Driving assistance devices

Abstract

To provide a driving support device capable of setting a driving condition of the own vehicle by performing a risk calculation in consideration of a deviation in a traveling track of the own vehicle due to an external environmental factor.SOLUTION: A driving support device for setting a vehicle driving condition based on a risk map generated by assigning risk potential to a risk object existing around a vehicle, comprises: one or more processors; and one or more memories communicably connected to the one or more processors, in which the processor performs a process that includes: acquiring information on a surrounding environment of the vehicle; acquiring information on an external environmental factor that may cause a deviation of a traveling track of the vehicle; and expanding a setting range of the risk potential of the risk object located in a direction of an assumed deviation based on the information on the external environmental factor.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present disclosure relates to a driving assistance device that sets driving conditions for a vehicle while taking into account risks present around the vehicle. [Background technology]

[0002] Driving assistance devices are known that set a driving trajectory or vehicle speed of a host vehicle by taking into account risks present around the host vehicle. For example, Patent Document 1 proposes a driving assistance device that sets a driving trajectory by taking into account both actual and potential risks. Specifically, Patent Document 1 discloses a driving assistance device that calculates a basic potential indicating a recommended driving position when the host vehicle is driving in accordance with road shape, an explicit potential based on actual risks indicated by risk object information, and a latent potential based on potential risks predicted from a prediction result of the driving scene of the host vehicle, and sets a driving trajectory for the host vehicle based on a potential field obtained by adding together the basic potential, the explicit potential, and the latent potential. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2018-192954 Summary of the Invention [Problem to be solved by the invention]

[0004] Patent Document 1 describes that the magnitude of risk varies depending on factors such as road conditions and weather, and specifically describes that the potential risk of a vehicle suddenly emerging from a blind spot varies depending on whether or not a guardrail is present and the width of the road. However, the driving assistance device in Patent Document 1 does not take into account that the vehicle's driving trajectory may deviate from a control target due to external environmental factors. Specifically, the driving assistance device in Patent Document 1 calculates risk based on actual and potential risks when the vehicle is driving along the control target, but does not consider the risk of the vehicle's driving trajectory deviating due to external environmental factors. Therefore, if the driving conditions of the vehicle are set based on the driving trajectory set by the driving assistance device in Patent Document 1, the vehicle's driving trajectory may deviate due to factors such as icy road surfaces, road gradients, and strong winds, causing the vehicle to approach a risk object or making the vehicle's occupants feel uneasy.

[0005] The present disclosure has been made in consideration of the above-mentioned problems, and an object of the present disclosure is to provide a driving assistance device that can perform risk calculations that take into account deviations in the vehicle's driving trajectory due to external environmental factors and set the driving conditions of the vehicle. [Means for solving the problem]

[0006] In order to solve the above problem, according to one aspect of the present disclosure, there is provided a driving assistance device that sets driving conditions for a vehicle based on a risk map that is generated by assigning risk potentials to risk objects present around the vehicle, the driving assistance device including one or more processors and one or more memories communicably connected to the one or more processors, wherein the processor acquires information about the environment around the vehicle, acquires information about external environmental factors that may cause a deviation in the vehicle's driving trajectory, and, based on the information about the external environmental factors, performs processing that includes expanding a setting range for the risk potential of risk objects located in the direction of the expected deviation.

[0007] Furthermore, in order to solve the above problem, according to another aspect of the present disclosure, there is provided a driving assistance device that sets driving conditions for a vehicle based on a risk map that is generated by assigning risk potentials to risk objects present around the vehicle, the driving assistance device including: an acquisition unit that acquires information on the environment around the vehicle and information on external environmental factors that may cause deviations in the vehicle's driving trajectory; a risk map generation unit that generates a risk map by expanding a setting range of risk potentials for risk objects located in the direction of an expected deviation based on the information on the external environmental factors; and a driving condition setting unit that sets driving conditions for the vehicle based on the risk map. [Effects of the Invention]

[0008] As described above, according to the present disclosure, it is possible to perform risk calculations that take into account deviations in the vehicle's travel trajectory due to external environmental factors, and set the driving conditions of the vehicle. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a schematic diagram illustrating a configuration example of a vehicle equipped with a driving assistance device according to an embodiment of the present disclosure. [Figure 2] 1 is a block diagram showing an example of the configuration of a driving assistance device according to a first embodiment. [Figure 3] FIG. 10 is an explanatory diagram showing an example of a risk potential set for a risk object. [Figure 4] FIG. 10 is an explanatory diagram showing an example of a target trajectory set based on a basic risk potential. [Figure 5] FIG. 10 is an explanatory diagram showing deviation of a running trajectory due to a low friction region. [Figure 6] 3 is an explanatory diagram showing an example of setting a risk potential and a target trajectory by the driving assistance device of the embodiment. FIG. [Figure 7] FIG. 4 is an explanatory diagram showing a risk potential that is set depending on road surface conditions. [Figure 8] 4 is a flowchart showing a processing operation performed by the driving assistance device of the embodiment. [Figure 9]4 is a flowchart showing a processing operation performed by the driving assistance device of the embodiment. [Figure 10] FIG. 10 is an explanatory diagram showing an example of setting a risk potential and a target trajectory by a driving assistance system according to a second embodiment. [Figure 11] 3 is an explanatory diagram showing an example of setting a risk potential and a target trajectory by the driving assistance device of the embodiment. FIG. [Figure 12] 4 is a flowchart showing a processing operation performed by the driving assistance device of the embodiment. [Figure 13] FIG. 10 is a block diagram showing an example of the configuration of a driving assistance device according to a third embodiment. [Figure 14] FIG. 10 is an explanatory diagram showing a risk potential that is set in accordance with the driver's sensitivity to risk objects. [Figure 15] 4 is a flowchart showing a processing operation performed by the driving assistance device of the embodiment. [Figure 16] FIG. 10 is a block diagram showing an example of the configuration of a driving assistance device according to a fourth embodiment. [Figure 17] FIG. 10 is an explanatory diagram showing deviation of a running trajectory due to the influence of wind. [Figure 18] 3 is an explanatory diagram showing an example of setting a risk potential and a target trajectory by the driving assistance device of the embodiment. FIG. [Figure 19] 4 is a flowchart showing a processing operation performed by the driving assistance device of the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In this specification and drawings, components having substantially the same functional configurations are designated by the same reference numerals, and redundant description will be omitted.

[0011] <<1. First Embodiment>> <1-1. Overall configuration of the vehicle> First, an example of the overall configuration of a vehicle to which a driving assistance device according to an embodiment of the present disclosure can be applied will be described.

[0012] Fig. 1 is a schematic diagram showing an example of the configuration of a vehicle 1 equipped with a driving assistance device 50 according to this embodiment. The vehicle 1 shown in Fig. 1 is configured as a four-wheel drive vehicle in which drive torque output from a drive force source 9 that generates drive torque for the vehicle is transmitted to a left front wheel 3LF, a right front wheel 3RF, a left rear wheel 3LR, and a right rear wheel 3RR (hereinafter collectively referred to as "wheels 3" unless a distinction is required). The drive force source 9 may be an internal combustion engine such as a gasoline engine or a diesel engine, a drive motor, or both an internal combustion engine and a drive motor.

[0013] The vehicle 1 may be an electric vehicle equipped with two drive motors, for example, a front-wheel drive motor and a rear-wheel drive motor, or an electric vehicle equipped with drive motors corresponding to each of the wheels 3. If the vehicle 1 is an electric vehicle or a hybrid electric vehicle, the vehicle 1 is equipped with a secondary battery that stores power to be supplied to the drive motors, and a motor or a generator such as a fuel cell that generates power to charge the battery.

[0014] The vehicle 1 is equipped with a driving force source 9, an electric steering device 15, and a brake fluid pressure control unit 20 as devices used to control the operation of the vehicle 1. The driving force source 9 outputs driving torque that is transmitted to the front drive shaft 5F and the rear drive shaft 5R via a transmission, a front wheel differential mechanism 7F, and a rear wheel differential mechanism 7R (not shown). The operation of the driving force source 9 and the transmission is controlled by a vehicle control device 41 that includes one or more electronic control units (ECUs: Electronic Control Units).

[0015] The front-wheel drive shaft 5F is provided with an electric steering device 15. The electric steering device 15 includes an electric motor and a gear mechanism (not shown), and is controlled by a vehicle control device 41 to adjust the steering angles of the left front wheel 3LF and the right front wheel 3RF. During manual driving, the vehicle control device 41 controls the electric steering device 15 based on the steering angle of the steering wheel 13 by the driver. During automatic driving, the vehicle control device 41 controls the electric steering device 15 based on a target steering angle set by the driving assistance device 50.

[0016] The brake system of the vehicle 1 is configured as a hydraulic brake system. A brake fluid pressure control unit 20 adjusts the hydraulic pressure supplied to brake calipers 17LF, 17RF, 17LR, and 17RR (hereinafter collectively referred to as "brake calipers 17" unless a distinction is required) provided on the front, rear, left, and right drive wheels 3LF, 3RF, 3LR, and 3RR, respectively, to generate braking force. The operation of the brake fluid pressure control unit 20 is controlled by a vehicle control device 41. If the vehicle 1 is an electric vehicle or a hybrid electric vehicle, the brake fluid pressure control unit 20 is used in conjunction with regenerative braking using the drive motor.

