases the restriction of the control value.
Fig. 3B is a schematic diagram showing a case where the restriction on the control value is released before the lapse of a predetermined time T. As in the case of Fig. 3A, when it is determined that the degree of influence is equal to or greater than a predetermined threshold at time t0, the familiarity determination unit 31 starts restricting the control value. When it is determined that the degree of influence is less than the threshold at time t4, which is prior to time t2, the familiarity determination unit 31 releases the restriction on the control value.

In this case, the familiarity degree determination unit 31 may gradually change the control value. In the example of Fig. 3B, the familiarity degree determination unit 31 gradually increases the maximum allowable speed from VL to V0 during the period from time t4 to time t5.
The familiarity level determination unit 31 may set the rate of change of the control value in the case where the restriction on the control value is lifted when a predetermined time T has elapsed since the start of the restriction on the control value as shown in Fig. 3A to be lower than the rate of change of the control value in the case where the restriction on the control value is lifted because the degree of influence becomes less than the threshold as shown in Fig. 3B. In other words, the time from time t2 to time t3 may be longer than the time from time t4 to time t5.

Please refer to Fig. 2. The familiarity level determination unit 31 may change the predetermined time T depending on the driving history of the vehicle 1 or the driver.
For example, the current driving scene in which the vehicle 1 is traveling may be determined based on the surrounding environment of the vehicle 1 acquired from the detection results of sensors such as the object sensor 11, and the predetermined time T may be set to be shorter when the vehicle has traveled through a similar driving scene to the current driving scene many times in the past rather than a few times. This is because it is considered that the vehicle will easily become accustomed to the surrounding environment if it has traveled through a similar driving scene many times in the past, and will become accustomed to the surrounding environment more quickly.

For example, if the number of times a similar driving scene has been driven in the past is less than a threshold, the predetermined time T may be set to an initial value T0, and if the number of times is equal to or greater than the threshold, the predetermined time T may be set to T1, which is shorter than the initial value T0. Also, for example, the greater the number of times a similar driving scene has been driven in the past, the shorter the predetermined time T may be set.
The number of times the vehicle 1 has traveled in a driving scene in the past may be stored in, for example, the storage device 17b as the driving history data 32. The driving history data 32 may be received from an external source via the communication device 16. The number of times the vehicle 1 has traveled may be the number of times the vehicle has traveled in a period from a time point a predetermined time before the present time to the present time, or the total number of times the vehicle has traveled up to now, or the driving frequency.

As the driving history data 32, for example, for each of a plurality of driving scenes classified according to the characteristics of the driving scene, the characteristics of each driving scene may be associated with information on the number of times the vehicle 1 has traveled and stored.
The familiarity determination unit 31 determines the characteristics of the current driving scene based on the surrounding environment of the vehicle 1 acquired from the detection results of the sensors of the object sensor 11, etc. Then, by comparing the characteristics of the current driving scene with the characteristics of each driving scene stored as the driving history data 32, it identifies a driving scene that corresponds to the current driving scene among the driving scenes stored as the driving history data 32. The familiarity determination unit 31 sets the predetermined time T according to the number of driving times stored for the corresponding driving scene. In other words, the predetermined time T is set shorter when the number of driving times is large than when it is small.

The features of the surrounding environment used for estimating the influence degree may be used as the features for classifying the driving scene. For example, the features for classifying the driving scene may be the distance between the preceding vehicle and the vehicle 1, the weather during driving, the structure of the road on which the vehicle is traveling, the shape of the lane on which the vehicle is traveling, the distance in the lane width direction between the vehicle 1 and an object existing around the vehicle 1, and the distance in the lane width direction between the vehicle 1 and a road boundary when traveling on a curved road.
For example, the driving scenes may be classified according to whether the distance between the preceding vehicle and the vehicle 1 is equal to or greater than a threshold value. The driving scenes may be classified stepwise into three or more types according to the distance between the preceding vehicle and the vehicle 1. For example, the driving scenes may be classified according to the weather during driving (e.g., sunny, cloudy, rainy, snowy, foggy, etc.). For example, the driving scenes may be classified according to whether the road is curved. For example, the driving scenes may be classified according to whether the curvature is equal to or greater than a threshold value. The driving scenes may be classified stepwise into three or more types according to the curvature. For example, the driving scenes may be classified according to whether the driving scene is in a tunnel.

