Driving support method and driving support device

Abstract

To suppress discomfort caused to a driver by driving support control that controls the travel of an own vehicle so as to reduce anxiety of the driver.SOLUTION: A driving support method includes: detecting a surrounding environment of an own vehicle (S1); calculating a control value of driving support control for controlling the travel of a driving support own vehicle on the basis of the surrounding environment detected for the driving support (S11); controlling the driving support own vehicle on the basis of the calculated driving support control value (S7); estimating an influence degree of the detected driving support surrounding environment on an anxiety of a driver (S10); restricting the driving support control value in a case where the estimated driving support influence degree is equal to or higher than a threshold (S13); and cancelling the restriction of the control value on the basis of an elapsed time after the start of the restriction of the driving support control value in a case where a state in which the estimated driving support influence degree is equal to or higher than the threshold continues (S17, S18).SELECTED DRAWING: Figure 5

Description

Technical Field

[0001] The present invention relates to a driving support method and a driving support device.

Background Art

[0002] Patent Document 1 proposes a driving control device that controls the driving state of a host vehicle so as to travel at a position where the degree of psychological pressure received by the driver of the host vehicle is low.

Prior Art Document

Patent Document

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, if driving control that suppresses the driver's sense of uneasiness continues, a driver who is accustomed to the driving environment may feel a lack of rhythm in driving the host vehicle and may feel a sense of discomfort. An object of the present invention is to suppress a driving support control for controlling the driving of a host vehicle so as to reduce the driver's sense of uneasiness from giving the driver the above-described sense of discomfort.

Means for Solving the Problems

[0005] In a vehicle control method according to an aspect of the present invention, the surrounding environment of the host vehicle is detected, a control value of driving support control for controlling the driving of the host vehicle is calculated based on the detected surrounding environment, the host vehicle is controlled based on the calculated control value, the degree of influence of the detected surrounding environment on the driver's sense of uneasiness is estimated, the control value is limited when the estimated degree of influence is equal to or greater than a threshold value, and the control value is released based on the elapsed time since the start of the limitation of the control value when the state where the estimated degree of influence is equal to or greater than the threshold value continues.

Effects of the Invention

[0006] According to the present invention, driving support control for controlling the driving of a host vehicle so as to reduce a driver's sense of unease can suppress giving the driver a sense of discomfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0007]

Figure 1

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[0008] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the description of the drawings, the same or similar parts are denoted by the same or similar reference numerals, and duplicate descriptions are omitted. Each drawing is schematic and may include cases different from the actual ones. The embodiments shown below illustrate 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 variously modified within the technical scope described in the claims.

[0009] (First Embodiment) FIG. 1 is a diagram showing an example of a schematic configuration of a vehicle equipped with the driving support device according to the embodiment. The host vehicle 1 includes a driving support device 10 that supports the driving of the host vehicle 1. The driving support device 10 detects the surrounding environment, which is the driving environment around the host vehicle 1, and supports the driving of the host vehicle 1 by the occupant (e.g., the driver) of the host vehicle 1 by automatically controlling the driving of the host vehicle 1 based on the detected surrounding environment. The driving support of the host vehicle 1 by the driving support device 10 may include, for example, an automatic vehicle speed control that automatically controls the vehicle speed of the host vehicle. For example, the automatic vehicle speed control may include an inter-vehicle distance control and a constant speed driving control. Also, for example, the driving support by the driving support device 10 may include a steering support control that automatically controls the steering angle. For example, the steering support control may be a lane departure prevention support. Also, for example, the driving support by the driving support device 10 may include an autonomous driving control that automatically drives the host vehicle 1 without the involvement of the occupant.

[0010] The driving support 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. Note that in the drawings, the map database is denoted as "map DB". The object sensor 11 detects an object within a predetermined distance range (e.g., the 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 object existing around the host vehicle 1 and the host vehicle 1, the distance between the host vehicle 1 and the object, and the direction in which the object exists. The object sensor 11 includes a plurality of different types of object detection sensors that detect objects around the host vehicle 1, such as a lidar, a millimeter-wave radar, a camera, and a LIDAR (Light Detection and Ranging, Laser Imaging Detection and Ranging) mounted on the host vehicle 1.

[0011] 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 provided on the host vehicle 1, a three-axis acceleration sensor (G sensor) that detects the acceleration (including deceleration) of the host vehicle 1 in three axial directions, 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.

[0012] The positioning device 13 includes a global navigation satellite system (GNSS) receiver, receives radio waves from a plurality of navigation satellites, and measures the current position of the host vehicle 1. The GNSS receiver may be, for example, a global positioning system (GPS) receiver or the like. The positioning device 13 may be, for example, an inertial navigation device. The map database 14 may store high-precision map information (hereinafter simply referred to as "high-precision map") suitable as a map for autonomous driving. The high-precision map is map data with higher precision than the map data for navigation (hereinafter simply referred to as "navigation map"), and includes lane unit information that is more detailed than road unit information. Hereinafter, the map represented by the map information in the map database 14 may be simply referred to as "map".

[0013] For example, the high-precision map includes, as lane unit information, information on lane nodes indicating reference points on the lane reference line (for example, the central line within the lane) and information on lane links indicating the section state of the lane between lane nodes. The information on the lane node includes the identification number of the lane node, the position coordinates, the number of connected lane links, and the identification numbers of the connected lane links. The information on the lane link includes the identification number of the lane link, the type of the lane, the width of the lane, the shape of the lane, the shape and type of the lane boundary line, and the shape of the lane reference line.

[0014] The navigation device 15 recognizes the current position of the host vehicle by the positioning device 13, and acquires map information at the current position from the map database 14. The navigation device 15 sets a route for each road to the destination input by the occupant, and guides the occupant along the set route. The controller 17 may automatically drive the host vehicle to travel 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 host vehicle 1. The communication method 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.

[0015] The actuator 18 operates the steering wheel, accelerator opening, and brake device of the host vehicle according to a control signal from the controller 17 to generate the vehicle behavior of the host vehicle. The 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 the host vehicle 1. That is, 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 the host vehicle 1.