[0017] The vehicle control device 41 includes one or more electronic control devices that control the drive of the driving force source 9 that outputs the driving torque of the vehicle 1, the electric steering device 15 that controls the steering wheel 13 or the steering angle of the steering wheels, and the brake fluid pressure control unit 20 that controls the braking force of the vehicle 1. The vehicle control device 41 may also have a function of controlling the drive of a transmission that changes the speed of the output output from the driving force source 9 and transmits it to the wheels 3. The vehicle control device 41 is configured to be able to acquire information transmitted from the driving assistance device 50, and is configured to be able to execute automatic driving control of the vehicle 1. Furthermore, when the vehicle 1 is being manually driven, the vehicle control device 41 acquires information on the amount of operation by the driver, and controls the drive of the driving force source 9 that outputs the driving torque of the vehicle 1, the electric steering device 15 that controls the steering wheel 13 or the steering angle of the steering wheels, and the brake fluid pressure control unit 20 that controls the braking force of the vehicle 1.

[0018] The vehicle 1 also has forward-facing cameras 31LF, 31RF, a rear-facing camera 31R, a LiDAR (Light Detection And Ranging) 31S, a road surface detection sensor 33, a vehicle condition sensor 35, a GPS (Global Positioning System) sensor 37, a navigation system 40, and an HMI (Human Machine Interface) 43.

[0019] The front photographing cameras 31LF, 31RF, the rear photographing camera 31R, and the LiDAR 31S constitute a surrounding environment sensor for acquiring information about the surrounding environment of the vehicle 1. The front photographing cameras 31LF, 31RF and the rear photographing camera 31R capture images of the area in front of or behind the vehicle 1 and generate image data. The front photographing cameras 31LF, 31RF and the rear photographing camera 31R are equipped with imaging elements such as CCDs (Charged-Coupled Devices) or CMOSs (Complementary Metal-Oxide-Semiconductors), and transmit the generated image data to the driving assistance device 50.

[0020] 1, the front imaging cameras 31LF, 31RF are configured as stereo cameras including a pair of left and right cameras, and the rear imaging camera 31R is configured as a so-called monocular camera, but each may be either a stereo camera or a monocular camera. In addition to the front imaging cameras 31LF, 31RF and the rear imaging camera 31R, the vehicle 1 may also be equipped with cameras that are provided on the side mirrors 11L, 11R, for example, to capture images of the left rear or right rear.

[0021] The LiDAR 31S transmits optical waves and receives reflected waves of the optical waves, and detects an object and the distance to the object based on the time between transmitting the optical waves and receiving the reflected waves. The LiDAR 31S transmits the detection data to the driving assistance device 50. The vehicle 1 may be equipped with one or more sensors, instead of or in addition to the LiDAR 31S, of a radar sensor such as a millimeter-wave radar and an ultrasonic sensor as a surrounding environment sensor for acquiring information about the surrounding environment.

[0022] The road surface detection sensor 33 is configured to include one or more sensors for detecting the frictional state of the road surface ahead of the vehicle 1. For example, the road surface detection sensor 33 is configured as a composite sensor including multiple sensors from the front imaging cameras 31LF, 31RF, a non-contact temperature sensor, a near-infrared sensor, and a laser light sensor (ToF (Time of Flight) sensor). The road surface detection sensor 33 transmits detection signals to the driving assistance device 50. The detection signals from the front imaging cameras 31LF, 31RF are used as information for detecting the color of the road surface. The detection signal from the non-contact temperature sensor is used as information for detecting at least one of the outside air temperature or the road surface temperature. The detection signal from the near-infrared sensor is used as information for detecting the moisture content of the road surface. The detection signal from the laser light sensor is used as information for detecting the roughness of the road surface.

[0023] The vehicle state sensor 35 is composed of one or more sensors that detect the operating state and behavior of the vehicle 1. The vehicle state sensor 35 includes at least one of a steering angle sensor, an accelerator position sensor, a brake stroke sensor, a brake pressure sensor, or an engine rotation speed sensor, and detects the operating state of the vehicle 1, such as the steering angle of the steering wheel 13 or the steering wheels, the accelerator opening, the amount of brake operation, or the engine rotation speed. The vehicle state sensor 35 also includes at least one of a vehicle speed sensor, an acceleration sensor, or an angular velocity sensor, and detects the behavior of the vehicle, such as the vehicle speed, longitudinal acceleration, lateral acceleration, and yaw rate. The vehicle state sensor 35 transmits sensor signals including the various detected information to the driving assistance device 50.

[0024] The navigation system 40 is a known navigation system that sets a driving route to a destination set by the occupant and notifies the driver of the driving route. A GPS sensor 37 is connected to the navigation system 40, and receives satellite signals from GPS satellites via the GPS sensor 37 to obtain position information on map data of the vehicle 1. Note that instead of the GPS sensor 37, an antenna that receives satellite signals from another satellite system that identifies the position of the vehicle 1 may be used.

[0025] The HMI 43 is driven by the driving assistance device 50 and presents various information to the driver by means of image display, audio output, etc. The HMI 43 includes, for example, a display device provided in the instrument panel and a speaker provided in the vehicle. The display device may have the function of the display device of the navigation system 40. The HMI 43 may also include a head-up display that displays an image on the front window of the vehicle 1.

[0026] <1-2. Driving assistance devices> Next, the driving assistance device 50 according to the first embodiment of the present disclosure will be specifically described.

[0027] (1-2-1. Configuration example) FIG. 2 is a block diagram showing an example of the configuration of the driving assistance device 50 according to this embodiment. The driving assistance device 50 is connected to an ambient environment sensor 31, a road surface detection sensor 33, and a vehicle state sensor 35 via a dedicated line or communication means such as a CAN (Controller Area Network) or a LIN (Local Inter Net). The driving assistance device 50 is also connected to a navigation system 40, a vehicle control device 41, and an HMI 43 via a dedicated line or communication means such as a CAN or a LIN. The driving assistance device 50 is not limited to an electronic control device mounted on the vehicle 1, and may be a terminal device such as a smartphone or a wearable device.

[0028] The driving assistance device 50 includes a control unit 51 and a memory unit 53. The control unit 51 is configured with one or more processors such as CPUs (Central Processing Units). Part or all of the control unit 51 may be configured with updatable firmware or the like, or may be a program module or the like executed by commands from the CPU or the like. The memory unit 53 is configured with memory such as RAM (Random Access Memory) or ROM (Read Only Memory). However, the number and type of memory units 53 are not particularly limited. The memory unit 53 records information such as computer programs executed by the control unit 51, various parameters used in arithmetic processing, detection data, and arithmetic results.

[0029] (1-2-2. Functional configuration) 2, the control unit 51 of the driving assistance device 50 includes an acquisition unit 61, a road surface friction state determination unit 63, a risk map generation unit 65, and a driving condition setting unit 67. Each of these units has a function realized by execution of a computer program by a processor such as a CPU, but some or all of each unit may be configured using analog circuits. Below, the function of each unit of the control unit 51 will be briefly described, and then the processing operation of the control unit 51 will be specifically described.

[0030] (Acquisition Department) The acquisition unit 61 executes a process of acquiring various information related to the host vehicle 1. Specifically, the acquisition unit 61 acquires information on the running state of the host vehicle 1 and information on the surrounding environment of the host vehicle 1 at predetermined calculation intervals, and records this information in the storage unit 53. The information on the running state of the host vehicle 1 includes information on the operation state of the vehicle 1, such as the steering angle of the steering wheel or steered wheels, accelerator opening, brake operation amount, or engine rotation speed, which are detected by the vehicle state sensor 35, and information on the behavior of the vehicle, such as the vehicle speed, longitudinal acceleration, lateral acceleration, and yaw rate.

[0031] Information on the surrounding environment of the host vehicle 1 includes information on the type, size (width, height, and depth), and position of risk objects present around the host vehicle 1, the distance from the host vehicle 1 to the risk objects, and the relative speed between the host vehicle 1 and the risk objects, which are detected by the surrounding environment sensor 31. Risk objects include, for example, other moving vehicles, parked vehicles, pedestrians, bicycles, side walls, curbs, guardrails, buildings, utility poles, traffic signs, traffic signals, natural objects, and any other objects present around the host vehicle 1 that may pose a risk of collision with the host vehicle 1. Information on the surrounding environment of the host vehicle 1 may also include information on road boundaries.

[0032] The information on the surrounding environment of the vehicle 1 also includes information on the friction state of the road surface in the traveling direction of the vehicle 1, which is detected by the road surface detection sensor 33. The information on the friction state of the road surface includes any one or more pieces of information on the road surface color, the outside air temperature or road surface temperature, the road surface moisture content, and the road surface roughness, which are detected by the front imaging cameras 31LF, 31RF and the road surface detection sensor 33.