For example, the driving scenes may be classified according to whether or not the distance in the lane width direction between the vehicle 1 and an object present around the vehicle 1, or the distance in the lane width direction between the vehicle 1 and a road boundary while the vehicle 1 is traveling on a curved road, is equal to or greater than a threshold value. The driving scenes may be classified into three or more types in stages according to the distance in the lane width direction.
When the degree of influence estimated using a plurality of different features is combined, the driving scenes may be classified by combining the plurality of different features.
For example, when classifying based on a combination of the distance between the preceding vehicle and the vehicle itself and whether or not the road is curved, the scenes may be classified into scenes where the distance between the vehicles is equal to or greater than a threshold and the driving is on a curved road, scenes where the distance between the vehicles is less than the threshold and the driving is on a curved road, scenes where the distance between the vehicles is equal to or greater than the threshold and the driving is on a straight road, and scenes where the distance between the vehicles is less than the threshold and the driving is on a straight road.

It should be noted here that in a state in which the control value is limited by the familiarity determination unit 31, the influence estimated by the influence estimation unit 30 continues to be equal to or greater than a predetermined threshold value. In other words, in a state in which the control value is limited, the current driving scene in which the host vehicle 1 is traveling has the same characteristics as the characteristics of the surrounding environment detected when the influence degree is estimated.
Therefore, by using the features of the surrounding environment used to estimate the impact level as features for classifying driving scenes, the specified time T can be set according to the driving history of past driving scenes that have the same features as the surrounding environment detected when estimating the impact level (i.e., the surrounding environment that affected the driver's sense of anxiety).

Also, for example, the predetermined time T may be set to be short when the number of override operations that occurred when the vehicle previously traveled through a driving scene similar to the current driving scene is small, rather than being large.
Here, the override operation may be, for example, an intervention operation in which the driver operates the steering wheel during steering control or steering assist control by the driving assist device 10. It may also be, for example, an intervention operation in which the driver operates the brakes during vehicle speed control by the driving assist device 10. It is considered that the fewer such override operations there are in a driving scene, the less anxious the driver will feel and the quicker he or she will become accustomed to the surrounding environment. In the following description, the number of override operations will be referred to as the "number of overrides."

For example, if the number of overrides when a similar driving scene was traveled in the past is equal to or greater than a threshold, the predetermined time T may be set to an initial value T0, and if the number of overrides is less than the threshold, the predetermined time T may be set to a value T1 that is shorter than the initial value T0. Also, for example, the fewer the number of overrides when a similar driving scene was traveled in the past, the shorter the predetermined time T may be set.
The number of overrides that occurred in driving scenes in which the vehicle 1 has traveled in the past may be stored in, for example, the storage device 17b as driving history data 32. The driving history data 32 may be received from an external source via the communication device 16. As the number of overrides, the number of overrides that occurred during a period from a predetermined time before the present time to the present time, all the numbers of overrides that have occurred up to now, and the occurrence frequency of override operations may be stored.

Also, for example, the predetermined time T may be set to be shorter when the number of times that an override operation by the driver of the vehicle 1 has occurred is small than when the number of times that the override operation has occurred is large.
For example, when the number of overrides by the driver of the vehicle 1 is equal to or greater than a threshold value, the predetermined time T may be set to an initial value T0, and when the number of overrides is less than the threshold value, the predetermined time T may be set to a value T1 that is shorter than the initial value T0. Also, for example, the fewer the number of overrides by the driver of the vehicle 1, the shorter the predetermined time T may be set.