[0016] The controller 17 is an electronic control unit that performs driving support control of the host vehicle 1. The controller 17 executes automatic driving control for controlling the travel 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 in 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 memories such as registers, cache memories, ROM and RAM used as main storage devices.

[0017] The functions of the controller 17 described below are realized, for example, when the processor 17a executes a computer program stored in the storage device 17b. Note that the controller 17 may be formed of dedicated hardware for executing each information process described below. For example, the controller 17 may include a functional logic circuit set in a general-purpose semiconductor integrated circuit. For example, it may have a programmable logic device such as a field programmable gate array (FPGA).

[0018] Hereinafter, the automatic driving control by the controller 17 will be described. The controller 17 executes driving support control for controlling the running of the host vehicle 1 by controlling the actuator 18 based on the detection result of the surrounding environment of the host vehicle 1 by the object sensor 11. At this time, the controller 17 calculates a control value of the driving support control for controlling the running of the host vehicle 1 based on the surrounding environment of the host vehicle 1. For example, the control value of the driving support control may be a target vehicle speed that is a target value of the vehicle speed Vh of the host vehicle 1. Also, for example, the control value of the driving support control may be a sequence of track points forming a target travel track of the host vehicle 1. In the following description, the control value of the driving support control may be simply referred to as "control value". The controller 17 controls the running of the host vehicle 1 by controlling the actuator 18 based on the calculated control value.

[0019] When such driving support control is executed, the driver may feel uneasy depending on the surrounding environment of the host vehicle 1. For example, when the inter-vehicle distance between the preceding vehicle and the host vehicle 1 is short, or when the weather at the current position of the host vehicle 1 is bad (for example, in the case of rain, snow, or fog), or when driving on a curved road or in a tunnel, the driver may feel uneasy if the vehicle speed Vh is high. Also, for example, when the distance in the lane width direction between an object existing around the host vehicle 1 (for example, another vehicle with a high vehicle height, a high wall, or the inner wall of a tunnel) and the host vehicle is short, or when the distance in the lane width direction between the road boundary and the host vehicle 1 during driving on a curved road is short, the driver may feel uneasy. Therefore, the controller 17 estimates the degree of influence of the detected surrounding environment on the driver's sense of unease. When the estimated degree of influence is equal to or greater than a threshold value, the control value is restricted.

[0020] For example, the upper limit value of the vehicle speed Vh is decreased. Thereby, when the distance between the preceding vehicle and the host vehicle 1 is short, or when the weather at the current position of the host vehicle 1 is bad (for example, in the case of rain, snow, or fog), or when driving in a curve or a tunnel, the driver's sense of unease can be reduced. Also, for example, the lower limit value of the lateral distance between an object around the host vehicle 1 and the host vehicle 1 or the lateral distance between the host vehicle 1 and the road boundary when driving on a curve is increased. Thereby, the sense of unease caused by approaching an object around the host vehicle 1 or the road boundary when driving on a curve can be reduced.

[0021] However, generally, a driver adapts to (i.e., gets used to) the surrounding environment as time passes. For this reason, it is considered that the actual degree of unease felt by the driver gradually deviates from the magnitude of the degree of influence estimated based on the surrounding environment as time passes. Therefore, if the control value is continuously restricted, a driver who has gotten used to the surrounding environment may feel that there is no rhythm in the driving of the host vehicle due to the driving support control and may feel a sense of discomfort. Therefore, when the state where the estimated degree of influence is equal to or greater than the threshold value continues, the controller 17 releases the restriction of the control value based on the elapsed time since the start of the restriction of the control value. For example, when the state where the estimated degree of influence is equal to or greater than the threshold value continues and a predetermined time has elapsed since the start of the restriction of the control value, the controller 17 may release the restriction of the control value. Thereby, it is possible to suppress a driver who has gotten used to the surrounding environment from feeling that there is no rhythm in the driving due to the driving support control and feeling a sense of discomfort.

[0022] FIG. 2 is a block diagram showing an example of the functional configuration of the controller 17 according to the first embodiment. The controller 17 includes an object detection unit 20, a host 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. Based on the detection signal of the object sensor 11, the object detection unit 20 detects the position, posture, size, speed, etc. of objects around the host vehicle 1, such as vehicles, motorcycles, pedestrians, obstacles, etc. The object detection unit 20 outputs a detection result that represents the two-dimensional position, posture, size, speed, etc. of the object, for example, in a top view (also called a plan view) that looks at the host vehicle 1 from the air.

[0023] Based on the measurement result by the positioning device 13 and the odometry using the detection result from the vehicle sensor 12, the host vehicle position estimation unit 21 measures the absolute position of the host vehicle 1, that is, the position, posture, and speed of the host vehicle 1 with respect to a predetermined reference point. The map acquisition unit 22 acquires map information indicating the structure of the road on which the host vehicle 1 travels from the map database 14. The map acquisition unit 22 may acquire map information from an external map data server through the communication device 16.

[0024] The detection integration unit 23 integrates the plurality of detection results obtained by the object detection unit 20 from each of the plurality of object detection sensors and outputs one detection result for each object. Specifically, based on the detection results (object position, posture, size, speed results) obtained from each of the object detection sensors, the most reasonable position with the least error is calculated considering the error characteristics of each object detection sensor, etc., and one two-dimensional position, posture, size, speed, etc. are output for each object. The detection integration unit 23 comprehensively evaluates the detection results obtained by multiple types of sensors by using known sensor fusion technology to obtain a more accurate detection result. The object tracking unit 24 tracks the objects detected by the object detection unit 20. Specifically, based on the detection result integrated by the detection integration unit 23, the identity verification (association) of the objects at different times is performed from the behavior of the objects output at different times, and based on the association, the behavior such as the speed and posture (for example, yaw angle) of the object is predicted.

[0025] The in-map position calculation unit 25 estimates the position and orientation of the host vehicle 1 on the map from the absolute position of the host vehicle 1 obtained by the host vehicle position estimation unit 21 and the map information obtained by the map acquisition unit 22. That is, it estimates the road on which the host vehicle 1 is traveling, the position of the host vehicle 1 in the road width direction within the road (for example, the distance in the road width direction between the road boundary and the host vehicle 1), the driving lane in which the host vehicle 1 is traveling, and the position of the host vehicle 1 in the lane width direction within the lane.

