Driving support control device and driving support method

Abstract

To appropriately calculate a risk degree by a scattered matter from a road surface.SOLUTION: A driving support control device 20 includes: a road surface state information acquisition section 24 for acquiring road surface state information which indicates a state of a road surface of a travel scheduled road of a vehicle; a vehicle information acquisition section 25; a scattered matter estimation section 26 for estimating an occurrence state of a scattered matter from the road surface of the travel scheduled road and property of the scattered matter based on the road surface state information and vehicle information; and a risk degree calculation section 27 for calculating a risk degree of damage occurrence of the vehicle due to the scattered matter on the travel scheduled road based on the estimated occurrence state and property of the scattered matter. The road surface state information acquisition section 24 acquires the road state information for enabling estimation of at least one of the kind of the scattered matter existing on the road surface and having a probability of scattering with a passage of the vehicle, a distribution on the road surface, and the amount of the scattered matter.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a driving assistance control device and a driving assistance method. [Background technology]

[0002] There is known a technology for detecting obstacles on a road, such as puddles, snow, gravel, and objects dropped from other vehicles, which may be scattered by the passing of a vehicle (see, for example, Patent Document 1). There is also known a technology for generating a risk map that expresses the degree of risk to the vehicle's traveling at each position around the vehicle, based on information on environmental elements around the vehicle, which includes at least road surface obstacles that the vehicle can overcome on the road surface (see, for example, Patent Document 2). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-177573 [Patent Document 2] Japanese Patent Publication No. 2022-083359 Summary of the Invention [Problem to be solved by the invention]

[0004] Tires can kick up and scatter dirt or dust that has accumulated on the road surface on which a vehicle is traveling, potentially causing damage such as scratches or stains to the vehicle, other vehicles in the vicinity, and objects on the roadside. Therefore, it is desirable to appropriately calculate the degree of risk of damage caused by debris that is blown off the road surface as a vehicle passes.

[0005] The present invention has been made in view of the above, and aims to appropriately calculate the degree of risk posed by debris flying from the road surface. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the object, the driving assistance control device of the present invention comprises a road surface condition information acquisition unit that acquires road surface condition information indicating the condition of the road surface of the road on which the vehicle is planned to travel, a vehicle information acquisition unit that acquires vehicle information including at least the speed of the vehicle, a scattered object estimation unit that estimates the occurrence status of debris from the road surface of the road on which the vehicle is planned to travel and the properties of the debris based on the road surface condition information acquired by the road surface condition information acquisition unit and the vehicle information acquired by the vehicle information acquisition unit, and a risk degree calculation unit that calculates the risk degree of damage to the vehicle due to the scattered object on the road on which the vehicle is planned to travel based on the occurrence status and properties of the scattered object estimated by the scattered object estimation unit, and the road surface condition information acquisition unit acquires road surface condition information that can estimate at least one of the type of debris that is present on the road surface and that may be scattered as the vehicle passes, its distribution on the road surface, and the amount of the scattered object.

[0007] The driving assistance method of the present invention includes a road surface condition information acquisition step of acquiring road surface condition information indicating the road surface condition of a road on which a vehicle is scheduled to travel; a vehicle information acquisition step of acquiring vehicle information including at least the speed of the vehicle; a scattered object estimation step of estimating the occurrence status of debris from the road surface of the road on which the vehicle is scheduled to travel and the properties of the debris based on the road surface condition information acquired by the road surface condition information acquisition step and the vehicle information acquired by the vehicle information acquisition step; and a risk degree calculation step of calculating the risk degree of damage to the vehicle due to the scattered object on the road on which the vehicle is scheduled to travel based on the occurrence status and properties of the scattered object estimated by the scattered object estimation step, wherein the road surface condition information acquisition step is performed by a driving assistance control device to acquire road surface condition information that can estimate at least one of the type of debris that is present on the road surface and that may be scattered as the vehicle passes, its distribution on the road surface, and the amount of the debris. [Effects of the Invention]

[0008] The present invention has the effect of being able to appropriately calculate the degree of risk posed by debris flying from the road surface. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram showing an outline of a driving assistance control device according to an embodiment. [Figure 2] FIG. 2 is an example of a table showing an example of classification of categories related to the properties of flying debris. [Figure 3] FIG. 3 is a diagram illustrating an example of a risk map image. [Figure 4] FIG. 4 is a flowchart showing the flow of processing in the driving assistance control device according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A detailed description of embodiments of a driving assistance control device and a driving assistance method according to the present invention will be given below with reference to the accompanying drawings. However, the present invention is not limited to the following embodiments.