[0033] (Road surface friction condition determination unit) The road surface friction condition determination unit 63 executes a process of determining the road surface friction condition in the traveling direction of the host vehicle 1 based on the information on the friction condition of the road surface acquired by the acquisition unit 61. The road surface friction condition determination unit 63 determines the road surface friction condition for each area of the road surface in the traveling direction of the host vehicle 1. The road surface friction condition determination unit 63 determines the road surface friction condition, for example, as one of "DRY," "WET," "SNOW," or "ICE." The road surface friction condition determination unit 63 may determine the road surface friction condition for each preset classification, or may generate a road surface friction condition map. Specifically, the road surface friction condition determination unit 63 not only determines the entire area of the road surface in the traveling direction of the host vehicle 1 as one of "DRY," "WET," "SNOW," or "ICE," but also, for example, if puddles, frozen areas, or snow remain on a part of the road surface, determines that area as one of "WET," "SNOW," or "ICE," and determines other areas as "DRY." Furthermore, when the road surface friction condition determination unit 63 determines that the road surface friction condition is "DRY," it may further estimate the road surface material. The road surface material is determined to be, for example, "asphalt," "concrete," or "gravel."

[0034] (Risk map generation section) During autonomous driving of the host vehicle 1, the risk map generation unit 65 executes a process for generating a risk map based on information on basic risk potentials of risk objects present around the host vehicle 1 and information on external environmental factors that may cause deviations in the driving trajectory of the host vehicle 1. In this embodiment, the risk map is generated based on information on road surface friction conditions in the traveling direction of the host vehicle 1 as information on external environmental factors. Specifically, the risk map generation unit 65 sets basic risk potentials for each of the risk objects present around the host vehicle 1. Furthermore, when the road surface condition in the traveling direction is "WET," "SNOW," or "ICE," the risk map generation unit 65 predicts deviations in the driving trajectory of the host vehicle 1 and performs a correction to expand the setting range of the risk potentials of risk objects located in the direction of the predicted deviation. The risk map generation unit 65 then calculates a risk map (potential field) that represents the risk of collision with multiple risk objects by adding up the spatial overlap of the risk potentials of each risk object.

[0035] (Operating condition setting section) The driving condition setting unit 67 sets the driving conditions of the host vehicle 1 based on the information of the risk map generated by the risk map generation unit 65 during autonomous driving of the host vehicle 1. Specifically, the driving condition setting unit 67 sets the driving trajectory that minimizes the risk value as the target trajectory of the host vehicle 1 during autonomous driving of the host vehicle 1. In this embodiment, the driving condition setting unit 67 sets the target vehicle speed of the host vehicle 1 so that the risk value is equal to or less than a predetermined risk threshold. The driving condition setting unit 67 sets the target steering angle and target acceleration / deceleration based on the information of the set target trajectory and target vehicle speed, and transmits this information to the vehicle control device 41. Having received the information on the driving conditions, the vehicle control device 41 controls the operation of each control device based on the information on the set driving conditions.

[0036] <1-3. Setting of driving conditions based on risk potential> (1-3-1. Overview of risk potential) Before describing the specific processing of the driving support device 50, a brief outline of the processing for setting driving conditions based on the risk potential executed by the driving support device 50 will be given.

[0037] Fig. 3 is an explanatory diagram showing the risk potential set for each risk object. Fig. 3 shows an example of the risk potential set for a vehicle. The value of the risk potential (risk value) RP at each coordinate position (x, y) in a two-dimensional coordinate system with the center of gravity (x, y = 0, 0) as the preset reference position of the vehicle 1 is i is maximum at a predetermined reference position (x0, y0) of the risk object (vehicle), and decreases as it moves away from the reference position. The reference position may be, for example, the position of the center of gravity of the vehicle 1 or the risk object when viewed from above. The risk value RP i can be expressed as an exponential function of the distance from the reference position, and is shown, for example, by the following formula (1).

[0038]

number

[0039] RP i :Risk value C i _var:gain σ i :Slope coefficient R: Coefficient representing road surface condition x0, y0: coordinates of the reference position of the risk object i: Numbering to distinguish risk objects

[0040] For example, risk value RP i The maximum value of is set to "1", and the risk value RP i is specified within the range of "0" to "1." However, the risk value RP iThe maximum value of RP may be set for each risk object as a value depending on the type of risk object. For example, if the risk object is a "vehicle" or a "low curb", the risk value RP is set for "vehicle" because the risk of collision with a vehicle is higher than the risk of collision with a low curb. i The maximum value of the risk value RP is set to "low curb" i is set to a value greater than the maximum value of

[0041] Gain C i _var is a weight value set according to the relative relationship between the vehicle 1 and the risk object, and the risk value RP i The maximum value of the risk value RP is specified. i If the maximum value of is "1", the gain C i _var is set within the range of "0" to "1". Gain C i _var is calculated using, for example, either the reciprocal of the shortest distance between the vehicle 1 and the risk object, or the relative speed of the risk object relative to the vehicle 1, or both. The shorter the shortest distance and the greater the relative speed, the greater the gain C i _var will be a large value.

[0042] Gradient coefficient σ i is a coefficient that defines the rate of decrease of the risk potential with respect to the distance from the reference position of the risk object. i may be adjusted according to the type of risk object. In addition, when the risk object is a moving object such as a vehicle, the risk in the direction of travel of the moving object is high, so the risk value RP i The setting range is the backward risk value RP i In this case, the forward risk value RP i The depth may be variable depending on the vehicle speed of the moving object or the relative vehicle speed with respect to the host vehicle 1.

[0043] The coefficient R representing the road surface condition is a coefficient used to expand the setting range of the risk potential of a risk object located in the direction of deviation of the driving trajectory of the host vehicle 1, and is set according to the road surface friction condition as the inverse of the road surface friction coefficient μ. The road surface friction coefficient μ is set within a range of "0" to "1" according to the road surface friction condition determined by the road surface friction condition determination unit 63. The coefficient R representing the road surface condition becomes larger as the road surface friction coefficient μ becomes smaller, that is, as the road surface becomes more slippery, and the setting range of the risk potential is expanded. The coefficient R representing the road surface condition of the risk potential of a risk object other than a risk object located in the direction of deviation of the driving trajectory of the host vehicle 1 is set to "1", and the coefficient R representing the road surface condition of the risk potential of a risk object located in the direction of deviation of the driving trajectory of the host vehicle 1 varies according to the road surface friction coefficient μ.

[0044] When setting the driving conditions of the host vehicle 1 using a risk potential, a risk potential is set for each risk object detected while the host vehicle 1 is traveling, and a risk map (potential field) representing the risks for multiple risk objects is obtained by adding up the spatial overlap of each risk potential. In this case, instead of the sum of the risk values at each coordinate position calculated using the risk potential set for each risk object, the largest risk value among them may be used as the risk value at that coordinate position.

[0045] In a risk map, the level of risk is displayed as contour lines on a two-dimensional plane. As described above, risk values have a two-dimensional distribution, making it possible to select a trajectory that reduces risk. A risk map may be calculated taking into account not only actual risk objects but also potential risks that have not yet been actualized (latent risks). For example, when a vehicle turns and passes through an area that is a blind spot due to an obstruction, a potential risk may be assigned based on the assumption that a pedestrian or vehicle may suddenly appear in the blind spot, and this may be reflected in the risk map.

[0046] In the driving assistance device 50 according to this embodiment, a risk potential is set for each risk object using the above formula (1). For risk objects located in the direction of a deviation estimated based on information about road friction conditions that may cause deviation of the vehicle's driving trajectory, the road friction conditions are reflected in the risk potential, and the setting range of the risk potential is expanded. This makes it easier to set the target trajectory away from the risk object, making it possible to set optimal driving conditions that take deviation of the driving trajectory into consideration.

[0047] (1-3-2. Specific examples of risk potential) Next, a specific example of the risk potential set by the driving assistance device 50 according to this embodiment will be described.

[0048] 4 to 6 show examples of risk potentials and target trajectories that are set when the host vehicle 1 passes by another vehicle (stopped vehicle) 91 that is stopped at the edge of the road ahead in the traveling direction of the host vehicle 1.

[0049] FIG. 4 shows an example in which the road surface condition is ideally dry. An ideal dry condition is, for example, a dry asphalt road surface. When no risk object is present, the target trajectory is basically set on a reference path Tv set, for example, in the center of the lane (dashed line in FIG. 4). When a stopped vehicle 91 is detected ahead of the host vehicle 1 in the traveling direction, the risk map generation unit 65 sets the risk potential RP_ve to the stopped vehicle 91 using the above equation (1). At this time, since there is no area with a low road surface friction coefficient μ between the host vehicle 1 and the stopped vehicle 91, the coefficient R representing the road surface condition in the above equation (1) is set to 1, and the risk potential RP_ve is set. The driving condition setting unit 67 sets the target trajectory T1 so that the host vehicle 1 passes through position P1 where the risk value is lowest in order to avoid a collision with the stopped vehicle 91. For example, the driving condition setting unit 67 sets the target trajectory T1 to pass through position P1 so that the steering angular velocity does not exceed a predetermined threshold.

[0050] 5 and 6 show an example in which a frozen portion (hereinafter also referred to as an "ICE area") 93 exists on a portion of the road surface between the host vehicle 1 and a stopped vehicle 91. For example, the "ICE area" 93 can occur when a puddle that existed on the road surface freezes. When the road surface condition of a portion of the road ahead of the host vehicle 1 in the traveling direction is determined to be "ICE," the risk map generating unit 65 predicts the direction of deviation of the traveling trajectory due to the "ICE area" 93. When the host vehicle 1 travels on the target trajectory T1 shown in FIG. 4 above, as shown in FIG. 5, it is predicted that the wheels of the host vehicle 1 will pass over the "ICE area" 93 just before passing by the stopped vehicle 91, causing the wheels to slip and causing the host vehicle 1 to head toward the stopped vehicle 91.