The number of times by the driver of the vehicle 1 may be stored in, for example, the storage device 17b as the driving history data 32. The driving history data 32 may be received from an external source via the communication device 16. As the number of overrides, the number of overrides that occurred during a period from a time point a predetermined time before the present time to the present time, all the numbers of overrides that have occurred up to now, and the occurrence frequency of override operations may be stored.
The number of overrides may be stored individually for a plurality of different drivers. The familiarity determination unit 31 may determine the driver who is in the vehicle 1, and set the predetermined time T according to the number of overrides stored for the driver currently in the vehicle 1. For example, a shorter predetermined time T may be set for a driver who has performed overrides less frequently than for a driver who has performed overrides more frequently.

The control command value calculation unit 33 generates a target driving trajectory. For example, when the driving assistance by the driving assistance device 10 is autonomous driving control, the control command value calculation unit 33 generates a route space map expressing the route around the vehicle 1 and the presence or absence of objects, and a risk map quantifying the risk of the driving field, based on the position and attitude of the vehicle 1 estimated by the intra-map position calculation unit 25, the positions and attitudes of objects around the vehicle 1, and the high-precision map. The control command value calculation unit 33 generates a driving action plan for automatically driving the vehicle 1 along the planned driving route, based on the planned driving route set by the navigation device 15, the route space map, and the risk map.
The control command value calculation unit 33 generates candidates for a target driving trajectory along which the vehicle 1 is to travel, based on the driving action plan, the motion characteristics of the vehicle 1, and the route space map. The control command value calculation unit 33 evaluates the future risk of each candidate based on the risk map, and selects the optimal target driving trajectory.

At this time, the control command value calculation unit 33 limits at least one of the target vehicle speed profile included in the target driving trajectory and the sequence of trajectory points forming the target driving trajectory by the limit value set by the familiarity degree determination unit 31 .
For example, the control command value calculation unit 33 limits the target vehicle speed profile to be equal to or lower than the maximum allowable speed set by the familiarity determination unit 31. In addition, for example, the control command value calculation unit 33 limits the distance in the lane width direction between the object around the vehicle 1 or the road boundary and the trajectory point sequence when traveling on a curved road so as not to be shorter than the allowable lower limit value set by the familiarity determination unit 31.
The control command value calculation unit 33 may generate candidates for the target driving trajectory so as to satisfy the limit value set by the familiarity determination unit 31, and may select an optimal trajectory from among the generated candidates that satisfies the limit value set by the familiarity determination unit 31 as the target driving trajectory.

The vehicle control unit 27 performs vehicle control based on the target driving trajectory generated by the trajectory generation unit 26. Specifically, the vehicle control unit 27 controls the steering actuator of the actuator 18 so that the host vehicle 1 travels along the target driving trajectory, and controls the accelerator opening actuator and the brake control actuator so that the vehicle speed Vh of the host vehicle 1 follows the target speed profile.
However, even if the vehicle route is not generated, control can be performed based on the relative distance of an object, and this does not limit the present invention.

FIG. 4 is a flowchart of an example of a driving assistance method according to the embodiment.
In step S<b>1 , the object detection unit 20 detects the position, orientation, size, speed, etc. of an object in the vicinity of the host vehicle 1 .
In step S2, the detection integration unit 23 integrates the detection results obtained from the object detection sensors. The object tracking unit 24 tracks each of the integrated objects and predicts the behavior of the objects in the vicinity of the host vehicle 1.

In step S3, the vehicle position estimating unit 21 measures the position, attitude and speed of the vehicle 1 relative to a predetermined reference point.
In step S4, the map acquisition unit 22 acquires map information indicating the structure of the road on which the host vehicle 1 is traveling.
In step S5, the in-map position calculation unit 25 estimates the position and attitude of the vehicle 1 on the map.
In step S6, the trajectory generating unit 26 executes a target trajectory generating process. The target trajectory generating process is a process for generating a target traveling trajectory that is a target value of the traveling trajectory of the host vehicle 1. Fig. 5 is a flowchart of an example of the target trajectory generating process.