[0011] [Embodiment] (driving assistance device) 1 is a block diagram showing an outline of a driving assistance control device according to an embodiment. The driving assistance device 1 calculates the degree of risk posed by debris flying from the road surface.

[0012] Road debris refers to debris that exists on the road surface and is scattered by passing vehicles. Examples of road debris include soil, pebbles, mud, dust, snow, rainwater, fallen leaves, and twigs. Examples of road debris include pollen, yellow sand, and particulate matter such as PM (Particulate Matter) 2.5.

[0013] The driving assistance device 1 may be configured as a portable terminal, a device installed in a vehicle, or a combination of several devices. The driving assistance device 1 includes a GNSS (Global Navigation Satellite System) receiving unit 11, an outside-vehicle camera 12, a map information storage unit 13, a notification unit 19, and a driving assistance control device 20.

[0014] The GNSS receiving unit 11 is configured with a GNSS receiver that receives GNSS signals from GNSS satellites, etc. The GNSS receiving unit 11 outputs the received GNSS signals to the position information acquiring unit 21.

[0015] The exterior camera 12 is a camera that captures images of the surroundings including the area in front of the vehicle. In this embodiment, the exterior camera 12 is described as a camera that can capture images of the entire 360° sky, but the present invention is not limited to this and may be a group of multiple cameras that capture images of the area around the vehicle. The exterior camera 12 is disposed in front of the vehicle. The exterior camera 12 is disposed, for example, in the front of the passenger compartment of the vehicle. The exterior camera 12 constantly captures images while the driving assistance device 1 is operating. The exterior camera 12 outputs the captured image data to the image acquisition unit 22 of the driving assistance control device 20.

[0016] The map information storage unit 13 stores map information. The map information is, for example, a road map including intersections. The map information may also include information on roadside objects installed on the roadside, such as guardrails, walls, and signs. The map information storage unit 13 outputs the stored map information to the map information acquisition unit 23 of the driving assistance control device 20. The map information storage unit 13 may be a storage device such as an external server device that acquires map information via a communication function of a communication unit (not shown).

[0017] The notification unit 19 notifies the vehicle occupants of the risk level of the road surface. The notification unit 19 includes at least one of a display unit and an audio output unit. The notification unit 19 controls to make a notification based on a control signal from the notification control unit 35.

[0018] The function of the notification unit 19 as a display unit will be described. The notification unit 19 is, for example, a display device specific to the driving assistance device 1, or a display device shared with other systems such as a navigation system. Based on the video signal output from the notification control unit 35, the notification unit 19 displays an image indicating the degree of risk, etc. on the display unit.

[0019] The function of the notification unit 19 as an audio output unit will be described. The notification unit 19 is, for example, an audio output unit specific to the driving assistance device 1, or an audio output unit shared with other systems such as a navigation system. When the notification unit 19 is an audio output unit, it outputs audio indicating the degree of risk, etc., based on the audio signal output from the notification control unit 35.

[0020] (Driver assistance control device) The driving assistance control device 20 is an arithmetic processing device including, for example, a CPU (Central Processing Unit). The driving assistance control device 20 may be implemented as a function of a navigation system or a drive recorder installed in a vehicle. The driving assistance control device 20 executes instructions included in a program stored in a storage unit (not shown). The driving assistance control device 20 calculates the degree of risk due to debris from the road surface. The driving assistance control device 20 includes a position information acquisition unit 21, an image acquisition unit 22, a map information acquisition unit 23, a road surface condition information acquisition unit 24, a vehicle information acquisition unit 25, a debris estimation unit 26, a risk degree calculation unit 27, an avoidance route search unit 31, a vehicle control unit 32, a risk map image generation unit 33, and a notification control unit 35. The driving assistance control device 20 may include at least one of the avoidance route search unit 31, the vehicle control unit 32, the risk map image generation unit 33, and the notification control unit 35.

[0021] The position information acquisition unit 21 acquires vehicle position information indicating the current position of the vehicle. In this embodiment, the position information acquisition unit 21 acquires the vehicle position information based on the radio wave signal acquired by the GNSS receiving unit 11.

[0022] The video acquisition unit 22 acquires video captured around the vehicle. More specifically, the video acquisition unit 22 acquires video output by the exterior camera 12. The acquired video is a moving image composed of images at, for example, 30 frames per second.

[0023] The map information acquisition unit 23 acquires map information from the map information storage unit 13. More specifically, the map information acquisition unit 23 acquires map information relating to the route from the current position of the vehicle to the destination from the map information storage unit 13, based on the current position of the vehicle and the location information of the destination acquired by the location information acquisition unit 21.

