Driving assistance device, method, non-transitory storage medium, and vehicle

Abstract

The present disclosure relates to a driving assistance device, a method, a non-transitory storage medium, and a vehicle. The driving assistance device for a vehicle is characterized by including one or more processors configured to: when the vehicle approaches a target existing in a traveling direction of the vehicle and a driver performs deceleration, calculate a perceptual risk estimation value indicating a characteristic of a driving operation of the driver of the vehicle; decide, based on the calculated perceptual risk estimation value, a driving assistance method to be applied to the driver; and execute the decided driving assistance method.

Description

Technical Field

[0001] This disclosure relates to driving assistance devices, methods, non-transitory storage media, and vehicles. Background Technology

[0002] Japanese Unexamined Patent Application Publication No. 2015-130069 (JP 2015-130069 A) discloses a driving assistance device that determines the timing of providing assistance to avoid a collision between the vehicle and a target located in front of the vehicle based on the driver's driving tendencies, lateral distance, and collision distance. Summary of the Invention

[0003] In driver assistance technologies used to help drivers avoid collisions with targets such as another vehicle traveling in front of them, there is a need to provide assistance tailored to the driver's driving characteristics.

[0004] This disclosure provides driving assistance devices, etc., which can perform appropriate driving assistance based on the driving characteristics of the vehicle's driver.

[0005] A first aspect of the technology according to this disclosure is a driving assistance device for a vehicle. The driving assistance device includes one or more processors configured to: calculate a perceived risk estimate indicating the characteristics of the driver's driving operation when the vehicle approaches a target present in the vehicle's direction of travel and the driver performs deceleration; determine a driving assistance method to be applied to the driver based on the calculated perceived risk estimate; and execute the determined driving assistance method.

[0006] A second aspect of the technology according to this disclosure is a method executed by a computer of a vehicle's driving assistance device, the method comprising: when the vehicle approaches a target present in the vehicle's direction of travel and the driver performs deceleration, calculating a perceived risk estimate indicating the characteristics of the driver's driving operation; determining a driving assistance method to be applied to the driver based on the perceived risk estimate; and executing the driving assistance method.

[0007] A third aspect of the technology according to this disclosure is a non-transitory storage medium that stores instructions executable by one or more processors and causing the one or more processors to perform functions, including: when a vehicle approaches a target present in the vehicle's direction of travel and the driver performs deceleration, calculating a perceived risk estimate indicating the characteristics of the driver's driving operation; determining a driving assistance method to be applied to the driver based on the perceived risk estimate; and executing the driving assistance method.

[0008] The aforementioned driving assistance devices and the like, according to this disclosure, can provide appropriate driving assistance based on the driving characteristics of the vehicle's driver. Attached Figure Description

[0009] The features, advantages, and technical and industrial significance of exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, wherein like reference numerals denote like elements, and wherein:

[0010] Figure 1 This is a schematic configuration diagram of a vehicle system including a driver assistance device according to an embodiment;

[0011] Figure 2 It is a flowchart of data collection, learning, and processing performed by the driver assistance device;

[0012] Figure 3 This is a diagram illustrating an example of the relationship between the distance and speed of a vehicle traveling ahead and the vehicle itself;

[0013] Figure 4 This is a flowchart of the driving assistance processes performed by the driving assistance device; and

[0014] Figure 5 This is an example of driver assistance content based on perceived risk estimates (PRE values). Detailed Implementation

[0015] When another vehicle (hereinafter referred to as the "vehicle traveling ahead") is present in the direction of travel of the vehicle being assisted (hereinafter referred to as "this vehicle" when it needs to be distinguished from other vehicles), the driving assistance device according to this disclosure performs steering assistance and deceleration assistance based on the speed and distance perception of the driver of this vehicle, which has been learned to date. This enables the provision of driving assistance that is suitable for the driver of the vehicle and also prioritizes safety.

[0016] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0017] Example

[0018] Configuration

[0019] Figure 1 This is a diagram illustrating a schematic configuration of a vehicle system 1 including a driver assistance device 20 according to an embodiment of the present disclosure. Figure 1 The vehicle system 1 shown includes an external sensor 11, a speed sensor 12, an acceleration sensor 13, a steering angle sensor 14, a driver assistance device 20, a human-machine interface (HMI) control unit 31, a power control unit 32, a steering control unit 33, and a brake control unit 34. The vehicle system 1 can be installed in a vehicle such as an automobile.