In step S10, the influence estimation unit 30 estimates the influence of the surrounding environment of the vehicle 1 on the driver's sense of anxiety.
In step S11, the control command value calculation unit 33 calculates a control value for the driving assistance control. For example, the control value is calculated based on a sequence of trajectory points of a target driving trajectory and a target speed profile.
In step S12, the familiarity determination unit 31 determines whether the degree of influence estimated by the influence estimation unit 30 is equal to or greater than a threshold. If the degree of influence is equal to or greater than the threshold (step S12: Y), the process proceeds to step S13. If the degree of influence is not equal to or greater than the threshold (step S12: N), the process proceeds to step S19.

In step S13, the familiarity determination unit 31 limits the control value of the driving assistance control. For example, the familiarity determination unit 31 sets a lower allowable maximum speed of the target vehicle speed profile. Also, for example, the familiarity determination unit 31 sets a longer allowable lower limit value of the distance in the lane width direction between the road boundary and the trajectory point sequence when traveling on an object around the vehicle 1 or a curved road.
In step S14, the familiarity determination unit 31 determines the current driving scene in which the vehicle 1 is currently traveling, and accumulates the driving history data 32. For example, the familiarity determination unit 31 updates the number of times the vehicle 1 has traveled in the same driving scene as the current driving scene. Also, if an override operation occurs, the familiarity determination unit 31 updates the number of times the vehicle 1 has been overridden in the same driving scene as the current driving scene. Also, for example, the familiarity determination unit 31 updates the number of times the driver has overridden the vehicle.

In step S<b>15 , the familiarity level determination unit 31 sets a predetermined time T based on the driving history data 32 .
In step S16, the familiarity level determination unit 31 calculates the elapsed time that has passed since the limitation of the control value was started.
In step S17, the familiarity determination unit 31 determines whether or not the elapsed time is longer than a predetermined time T. If the elapsed time is longer than the predetermined time T (step S17: Y), the process proceeds to step S18. If the elapsed time is not longer than the predetermined time T (step S17: N), the process proceeds to step S19.

In step S18, the control command value calculation unit 33 releases the restriction on the control value. For example, the familiarity determination unit 31 returns the maximum allowable speed of the target vehicle speed profile to the value before the restriction. Also, for example, the allowable lower limit of the distance in the lane width direction between the road boundary and the trajectory point sequence when traveling on an object around the vehicle 1 or a curved road is returned to the value before the restriction.
In step S<b>19 , the control command value calculation unit 33 limits the target driving trajectory so as to satisfy the limit value set by the familiarity level determination unit 31 .

See FIG. 4. In step S7, the vehicle control unit 27 controls the host vehicle 1 so that the host vehicle 1 travels according to the target travel trajectory generated by the trajectory generation unit 26. Then, the process ends.

Second Embodiment
6 is a block diagram showing an example of the functional configuration of the controller 17 according to the second embodiment. The same components as those in the controller 17 according to the first embodiment are denoted by the same reference numerals.
The controller 17 of the second embodiment estimates an anxiety level, which is the degree of anxiety of the driver, based on the surrounding environment of the vehicle 1 acquired from the detection results of the sensors of the object sensor 11. The controller 17 limits a control value based on the estimated anxiety level.

The controller 17 of the second embodiment includes a habituation reflecting unit 34. When the influence level estimated by the influence level estimating unit 30 becomes equal to or greater than a predetermined threshold, the habituation reflecting unit 34 starts limiting the control value, and estimates the driver's anxiety level at the time when the influence level becomes equal to or greater than the predetermined threshold as an initial anxiety level Ai.
For example, the habituation reflection unit 34 may estimate the initial anxiety level Ai based on the influence level at the time when the influence level estimated by the influence level estimation unit 30 becomes equal to or greater than a predetermined threshold. For example, the influence level estimated at the time when the influence level becomes equal to or greater than a predetermined threshold may be used as the initial anxiety level Ai.
The habituation reflecting unit 34 estimates the anxiety level of the driver who becomes accustomed to the surrounding environment over time based on the estimated initial anxiety level Ai. For example, the habituation reflecting unit 34 may estimate the anxiety level such that the driver's anxiety level decreases as the time elapsed since the start of the restriction of the control value increases. The habituation reflecting unit 34 releases the restriction of the control value according to the estimated driver's anxiety level.