[0024] The road surface condition information acquisition unit 24 acquires road surface condition information that indicates the road surface condition of the road surface on which the vehicle is scheduled to travel. The road surface condition information acquisition unit 24 acquires road surface condition information that can estimate at least one of the type of debris that exists on the road surface and that may be scattered as the vehicle passes, its distribution on the road surface, and the amount of the debris.

[0025] For example, if a destination of the vehicle has been set, the planned driving road may be the entire route from the current position of the vehicle to the destination, or a range of about several kilometers ahead of the current position on the route.For example, if a destination of the vehicle has not been set, the planned driving road may be a range of up to about 1 kilometer ahead in the direction of travel of the road on which the vehicle is traveling.

[0026] The road surface condition information acquisition unit 24 recognizes flying objects from the video captured by the video acquisition unit 22 and acquires road surface condition information that can be used to estimate the road surface condition. More specifically, the road surface condition information acquisition unit 24 performs image recognition processing on the video and performs pattern matching with a pre-stored recognition dictionary to recognize flying objects captured in the video as road surface condition information. A known method can be used to recognize flying objects from the video, and is not limited to this method. The road surface condition information acquisition unit 24 may recognize roadside objects placed on the roadside from the video captured by the video acquisition unit 22 and acquire surrounding condition information that can be used to estimate a situation in which flying objects from the vehicle collide with the roadside object and bounce back again.

[0027] The road surface condition information acquisition unit 24 may acquire, as the road surface condition information, a detection result by a sensor including a LiDAR.

[0028] The road surface condition information acquisition unit 24 may acquire road surface condition information from a management server device installed by a management company that inspects and manages roads. In this case, it is sufficient to detect the road surface conditions from the current position to the destination on the road on which the vehicle is scheduled to travel.

[0029] The road surface condition information acquisition unit 24 may acquire, via a network, road surface condition information acquired by a driving assistance device 1 mounted on another vehicle that has passed through the planned driving route of the vehicle. In this case, it is sufficient to detect the road surface condition from the current position to the destination on the road that the vehicle is scheduled to travel.

[0030] The road surface condition information acquisition unit 24 may acquire road surface condition information detected by a camera or a sensor including a LiDAR mounted on a roadside device (not shown) installed on the planned travel route of the vehicle via a network. In this case, it is sufficient to detect the road surface condition from the current position to the destination on the road on which the vehicle is planned to travel.

[0031] The vehicle information acquisition unit 25 acquires vehicle information including at least information indicating the vehicle speed via a CAN (Controller Area Network) interface, etc. The vehicle information may include information on the arrangement, position, width, diameter, and number of tires of the vehicle, as well as information on the vehicle height, vehicle model, destination, car wash history, and risk tolerance for damage caused by flying debris.

[0032] The flying object estimation unit 26 estimates the occurrence status of flying objects from the road surface of the planned road and the properties of the flying objects based on the road surface condition information acquired by the road surface condition information acquisition unit 24 and the vehicle information acquired by the vehicle information acquisition unit 25.

[0033] For example, when the road surface condition information is a video, the flying object estimation unit 26 recognizes flying objects from the video and estimates the occurrence status and properties of the flying objects. More specifically, the flying object estimation unit 26 performs image recognition processing on the video and performs pattern matching with a pre-stored recognition dictionary to recognize the flying objects captured in the video and estimate the occurrence status and properties of the flying objects. When recognizing flying objects, the flying object estimation unit 26 recognizes the type of flying object, the amount of accumulated flying object, the range of accumulated flying object, etc. The method of recognizing flying object from video can be any known method and is not limited thereto.

[0034] The following describes the estimation of the occurrence status of flying objects by the flying object estimation unit 26. The flying object estimation unit 26 estimates the occurrence status of flying objects from the road surface, in other words, the likelihood of flying objects occurring, based on at least one of road surface condition information and vehicle information.

[0035] For example, if there is flying matter in the air above the road surface based on road surface condition information, the flying matter estimation unit 26 estimates that flying matter has already been generated.

[0036] For example, if there are fallen leaves or the like on the road surface based on road surface condition information, the flying object estimation unit 26 estimates that the situation is likely to produce flying objects.

[0037] The flying object estimation unit 26 estimates that the greater the overlap between the vehicle's running position and the accumulation range of the flying object, the more likely the flying object will be scattered, based on the relative positional relationship between the running position, which is the contact position of the vehicle's tires on the planned road, and the flying object present on the road surface.