[0020] External sensor 11 is a sensor used to detect / acquire information about the vehicle's surroundings. Specifically, external sensor 11 is mounted at the front of the vehicle to detect targets primarily present in the surroundings in front of the vehicle, such as vehicles traveling ahead, two-wheeled vehicles, etc., and to acquire information about the detected targets (type, speed, distance, etc.). Examples that can be used as external sensor 11 include radar sensors using lasers, millimeter waves, microwaves, or ultrasound, and camera sensors using charge-coupled devices (CCDs) or complementary metal-oxide-semiconductor (CMOS). The information about the vehicle's surroundings (information about targets, etc.) detected / acquired by external sensor 11 is output to driver assistance device 20.

[0021] Speed ​​sensor 12 is a sensor used to detect / acquire the speed of a vehicle. Examples that can be used as speed sensor 12 include wheel speed sensors that detect the rotational speed (or amount of rotation) of the wheels and are mounted on each wheel of the vehicle. The vehicle speed detected / acquired by speed sensor 12 is output as information about the vehicle to driver assistance device 20.

[0022] Accelerometer 13 is a sensor used to detect / acquire the magnitude of the acceleration (G-force) experienced by the vehicle. For example, a triaxial accelerometer installed at a predetermined location on the vehicle to detect acceleration in the longitudinal, width, and vertical directions can be used as acceleration sensor 13. The acceleration information detected / acquired by acceleration sensor 13 is output as vehicle-related information to driver assistance device 20.

[0023] Steering angle sensor 14 is a sensor that detects / acquires the steering angle of the steering wheel corresponding to the driver's steering operation. Steering angle sensor 14 is installed, for example, in the vehicle's steering control unit 33. The information about the steering wheel's steering angle detected / acquired by steering angle sensor 14 is output as vehicle-related information to driver assistance device 20.

[0024] The HMI control unit 31 is a device capable of controlling the presentation of information such as the operating status of the driving assistance to the driver of the vehicle based on instructions output from the driving assistance device 20. Various types of devices (omitted from the illustration) such as head-up displays (HUD), navigation system monitors, instrument panels, and speakers are used to present information.

[0025] The power control unit 32 is a device that can control actuators (omitted from the figure) that serve as vehicle power sources, such as internal combustion engines or traction motors, according to instructions output from the driving assistance device 20, in order to control the driving and braking forces generated by each of these power sources.

[0026] The steering control unit 33 is a device that can control the force used to assist vehicle steering, for example, through an electric steering mechanism (omitted from the figure), according to instructions output from the driving assistance device 20.

[0027] The brake control unit 34 is a device that can control the braking force generated at the wheels via the vehicle's braking system according to instructions output from the driving assistance device 20, such as via an electric braking mechanism (omitted in the figure).

[0028] Based on information about the vehicle and its surroundings (such as information about targets) obtained from external sensors 11, speed sensor 12, acceleration sensor 13, steering angle sensor 14, etc., the driver assistance device 20 issues control commands to the HMI control unit 31, power control unit 32, steering control unit 33 and brake control unit 34 to perform appropriate driving assistance to the driver of the vehicle.

[0029] The driving assistance device 20 can typically be configured as part or all of an electronic control unit (ECU) including a processor, memory, input / output interfaces, etc. In this embodiment, the driving assistance device 20 implements the functions of the collection unit 21, calculation unit 22, decision unit 23, and execution unit 24 by reading and executing programs stored in the memory through a processor, which will be described below.

[0030] The collection unit 21 collects driving data from external sensors 11, speed sensor 12, acceleration sensor 13, steering angle sensor 14, etc., including information about the vehicle and its surroundings (information about targets, etc.) required for driving assistance. Details of the driving data will be described later. The calculation unit 22 learns the content of the driver's driving operations (driving characteristics, driving perception) and calculates a perceived risk estimate (PRE) reflecting the learning results regarding the vehicle's forward and backward directions. This perceived risk estimate (PRE) is a quantitative characteristic of the driving operations when the vehicle approaches a vehicle ahead and the driver decelerates to avoid contact. Details of this perceived risk estimate (PRE) will be described later. The decision unit 23, based on the perceived risk estimate (PRE) calculated by the calculation unit 22, determines the content of driving assistance (driving assistance method) suitable for the driver. The determined driving assistance content is suitable for the driver's perception. Details of the method for determining the driving assistance content will be described later. The execution unit 24 assists the driver in driving according to the driving assistance content determined by the decision unit 23.