[0051] When setting the risk potential RP_ve for a stopped vehicle 91 located in the direction of deviation from the travel trajectory, the risk map generator 65 sets the coefficient R, which represents the road surface condition in the above equation (1), according to the road surface friction coefficient μ, and sets the risk potential RP_ve_r1. As a result, the setting range for the risk potential RP_ve_r1 is expanded compared to the setting range for the risk potential RP_ve, as shown in Figure 6. As a result, the target trajectory T2 is set so that the stopped vehicle 91 passes through position P2, which is farther away from the stopped vehicle 91 than position P1 shown in Figure 4.

[0052] In the above formula (1), the gradient that defines the rate of decrease of the risk potential with respect to the distance from the coordinates x0, y0 of the reference position of the risk object is set to depend on the square of the coefficient R (the inverse of the road friction coefficient μ) that represents the road surface condition. Therefore, the smaller the road friction coefficient μ, the gentler the gradient becomes, and the risk potential RP i Specifically, as shown in FIG. 7, the setting range of the risk potential RP_ve_r1 set for the stopped vehicle 91, reflecting the road surface conditions, increases in the order of "DRY," "WET," "SNOW," and "ICE." Therefore, the more slippery the road surface, the farther the target trajectory is set to pass through a position away from the stopped vehicle 91, thereby reducing the risk of a collision with the stopped vehicle 91.

[0053] <1-4. Operation of driving assistance devices> Next, an example of the operation of the driving assistance device 50 according to this embodiment will be specifically described.

[0054] 8 and 9 are flowcharts showing an example of the processing operation of the driving support device 50. FIG. First, when the in-vehicle system including the driving assistance device 50 is started (step S11), the acquisition unit 61 acquires information on the driving state of the vehicle 1 (step S13). Specifically, based on the detection signal transmitted from the vehicle state sensor 35, the acquisition unit 61 acquires information on the operation state of the vehicle 1, such as the steering angle of the steering wheel or steering wheels, the accelerator opening, the brake operation amount, or the engine rotation speed, as well as information on the behavior of the vehicle, such as the vehicle speed, longitudinal acceleration, lateral acceleration, and yaw rate.

[0055] Next, the acquisition unit 61 acquires information on the surrounding environment of the vehicle 1 (step S15). Specifically, the acquisition unit 61 acquires information on the type, size (width, height, and depth), and position of risk objects present around the vehicle 1, the distance from the vehicle 1 to the risk objects, and the relative speed between the vehicle 1 and the risk objects, based on the detection signal transmitted from the surrounding environment sensor 31. The acquisition unit 61 may also acquire information on boundary lines on the road based on the detection signal transmitted from the surrounding environment sensor 31. Note that the acquisition unit 61 may acquire one or more pieces of information on the surrounding environment from an external device via vehicle-to-vehicle communication, road-to-vehicle communication, or mobile communication.

[0056] The acquisition unit 61 also acquires information about the friction state of the road surface in the traveling direction of the vehicle 1 based on detection signals transmitted from the ambient environment sensor 31 and the road surface detection sensor 33. Specifically, the acquisition unit 61 acquires information about the color of the road surface in the traveling direction of the vehicle 1 based on detection signals transmitted from the forward-facing cameras 31LF and 31RF. The acquisition unit 61 also acquires information about the outside air temperature or road surface temperature based on detection signals transmitted from the non-contact temperature sensor included in the road surface detection sensor 33. The acquisition unit 61 also acquires information about the moisture content of the road surface based on detection signals from the near-infrared sensor included in the road surface detection sensor 33. More specifically, when near-infrared rays are irradiated onto the road surface, if the road surface is highly moist, the amount of near-infrared rays reflected is small, whereas if the road surface is less moist, the amount of near-infrared rays reflected is large. Therefore, the acquisition unit 61 can acquire the moisture content of the road surface based on the detection signals from the near-infrared sensor. The acquisition unit 61 also acquires information about the roughness of the road surface based on detection signals from the laser light sensor included in the road surface detection sensor 33. More specifically, the acquisition unit 61 acquires information about the roughness of the road surface ahead of the vehicle 1 based on the time from when the laser light is emitted until the reflected light is detected. Information about the friction state of the road surface acquired based on the detection signals transmitted from the front imaging cameras 31LF, 31RF and the road surface detection sensor 33 is associated with information about relative positions defined by the direction and distance as seen from the vehicle 1.

[0057] Next, the road surface friction condition determination unit 63 determines the road surface friction condition in the traveling direction of the vehicle 1 based on the information on the friction condition of the road surface acquired by the acquisition unit 61 (step S17). For example, the road surface friction condition determination unit 63 determines the road surface condition as one of "DRY", "WET", "SNOW", or "ICE" based on the information on the friction condition of the road surface. For example, the road surface friction condition determination unit 63 may determine the road surface condition using a known three-dimensional map in which normalized values of the road surface temperature or outside air temperature, road surface roughness, and road surface moisture content are used as parameters.

[0058] Furthermore, when the road surface friction condition determination unit 63 determines the road surface condition as "DRY," it compares the image data transmitted from the forward-facing cameras 31LF, 31RF with pre-recorded image data of "asphalt," "concrete," and "gravel" to determine the degree of similarity with each data and determine the road surface material. The image data of "asphalt" to be compared may be subdivided into image data of "new pavement," "normal pavement," "pavement wear," and "excessive tar." The image data of "concrete" to be compared may be subdivided into image data of "new pavement," "normal pavement," and "pavement wear." The image data of "gravel" to be compared may be subdivided into image data of "simple pavement" and "fine gravel." Even when the road surface friction condition determination unit 63 determines the road surface condition as "SNOW" or "ICE," it may perform a matching process based on the acquired image data and further determine the road surface condition. The road surface condition determination may be performed using a machine learning learning model.

[0059] The road surface friction condition determination unit 63 reflects the determined road surface condition in a database in which a relationship between the road surface condition and the road surface friction coefficient μ is preset, and calculates the road surface friction coefficient μ. For example, the database of road surface friction coefficient μ may be one in which the road surface friction coefficient μ is preset according to the subdivided road surface condition items.

[0060] As described above, each piece of information relating to the friction state of the road surface is associated with information on the relative position of the vehicle 1, and the road surface friction state determination unit 63 divides the road surface ahead in the traveling direction of the vehicle 1 into regions according to the road surface state or the road surface friction coefficient μ. For example, the road surface friction state determination unit 63 sets regions such as a "DRY region," a "WET region," a "SNOW region," and an "ICE region" for each road surface state or road surface friction coefficient μ. More detailed region setting may also be performed.

[0061] Next, the risk map generation unit 65 generates a risk map based on the information on the surrounding environment acquired by the acquisition unit 61 (step S19). Specifically, the risk map generation unit 65 sets a risk potential for each detected risk object using the above formula (1), and generates a basic risk map (potential field) representing the risk for multiple risk objects by adding up the spatial overlap of each risk potential (see FIG. 4). In step S19, the risk potential is set with the coefficient R representing the road surface condition set to "1" regardless of the road surface condition, and the basic risk map is generated.

[0062] Next, the driving condition setting unit 67 sets driving conditions for the host vehicle 1 based on the generated basic risk map and information on the driving state of the host vehicle 1 (step S21). Specifically, the driving condition setting unit 67 sets a target trajectory and a target vehicle speed based on information on a reference path set in the center of a lane, for example, and the generated basic risk map, so that the host vehicle 1 passes through a position where the risk value is lowest. In this case, the driving condition setting unit 67 may set the target trajectory based on information on the current traveling direction, steering angle, vehicle speed, and acceleration / deceleration of the host vehicle 1 so that the steering angular velocity does not exceed a preset threshold.

[0063] Next, the driving condition setting unit 67 predicts a deviation in the traveling direction of the host vehicle 1 due to slippage when the host vehicle 1 travels along the target trajectory, based on the information on the road surface condition and the road surface friction coefficient μ calculated by the road surface friction state determination unit 63 (step S23). For example, the driving condition setting unit 67 determines whether the host vehicle 1 will pass through a "wet area," a "snow area," or an "ice area" when traveling along the set target trajectory, and predicts a deviation in the traveling direction of the host vehicle 1 (see FIG. 5). For example, the driving condition setting unit 67 determines which wheels of the host vehicle 1 will pass through the "wet area," the "snow area," or the "ice area," and predicts the possibility of the host vehicle 1 slipping and a deviation in the traveling direction due to the slippage based on information on the steering angle, vehicle speed, and acceleration / deceleration when the wheels pass through the "wet area," the "snow area," or the "ice area." At this time, the slippage may be predicted using the road surface friction coefficient μ of each of the "wet area," the "snow area," and the "ice area." Furthermore, if the vehicle 1 is provided with a function for estimating the state of tire wear, the state of tire wear may be used to predict slippage.

[0064] Next, the driving condition setting unit 67 determines whether or not there is a risk object in the direction of deviation of the traveling direction of the vehicle 1 (step S25). For example, the driving condition setting unit 67 determines whether or not there is a risk object that overlaps with the traveling direction of the vehicle 1 when the vehicle 1 moves in the direction of deviation of the predicted traveling direction.