[0026] The trajectory generation unit 26 generates a target driving trajectory that is a target value of the driving trajectory of the host vehicle 1. For example, the target driving trajectory may include a trajectory point sequence that is a sequence of points forming the target driving trajectory and the target vehicle speed of the host vehicle 1 at each of the trajectory point sequences. In the following description, the target speed at each of the trajectory point sequences forming the target driving trajectory may be referred to as the "target vehicle speed profile". The target driving trajectory (that is, the trajectory point sequence that is a sequence of points forming the target driving trajectory and the target vehicle speed profile) is an example of the "control value of the driving support control" described in the claims.

[0027] The trajectory generation unit 26 includes an influence degree estimation unit 30, a familiarity 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 that the surrounding environment of the host vehicle 1 has on the driver's sense of uneasiness based on the surrounding environment of the host vehicle 1 obtained from the detection results of sensors such as the object sensor 11. Hereinafter, the influence degree estimated by the influence degree estimation unit 30 is simply referred to as the "influence degree".

[0028] For example, the influence degree estimation unit 30 may estimate the influence degree according to the characteristics of the surrounding environment obtained from the detection results of sensors such as the object sensor 11. For example, the characteristics of the surrounding environment used for estimating the influence degree may be the inter-vehicle distance between the preceding vehicle and the own vehicle 1. For example, the influence degree is estimated such that it becomes higher when the inter-vehicle distance between the preceding vehicle and the own vehicle 1 is short compared to when it is long. For example, the influence degree may be set to 0 when the inter-vehicle distance between the preceding vehicle and the own vehicle 1 is equal to or greater than the threshold value, and a predetermined value greater than 0 when it is less than the threshold value. Also, the shorter the inter-vehicle distance between the preceding vehicle and the own vehicle 1, the higher the influence degree may be set.

[0029] Also, for example, the characteristics of the surrounding environment used for estimating the influence degree may be the weather at the current position of the own vehicle 1. For example, the influence degree is estimated such that it becomes higher in case of bad weather compared to when the weather is good. For example, the influence degree is estimated such that it becomes higher in case of rain, snow, or fog compared to when the weather is sunny or cloudy. For example, the influence degree may be set to 0 when the weather is sunny or cloudy, and a predetermined value greater than 0 in case of rain, snow, or fog. The influence degree estimation unit 30 may recognize the weather at the current position of the own vehicle 1 based on, for example, the detection results of sensors such as the object sensor 11. For example, by performing image recognition on the video captured by the camera of the object sensor 11, it may be recognized whether the weather at the current position of the own vehicle 1 is sunny, cloudy, rainy, snowy, or foggy. Also, the weather data at the current position of the own vehicle 1 may be acquired from an external weather data server system by the communication device 16.

[0030] Also, for example, the characteristics of the surrounding environment used for estimating the degree of influence may be the structure of the road on which the host vehicle 1 travels (hereinafter sometimes referred to as the "host vehicle road") or the lane shape of the lane on which the host vehicle 1 travels (hereinafter sometimes referred to as the "host vehicle lane"). For example, the degree of influence is estimated such that it becomes higher when traveling on a curved road compared to when traveling on a straight road. For example, the degree of influence when traveling on a straight road may be set to 0, and the degree of influence when traveling on a curved road may be set to a predetermined value greater than 0. The degree of influence may be estimated according to the curvature of the curved road. For example, the degree of influence may be set to 0 when the curvature is less than the threshold value, and the degree of influence 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 degree of influence is estimated such that it becomes higher when the curvature is large compared to when it is small. For example, the degree of influence may be made higher as the curvature becomes smaller. Also, for example, the degree of influence is estimated such that it becomes higher when traveling inside a tunnel compared to 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.

[0031] Also, for example, the characteristics of the surrounding environment used for estimating the degree of influence may be the distance in the lane width direction between an object existing around the host vehicle 1 (for example, another vehicle with a high vehicle height, a high wall, the inner wall of a tunnel) and the host vehicle, or the distance in the lane width direction between the host vehicle 1 and the road boundary when traveling on a curved road. For example, the degree of influence is estimated such that it becomes higher when the distance in the lane width direction is short compared to when it is long. For example, the degree of influence may be set to 0 when the distance in the lane width direction is equal to or greater than the threshold value, and the degree of influence may be set to a predetermined value greater than 0 when the distance in the lane width direction is less than the threshold value. The degree of influence may be made higher as the distance in the lane width direction becomes shorter.

[0032] Among the above influence degrees, the influence degree estimated based on the inter-vehicle distance between the preceding vehicle and the host vehicle 1, the influence degree estimated based on the weather at the current position of the host vehicle 1, and the influence degree estimated based on the road structure of the host road and the lane shape of the host lane affect the vehicle speed Vh of the host vehicle 1 at which the driver feels uneasy. That is, when the inter-vehicle distance between the preceding vehicle and the host vehicle 1 is short, when the weather at the current position of the host vehicle 1 is bad, or when driving in a curved road or a tunnel, the vehicle speed Vh at which the driver can drive without feeling uneasy becomes low.

[0033] On the other hand, the lateral distance in the lane width direction between an object around the host vehicle 1 and the host vehicle 1 or the lateral distance in the lane width direction between the host vehicle 1 and the road boundary when driving on a curved road affects the driving trajectory of the host vehicle 1 at which the driver feels uneasy. That is, when there is an object around the host vehicle 1 or when driving on a curved road, the range within which a driving trajectory that does not give the driver a sense of unease can be generated becomes narrow. Therefore, the influence degree estimation unit 30 may separately estimate the influence degree of the surrounding environment that affects the vehicle speed Vh of the host vehicle and the influence degree of the surrounding environment that affects the driving trajectory of the host vehicle 1. Hereinafter, the influence degree related to the vehicle speed Vh may be referred to as the "first influence degree", and the influence degree related to the driving trajectory may be referred to as the "second influence degree".