[0031] control

[0032] Next, we will refer to Figures 2 to 5The processes performed by the driving assistance device 20 according to this embodiment are described. The processes performed by the driving assistance device 20 include data collection and learning processes and driving assistance processes.

[0033] (1) Data collection, learning, and processing

[0034] Figure 2 This is a flowchart illustrating the data collection and learning process performed by the collection unit 21 and the computing unit 22 of the driving assistance device 20. For example, it involves detecting targets such as vehicles traveling ahead in the direction of travel of this vehicle. Figure 2 The example illustrates data collection, learning, and processing.

[0035] Step S201

[0036] When the vehicle approaches a vehicle ahead and the driver begins to decelerate (when approaching a vehicle ahead), the collection unit 21 of the driver assistance device 20 collects vehicle driving data. This deceleration action is the driver's action of interrupting the reduction of distance to the vehicle ahead, and includes actions such as pressing the brake pedal, releasing the accelerator pedal, and downshifting to apply engine braking. The collected vehicle driving data includes at least the vehicle's speed Vs, the relative speed Vr of the vehicle ahead relative to the vehicle, the relative acceleration or deceleration Ar of the vehicle ahead relative to the vehicle, and the distance D between the vehicle and the vehicle ahead in the longitudinal direction. Figure 3 This is a diagram illustrating an example of the relationship between distance and speed between the vehicle traveling on the road and a vehicle (target) moving in the same direction ahead of the vehicle. Vehicle travel data can be, for example, instantaneous values ​​when the vehicle begins to decelerate, or it can be an average value over a predetermined time period after the vehicle begins to decelerate. After collecting vehicle travel data as it approaches the vehicle ahead, the process proceeds to step S202.

[0037] Step S202

[0038] The computing unit 22 of the driving assistance device 20 uses vehicle driving data collected by the collection unit 21 when approaching a vehicle traveling in front to learn the content of the driver's driving operations (driving characteristics, driving perception). According to this embodiment, the computing unit 22 learns each of the driver's speed perception α, the driver's acceleration or deceleration perception β, and the driver's perception n regarding distance prediction in the forward and backward directions. The driver's speed perception α can be considered a parameter representing the difference between the driver's perception of the vehicle's speed and reality. The driver's acceleration or deceleration perception β can be considered a parameter representing the difference between the driver's perception of the relative acceleration or deceleration between the vehicle and the vehicle traveling in front and reality. The driver's perception n regarding distance prediction in the forward and backward directions can be considered a parameter representing the difference between the driver's estimate of the distance between the vehicle and the vehicle traveling in front in the forward and backward directions and reality.

[0039] Based on the correlations between vehicle distance and time of collision (TTC), between vehicle distance and relative speed, and between vehicle distance, speed, and time to start braking, each of these perceptual parameters α, β, and n is obtained as parameters for the individual driver of this vehicle. These correlations can be obtained, for example, by measuring through experimental driving with test drivers or by estimating through simulation.

[0040] When approaching a vehicle traveling ahead, the calculation unit 22 updates the driver's perception values ​​α, β, and n with new values ​​obtained from the vehicle driving data collected in step S201 above, as learned values. Once the driver's perception values ​​α, β, and n are learned, the process proceeds to step S203.

[0041] Step S203

[0042] The calculation unit 22 of the driver assistance device 20 uses the values ​​of the driver's speed perception α, the driver's acceleration or deceleration perception β, and the driver's distance prediction perception n in the forward and backward directions, which were learned and updated in step S202 above, to calculate the perceived risk estimate (PRE value) according to the following expression. When the perceived risk estimate (PRE value) is calculated, the process then proceeds to step S204.

[0043]

[0044] Step S204

[0045] The computing unit 22 of the driver assistance device 20 stores the newly calculated perceived risk estimate (PRE value) from step S203 in association with the driver's information regarding the deceleration action of the vehicle. Note that the driver's identity can be identified using known methods, such as by the unique ID of the electronic key carried by the driver, by adjusting the driver's (seat) position, or by analyzing images from a driver's camera. The data collection and learning process ends when the perceived risk estimate (PRE value) is stored in association with driver information that identifies the individual.

[0046] (2) Driving assistance processing

[0047] Figure 4 This is a flowchart illustrating the procedure for driving assistance processing performed by the decision unit 23 and execution unit 24 of the driving assistance device 20. For example, it involves detecting a target such as a vehicle traveling ahead in the driving direction of this vehicle. Figure 4 The example shown is a driver assistance system.