[0038] The flying object estimation unit 26 estimates, for example, based on road surface condition information, that the greater the overlap between the vehicle's running position on the road, which is the tire position of the vehicle, and the accumulation range of the flying object, in other words, the greater the proportion of the accumulation range of the flying object in the vehicle's contact area, the more likely the flying object will be generated.The flying object estimation unit 26 estimates, for example, based on road surface condition information, that the smaller the overlap between the vehicle's running position on the road and the accumulation range of the flying object, in other words, the smaller the proportion of the accumulation range of the flying object in the vehicle's contact area, the less likely the flying object will be generated.

[0039] The tire positions of the vehicle may be estimated in the width direction of the planned road, for example, by taking into account vehicle information such as the arrangement and position of the tires on the vehicle based on, for example, an image of the area around the vehicle acquired by the image acquisition unit 22, distance data detected by a sensor (such as a LiDAR) mounted on the vehicle (not shown), and distance data detected by a camera or sensor mounted on a roadside device acquired by the road surface condition information acquisition unit 24. The method for estimating the tire positions of the vehicle is not particularly limited, and may be estimated by a known method. The tire positions of the vehicle may be determined, for example, based on map information acquired by the map information acquisition unit 23, such that the vehicle is traveling in the center of the lane when lanes are provided on the road on which the vehicle is traveling. The tire positions of the vehicle may be determined, for example, based on map information acquired by the map information acquisition unit 23, such that the vehicle is traveling in the center of the road when lanes are not provided on the road on which the vehicle is traveling.

[0040] For example, based on road surface condition information, the flying object estimation unit 26 estimates that flying object is likely to occur on the side of the vehicle when the flying object accumulation area and the vehicle's tire position on the road overlap on the side of the vehicle, in other words, on the outside of the tire. For example, based on road surface condition information, the flying object estimation unit 26 estimates that flying object is unlikely to occur on the side of the vehicle when the flying object accumulation area and the vehicle's tire position on the road overlap on the inside of the tire.

[0041] The flying object estimation unit 26 estimates, for example, based on the road surface condition information and the vehicle information, that the higher the vehicle speed, the more likely flying object will be generated. For example, based on the road surface condition information and the vehicle information, the flying object estimation unit 26 estimates that flying object will not be generated if the vehicle speed is equal to or lower than a predetermined speed. The vehicle speed may be estimated, for example, by estimating the vehicle speed in the traveling direction of the planned road from the speed of the vehicle's current position acquired by the vehicle information acquisition unit 25.

[0042] The flying object estimation unit 26 may further estimate the occurrence status by taking into account the presence or absence of scattering prevention devices such as fenders or rubber mudguards on the vehicle. For example, if the vehicle is equipped with scattering prevention devices, the flying object estimation unit 26 estimates that flying object is less likely to occur. For example, if the vehicle is not equipped with scattering prevention devices, the flying object estimation unit 26 estimates that flying object is more likely to occur.

[0043] The flying object estimation unit 26 may make an estimation by taking into account information such as the arrangement, width, diameter, number of tires of the vehicle, etc. For example, the flying object estimation unit 26 estimates that large trucks are more likely to generate flying objects because the width, diameter, and number of tires are greater than those of standard cars, and the contact area with the object is larger.

[0044] The flying object estimation unit 26 may estimate the occurrence situation by further taking into account weather information acquired from an external server device via a network.

[0045] If the flying object estimation unit 26 estimates that flying objects are likely to be generated, it may further estimate the flying object's scattering distance, scattering height, scattering direction, etc. from the properties of the flying objects and the vehicle speed.

[0046] The estimation of the properties of the scattered objects by the scattered object estimation unit 26 will be described below. When it is estimated that scattered objects are likely to be generated, the scattered object estimation unit 26 further estimates the properties of the scattered objects. Based on road surface condition information, the scattered object estimation unit 26 estimates the properties of the scattered objects indicated by, for example, size, hardness, water content, scattering property, adhesive property, etc. The scattered object estimation unit 26 estimates the properties from the type of scattered object, etc.

[0047] For example, when the road surface condition information is a video, the flying object estimation unit 26 estimates the type of flying object based on the video data. The flying object estimation unit 26 detects objects from the video data, classifies the detected objects, and estimates the type of flying object from the video data. The flying object estimation unit 26 may perform processing using, for example, image recognition technology that classifies pixels of the video data into classes that indicate the type of flying object. Note that the type of flying object may be set arbitrarily, and classes such as "soil," "mud," "sand," and "pebbles" may be set. The flying object estimation unit 26 estimates the type of flying object according to the arbitrarily set class.