[0034] The influence degree estimation unit 30 may estimate any one of the influence degree estimated based on the inter-vehicle distance between the preceding vehicle and the host vehicle 1, the influence degree estimated based on the weather at the current position of the host vehicle 1, and the influence degree estimated based on the road structure of the host road and the lane shape of the host lane as the first influence degree. The first influence degree may be estimated by combining a plurality of these influence degrees. For example, the average value, median value, maximum value, or minimum value of the plurality of influence degrees may be obtained as the first influence degree, or the weighted sum of the plurality of influence degrees may be obtained as the first influence degree.

[0035] Further, the influence degree estimation unit 30 estimates, 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 another vehicle with a high vehicle height existing 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 existing around the host vehicle 1, the influence degree estimated based on the lane width direction distance between the host vehicle 1 and the inner wall of a tunnel existing around the host vehicle 1, and the influence degree estimated based on the lane width direction distance between the host vehicle 1 and the road boundary when driving on a curved road. The second influence degree may be estimated by combining a plurality of these influence degrees. For example, the average value, median value, maximum value, or minimum value of the plurality of influence degrees may be obtained as the second influence degree, or the weighted sum of the plurality of influence degrees may be obtained as the second influence degree.

[0036] The familiarity determination unit 31 determines whether the influence degree estimated by the influence degree estimation unit 30 is equal to or greater than a predetermined threshold. When the influence degree is equal to or greater than the predetermined threshold, the familiarity determination unit 31 restricts the control value of the driving support control by the driving support device 10. For example, the familiarity determination unit 31 may restrict the control value of the driving support control by narrowing the allowable range of the control value of the driving support 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 restrict the target vehicle speed profile (i.e., the target vehicle speed) of the host vehicle 1 by setting a lower allowable maximum speed for the target vehicle speed profile generated by the trajectory generation unit 26. Also, for example, 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 restrict the trajectory point sequence of the target driving trajectory of the host vehicle 1 by setting a longer allowable lower limit value for 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 host vehicle 1. Further, the trajectory point sequence may be restricted by setting a longer allowable lower limit value for the lane width direction distance between the road boundary when driving on a curved road and the position of the trajectory point sequence.

[0037] Next, the familiarity determination unit 31 determines the degree of familiarity, which is the degree to which the driver gets used to the surrounding environment. For example, when the state where the influence degree is equal to or higher than the threshold value continues, the degree of familiarity of the driver is determined based on the elapsed time from the time when the restriction of the control value is started (for example, the time when the influence degree becomes equal to or higher than the predetermined threshold value). For example, the familiarity determination unit 31 of the first embodiment determines that the driver is sufficiently familiar with the surrounding environment when the state where the influence degree is equal to or higher than the threshold value continues and a predetermined time T has elapsed since the start of the restriction of the control value.

[0038] The familiarity determination unit 31 releases the restriction of the control value according to the degree of familiarity of the driver. The familiarity determination unit 31 of the first embodiment releases the restriction of the control value when it is determined that the driver is sufficiently familiar with the surrounding environment (that is, when the state where the influence degree is equal to or higher than the threshold value continues and a predetermined time T has elapsed since the start of the restriction of the control value). FIG. 3(a) is a schematic diagram showing an example of the restriction and release of the control value. When the familiarity determination unit 31 determines that the influence degree becomes equal to or higher than the predetermined threshold value at time t0, the restriction of the control value is started. In the example of FIG. 3(a), the allowable maximum speed of the target vehicle speed profile is restricted from V0 to VL which is lower than V0. When restricting the control value, the familiarity determination unit 31 may gradually change the control value. In the example of FIG. 3(a), the allowable maximum speed is gradually decreased from V0 to VL during the period from time t0 to time t1.

[0039] The familiarity determination unit 31 releases the restriction of the control value at time t2 which is a predetermined time T later than time t0 when the restriction of the control value is started. Note that the restriction of the control value may be released at a time which is a predetermined time T later than time t1 instead of time t0. In the example of FIG. 3(a), the restriction of the control value is released by resetting the allowable maximum speed of the target vehicle speed profile to V0 again. The familiarity determination unit 31 may also gradually change the control value when releasing the restriction of the control value. In the example of FIG. 3(a), the familiarity determination unit 31 gradually increases the allowable maximum speed from VL to V0 during the period from time t2 to time t3. Incidentally, the familiarity determination unit 31 may set the rate of change of the control value (i.e., the amount of change in the control value per unit time) when releasing the restriction on the control value when a predetermined time T has elapsed 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. That is, the time from time t2 to time t3 may be made longer than the time from time t0 to time t1.

[0040] On the other hand, if the degree of influence becomes less than the threshold value before the predetermined time T has elapsed after the start of the restriction on the control value, it is not necessary to continue the restriction on the control value. For this reason, the familiarity determination unit 31 releases the restriction on the control value. FIG. 3(b) is a schematic diagram when releasing the restriction on the control value before the predetermined time T has elapsed. Similar to the case of FIG. 3(a), when it is determined that the degree of influence becomes equal to or greater than a predetermined threshold value at time t0, the familiarity determination unit 31 starts restricting the control value. When it is determined that the degree of influence becomes less than the threshold value at time t4 before time t2, the familiarity determination unit 31 releases the restriction on the control value.

[0041] Also in this case, the familiarity determination unit 31 may gradually change the control value. In the example of FIG. 3(b), the familiarity determination unit 31 gradually increases the allowable maximum speed from VL to V0 during the period from time t4 to time t5. Incidentally, the familiarity determination unit 31 may set the rate of change of the control value when releasing the restriction on the control value when a predetermined time T has elapsed since the start of the restriction on the control value as shown in FIG. 3(a) to be lower than the rate of change of the control value when releasing the restriction on the control value because the degree of influence becomes less than the threshold value as shown in FIG. 3(b). That is, the time from time t2 to time t3 may be made longer than the time from time t4 to time t5.

[0042] Referring to FIG. 2, the familiarity determination unit 31 may change the predetermined time T according to the driving history of the host vehicle 1 or the driver. For example, based on the surrounding environment of the host vehicle 1 obtained from the detection results of sensors such as the object sensor 11, the current driving scene in which the host vehicle 1 is traveling is determined, and the predetermined time T may be set shorter when the number of driving times in the past for a driving scene similar to the current driving scene is larger than when it is smaller. This is because if the same driving scene has been traveled many times in the past, it is easier to get used to the surrounding environment, and it is considered that the surrounding environment can be gotten used to earlier.