[0048] Step S401

[0049] The decision unit 23 of the driver assistance device 20 retrieves the perceived risk estimate (PRE value) stored in association with driver information from a predetermined memory or the like. Individual driver identification of the vehicle is performed as described above. Note that when the same driver completes a full journey from ignition-on to ignition-off, each time the perceived risk estimate (PRE value) is calculated in step S203 of the data collection and learning process described above, a newly calculated perceived risk estimate (PRE value) is retrieved during the next vehicle deceleration maneuver. Upon retrieval of the perceived risk estimate (PRE value) associated with the vehicle's driver, the process proceeds to step S402.

[0050] Step S402

[0051] The decision unit 23 of the driver assistance device 20 determines whether the perceived risk estimate (PRE value) obtained in step S401 is high or low. As an example, the decision unit 23 can determine whether the perceived risk estimate (PRE value) is high or low based on whether it is greater than a preset threshold M. This threshold M can be appropriately set based on statistical results from driving data obtained from a large number of drivers.

[0052] When the perceived risk estimate (PRE value) is equal to or less than the threshold M ("Yes" in step S402), it is determined that the driver's acceptance of the safety assistance is high, and the process proceeds to step S403. On the other hand, when the perceived risk estimate (PRE value) is greater than the threshold M ("No" in step S402), it is determined that the driver's acceptance of the safety assistance is low, and the process proceeds to step S404.

[0053] Step S403

[0054] When the perceived risk estimate (PRE value) is equal to or less than the threshold M (PRE≤M), the decision unit 23 of the driver assistance device 20 decides to execute driver assistance as intended for the driver of the vehicle. That is, in this case, it is decided to execute driver assistance for a driver with a “high” acceptance of safety assistance. The execution unit 24 of the driver assistance device 20 then executes the driver assistance determined by the decision unit 23 relative to the driver of the vehicle. Figure 5 The left column shows the driving assistance features when the acceptance level for safety assistance is "high".

[0055] In this embodiment, when the acceptance level of safety assistance is "high," the assistance that performs control to guide the vehicle towards a safe side (i.e., control regarding vehicle safety) has a higher priority than assistance that performs other controls. More specifically, for example, compared to the standard (default) setting or when the acceptance level of safety assistance is "other," control assistance is initiated at an earlier time to increase the amount of assistance (stronger effect) in performing control.

[0056] Furthermore, when the acceptance of safety assistance is "high," assistance from the HMI (i.e., controls related to vehicle safety) used to guide safe driving takes precedence over other HMI assistance. Typical examples of HMI assistance implemented when "high" include using the HMI control unit 31 to control the activation of display notifications via the instrument panel, HUD, etc., controlling the activation of audio notifications via speakers, etc., and using the steering control unit 33 to strongly control tactile notifications that vibrate the steering wheel, etc.

[0057] In addition to the assistance provided by the HMI mentioned above, controls can be implemented to proactively suggest various types of safety-related assistance to the driver of the vehicle when the acceptance of safety assistance is "high".

[0058] When the driver's acceptance of safety assistance is "high", the driving assistance process ends when the driver is given driving assistance.

[0059] Step S404

[0060] When the perceived risk estimate (PRE value) is greater than the threshold M (PRE>M), the decision unit 23 of the driver assistance device 20 decides to execute driver assistance as intended for the vehicle's driver. That is, in this case, it is decided to execute driver assistance for drivers who fall under the "other" category and do not have a high tolerance for safety assistance. The execution unit 24 of the driver assistance device 20 then executes the driver assistance determined by the decision unit 23 relative to the vehicle's driver. Figure 5 The right column shows the driver assistance options available when the driver's acceptance level for safety assistance is set to "Other".

[0061] In this embodiment, when the acceptance level for safety assistance is "Other," the priority of assisting in guiding the vehicle to a safe side (i.e., control regarding vehicle safety) is no higher than that of assisting in performing other controls. More specifically, for example, compared to the standard (default) setting or a "High" acceptance level for safety assistance, control assistance is initiated at a later time to reduce the amount of assistance (weaker effect) performing control. Furthermore, when the acceptance level for safety assistance is "Other," the vehicle's driver can optionally choose whether to implement control assistance to guide the vehicle to a safe side.