For example, the habituation reflection unit 34 may release the restriction on the control value when the driver's anxiety level becomes equal to or lower than a threshold value Ath.
Fig. 7(a) is a schematic diagram of an example of the driver's anxiety level calculated by the familiarity reflecting unit 34, and Fig. 7(b) is a schematic diagram showing an example of limiting the control value based on the anxiety level of Fig. 7(a). When the familiarity level determining unit 31 determines that the influence level is equal to or greater than a predetermined threshold at time t0, it starts limiting the control value and gradually reduces the allowable maximum speed of the target vehicle speed profile from V0 to VL during the period from time t0 to time t1. The familiarity level determining unit 31 also calculates the initial anxiety level Ai at time t0.

Thereafter, the familiarity determination unit 31 calculates the driver's anxiety level so that it gradually decreases from the initial anxiety level Ai according to the elapsed time from time t0. When the anxiety level becomes equal to or lower than the threshold value Ath at time t2, the familiarity reflection unit 34 starts removing the restriction on the control value, and gradually increases the maximum allowable speed from VL to V0 during the period from time t2 to time t3.
The rate of change of the control value when the restriction of the control value is released when the anxiety level becomes equal to or lower than the threshold Ath may be set lower than the rate of change of the control value when the control value is restricted. In other words, the time from time t2 to time t3 may be set longer than the time from time t0 to time t1.

The familiarity level determination unit 31 may change the rate of change in the driver's anxiety level over time (i.e., the amount of change in anxiety level per unit time) depending on the driving history of the vehicle 1 or the driver.
For example, the change speed may be set high so that the release of the restriction on the control value starts sooner when the number of times a driving scene similar to the current driving scene has been driven in the past is greater than when it is small. Also, for example, the change speed may be set high when the number of times overrides have been performed when a driving scene similar to the current driving scene has been driven in the past is smaller than when it is large. Also, for example, the change speed may be set high when the number of times overrides have been performed in which an override operation has been performed by the driver of the vehicle 1 is smaller than when it is large.
The rate of change in the anxiety level in FIG. 8A, which will be described later, may also be changed in accordance with the driving history in a similar manner.

Also, for example, the habituation reflection unit 34 may gradually release the restriction on the control value as the driver's anxiety level decreases.
Fig. 8(a) is a schematic diagram of another example of the driver's anxiety level calculated by the familiarity reflecting unit 34, and Fig. 8(b) is a schematic diagram showing another example of the restriction of the control value based on the anxiety level of Fig. 8(a). When the familiarity level determining unit 31 determines that the influence level is equal to or greater than a predetermined threshold at time t0, it starts restricting the control value and gradually reduces the allowable maximum speed of the target vehicle speed profile from V0 to VL during the period from time t0 to time t1. The familiarity level determining unit 31 also calculates the initial anxiety level Ai at time t0.

Thereafter, the familiarity determination unit 31 calculates the driver's anxiety level so that it gradually decreases from the initial anxiety level Ai according to the time elapsed from time t1. The familiarity determination unit 31 gradually releases the restriction on the allowable maximum speed as the driver's anxiety level decreases. In other words, the familiarity determination unit 31 gradually increases the allowable maximum speed as the driver's anxiety level decreases. As a result, the allowable maximum speed returns to the original value V0 at time t3.
In this way, by gradually releasing the limit on the control value as the driver's anxiety level decreases, the limit on the control value can be controlled more finely.

(Effects of the embodiment)
(1) The object sensor 11 detects the surrounding environment of the vehicle 1. The controller 17 calculates a control value of the driving assistance control for controlling the traveling of the vehicle 1 based on the detected surrounding environment, and controls the vehicle 1 based on the calculated control value. The controller 17 also estimates the degree of influence of the detected surrounding environment on the driver's sense of anxiety, limits the control value when the estimated influence is equal to or greater than a threshold, and releases the restriction on the control value based on the elapsed time since the start of the restriction on the control value when the state in which the estimated influence is equal to or greater than the threshold continues.
This reduces the driver's sense of anxiety due to the surrounding environment of the vehicle 1, and also prevents a driver who is accustomed to the surrounding environment from feeling uncomfortable due to a lack of sharpness in the driving of the vehicle under driving assistance control.