[0043] For example, when the number of driving times in the past for a similar driving scene is less than the threshold value, the predetermined time T may be set to the initial value T0, and when it is equal to or greater than the threshold value, it may be set to T1 which is shorter than the initial value T0. Also, for example, the more the number of driving times in the past for a similar driving scene, the shorter the predetermined time T may be set. The number of driving times of the driving scenes that the host vehicle 1 has traveled in the past may be stored, for example, in the storage device 17b as the driving history data 32. The driving history data 32 may be received from the outside by the communication device 16. As the number of driving times, the number of driving times in the period from a point in time a predetermined time before the current time to the current time, the total number of all driving times up to now, or the driving frequency may be stored.

[0044] 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 the information on the number of driving times that the host 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 host vehicle 1 obtained from the detection results of sensors such as the object sensor 11. Then, by comparing the characteristics of the current driving scene with the characteristics of each driving scene stored as the driving history data 32, the driving scene corresponding to the current driving scene among the driving scenes stored as the driving history data 32 is specified. The familiarity determination unit 31 sets the predetermined time T according to the number of driving times stored for the corresponding driving scene. That is, the predetermined time T is set shorter when the number of driving times is larger than when it is smaller.

[0045] As a feature for classifying the driving scene, the features of the surrounding environment used for estimating the influence degree may be used. For example, as a feature for classifying the driving scene, the distance between the preceding vehicle and the host vehicle 1, the weather during driving, the structure of the host vehicle road, the lane shape of the host vehicle lane, the distance in the lane width direction between the object existing around the host vehicle 1 and the host vehicle, and the distance in the lane width direction between the host vehicle 1 and the road boundary during driving on a curved road may be used. For example, the driving scene may be classified according to whether the distance between the preceding vehicle and the host vehicle 1 is equal to or greater than a threshold value. The driving scene may be classified step by step into three or more types according to the distance between the preceding vehicle and the host vehicle 1. Also, for example, the driving scene may be classified according to the weather during driving (for example, sunny, cloudy, rainy, snowy, foggy, etc.). Also, for example, the driving scene may be classified according to whether it is a curved road. Also, the driving scene may be classified according to whether the curvature is equal to or greater than a threshold value. The driving scene may be classified step by step into three or more types according to the curvature. Also, for example, the driving scene may be classified according to whether the vehicle is driving inside a tunnel.

[0046] Also, for example, the driving scene may be classified according to whether the distance in the lane width direction between the object existing around the host vehicle 1 and the host vehicle and the distance in the lane width direction between the host vehicle 1 and the road boundary during driving on a curved road are equal to or greater than a threshold value. The driving scene may be classified step by step into three or more types according to the distance in the lane width direction. When combining the influence degrees estimated using a plurality of different features respectively, these plurality of different features may be combined to classify the driving scene. For example, when classifying by combining the distance between the preceding vehicle and the host vehicle 1 and whether it is a curved road, it may be classified into a driving scene where the distance is equal to or greater than the threshold value and it is a curved road, a driving scene where the distance is less than the threshold value and it is a curved road, a driving scene where the distance is equal to or greater than the threshold value and it is a straight road, and a driving scene where the distance is less than the threshold value and it is a straight road.

[0047] Here, it should be noted that when the control value is restricted by the familiarity determination unit 31, the state where the influence degree estimated by the influence degree estimation unit 30 is equal to or greater than a predetermined threshold continues. That is, when the control value is restricted, the current driving scene in which the host vehicle 1 is traveling has the same characteristics as the characteristics of the surrounding environment detected at the time of estimating the influence degree. Therefore, by using the characteristics of the surrounding environment used for estimating the influence degree as the characteristics for classifying the driving scene, a predetermined time T can be set according to the driving history of traveling in a driving scene having the same characteristics as the surrounding environment (that is, the surrounding environment that affected the driver's sense of uneasiness) detected at the time of estimating the influence degree.

[0048] Also, for example, the predetermined time T may be set shorter when the number of override operations that occurred when traveling in a driving scene similar to the current driving scene in the past is less than when the number is large. Here, the override operation may be, for example, an intervention operation in which the driver operates the steering wheel during the steering control or steering assist control by the driving support device 10. Also, for example, it may be an intervention operation in which the driver operates the brake during the vehicle speed automatic control by the driving support device 10. It is considered that the driver is less likely to feel uneasy in a driving scene with fewer such override operations, and it is also considered that the driver gets used to the surrounding environment earlier. In the following description, the number of override operations is referred to as the "override count".

[0049] For example, when the override count when traveling in a similar driving scene in the past is equal to or greater than the threshold, the predetermined time T may be set to the initial value T0, and when it is less than the threshold, it may be set to T1 shorter than the initial value T0. Also, for example, the less the override count when traveling in a similar driving scene in the past, the shorter the predetermined time T may be set. The number of overrides that have occurred in the driving scenes in which the host vehicle 1 has traveled in the past may be stored as driving history data 32, for example, in the storage device 17b. The driving history data 32 may be received from the outside by the communication device 16. As the number of overrides, the number of overrides that have occurred during the period from a point in time a predetermined time before the current time to the current time, all the overrides that have occurred so far, or the occurrence frequency of override operations may be stored.

[0050] Also, for example, the predetermined time T may be set shorter when the number of overrides caused by the driver of the host vehicle 1 is smaller than when the number of overrides is larger. For example, when the number of overrides by the driver of the host vehicle 1 is equal to or greater than the threshold value, the predetermined time T may be set to the initial value T0, and when it is less than the threshold value, it may be set to T1, which is shorter than the initial value T0. Also, for example, the shorter the number of overrides by the driver of the host vehicle 1, the shorter the predetermined time T may be set.