[0062] Similarly, when the acceptance level for safety assistance is "Other," the priority of HMI assistance for guiding safe driving (i.e., control over vehicle safety) is no higher than other HMI assistance. More typically, the use of HMI for guiding safe driving can be optionally selected by the vehicle's driver. Typical examples of HMI assistance implemented when "Other" include using the HMI control unit 31 to control the activation of display notifications via the instrument panel, HUD, etc., controlling the deactivation of audio notifications via speakers, etc., and using the steering control unit 33 to subtly control tactile notifications that vibrate the steering wheel, etc.

[0063] When the acceptance level for safety assistance is "Other", the driving assistance process ends when the driver is given driving assistance.

[0064] The aforementioned data collection and learning processing (steps S201 to S204) and driving assistance processing (steps S401 to S404) enable driving assistance to be performed based on the driving characteristics of the driver who prioritizes safety and is suitable for the vehicle.

[0065] Furthermore, as described in this embodiment, driver assistance content based on perceived risk estimate (PRE value) is categorized into two cases: "high" acceptance of safety assistance and "other". However, in addition to this classification example, driver assistance content can be categorized into more than three categories, for example, based on differences in driver driving types obtained through learning driver perception, etc.

[0066] Operation and Effect

[0067] As described above, in the driving assistance device according to embodiments of this disclosure, when a target such as a vehicle traveling ahead is present in the vehicle's direction of travel, and the driver performs a deceleration action while the vehicle approaches the vehicle ahead, the device collects data on the distance between the vehicle and the vehicle ahead in the longitudinal direction, the vehicle's speed, the relative speed of the vehicle ahead relative to the vehicle, and the relative acceleration or deceleration of the vehicle ahead relative to the vehicle. This collected data is then used to learn each parameter in the driver's speed perception α, the driver's acceleration or deceleration perception β, and the driver's perception n regarding distance prediction in the longitudinal direction. Based on the parameters obtained through this learning, the driver's perception of distance and speed is estimated relative to the actual distance and speed, and a driver-specific perceived risk estimate (PRE value) is calculated.

[0068] Using perceived risk estimates (PRE values) calculated in this way to assist driver operations enables the provision of driver assistance that prioritizes safety and is tailored to the driver's driving characteristics (driving perception) based on the vehicle. This prevents drivers from feeling uneasy or annoyed by the uniformity of driver assistance features in conventional configurations and prevents a decline in driver acceptance of driver assistance functions.

[0069] Although embodiments of the present disclosure have been described above, the present disclosure can be understood as a driving assistance device, a method executed by a driving assistance device including a processor and a memory, a control program for executing the method, a computer-readable non-transitory storage medium storing the control program, and a vehicle equipped with a driving assistance device.

[0070] The driving assistance devices and the like disclosed herein can be used in vehicles and the like, and are useful when it is desired to provide appropriate driving assistance based on the driving characteristics of the vehicle's driver.

Claims

1. A driving assistance device for a vehicle, characterized in that it includes one or more processors, said one or more processors being configured to: When the vehicle approaches a target existing in the vehicle's direction of travel and the driver decelerates, based on the distance between the vehicle and the target in the longitudinal direction, the driver's perception of the distance in the longitudinal direction, the vehicle's speed, the relative speed between the vehicle and the target, the relative acceleration or deceleration between the vehicle and the target, the driver's perception of the speed, and the driver's perception of the relative acceleration or deceleration, a perceived risk estimate indicating the characteristics of the driver's driving operation of the vehicle is calculated according to the following expression. The driving assistance method to be applied to the driver is determined based on the calculated perceived risk estimate. as well as The driving assistance method described in the decision; Where PRE is the driver's perceived risk estimate, D is the distance between the vehicle and the target in the longitudinal direction, n is the driver's perception of the distance in the longitudinal direction, Vs is the vehicle's speed, Vr is the relative speed between the vehicle and the target, Ar is the relative acceleration or deceleration between the vehicle and the target, α is the driver's perception of the speed, and β is the driver's perception of the relative acceleration or deceleration. Based on the correlation between the distance between vehicles and the collision time, the correlation between the distance between vehicles and the relative speed, and the correlation between the distance between vehicles, the speed, and the time of initiation of braking, each of the driver's perceptions α, β, and n is obtained as a parameter for the individual driver of the vehicle. The values ​​learned during actual vehicle operation are used to estimate the driver's perception of distance and speed relative to the actual distance and speed; When the perceived risk estimate is equal to or less than a predetermined threshold, control assistance is initiated at an earlier time compared to other controls, and control is executed with an increased amount of assistance. When the perceived risk estimate is greater than a predetermined threshold, the driving assistance method decides not to prioritize the execution of controls related to the safety of the vehicle compared to other controls.