[0048] The flying object estimation unit 26 estimates the properties of the flying object based on, for example, the general properties of the main materials that make up the flying object (such as materials with a high content ratio or materials that affect the properties) that are estimated from the type of flying object that has been identified.

[0049] This will be explained in detail using FIG. 2. FIG. 2 is an example of a table showing an example of classification of categories related to the properties of scattered debris. As shown in FIG. 2, the categories related to the properties of scattered debris are set as "size," "hardness," "moisture content," "scatterability," and "adhesiveness," and the properties of scattered debris related to these categories are inferred. Multiple classes are set for each of these categories, and the class into which the scattered debris falls is determined for each of these categories. For example, if the type of scattered debris is soil, it is composed mainly of a collection of hard particles, but the particle diameter is small, so the properties of the scattered debris are inferred as "medium hardness," "low moisture content," "medium scatterability," and "medium adhesiveness." For example, if the type of scattered debris is mud, the soil absorbs water due to rainfall and becomes semi-liquid, and is composed mainly of a collection of hard particles that contain a lot of water, so the properties of the scattered debris are inferred as "medium hardness," "high moisture content," "high scatterability," and "high adhesiveness." For example, if the type of scattered material is sand, it contains a large number of hard particles that are larger in diameter than soil, so the scattered material's properties are estimated to be "hard" in hardness, "low" in moisture content, "medium" in dispersibility, and "low" in adhesion. For example, if the type of scattered material is wet sand, it contains a large number of hard particles that are larger in diameter than water and soil, so the scattered material's properties are estimated to be "hard" in hardness, "high" in moisture content, "low" in dispersibility, and "low" in adhesion. For example, if the type of scattered material is pebbles, it contains a large number of hard particles that are larger in diameter than sand, so it has low water retention, so the scattered material's properties are estimated to be "hard" in hardness, "low" in moisture content, "medium" in dispersibility, and "low" in adhesion, regardless of whether it is wet or dry. For example, if the type of scattered material is dry fallen leaves, it will mainly be soft, light, and flat small pieces, so the properties of the scattered material are estimated to be "soft" in hardness, "low" in moisture content, "high" in scattering properties, and "low" in adhesion. For example, if the type of scattered material is wet fallen leaves, it will mainly be soft, slightly heavy small pieces containing water, so the properties of the scattered material are estimated to be "soft" in hardness, "high" in moisture content, "low" in scattering properties, and "medium" in adhesion. For example, if the type of scattered material is fresh snow, it will be estimated to be "soft" in hardness, "high" in moisture content, "low" in scattering properties, and "high" in adhesion. Furthermore, since there is little mud or dust mixed in, it may be estimated that the pollution potential is low. When there is fresh snow, some of it is blown away by wind pressure, but since it is compacted by wheels, etc., the likelihood of it being blown away is estimated to be "low." However, when the snow melts (including when it rains after snowfall), the likelihood of it being blown away can be estimated to be "high."The properties of other flying debris may also be estimated in a similar manner.

[0050] The flying object estimation unit 26 estimates the properties of each flying object, but if multiple different types of flying objects are present overlapping on the road surface, the estimation may take into account the contribution of the properties of the flying objects depending on the area occupied by each flying object, etc.

[0051] The flying object estimation unit 26 may acquire weather information (rainfall, snowfall, precipitation amount, wind speed, etc.) for the road on which the vehicle is scheduled to travel, and estimate the properties of the flying object by taking the weather information into consideration. For example, if the type of flying object is sand and the weather information for the road on which the vehicle is scheduled to travel is rainfall, the estimated properties may be that the sand absorbs water due to rainfall and becomes wet sand, and therefore has a lower tendency to fly than dry sand.

[0052] The flying object estimation unit 26 may determine the properties of flying objects from the video data, for example, using a trained model that has learned the relationship between the video data and the properties of flying objects.

[0053] The categories and classes shown in FIG. 2 are merely examples and are not limiting, and any categories and classes may be set.

[0054] The risk degree calculation unit 27 calculates the degree of risk of damage to the vehicle caused by flying debris on the planned travel road, based on the occurrence status and properties of the flying debris estimated by the flying debris estimation unit 26. More specifically, the risk degree calculation unit 27 calculates, as the risk degree, the degree of damage caused by scratches or stains caused by flying debris, for example, based on the occurrence status and properties of the flying debris estimated by the flying debris estimation unit 26. The risk degree calculation unit 27 stores information indicating the calculated risk degree and location information in the storage unit in association with each other.