[0051] The number of times by the driver of the host vehicle 1 may be stored as driving history data 32, for example, in the storage device 17b. The driving history data 32 may be received from the outside by the communication device 16. As the number of overrides, the number of overrides that have occurred during the period from a point in time a predetermined time before the current time to the current time, all the overrides that have occurred so far, or 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 discriminate the driver boarding the host vehicle 1 and set the predetermined time T according to the number of overrides stored for the driver currently riding in the host vehicle 1. For example, a shorter predetermined time T may be set for a driver with a smaller number of overrides than for a driver with a larger number of overrides.

[0052] The control command value calculation unit 33 generates a target travel trajectory. For example, when the driving support by the driving support device 10 is autonomous driving control, the control command value calculation unit 33 is based on the position and attitude of the host vehicle 1 estimated by the in-map position calculation unit 25, the positions and attitudes of the objects around the host vehicle 1, and the high-precision map, and generates a route space map representing the route around the host vehicle 1 and the presence or absence of objects, and a risk map in which the risk level of the driving field is quantified. The control command value calculation unit 33 generates a driving action plan for automatically driving the host vehicle 1 on the planned travel route based on the planned travel 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 the target travel trajectory for driving the host vehicle 1 based on the driving action plan, the motion characteristics of the host 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 travel trajectory.

[0053] At this time, the control command value calculation unit 33 restricts at least one of the target vehicle speed profile included in the target travel trajectory and the sequence of trajectory points forming the target travel trajectory by the limit value set by the familiarity determination unit 31. For example, the control command value calculation unit 33 restricts the target vehicle speed profile to be equal to or lower than the maximum allowable speed set by the familiarity determination unit 31. Also, for example, the distance in the lane width direction between the objects around the host vehicle 1 and the road boundary when driving on a curved road and the sequence of trajectory points is restricted so as not to be shorter than the lower allowable limit value set by the familiarity determination unit 31. The control command value calculation unit 33 may generate candidates for the target travel trajectory so as to satisfy the limit value set by the familiarity determination unit 31, and among the generated candidates, any optimal trajectory may be selected as the target travel trajectory from among the candidates that satisfy the limit value set by the familiarity determination unit 31.

[0054] The vehicle control unit 27 performs vehicle control based on the target travel trajectory generated by the trajectory generation unit 26. Specifically, it controls the steering actuator of the actuator 18 to travel along the target travel 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 when not generating the host vehicle route, it is also possible to perform control based on the relative distance of an object or the like, and the present invention is not limited thereto.

[0055] FIG. 4 is a flowchart of an example of the driving support method according to the embodiment. In step S1, the object detection unit 20 detects the position, attitude, size, speed, etc. of the objects around the host vehicle 1. In step S2, the detection integration unit 23 integrates the plurality of detection results obtained from each of the plurality of object detection sensors. The object tracking unit 24 tracks each integrated object and predicts the behavior of the objects around the host vehicle 1.

[0056] In step S3, the host vehicle position estimation unit 21 measures the position, attitude, and speed of the host vehicle 1 with respect 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 travels. In step S5, the in-map position calculation unit 25 estimates the position and attitude of the host vehicle 1 on the map. In step S6, the trajectory generation unit 26 executes a target trajectory generation process. The target trajectory generation process is a process of generating a target travel trajectory that is a target value of the travel trajectory of the host vehicle 1. FIG. 5 is a flowchart of an example of the target trajectory generation process.

[0057] In step S10, the influence degree estimation unit 30 estimates the influence degree of the surrounding environment of the host vehicle 1 on the driver's sense of unease. In step S11, the control command value calculation unit 33 calculates the control value of the driving support control. For example, it calculates the trajectory point sequence of the target travel trajectory and the target speed profile as the control values. In step S12, the familiarity determination unit 31 determines whether the influence degree estimated by the influence degree estimation unit 30 is equal to or greater than a threshold value. If the influence degree is equal to or greater than the threshold value (step S12: Y), the process proceeds to step S13. If the influence degree is less than the threshold value (step S12: N), the process proceeds to step S19.

[0058] In step S13, the familiarity determination unit 31 restricts the control value of the driving support control. For example, the familiarity determination unit 31 sets a lower allowable maximum speed for the target vehicle speed profile. Also, for example, the familiarity determination unit 31 sets a longer allowable lower limit value for the lane width direction distance between an object around the host vehicle 1 and the track point sequence when driving on a curved road. In step S14, the familiarity determination unit 31 determines the current driving scene in which the current host vehicle 1 is traveling and accumulates the driving history data 32. For example, the familiarity determination unit 31 updates the number of times of traveling in the same driving scene as the current driving scene. Also, when an override operation occurs, the familiarity determination unit 31 updates the number of override times in the same driving scene as the current driving scene. Also, for example, the familiarity determination unit 31 updates the number of override times of the driver.

[0059] In step S15, the familiarity determination unit 31 sets a predetermined time T based on the driving history data 32. In step S16, the familiarity determination unit 31 calculates the elapsed time since the start of the restriction of the control value. In step S17, the familiarity determination unit 31 determines whether the elapsed time is longer than the 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.

[0060] In step S18, the control command value calculation unit 33 releases the restriction of the control value. For example, the familiarity determination unit 31 returns the allowable maximum speed of the target vehicle speed profile to the value before the restriction. Also, for example, the familiarity determination unit 31 returns the allowable lower limit value of the lane width direction distance between an object around the host vehicle 1 and the track point sequence when driving on a curved road to the value before the restriction. In step S19, the control command value calculation unit 33 restricts the target travel trajectory so as to satisfy the limit value set by the familiarity determination unit 31.

[0061] Referring to 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.

[0062] (Second Embodiment) FIG. 6 is a block diagram of an example of the functional configuration of the controller 17 according to the second embodiment. The same components as those of the controller 17 in the first embodiment are denoted by the same reference numerals. The controller 17 according to the second embodiment estimates the degree of driver's anxiety, which is the degree of anxiety, based on the surrounding environment of the host vehicle 1 obtained from the detection results of sensors such as the object sensor 11. The controller 17 restricts the control value based on the estimated degree of anxiety.