[0065] If there is no risk object in the direction of deviation of the vehicle's 1 direction of travel (S25 / No), the driving condition setting unit 67 proceeds directly to step S31, sets the target steering angle and target acceleration / deceleration based on the information on the target trajectory and target vehicle speed set in step S21, and transmits this information to the vehicle control device 41 (step S31).

[0066] On the other hand, if a risk object exists in the direction of deviation of the vehicle 1's traveling direction (S25 / Yes), the risk map generation unit 65 performs a correction to expand the setting range of the risk potential of the risk object existing in the direction of deviation of the vehicle's traveling direction (step S27). Specifically, the risk map generation unit 65 sets a coefficient R representing the road surface condition of the risk potential of the corresponding risk object based on the road surface friction coefficient μ of the "WET area," "SNOW area," or "ICE area," which is a factor in the deviation of the vehicle's traveling direction. The risk map generation unit 65 sets the risk potential of the risk object according to the above formula (1) using the coefficient R representing the set road surface condition. As a result, at least the setting range of the risk potential is expanded (see FIG. 6).

[0067] Next, the driving condition setting unit 67 sets the driving conditions of the host vehicle 1 based on the risk map reflecting the corrected risk potential and information on the driving state of the host vehicle 1 (step S29). Specifically, the driving condition setting unit 67 sets a target trajectory and a target vehicle speed in accordance with the processing of step S21 so that the host vehicle 1 passes through a position where the risk value is lowest. The set target trajectory is set so that the host vehicle 1 passes through a position farther away from the risk object than the target trajectory set in step S21.

[0068] Next, the driving condition setting unit 67 sets a target steering angle and a target acceleration / deceleration based on the information on the set target trajectory and target vehicle speed, and transmits this information to the vehicle control device 41 (step S31).

[0069] Next, the driving condition setting unit 67 determines whether the in-vehicle system has stopped (step S33). If the in-vehicle system has stopped (S33 / Yes), the processing by the control unit 51 ends. On the other hand, if the in-vehicle system has not stopped (S33 / No), the process returns to step S13 and the processing of each step described so far is repeatedly executed.

[0070] As described above, when a low-friction area with a low road surface friction coefficient μ exists ahead of the host vehicle 1 in the traveling direction, the driving assistance device 50 according to the first embodiment of the present disclosure predicts a deviation in the traveling direction of the host vehicle 1 due to the low-friction area. Furthermore, when a risk object exists in the direction of deviation of the traveling direction of the host vehicle 1, the driving assistance device 50 expands the set range of the risk potential of the risk object. As a result, the target trajectory (T2) is set so as to pass through a position farther away from the risk object than the target trajectory (T1) set based on the risk potential before expansion. Therefore, even if the host vehicle 1 slips due to the low-friction area, the risk of collision with the risk object can be reduced.

[0071] Furthermore, the driving assistance device 50 according to this embodiment sets the risk potential of the risk object using the above formula (1), in which the road surface friction coefficient μ is reflected in the gradient of the risk potential. Therefore, the setting range of the risk potential is expanded according to the road surface condition in the low friction region, and it is possible to prevent the difference between the target trajectory before and after correction from becoming excessively large.

[0072] <<2. Second Embodiment>> Next, a driving assistance device according to a second embodiment of the present disclosure will be described. In the driving assistance device according to the second embodiment, the setting range of the risk potential of a risk object existing in the deviation direction of the traveling direction of the host vehicle 1 is expanded, and the risk map is corrected according to the condition of the road surface in the traveling direction. Specifically, if the road surface friction coefficient of a part of the road surface in the traveling direction of the host vehicle 1 is lower than the friction coefficient of the surrounding road surfaces, the risk potential is also set for the road surface in that part of the road surface. Furthermore, if the road surface friction coefficient of the entire traveling direction of the host vehicle 1 is lower than the friction coefficient of the surrounding road surfaces, the value of the risk potential of the risk object existing in the deviation direction of the traveling direction is increased.

[0073] The driving assistance device according to this embodiment can have the same configuration as the example configuration of the driving assistance device 50 according to the first embodiment shown in Fig. 2. Hereinafter, specific processing of the driving assistance device 50 according to this embodiment will be described, focusing mainly on differences from the first embodiment.

[0074] <2-1. Specific examples of risk potential> 10 and 11 show examples of risk potentials and target trajectories generated by the driving assistance device 50 according to this embodiment. The examples of risk potentials and target trajectories shown in FIGS. 10 and 11 correspond to the example of risk potentials and target trajectories set in the scene shown in FIG.

[0075] Similar to the example shown in FIG. 6, FIG. 10 shows an example in which an "ICE area" 93 exists on part of the road surface between the host vehicle 1 and the stopped vehicle 91. In this case, as shown in FIG. 5, it is predicted that the wheels of the host vehicle 1 will pass over the "ICE area" 93 before passing beside the stopped vehicle 91, causing the wheels to slip and causing the host vehicle 1 to head towards the stopped vehicle 91. For this reason, as shown in FIG. 6, the risk map generation unit 65 corrects the setting range of the risk potential RP_ve for the stopped vehicle 91 in the direction of the deviation from the traveling trajectory by expanding it, and sets the corrected risk potential RP_ve_r1.

[0076] In this embodiment, when a part of the road surface is determined to be a "wet area," "snow area," or "ice area" (hereinafter, these may be collectively referred to as "low friction area"), the risk map generating unit 65 also sets the risk potential RP_rd for the "low friction area." i On the other hand, the risk potential RP_rd set for the low friction region is expressed by, for example, the following equation (2).

[0077]

number

[0078] RP_rd: Risk value for low friction region v0: Coefficient representing the speed of vehicle 1 R: Coefficient representing road surface condition x 0r , y 0r : Coordinates of the low friction area

[0079] The coefficient v0 representing the vehicle speed of the host vehicle 1 is standardized in accordance with a preset standard and is defined within the range of "0" to "1." The coefficient v0 representing the vehicle speed of the host vehicle 1 becomes larger as the vehicle speed increases. The coordinate x of the low friction region 0r , y 0r may be the coordinate of the position where the road surface friction coefficient μ is the minimum value in the low friction region. Alternatively, the coordinate x 0r , y 0r may be the position of the center of gravity when the low friction region is viewed from above.

[0080] The risk map generator 65 uses the above equation (2) to set the risk potential RP_rd also for road surfaces in areas where the road surface friction coefficient μ is smaller than the friction coefficients of the surrounding road surfaces. As a result, as shown in Figure 10, the target trajectory T3 is set so as to avoid the "ICE area" 93, and the target trajectory T3 is set so as to pass through a position P3 that is further away from the stopped vehicle 91 than the position P2 shown in Figure 6. This also reduces the possibility that the wheels of the host vehicle 1 will pass through the "ICE area" 93, further reducing the risk of a collision with the stopped vehicle 91 due to slippage.

[0081] In the above formula (2), the gradient that defines the rate of decrease of the risk potential with respect to the distance from the coordinates x0, y0 of the low friction area is set to depend on the square of the coefficient R (the inverse of the road friction coefficient μ) that represents the road surface condition. Therefore, the smaller the road friction coefficient μ, the gentler the gradient becomes, and the risk potential RP i Therefore, the more likely the low-friction area is to slip, the more the target trajectory is set to pass through a position farther away from the coordinates x0, y0 of the low-friction area, thereby reducing the risk of slipping when the wheels pass through the low-friction area.

[0082] FIG. 11 shows an example in which an "ICE area" is formed on the entire road surface ahead of the host vehicle 1 in the direction of travel. In this case, no matter which trajectory the host vehicle 1 follows, there is a risk of the wheels slipping and colliding with a stopped vehicle 91. For this reason, in this embodiment, when the overall road surface friction coefficient μ ahead of the host vehicle 1 is smaller than the road surface friction coefficient μ of a dry road surface, the risk map generation unit 65 increases the value of the risk potential of a risk object located in the direction of the expected deviation. When the road surface friction coefficient μ of the entire road surface ahead of the host vehicle 1 in the direction of travel is small, the risk potential RP i is expressed by, for example, the following formula (3).

[0083]

number

[0084] RP i :Risk value C i _var:gain σ i :Slope coefficient R: Coefficient representing road surface condition x0, y0: coordinates of the reference position of the risk object i: Numbering to distinguish risk objects

[0085] Compared with the above formula (1), the risk value RP in the above formula (3) i The gain C determines the maximum value of i _var is multiplied by a coefficient R that represents the road surface condition. The coefficient R that represents the road surface condition becomes larger as the road surface friction coefficient μ becomes smaller, that is, the more slippery the road surface is. Therefore, the smaller the road surface friction coefficient μ, the lower the risk value RP i In addition, in the above formula (3), the gradient coefficient σ , which defines the rate of decrease of the risk potential with respect to the distance from the reference position of the risk object, is used. i The risk potential RP is set to be multiplied by the cube of the coefficient R, which represents the road surface condition. Therefore, the smaller the road friction coefficient μ, the gentler the gradient, and thei The setting range will be expanded.

[0086] Therefore, as shown in FIG. 11, the set risk potential RP i _ve_r2 is generally larger than the basic risk potential RPi shown in Figure 4. As a result, the target trajectory T4 is set so that the vehicle passes through position P4, which is further away from the stopped vehicle 91 than position P3 shown in Figure 10. Furthermore, the more slippery the road surface is, the more the target trajectory is set so that the vehicle passes through a position farther away from the stopped vehicle 91. In this way, when the road surface friction coefficient μ of the entire road surface is small, the risk of a collision with the stopped vehicle 91 can be further reduced.