[0055] For example, if the flying matter is dry sand and there is a large amount of it, the risk degree calculation unit 27 calculates that there is a high risk of minor scratches on the vehicle. For example, if the flying matter is mud, the risk degree calculation unit 27 calculates that there is a high risk of the vehicle becoming dirty. For example, if the flying matter is fresh snow, the risk degree calculation unit 27 calculates that even if flying matter occurs, the degree of scratches or dirt is low, in other words, the risk is low.

[0056] For example, the risk degree calculation unit 27 may calculate the risk degree using a score or a rank, with a smaller number indicating a lower risk degree.

[0057] The risk degree calculation unit 27 may further calculate the risk degree by taking into account information on other vehicles or roadside objects installed on the roadside around the vehicle. For example, when the distance between the vehicle and the surrounding objects is short, there is a risk that debris from the vehicle will collide with the surrounding objects and bounce back again, so the risk of damage to the vehicle and the surrounding objects may be calculated to be high.

[0058] The risk degree calculation unit 27 may calculate the risk degree by taking into account the vehicle height of the vehicle stored in a storage unit (not shown). For example, even if the scattering height is low, the risk degree calculation unit 27 may calculate that the risk of damage is high if the vehicle height is low.

[0059] The risk degree calculation unit 27 may calculate the risk degree by taking into account the risk tolerance set for each vehicle type or the risk tolerance set by the user. For example, the risk degree calculation unit 27 may calculate the risk degree for a construction truck by increasing the risk tolerance for scratches and dirt. As a result, the risk degree calculation unit 27 may calculate the risk degree for a construction truck to be lower than that for other vehicle types such as passenger cars.

[0060] The risk degree calculation unit 27 may calculate the risk degree by taking into account a risk tolerance degree according to the destination of the vehicle. For example, when the destination of the vehicle is a tourist spot, a commercial facility, an acquaintance's house, an urban area, etc., the risk degree calculation unit 27 may lower the risk tolerance degree and calculate the risk degree to be higher than in other cases. For example, when the destination of the vehicle is the home, etc., the risk degree calculation unit 27 may raise the risk tolerance degree and calculate the risk degree to be lower than in other cases.

[0061] The risk degree calculation unit 27 may calculate the risk degree by taking into account the risk tolerance degree according to the time period that has elapsed since the car was washed. For example, if the time period that has elapsed since the car was washed is short, such as a few days, the risk degree calculation unit 27 may lower the risk tolerance degree and calculate the risk degree to be higher than in other cases. For example, if the time period that has elapsed since the car was washed is long, such as one week or more, the risk degree calculation unit 27 may raise the risk tolerance degree and calculate the risk degree to be lower than in other cases.

[0062] The risk degree calculation unit 27 may calculate the risk degree taking into account a risk tolerance degree according to weather information. For example, when there is a forecast that good weather will continue, the risk degree calculation unit 27 may lower the risk tolerance degree and calculate the risk degree to be higher than in other cases. For example, when there is a forecast that the weather will worsen, the risk degree calculation unit 27 may raise the risk tolerance degree and calculate the risk degree to be lower than in other cases.

[0063] The avoidance route search unit 31 searches for an avoidance route in accordance with the degree of risk of damage calculated by the risk degree calculation unit 27.

[0064] For example, if the user sets the risk tolerance level as "scratches are strictly prohibited" and "dirt is acceptable," the avoidance route search unit 31 may set a route with a low risk of scratches and a medium or lower risk of dirt.

[0065] The avoidance route searching unit 31 searches for an avoidance route that avoids a route with a high risk level, based on the risk level of the road calculated by the risk level calculation unit 27. More specifically, the avoidance route searching unit 31 searches for an avoidance route based on vehicle position information, destination position information, and the risk level of the road determined by the risk level calculation unit 27. The avoidance route searching unit 31 may search for a route by reducing the possibility that a road determined by the risk level calculation unit 27 to have a relatively high risk level will be selected. The method of searching for a route may be any known route searching method and is not limited to this. The avoidance route searching unit 31 may provide route guidance along the found avoidance route. The method of providing guidance on the avoidance route may be any known route guidance method and is not limited to this.

[0066] The vehicle control unit 32 controls the vehicle to avoid flying debris according to the degree of risk of damage calculated by the risk degree calculation unit 27. The vehicle control unit 32 may generate a control signal to perform a steering operation to avoid areas of the road determined by the risk degree calculation unit 27 to have a relatively high risk degree. The vehicle control unit 32 may generate a control signal to decelerate the vehicle in areas of the road determined by the risk degree calculation unit 27 to have a relatively high risk degree.