(2) Furthermore, the controller 17 may release the restriction on the control value when the estimated degree of influence continues to be equal to or greater than the threshold value and a predetermined time has elapsed since the controller 17 started restricting the control value.
This makes it possible to prevent a driver who has become accustomed to the surrounding environment over time from feeling that the driving of the vehicle under driving assistance control is lacking in sharpness and feeling uncomfortable.
(3) For each of a plurality of driving scenes classified in advance, the number of times the vehicle 1 has traveled may be stored in the storage device. The controller 17 may determine the driving scene of the vehicle 1 based on the detected surrounding environment, and set a shorter predetermined time when the number of times the vehicle 1 has traveled for a driving scene corresponding to the detected driving scene among the plurality of driving scenes is large compared to when the number of times is small.
This makes it possible to quickly release the restriction on the control value in cases where the driver has driven in a similar driving situation many times in the past and is more likely to become accustomed to the situation.

(4) The number of times an override operation has occurred for each of a plurality of driving scenes classified in advance may be stored in the storage device as the number of overrides. The controller 17 may determine the driving scene of the vehicle 1 based on the detected surrounding environment, and set a shorter predetermined time when the number of overrides stored for a driving scene corresponding to the detected driving scene among the plurality of driving scenes is small compared to when the number of overrides stored for the driving scene is large.
This makes it possible to quickly release the restriction on the control value in driving situations where the number of past overrides is small and the driver is unlikely to feel anxious.

(5) The number of times an override operation has been performed by the driver may be stored in a storage device as the number of overrides. The controller 17 may set a shorter predetermined time period when the number of stored overrides is small compared to when the number of stored overrides is large.
This makes it possible to expedite the release of restrictions on the control value when the number of past overrides is small and the driver is unlikely to feel uneasy about the driving assist control.
(6) The controller 17 may set the rate of change of the control value when lifting the restriction on the control value based on the elapsed time since the start of the restriction on the control value to be lower than the rate of change of the control value when restricting the control value.
This allows the control value to be released in accordance with the driver's sense of familiarity with the surrounding environment.

(7) The controller 17 may release the control value limit when the estimated impact level after limiting the control value becomes less than a threshold value, and may set the rate of change of the control value when the control value limit is released based on the elapsed time since the control value limit was started to be lower than the rate of change of the control value when the control value limit is released because the impact level becomes less than the threshold value.
This allows the control value to be released in accordance with the driver's sense of familiarity with the surrounding environment.
(8) The controller 17 may estimate the driver's anxiety level based on the estimated degree of impact and the elapsed time since the estimated degree of impact continues to be above a threshold and the control value restriction began, and may release the restriction on the control value based on the estimated degree of anxiety.
This makes it possible to prevent a driver who has become accustomed to the surrounding environment over time from feeling that the driving of the vehicle under driving assistance control is lacking in sharpness and feeling uncomfortable.

(9) The control value may include the vehicle speed of the vehicle 1. This reduces the driver's anxiety by limiting the vehicle speed in accordance with the surrounding environment, and enables the limit to be released when the driver becomes accustomed to the high vehicle speed.
(10) The control value may include a sequence of trajectory points forming a target driving trajectory of the host vehicle 1. The controller 17 may limit the sequence of trajectory points by increasing a lower limit value of the distance in the lane width direction between the host vehicle 1 and an object around the host vehicle 1 or the distance in the lane width direction between the host vehicle 1 and a road boundary when traveling on a curved road. This reduces the driver's anxiety caused by approaching the object around the host vehicle 1 or the road boundary of the curved road, and enables the restriction to be released when the driver becomes accustomed to the object around the host vehicle 1 or the road boundary of the curved road.