[0087] <2-2. Operation of driving assistance device> Next, an example of the operation of the driving assistance device 50 according to this embodiment will be specifically described.

[0088] Fig. 12 is a flowchart showing an example of the processing operation of the driving support device 50. The flowchart shown in Fig. 12 can be replaced with Fig. 9 showing an example of the operation of the driving support device 50 according to the first embodiment, and the processing operation of the driving support device 50 according to this embodiment is shown in Fig. 8 and Fig. 12. More specifically, in this embodiment, the processing of steps S41 to S47 shown in the flowchart of Fig. 12 is executed instead of the processing of step S27 shown in the flowchart of Fig. 9.

[0089] The driving assistance device 50 executes the processes from step S11 to step S23 shown in Fig. 8 in accordance with the procedure described in the first embodiment. Next, the driving condition setting unit 67 determines whether or not a risk object exists in the direction of deviation of the traveling direction of the host vehicle 1 (step S25). If a risk object does not exist in the direction of deviation of the traveling direction of the host vehicle 1 (S25 / No), the driving condition setting unit 67 proceeds directly to step S31, sets a target steering angle and a target acceleration / deceleration based on the information on the target trajectory and target vehicle speed set in step S21, and transmits this information to the vehicle control device 41 (step S31).

[0090] On the other hand, if a risk object exists in the direction of deviation of the traveling direction of the host vehicle 1 (S25 / Yes), the risk map generation unit 65 determines whether or not the entire road surface in the traveling direction of the host vehicle 1 is a low-friction area based on the determination result of step S17 (step S41). For example, the risk map generation unit 65 may determine whether or not the entire road surface within a predetermined range of distance from the host vehicle 1 ahead of the road on which the host vehicle 1 is traveling is a low-friction area. The risk map generation unit 65 may also determine whether or not a predetermined percentage (e.g., 80%) or more of the road surface within the predetermined range is a low-friction area.

[0091] If the entire road surface in the traveling direction of the host vehicle 1 is not a low friction area (S41 / No), the risk map generation unit 65 performs a correction to expand the setting range of the risk potential of the risk object existing in the direction of deviation from the traveling direction (step S43), similar to the processing of step S27 described in the first embodiment. Specifically, the risk map generation unit 65 sets the risk potential of the risk object using the above formula (1) using the coefficient R that represents the road surface condition of the low friction area.

[0092] Next, the risk map generation unit 65 sets a risk potential for the low friction region (step S45). Specifically, the risk map generation unit 65 sets the risk potential for the low friction region using the coefficient R that represents the road surface condition of the low friction region, according to the above formula (2).

[0093] On the other hand, if the entire road surface in the traveling direction of the vehicle 1 is a low friction area (S41 / Yes), the risk map generation unit 65 increases the risk value of the risk potential of the risk object existing in the direction of deviation from the traveling direction and corrects it by expanding the setting range (step S47). Specifically, the risk potential of the risk object is set by the above formula (3) using the coefficient R that represents the road surface condition of the low friction area.

[0094] Next, the driving condition setting unit 67 sets the driving conditions of the vehicle 1 based on the risk map reflecting the corrected risk potential and information on the driving state of the vehicle 1 (step S29). Next, the driving condition setting unit 67 sets the target steering angle and target acceleration / deceleration based on the information on the set target trajectory and target vehicle speed, and transmits this information to the vehicle control device 41 (step S31).

[0095] Next, the driving condition setting unit 67 determines whether the in-vehicle system has stopped (step S33). If the in-vehicle system has stopped (S33 / Yes), the processing by the control unit 51 ends. On the other hand, if the in-vehicle system has not stopped (S33 / No), the process returns to step S13 and the processing of each step described so far is repeatedly executed.

[0096] As described above, the driving assistance device 50 according to the second embodiment of the present disclosure, like the driving assistance device 50 according to the first embodiment, expands the setting range of the risk potential of a risk object when a risk object exists in the direction of deviation from the traveling direction of the host vehicle 1. Furthermore, when a low-friction area is formed on a portion of the road surface ahead in the traveling direction of the host vehicle 1, the driving assistance device 50 according to this embodiment sets the risk potential for the low-friction area. As a result, the target trajectory (T3) is set so that the host vehicle 1 passes through a position further away from the risk object compared to the target trajectory (T1) set based on the risk potential before expansion. Furthermore, the risk of the wheels of the host vehicle 1 passing through the low-friction area is reduced, and the possibility of the host vehicle 1 slipping can be reduced. Therefore, the risk of a collision with the risk object can be reduced.

[0097] Furthermore, when the entire road surface ahead of the host vehicle 1 in the traveling direction is a low-friction area, the driving assistance device 50 according to this embodiment increases the risk potential of the risk object and expands the setting range of the risk potential. This causes the target trajectory (T4) to be set so that the host vehicle 1 passes through a position further away from the risk object. This reduces the risk of collision with the risk object.

[0098] Furthermore, the driving assistance device 50 according to this embodiment sets the risk potential in the low-friction region using the above-mentioned formula (2), in which the road surface friction coefficient μ is reflected in the gradient of the risk potential. Therefore, the setting range of the risk potential is expanded depending on the road surface condition in the low-friction region, and it is possible to prevent the difference between the target trajectory before and after correction from becoming excessively large.

[0099] Furthermore, the driving assistance device 50 according to this embodiment sets the risk potential of the risk object using the above-mentioned formula (3), in which the road surface friction coefficient μ is reflected in the maximum value and gradient of the risk potential. Therefore, the risk potential is increased and the setting range is expanded according to road surface conditions in the low friction region, making it possible to prevent the difference between the target trajectory before and after correction from becoming excessively large.

[0100] <<3. Third Embodiment>> Next, a driving assistance device according to a third embodiment of the present disclosure will be described. The driving assistance device according to the third embodiment is configured to further correct the risk potential of each risk object based on the sensitivity of the driver of the vehicle 1 to the risk object in the driving assistance device 50 according to the first or second embodiment, and to reflect this in the risk map. Below, the driving assistance device according to this embodiment will be described, focusing mainly on the differences from the first and second embodiments.

[0101] <3-1.Configuration example> FIG. 13 is a block diagram showing an example of the configuration of a driving assistance device 70 according to this embodiment. The driving assistance device 70 includes a control unit 51 and a storage unit 53. The surrounding environment sensor 31, road surface detection sensor 33, and vehicle state sensor 35 are connected to the driving assistance device 70 via a dedicated line or communication means such as CAN or LIN. The navigation system 40, vehicle control device 41, and HMI 43 are also connected to the driving assistance device 70 via a dedicated line or communication means such as CAN or LIN. The driving assistance device 70 according to this embodiment is further connected to an in-vehicle camera 39, a driver database 71, and a driving characteristics database 73 via a dedicated line or communication means such as CAN or LIN.

[0102] The interior camera 39 is positioned so as to be able to capture an image of the driver of the vehicle 1 and is used to identify the driver. The interior camera 39 is equipped with an imaging element such as a CCD or CMOS, captures an image of the interior of the vehicle, and generates image data. The interior camera 39 transmits the generated image data to the driving assistance device 70. Only one interior camera 39 may be installed, or multiple interior cameras 39 may be installed. However, the means for identifying the driver is not limited to the interior camera 39, and the device may be configured so that the driver can register identification information in the driving assistance device 70, for example.

[0103] <3-2. Database> The driving assistance device 70 is communicatively connected to a driver database 71 and a driving characteristics database 73. The driver database 71 and the driving characteristics database 73 are each configured by a memory element such as a RAM, or an updatable recording medium such as a hard disk drive (HDD), a compact disk (CD), a digital versatile disk (DVD), a solid state drive (SSD), a USB flash drive, or a storage device. However, the type of recording medium is not particularly limited. One or all of the driver database 71 and the driving characteristics database 73 may be mounted on the vehicle 1, or may be stored on a server that can communicate with the driving assistance device 50 via wireless communication means such as mobile communication. Alternatively, each database may be configured as a single database.

[0104] (driver database) The driver database 71 is a database that records identification information for identifying the driver of the vehicle 1. The identification information may be, for example, an identification number or an identification symbol. However, the identification information is not limited to the above examples.

[0105] (Driving characteristics database) The driving characteristics database 73 is a database that records information on the sensitivity of each driver to each risk object. The sensitivity to a risk object is set, for example, at multiple levels for each risk object. The information on each sensitivity is information that indicates the distance at which each driver feels anxiety or fear for each risk object while traveling with the host vehicle 1, and is calculated based on a basic risk potential RP that is set in advance for each risk object. i This is reflected in the coefficient that corrects the

[0106] The sensitivity of each driver to risk objects may be information set based on, for example, information on the results of responses to a questionnaire obtained in advance. Specifically, each driver may be asked to answer questions about which risk objects and how close they need to be to feel anxiety or fear, and the data may be used to evaluate the distance to the risk object on a multiple-level scale. However, the method of collecting and setting the sensitivity information to each risk object is not limited to the above example, and may be set by any appropriate method. The sensitivity information is recorded in association with the driver's identification information.