[0067] The risk map image generating unit 33 generates a risk map image according to the degree of risk of damage calculated by the risk degree calculating unit 27 .

[0068] The risk map image will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of a risk map image. The risk map image is an image in which areas of the road that are calculated to have a high risk level are colored dark, and areas that are calculated to have a low risk level are colored light.

[0069] The notification control unit 35 controls to notify the user of the risk degree calculated by the risk degree calculation unit 27. The notification control unit 35 may, for example, notify the user only when the calculated risk degree requires notification to the user.

[0070] The notification control unit 35 controls the notification unit 19 to notify the degree of risk.

[0071] A case where the notification unit 19 is a display unit will be described. The notification control unit 35 outputs a video signal that causes the display unit to display a video indicating the risk level. The notification control unit 35 may display a risk map image, for example, as shown in FIG. 3.

[0072] A case will be described where the notification unit 19 is an audio output unit. The notification control unit 35 outputs an audio signal that causes the audio output unit to output an audio indicating the degree of risk.

[0073] (Information processing in driving assistance devices) Next, information processing in the driving assistance device 1 will be described with reference to Fig. 4. Fig. 4 is a flowchart showing the flow of processing in the driving assistance control device according to the embodiment. While the driving assistance device 1 is running, the processing of the flowchart shown in Fig. 4 is executed at predetermined time intervals. While the processing of the flowchart shown in Fig. 4 is being executed, video is captured by the outside-vehicle camera 12.

[0074] The driving assistance control device 20 acquires road surface condition information (step S101). The driving assistance control device 20 acquires road surface condition information that can estimate at least one of the type of debris that exists on the road surface and that may be scattered as a vehicle passes, its distribution on the road surface, and the amount of the debris, using the road surface condition information acquisition unit 24. The driving assistance control device 20 proceeds to step S102.

[0075] The driving assistance control device 20 acquires the vehicle speed (step S102). The driving assistance control device 20 acquires vehicle information including at least information indicating the vehicle speed via a CAN interface or the like using the vehicle information acquisition unit 25. The driving assistance control device 20 proceeds to step S103.

[0076] The driving assistance control device 20 estimates the flying object (step S103). When the road surface condition information is an image, the driving assistance control device 20 recognizes the flying object from the image and estimates the occurrence status and properties of the flying object using the flying object estimation unit 26. The driving assistance control device 20 proceeds to step S104.

[0077] The driving assistance control device 20 calculates the degree of risk (step S104). The driving assistance control device 20 uses the risk degree calculation unit 27 to calculate, as the risk degree, the degree of damage caused by scratches or stains caused by flying objects, based on the occurrence status and properties of the flying objects estimated by the flying object estimation unit 26. The driving assistance control device 20 associates information indicating the degree of risk determined by the risk degree calculation unit 27 with the position information and stores them in the memory unit. The driving assistance control device 20 proceeds to step S105.

[0078] The driving assistance control device 20 determines whether or not the calculated risk degree requires notification (step S105). More specifically, the driving assistance control device 20 determines that notification is necessary only when the calculated risk degree is high. If the driving assistance control device 20 determines that the calculated risk degree requires notification (Yes in step S105), the process proceeds to step S106. If the driving assistance control device 20 does not determine that the calculated risk degree requires notification (No in step S105), the process proceeds to step S107.

[0079] When it is determined that the calculated risk degree requires notification (Yes in step S105), the driving assistance control device 20 notifies the risk degree (step S106). The driving assistance control device 20 controls the notification control unit 35 to notify the risk degree via the notification unit 19. The driving assistance control device 20 proceeds to step S107.

[0080] The driving assistance control device 20 determines whether or not to end the processing of this flowchart (step S107). More specifically, the driving assistance control device 20 determines to end the processing of this flowchart when any condition is met, for example, when the vehicle has stopped or when an end operation by the user has been detected. When the driving assistance control device 20 determines to end the processing of this flowchart (Yes in step S107), it ends the processing of this flowchart. When the driving assistance control device 20 does not determine to end the processing of this flowchart (No in step S107), it executes the processing of step S101 again.

[0081] (effect) As described above, in this embodiment, it is possible to calculate the degree of risk of damage to a vehicle caused by flying debris on a planned road based on the occurrence status and properties of the flying debris. According to this embodiment, it is possible to appropriately calculate the degree of risk caused by flying debris from the road surface.