1...Vehicle, 10...Driving assistance device, 11...Object sensor, 12...Vehicle sensor, 13...Positioning device, 14...Map database, 15...Navigation device, 16...Communication device, 17...Controller, 18...Actuator, 20...Object detection unit, 21...Vehicle position estimation unit, 22...Map acquisition unit, 23...Detection integration unit, 24...Object tracking unit, 25...Map position calculation unit, 26...Trajectory generation unit, 27...Vehicle control unit, 30...Influence estimation unit, 31...Familiarity determination unit, 32...Driving history data, 33...Control command value calculation unit, 34...Familiarity reflection unit, 37...Vehicle route generation unit

Claims (11)

Detect the surrounding environment of the vehicle;
Calculating a control value of a driving assistance control for controlling driving of the host vehicle based on the detected surrounding environment;
Controlling the host vehicle based on the calculated control value;
Estimating the degree of influence of the detected surrounding environment on the driver's sense of anxiety;
limiting the control value when the estimated degree of influence is equal to or greater than a threshold, and releasing the restriction on the control value based on the elapsed time from when the state in which the estimated degree of influence is equal to or greater than the threshold continues.
A driving assistance method comprising: The driving assistance method according to claim 1, characterized in that the restriction on the control value is released when the estimated degree of influence continues to be equal to or greater than a threshold and a predetermined time has elapsed since the restriction on the control value was started. storing the number of times the vehicle has traveled for each of a plurality of travel scenes classified in advance;
determining a driving scene of the host vehicle based on the detected surrounding environment;
a shorter predetermined time is set when the number of times of travel stored for a traveling scene corresponding to the detected traveling scene among the plurality of traveling scenes is large compared to when the number of times of travel stored for the traveling scene corresponding to the detected traveling scene is small;
The driving assistance method according to claim 2 . The number of times an override operation has occurred for each of a plurality of driving scenes classified in advance is stored as an override number,
determining a driving scene of the host vehicle based on the detected surrounding environment;
a shorter predetermined time is set when the number of overrides stored for a traveling scene corresponding to the detected traveling scene among the plurality of traveling scenes is small compared to when the number of overrides stored for the traveling scene is large;
The driving assistance method according to claim 2 . storing the number of times an override operation by the driver has occurred as an override count;
3. The driving support method according to claim 2, wherein the predetermined time is set to be shorter when the stored number of overrides is small compared to when the number of overrides is large. The driving assistance method according to any one of claims 1 to 5, characterized in that the rate of change of the control value when the control value restriction is released based on the elapsed time since the control value restriction was started is set to be lower than the rate of change of the control value when the control value is restricted. When the estimated degree of influence becomes less than a threshold value after the control value is limited, the limit on the control value is released;
The driving assistance method according to any one of claims 1 to 6, characterized in that a rate of change of the control value when the limit on the control value is lifted based on the elapsed time since the limit on the control value was started is made lower than a rate of change of the control value when the limit on the control value is lifted because the influence degree becomes less than a threshold value. estimating a degree of anxiety of the driver based on the estimated degree of influence and an elapsed time from when the state in which the estimated degree of influence is equal to or greater than a threshold continues and when the control value is started;
The driving support method according to claim 1 , further comprising removing the restriction on the control value based on the estimated degree of anxiety. The driving assistance method according to any one of claims 1 to 8, characterized in that the control value includes the vehicle speed of the host vehicle. the control value includes a sequence of trajectory points that form a target driving trajectory of the host vehicle;
The driving assistance method according to any one of claims 1 to 9, characterized in that the sequence of trajectory points is limited by increasing a lower limit value of a distance in a lane width direction between the host vehicle and an object around the host vehicle, or a distance in a lane width direction between the host vehicle and a road boundary when traveling on a curved road. A sensor for detecting the surrounding environment of the vehicle;
a controller that calculates a control value of a driving assistance control for controlling traveling of the vehicle based on the detected surrounding environment, controls the vehicle based on the calculated control value, estimates a degree of influence of the detected surrounding environment on a driver's anxiety, limits the control value when the estimated degree of influence is equal to or greater than a threshold, and releases the restriction on the control value based on an elapsed time since the restriction on the control value was started when the state in which the estimated degree of influence is equal to or greater than the threshold continues;
A driving assistance device comprising:

Images