[0107] The information on the sensitivity of each driver to risk objects may be data obtained by learning the risks that the driver perceives from risk objects when manually driving the vehicle 1.

[0108] <3-3. Functional configuration> 13, the control unit 51 of the driving assistance device 70 includes the acquisition unit 61, road surface friction state determination unit 63, risk map generation unit 65, and driving condition setting unit 67 described in the first embodiment, as well as a driver determination unit 69. Each of these units is a function realized by the execution of a computer program by a processor such as a CPU, but some or all of the units may be configured using analog circuits.

[0109] The driver determination unit 69 executes a process of identifying the driver of the vehicle 1 based on image data transmitted from the in-vehicle camera 39. The driver determination unit 69 may also identify the driver of the vehicle 1 based on information registered by the driver or a passenger via an input device such as a touch panel.

[0110] The basic functions of the acquisition unit 61, road surface friction state determination unit 63, risk map generation unit 65, and driving condition setting unit 67 may be similar to those of the respective units of the driving assistance device 50 according to the first and second embodiments. However, in this embodiment, the risk map generation unit 65 further generates the risk map using information on the sensitivity of each individual driver to risk objects.

[0111] The risk map generation unit 65 reflects information on the driver's sensitivity to risk objects when setting the risk potential in the processing operation executed by the driving assistance device 50 according to the first or second embodiment. Specifically, the risk map generation unit 65 is configured to widen the setting range of the risk potential as the driver's sensitivity to risk objects increases.

[0112] For example, the risk potential RP i In the above equations (1) and (3), the gradient coefficient σ , which defines the rate of decrease of the risk potential with respect to the distance from the coordinates x0, y0 of the reference position of the risk object, is iIn other words, in this embodiment, the risk potential RP of the risk object is calculated using the following equations (4) and (5) instead of the above equations (1) and (3). i is set.

[0113]

number

[0114] RP i :Risk value C i _var:gain σ i :Slope coefficient R: Coefficient representing road surface condition x0, y0: coordinates of the reference position of the risk object s: Coefficient representing sensitivity to risk object i: Numbering to distinguish risk objects

[0115]

number

[0116] RP i :Risk value C i _var:gain σ i :Slope coefficient R: Coefficient representing road surface condition x0, y0: coordinates of the reference position of the risk object s: Coefficient representing sensitivity to risk object i: Numbering to distinguish risk objects

[0117] The coefficient s, which represents the sensitivity to risk objects, can be a value obtained by evaluating the distance at which each driver feels anxiety or fear towards the risk object on a scale of, for example, "1" to "4." The greater the distance at which anxiety or fear is felt, the greater the value of the coefficient s. In other words, the coefficient s, which represents the sensitivity to risk objects, becomes greater the farther the distance at which anxiety or fear is felt towards the risk object. Therefore, as shown in FIG. 14, the higher the sensitivity to risk objects, the wider the setting range of the risk potential.

[0118] The coefficient s representing the sensitivity to the risk object may be set according to not only the distance from the host vehicle 1 to the risk object, but also the speed of the host vehicle 1 or the relative speed of the risk object to the host vehicle 1.

[0119] <3-4. Operation of driving assistance devices> Next, an example of the operation of the driving support device 70 according to this embodiment will be specifically described.

[0120] FIG. 15 is a flowchart showing an example of the processing operation of the driving assistance device 70. The flowchart shown in FIG. 15 can be substituted for FIG. 8, which shows an example of the operation of the driving assistance device 50 according to the first embodiment, and the processing operation of the driving assistance device 70 according to this embodiment is shown in FIGS. 15 and 9, or in FIGS. 15 and 12. More specifically, in this embodiment, steps S51 and S53 shown in FIG. 15 are added to the flowchart shown in FIG. 8. Furthermore, in this embodiment, when setting the risk potential for the risk object in step S19 shown in FIG. 15, step S27 shown in FIG. 9, and step S43 shown in FIG. 12, the above formula (4) is used instead of the above formula (1). Furthermore, in this embodiment, when setting the risk potential for the risk object in step S47 shown in FIG. 12, the above formula (5) is used instead of the above formula (3).

[0121] Specifically, when the in-vehicle system is started (step S11), the driver determination unit 69 of the control unit 51 executes a process of identifying the driver of the vehicle 1 (step S51). For example, the driver determination unit 69 executes a facial recognition process using image data transmitted from the in-vehicle camera 39 to detect the driver of the vehicle 1 sitting in the driver's seat. The driver determination unit 69 also extracts facial features of the driver and identifies the corresponding driver in light of the feature data accumulated in the driver database 71. The driver determination unit 69 records identification information of the identified driver in the storage unit 53. If data of the corresponding driver is not recorded in the driver database 71, the driver determination unit 69 records in the storage unit 53 that identification information of the driver does not exist.

[0122] Furthermore, after executing the process of acquiring information on the driving state of the vehicle 1 (step S13), the process of acquiring information on the surrounding environment (step S15), and the process of determining the road surface friction state (step S17), the risk map generation unit 65 determines the driver's sensitivity to the detected risk object (step S53). Specifically, the risk map generation unit 65 refers to the driving characteristics database 73 and reads out information on the sensitivity to the risk object corresponding to the identification information of the specified driver. Furthermore, the risk map generation unit 65 sets a coefficient s representing the sensitivity to the risk object based on the sensitivity information corresponding to the type of the detected risk object.

[0123] Thereafter, the control unit 51 executes the processes of steps S19 to S23, and further executes the processes of each step in accordance with the flowchart of Fig. 9 or 12. As a result, the risk potential set for each risk object in steps S19, S27, S43, and S47 reflects the driver's sensitivity to the risk object, and the setting range of the risk potential set is wider for risk objects that are more likely to cause anxiety or fear. Therefore, not only is the risk of collision with the risk object due to slippage of the host vehicle 1 reduced, but a target trajectory can be set that can suppress the driver's anxiety or fear regarding the risk object.

[0124] <<4. Fourth Embodiment>> Next, a driving assistance device according to a fourth embodiment of the present disclosure will be described. In the driving assistance devices according to the first to third embodiments, information on road surface friction conditions is used as information on external environmental factors that may cause deviations in the vehicle's driving trajectory, but in the driving assistance device according to the fourth embodiment, information on wind direction is used as information on external environmental factors.

[0125] When the wind speed around the host vehicle 1 is high, there is a possibility that the traveling trajectory of the host vehicle 1 will deviate downwind. For this reason, in this embodiment, when it is predicted that the traveling trajectory of the host vehicle 1 will deviate due to the influence of the wind, the setting range of the risk potential of the risk object located in the direction of the deviation is expanded. Below, an example of the driving assistance device according to this embodiment will be described, in which wind direction information is used instead of road surface friction state information in the driving assistance device shown in the first embodiment.

[0126] FIG. 16 is a block diagram showing an example of the configuration of a driving support device 80 according to this embodiment. Instead of a road surface detection sensor, a wind speed sensor 81 is connected to the driving assistance device 80. Note that, in the case where information on road surface friction conditions is to be reflected in the risk potential together with information on wind direction, a road surface detection sensor 33 may be connected to the driving assistance device 80, and a road surface friction condition determination unit 63 may be provided in the control unit 51.

[0127] The wind speed sensor 81 detects the wind speed and wind direction in the driving area of the host vehicle 1, and transmits information on the detected wind speed and wind direction to the driving assistance device 80. The acquisition unit 61 of the control unit 51 may acquire weather information from a telematics service or the like via mobile communication means, in addition to the information on the wind speed and wind direction detected by the wind speed sensor 81.

[0128] The risk map generation unit 65 executes a process of generating a risk map based on information on the basic risk potential of risk objects present around the host vehicle 1 and information on wind speed and wind direction acquired by the acquisition unit 61. Specifically, when it is determined that the traveling trajectory of the host vehicle 1 is affected by wind, the risk map generation unit 65 is configured to expand the setting range of the risk potential as the wind speed increases.

[0129] For example, if it is determined that wind exceeding a preset wind speed threshold will blow in the direction from the host vehicle 1 toward the risk object while the host vehicle 1 is traveling on a target trajectory for the host vehicle 1 that is set without taking into account the effects of wind, the risk map generation unit 65 expands the setting range of the risk potential according to the wind speed. The wind speed threshold may be a fixed value, or may be a value set according to the weight of the host vehicle 1 and the road surface friction state.

[0130] For example, the risk potential RP i In the above formula (1), the gradient coefficient σ , which defines the rate of decrease of the risk potential with respect to the distance from the coordinates x0, y0 of the reference position of the risk object, is i In other words, in this embodiment, the risk potential RP of the risk object is calculated using the following equation (6) instead of the above equation (1): i is set.

[0131]

number

[0132] RP i :Risk value C i _var:gain σ i :Slope coefficient w: Coefficient representing the effect of wind x0, y0: coordinates of the reference position of the risk object i: Numbering to distinguish risk objects

[0133] The coefficient w representing the influence of wind may be a value set in accordance with the magnitude of the wind speed from among a plurality of preset values, or may be a value determined within a predetermined range by standardizing the reciprocal of the wind speed in accordance with a preset standard. The coefficient w representing the influence of wind increases as the wind speed increases, and the setting range of the risk potential increases accordingly.