2. The driving assistance device according to claim 1, characterized in that, The one or more processors are configured as follows: When the perceived risk estimate is equal to or less than a predetermined threshold, the driving assistance method is determined to include human-machine interface assistance. and When the perceived risk estimate is greater than a predetermined threshold, the driving assistance method is determined based on the driver's choice of whether or not the driver uses the assistance provided by the human-machine interface.

3. A method executed by a computer of a vehicle's driving assistance device, the method being characterized by comprising: When the vehicle approaches a target existing in the vehicle's direction of travel and the driver decelerates, based on the distance between the vehicle and the target in the longitudinal direction, the driver's perception of the distance in the longitudinal direction, the vehicle's speed, the relative speed between the vehicle and the target, the relative acceleration or deceleration between the vehicle and the target, the driver's perception of the speed, and the driver's perception of the relative acceleration or deceleration, a perceived risk estimate indicating the characteristics of the driver's driving operation of the vehicle is calculated according to the following expression. The driving assistance method to be applied to the driver is determined based on the perceived risk estimate. as well as Execute the driving assistance method; Where PRE is the driver's perceived risk estimate, D is the distance between the vehicle and the target in the longitudinal direction, n is the driver's perception of the distance in the longitudinal direction, Vs is the vehicle's speed, Vr is the relative speed between the vehicle and the target, Ar is the relative acceleration or deceleration between the vehicle and the target, α is the driver's perception of the speed, and β is the driver's perception of the relative acceleration or deceleration. Based on the correlation between the distance between vehicles and the collision time, the correlation between the distance between vehicles and the relative speed, and the correlation between the distance between vehicles, the speed, and the time of initiation of braking, each of the driver's perceptions α, β, and n is obtained as a parameter for the individual driver of the vehicle. The values ​​learned during actual vehicle operation are used to estimate the driver's perception of distance and speed relative to the actual distance and speed; When the perceived risk estimate is equal to or less than a predetermined threshold, control assistance is initiated at an earlier time compared to other controls, and control is executed with an increased amount of assistance. When the perceived risk estimate is greater than a predetermined threshold, the driving assistance method decides not to prioritize the execution of controls related to the safety of the vehicle compared to other controls.

4. A non-transitory storage medium storing instructions executable by one or more processors and causing said one or more processors to perform functions, the functions being characterized by including: When a vehicle approaches a target existing in the vehicle's direction of travel and the driver decelerates, based on the distance between the vehicle and the target in the longitudinal direction, the driver's perception of the distance in the longitudinal direction, the vehicle's speed, the relative speed between the vehicle and the target, the relative acceleration or deceleration between the vehicle and the target, the driver's perception of the speed, and the driver's perception of the relative acceleration or deceleration, a perceived risk estimate indicating the characteristics of the driver's driving operation of the vehicle is calculated according to the following expression. The driving assistance method to be applied to the driver is determined based on the perceived risk estimate. as well as Execute the driving assistance method; Where PRE is the driver's perceived risk estimate, D is the distance between the vehicle and the target in the longitudinal direction, n is the driver's perception of the distance in the longitudinal direction, Vs is the vehicle's speed, Vr is the relative speed between the vehicle and the target, Ar is the relative acceleration or deceleration between the vehicle and the target, α is the driver's perception of the speed, and β is the driver's perception of the relative acceleration or deceleration. Based on the correlation between the distance between vehicles and the collision time, the correlation between the distance between vehicles and the relative speed, and the correlation between the distance between vehicles, the speed, and the time of initiation of braking, each of the driver's perceptions α, β, and n is obtained as a parameter for the individual driver of the vehicle. The values ​​learned during actual vehicle operation are used to estimate the driver's perception of distance and speed relative to the actual distance and speed; When the perceived risk estimate is equal to or less than a predetermined threshold, control assistance is initiated at an earlier time compared to other controls, and control is executed with an increased amount of assistance. When the perceived risk estimate is greater than a predetermined threshold, the driving assistance method decides not to prioritize the execution of controls related to the safety of the vehicle compared to other controls.

5. A vehicle, characterized in that... Includes the driving assistance device according to claim 1 or 2.

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