[0063] The controller 17 according to the second embodiment includes a familiarity reflection unit 34. When the degree of influence estimated by the degree of influence estimation unit 30 becomes equal to or greater than a predetermined threshold value, the familiarity reflection unit 34 starts restricting the control value, and estimates the degree of driver's anxiety at the time when the degree of influence becomes equal to or greater than the predetermined threshold value as the initial anxiety degree Ai. For example, the familiarity reflection unit 34 may estimate the initial anxiety degree Ai based on the degree of influence at the time when the degree of influence estimated by the degree of influence estimation unit 30 becomes equal to or greater than a predetermined threshold value. For example, the degree of influence estimated at the time when the degree of influence becomes equal to or greater than the predetermined threshold value may be used as the initial anxiety degree Ai. Based on the estimated initial anxiety degree Ai, the familiarity reflection unit 34 estimates the degree of driver's anxiety of getting used to the surrounding environment over time. For example, the familiarity reflection unit 34 may estimate the degree of anxiety such that the degree of driver's anxiety becomes smaller as the elapsed time since the start of restricting the control value becomes longer. The familiarity reflection unit 34 releases the restriction on the control value according to the estimated degree of driver's anxiety.

[0064] For example, the familiarity reflection unit 34 may release the restriction on the control value when the degree of driver's anxiety becomes equal to or less than the threshold value Ath. Fig. 7(a) is a schematic diagram of an example of the driver's anxiety level calculated by the familiarity reflection unit 34, and Fig. 7(b) is a schematic diagram showing an example of the restriction of the control value based on the anxiety level in Fig. 7(a). When the familiarity determination unit 31 determines that the influence degree has become equal to or higher than a predetermined threshold value at time t0, it starts restricting the control value, and gradually decreases the allowable maximum speed of the target vehicle speed profile from V0 to VL during the period from time t0 to time t1. Further, the familiarity determination unit 31 calculates an initial anxiety level Ai at time t0.

[0065] After that, the familiarity determination unit 31 calculates the driver's anxiety level so as to gradually decrease 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 releasing the restriction of the control value, and gradually increases the allowable maximum speed from VL to V0 during the period from time t2 to time t3. Note that the change speed of the control value when releasing the restriction of the control value when the anxiety level becomes equal to or lower than the threshold value Ath may be made lower than the change speed of the control value when restricting the control value. That is, the time from time t2 to time t3 may be made longer than the time from time t0 to t1.

[0066] The familiarity determination unit 31 may change the change speed of the driver's anxiety level (that is, the change amount of the anxiety level per unit time) according to the driving history of the host vehicle 1 or the driver as time elapses. For example, when the number of driving times in the past in a driving scene similar to the current driving scene is larger than when it is smaller, the change speed may be set higher so that the release of the restriction of the control value starts earlier. Also, for example, the change speed may be set higher when the number of override times when driving in a driving scene similar to the current driving scene in the past is smaller than when it is larger. Also, for example, the change speed may be set higher when the number of override times in which an override operation by the driver of the host vehicle 1 has occurred is smaller than when it is larger. The change speed of the anxiety level in Fig. 8(a) described later may also be changed according to the driving history in the same manner.

[0067] For example, the familiarity 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 reflection unit 34, and FIG. 8(b) is a schematic diagram showing another example of the restriction on the control value based on the anxiety level in FIG. 8(a). When the familiarity determination unit 31 determines that the influence degree has become equal to or higher than a predetermined threshold value at time t0, it starts restricting the control value, and gradually decreases the allowable maximum speed of the target vehicle speed profile from V0 to VL during the period from time t0 to time t1. Further, the familiarity determination unit 31 calculates an initial anxiety level Ai at time t0.

[0068] After that, the familiarity determination unit 31 calculates the driver's anxiety level so as to gradually decrease from the initial anxiety level Ai according to the elapsed time from time t1. The familiarity determination unit 31 gradually releases the restriction on the allowable maximum speed as the driver's anxiety level decreases. That is, the allowable maximum speed is gradually increased 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 restriction on the control value as the driver's anxiety level decreases, the restriction on the control value can be controlled in more detail.

[0069] (Effects of the Embodiment) (1) The object sensor 11 detects the surrounding environment of the host vehicle 1. The controller 17 calculates a control value for driving support control for controlling the running of the host vehicle 1 based on the detected surrounding environment, and controls the host vehicle 1 based on the calculated control value. Further, the controller 17 estimates the influence degree of the detected surrounding environment on the driver's sense of uneasiness, restricts the control value when the estimated influence degree is equal to or higher than the threshold value, 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 where the estimated influence degree is equal to or higher than the threshold value continues. Thereby, it is possible to reduce the driver's sense of uneasiness due to the surrounding environment of the host vehicle 1, and it is possible to suppress a driver who is familiar with the surrounding environment from feeling that the driving of the host vehicle by the driving support control lacks a sense of rhythm and feeling uncomfortable.

[0070] (2) Further, the controller 17 may cancel the restriction of the control value when the state where the estimated influence degree is equal to or greater than the threshold value continues and a predetermined time has elapsed since the start of the restriction of the control value. Thereby, it is possible to suppress a driver who has become accustomed to the surrounding environment over time from feeling a lack of rhythm and discomfort in driving the host vehicle by the driving support control. (3) For each of a plurality of pre-classified driving scenes, the number of driving times that the host vehicle 1 has traveled may be stored in the storage device. The controller 17 determines the driving scene of the host vehicle 1 based on the detected surrounding environment, and sets a shorter predetermined time when the number of stored driving times for the driving scene corresponding to the detected driving scene among the plurality of driving scenes is larger than when it is smaller. Thereby, when the number of times of traveling in a similar driving scene in the past is large and the driver is more likely to get used to it, the cancellation of the restriction of the control value can be accelerated.

[0071] (4) For each of a plurality of pre-classified driving scenes, the number of override operations that have occurred may be stored in the storage device as the override count. The controller 17 determines the driving scene of the host vehicle 1 based on the detected surrounding environment, and sets a shorter predetermined time when the number of stored override counts for the driving scene corresponding to the detected driving scene among the plurality of driving scenes is smaller than when it is larger. Thereby, in a driving scene where the past number of override operations is small and the driver is less likely to feel anxiety, the cancellation of the restriction of the control value can be accelerated.