[0134] 17 and 18 show examples of risk potentials and target trajectories generated by the driving assistance system 80 according to this embodiment. The examples of risk potentials and target trajectories shown in FIGS. 17 and 18 correspond to the example of risk potentials and target trajectories set in the scene shown in FIG.

[0135] As shown in FIG. 17, if a strong wind is blowing from the right side of the host vehicle 1's direction of travel, the host vehicle 1's travel trajectory will deviate from the target trajectory T1, which is set without taking the effects of the wind into consideration, and the host vehicle 1 is predicted to head towards the stopped vehicle 91. For this reason, as shown in FIG. 18, the risk map generation unit 65 corrects the risk potential RP_ve by expanding the range of the risk potential RP_ve for the stopped vehicle 91 located in the direction of the deviation of the host vehicle's travel trajectory, and sets a corrected risk potential RP_ve_r3. As a result, the target trajectory T5 is set so that the host vehicle 1 passes through position P5, which is farther from the stopped vehicle 91 than position P1 shown in FIG. 17. Therefore, even if the host vehicle 1's travel trajectory deviates due to the effects of the wind, the risk of a collision with the stopped vehicle 91 can be reduced.

[0136] FIG. 19 is a flowchart showing an example of the processing operation of the driving assistance device 80. The flowchart shown in FIG. 19 can be substituted for FIG. 8, which shows an example of the operation of the driving assistance device 80 according to the first embodiment, and the processing operation of the driving assistance device 80 according to this embodiment is shown in FIGS. 19 and 9. More specifically, in this embodiment, step S17 shown in FIG. 8 is omitted, and step S23 is replaced with step S61. Furthermore, in this embodiment, when setting the risk potential for the risk object in step S19 shown in FIG. 19 and step S27 shown in FIG. 9, the above formula (6) is used instead of the above formula (1).

[0137] Specifically, when the in-vehicle system is started (step S11), the control unit 51 executes a process of acquiring information on the driving state of the vehicle 1 (step S13), a process of acquiring information on the surrounding environment (step S15), a process of generating a basic risk map (step S19), and a process of setting driving conditions (step S21). In step S19, the basic risk potential is set by setting the coefficient s representing the influence of wind in the above equation (6) to "1".

[0138] Next, the driving condition setting unit 67 determines whether or not the traveling trajectory of the host vehicle 1 is affected by wind, based on the information on wind speed and wind direction acquired by the acquisition unit 61. For example, the driving condition setting unit 67 determines that the traveling trajectory of the host vehicle 1 is affected by wind when it is determined that wind of a speed equal to or greater than a preset wind speed threshold is blowing in the direction from the host vehicle 1 toward the risk object while the host vehicle 1 is traveling on the target trajectory of the host vehicle 1 set in step S21. As described above, the wind speed threshold may be a fixed value, or may be a value set depending on the weight of the host vehicle 1 and the road surface friction state.

[0139] Thereafter, the control unit 51 executes the processing of each step in accordance with the flowchart of Fig. 9. If it is predicted that the traveling trajectory of the host vehicle 1 will be affected by wind (S25 / Yes), the influence of wind is reflected in the risk potential set for each risk object in step S27, and the setting range of the risk potential is expanded as the wind speed increases. Therefore, it is possible to set a target trajectory that can reduce the risk of collision with a risk object, taking into account the deviation of the traveling trajectory of the host vehicle 1 predicted due to the influence of wind.

[0140] As described above, the driving assistance device 80 according to the fourth embodiment of the present disclosure predicts a deviation in the traveling direction of the host vehicle 1 due to the influence of the wind when the host vehicle 1 is traveling in strong winds. Furthermore, if a risk object exists in the direction of deviation of the traveling direction of the host vehicle 1, the driving assistance device 80 expands the set range of the risk potential of the risk object. As a result, the target trajectory (T5) is set so that the host vehicle 1 passes through a position farther away from the risk object than the target trajectory (T1) set based on the risk potential before expansion. Therefore, even if the traveling trajectory of the host vehicle 1 deviates due to the influence of the wind, the risk of collision with the risk object can be reduced.

[0141] Furthermore, the driving assistance device 50 according to this embodiment sets the risk potential of the risk object using the above-mentioned formula (6), in which the coefficient w representing the influence of wind is reflected in the gradient of the risk potential. As a result, the setting range of the risk potential is expanded depending on the wind speed, and it is possible to prevent the difference between the target trajectory before and after correction from becoming excessively large.

[0142] The driving assistance device 80 according to the fourth embodiment may be configured in combination with the driving assistance devices 50, 70 according to the first, second, or third embodiment. In this case, the above formulas (1), (3), (4), and (5) indicating the risk potential set for the risk object may be configured with the gradient coefficient σ i is set to multiply by a coefficient w that represents the effect of wind.

[0143] Furthermore, in the fourth embodiment, information on wind speed and wind direction was used as information on external environmental factors that could cause deviation of the vehicle's trajectory. However, the information on the external environmental factors may also be information on the road's inclination angle. Specifically, when the road's inclination angle is large, the vehicle's trajectory may deviate downward on the inclined road due to the gravity of the vehicle or the inertial force generated by the vehicle's travel. Therefore, the driving assistance device is configured to acquire a sensor signal transmitted from an inclination sensor that detects the road gradient, and is configured to expand the range of the risk potential set for the risk object as the downward gradient from the vehicle's trajectory toward the risk object increases. This allows the target trajectory (T5) to be set so that the vehicle passes through a position farther away from the risk object than the target trajectory (T1) set based on the risk potential before expansion. Therefore, even if the vehicle's trajectory deviates due to the influence of the road gradient, the risk of collision with the risk object can be reduced.

[0144] Although the preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, the present disclosure is not limited to such examples. It is clear that a person skilled in the art to which the present disclosure pertains can conceive of various modifications or alterations within the scope of the technical ideas set forth in the claims, and it is understood that these also naturally fall within the technical scope of the present disclosure.

[0145] For example, in the above embodiment, all of the functions of the driving assistance device are installed in the vehicle 1, but the present disclosure is not limited to such an example. For example, some of the functions of the driving assistance device may be provided in a server device that can communicate via mobile communication means, and the driving assistance device may be configured to transmit and receive data to and from the server device.

[0146] The following aspects also fall within the technical scope of the present disclosure. A driving assistance device in which, when the coefficient of friction of a road surface in a partial area ahead of the vehicle is smaller than the coefficient of friction of the road surfaces in the surrounding area, the processor sets a risk potential for the road surface in the partial area. A driving assistance device, wherein the processor increases the risk potential value of risk objects located in the direction of the expected deviation when the overall road friction coefficient ahead of the vehicle is smaller than the road friction coefficient when the road surface is dry. A computer program applicable to a driving assistance device that sets driving conditions for a vehicle based on a risk map generated by assigning risk potentials to risk objects present around the vehicle, the computer program causing one or more processors to execute processes including acquiring information on the vehicle's surrounding environment, acquiring information on external environmental factors that may cause the vehicle's driving trajectory to deviate, and expanding the setting range of risk potentials for risk objects located in the direction of the expected deviation based on the information on the external environmental factors, and a recording medium having the computer program recorded thereon. [Explanation of symbols]

[0147] 1: vehicle (host vehicle), 31: ambient environment sensor, 33: road surface detection sensor, 35: vehicle condition sensor, 41: vehicle control device, 50: driving assistance device, 51: control unit, 53: memory unit, 61: acquisition unit, 63: road surface friction condition determination unit, 65: risk map generation unit, 67: driving condition setting unit, 69: driver determination unit, 70: driving assistance device, 71: driver database, 73: driving characteristic database, 80: driving assistance device, 81: wind speed sensor, 91: stopped vehicle, T1·T2·T3·T4·T5: target trajectory

Claims

1. 1. A driving assistance device that sets a target steering angle and a target acceleration / deceleration of a vehicle based on a risk map that is generated by assigning risk potentials to risk objects present around the vehicle, one or more processors; and one or more memories communicatively coupled to the one or more processors; the one or more processors: acquiring information about the surrounding environment of the vehicle; acquiring information on road surface friction conditions ahead of the vehicle as information on external environmental factors that may cause deviation of the vehicle's travel trajectory; expanding a set range of risk potentials of risk objects located in the direction of the expected deviation based on information about road surface friction conditions ahead of the vehicle; Furthermore, when the road surface friction coefficient of a partial area in front of the vehicle is smaller than the road surface friction coefficient of the surrounding area, a risk potential is set for the road surface of the partial area. A driving assistance device that performs processing including the steps of:

2. 1. A driving assistance device that sets a target steering angle and a target acceleration / deceleration of a vehicle based on a risk map that is generated by assigning risk potentials to risk objects present around the vehicle, an acquisition unit that acquires information about the environment surrounding the vehicle and information about a road surface friction state ahead of the vehicle as information about external environmental factors that may cause deviation of the vehicle's traveling trajectory; a risk map generator that expands a setting range of risk potentials for risk objects located in the direction of the expected deviation based on information about the road surface friction state ahead of the vehicle, and further, when the road surface friction coefficient of a partial area ahead of the vehicle is smaller than the road surface friction coefficient of the surrounding area, sets a risk potential for the road surface in the partial area to generate a risk map; a driving condition setting unit that sets a target steering angle and a target acceleration / deceleration of the vehicle based on the risk map; A driving assistance device equipped with the above.

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