[0072] (5) The number of override operations performed by the driver may be stored in the storage device as the override count. The controller 17 may set a shorter predetermined time when the stored override count is smaller than when it is larger. Thereby, when the past number of override operations is small and the driver is less likely to feel anxiety about the driving support control, the cancellation of the restriction of the control value can be accelerated. (6) The controller 17 may make the change rate of the control value when releasing the restriction of the control value based on the elapsed time since the start of the restriction of the control value lower than the change rate of the control value when restricting the control value. Thereby, the control value can be released in accordance with the sense of familiarity with the surrounding environment.

[0073] (7) When the estimated influence degree after restricting the control value becomes less than the threshold value, the controller 17 releases the restriction of the control value, and makes the change rate of the control value when releasing the restriction of the control value based on the elapsed time since the start of the restriction of the control value lower than the change rate of the control value when releasing the restriction of the control value due to the estimated influence degree becoming less than the threshold value. Thereby, the control value can be released in accordance with the sense of familiarity with the surrounding environment. (8) The controller 17 may estimate the driver's anxiety level based on the estimated influence degree and the elapsed time since the start of the restriction of the control value in a state where the estimated influence degree is equal to or greater than the threshold value continues, and release the restriction of the control value based on the estimated anxiety level. Thereby, it is possible to suppress a driver who has become accustomed to the surrounding environment over time from feeling a lack of rhythm and experiencing discomfort in the driving of the host vehicle by the driving support control.

[0074] (9) The control value may include the vehicle speed of the host vehicle 1. Thereby, by restricting the vehicle speed according to the surrounding environment, the driver's sense of uneasiness can be reduced, and the restriction can be released when the driver has become accustomed to the state of high vehicle speed. (10) The control value may include a sequence of track points forming the target travel trajectory of the host vehicle 1. The controller 17 may restrict the sequence of track points by increasing the lower limit value of the lateral distance between an object around the host vehicle 1 and the host vehicle 1 or the lateral distance between the host vehicle 1 and the road boundary when traveling on a curved road. Thereby, the driver's sense of uneasiness due to approaching an object around the host vehicle 1 or the road boundary of a curved road can be reduced, and the restriction can be released when the driver has become accustomed to an object around the host vehicle 1 or the road boundary of a curved road.

Description of Signs

[0075] 1…Self-vehicle, 10…Driving support 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…Self-vehicle position estimation unit, 22…Map acquisition unit, 23…Detection integration unit, 24…Object tracking unit, 25…In-map position calculation unit, 26…Trajectory generation unit, 27…Vehicle control unit, 30…Influence degree estimation unit, 31…Familiarity determination unit, 32…Driving history data, 33…Control command value calculation unit, 34…Familiarity reflection unit, 37…Self-vehicle route generation unit

Claims

1. Detect the surrounding environment of the host vehicle, Calculate a control value for driving support control that controls the driving of the host vehicle based on the detected surrounding environment, Control the host vehicle based on the calculated control value, Estimate the degree of influence of the detected surrounding environment on the driver's sense of unease, Restrict the control value when the estimated degree of influence is equal to or greater than a threshold value, and release the restriction of the control value based on the elapsed time since the start of the restriction of the control value when the state where the estimated degree of influence is equal to or greater than the threshold value continues. A driving support method characterized by the above.

2. The driving support method according to claim 1, wherein the restriction of the control value is released when the state where the estimated degree of influence is equal to or greater than a threshold value continues and a predetermined time has elapsed since the start of the restriction of the control value.

3. For each of a plurality of pre-classified driving scenes, store the number of driving times that the host vehicle has traveled, Determine the driving scene of the host vehicle based on the detected surrounding environment, Set a shorter said predetermined time when the number of driving times stored for the driving scene corresponding to the detected driving scene is larger than when it is smaller among the plurality of driving scenes. The driving support method according to claim 2, characterized by the above.

4. For each of a plurality of pre-classified driving scenes, store the number of override operations that have occurred as the override count, Determine the driving scene of the host vehicle based on the detected surrounding environment, Set a shorter said predetermined time when the override count stored for the driving scene corresponding to the detected driving scene is smaller than when it is larger among the plurality of driving scenes. The driving support method according to claim 2, characterized by the above.

5. Store the number of override operations performed by the driver as the override count, The driving support method according to claim 2, characterized in that a shorter said predetermined time is set when the stored override count is smaller than when it is larger.

6. The driving support method according to any one of claims 1 to 5, characterized in that the change speed of the control value when releasing the restriction of the control value based on the elapsed time since the start of the restriction of the control value is made lower than the change speed of the control value when restricting the control value.

7. When the estimated influence degree becomes less than the threshold value after restricting the control value, the restriction of the control value is released. The driving support method according to any one of claims 1 to 6, characterized in that the change rate of the control value when releasing the restriction of the control value based on the elapsed time since the start of the restriction of the control value is made lower than the change rate of the control value when releasing the restriction of the control value due to the estimated influence degree becoming less than the threshold value.

8. Estimate the driver's anxiety level based on the estimated influence degree and the state where the estimated influence degree is equal to or greater than the threshold value continues and the elapsed time since the start of the restriction of the control value. The driving support method according to claim 1, characterized in that the restriction of the control value is released based on the estimated anxiety level.

9. The driving support method according to any one of claims 1 to 8, characterized in that the control value includes the vehicle speed of the host vehicle.

10. The control value includes a sequence of track points forming the target driving trajectory of the host vehicle. The driving support method according to any one of claims 1 to 9, characterized in that the sequence of track points is restricted by increasing the lower limit value of the lateral distance between an object around the host vehicle and the host vehicle or the lateral distance between the host vehicle and the road boundary when driving on a curved road.

11. A sensor for detecting the surrounding environment of the host vehicle. A controller that calculates a control value for driving support control for controlling the driving of the host vehicle based on the detected surrounding environment, controls the host vehicle based on the calculated control value, estimates the influence degree of the detected surrounding environment on the driver's sense of uneasiness, restricts the control value when the estimated influence degree is equal to or greater than the threshold value, and releases the restriction of the control value based on the elapsed time since the start of the restriction of the control value when the state where the estimated influence degree is equal to or greater than the threshold value continues. A driving support device, characterized by comprising the above.

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