[0082] In this embodiment, the calculated risk level can be notified. According to this embodiment, the risk level due to debris from the road surface can be appropriately notified. According to this embodiment, the driver can check the notified risk level and take appropriate measures, such as avoiding areas with a high risk level or slowing down.

[0083] In this embodiment, based on the relative positional relationship between the vehicle's traveling position on the road and the debris present on the road surface, it can be estimated that the greater the overlap between the vehicle's traveling position and the accumulation area of ​​the debris, the more likely the debris will be scattered. According to this embodiment, it is possible to appropriately calculate the degree of risk posed by debris from the road surface.

[0084] In this embodiment, if the flying debris is dry sand and there is a large amount of it, it is determined that there is a high risk of minor scratches on the vehicle, and if the flying debris is mud, it is determined that there is a high risk of the vehicle becoming dirty. According to this embodiment, it is possible to appropriately calculate the degree of risk posed by flying debris from the road surface.

[0085] The components of the illustrated driving assistance device are conceptual functional components and do not necessarily have to be physically configured as shown in the drawings. In other words, the specific form of each device is not limited to that shown in the drawings, and all or part of each device may be functionally or physically distributed or integrated in any unit depending on the processing load and usage status of each device.

[0086] The configuration of the driving assistance device is realized, for example, as software, by a program loaded into a memory. In the above embodiment, the configuration has been described as functional blocks realized by the cooperation of these hardware and software. In other words, these functional blocks can be realized in various forms, using only hardware, only software, or a combination of these.

[0087] The components described above include those that can be easily imagined by a person skilled in the art and those that are substantially the same. Furthermore, the configurations described above can be combined as appropriate. Furthermore, various omissions, substitutions, or modifications of the configurations are possible within the scope of the gist of the present invention. [Explanation of symbols]

[0088] 1 Driving assistance devices 11 GNSS receiver 12. Exterior camera 13 Map information storage unit 19 Notification Department 20 Driving assistance control device 21 Location information acquisition unit 22 Video acquisition unit 23 Map information acquisition unit 24 Road condition information acquisition unit 25 Vehicle Information Acquisition Unit 26 Flying object estimation section 27 Risk Degree Calculation Section 31 Avoidance route search unit 32 Vehicle control unit 33 Risk map image generation unit 35 Notification control section

Claims

1. a road surface condition information acquisition unit that acquires road surface condition information indicating the condition of the road surface of a road on which the vehicle is to travel; a vehicle information acquisition unit that acquires vehicle information including at least the speed of the vehicle; a scattered object estimation unit that estimates the occurrence status of scattered objects from the road surface of the planned travel road and the properties of the scattered objects based on the road surface condition information acquired by the road surface condition information acquisition unit and the vehicle information acquired by the vehicle information acquisition unit; a risk degree calculation unit that calculates a risk degree of damage to the vehicle caused by the flying object on the planned travel road based on the occurrence status and properties of the flying object estimated by the flying object estimation unit; Equipped with the road surface condition information acquisition unit acquires road surface condition information that can estimate at least one of the type of debris that exists on the road surface and that may be scattered when a vehicle passes, its distribution on the road surface, and the amount of the debris; Driver assistance control device.

2. a notification control unit that controls to notify the risk degree calculated by the risk degree calculation unit; The driving assistance control device according to claim 1 , comprising:

3. the scattered object estimation unit estimates that the greater the overlap between the vehicle's traveling position and the area where the scattered object accumulates, the more likely the scattered object will be scattered, based on a relative positional relationship between the traveling position, which is the contact position of the tires of the vehicle on the planned traveling road, and the scattered object present on the road surface; The driving assistance control device according to claim 2 .

4. the flying object estimation unit estimates the properties of the flying object using a table that defines the properties for each type of flying object; The driving assistance control device according to claim 2 or 3.

5. a road surface condition information acquisition step of acquiring road surface condition information indicating the road surface condition of a road on which the vehicle is to travel; a vehicle information acquisition step of acquiring vehicle information including a speed of the vehicle; a scattered object estimation step of estimating the occurrence status of scattered objects from the road surface of the planned road and the properties of the scattered objects based on the road surface condition information acquired in the road surface condition information acquisition step and the vehicle information acquired in the vehicle information acquisition step; a risk degree calculation step of calculating a risk degree of damage to the vehicle caused by the flying debris on the planned travel road based on the occurrence status and properties of the flying debris estimated in the flying debris estimation step; Including, the road surface condition information acquisition step acquires road surface condition information that can estimate at least one of the type of debris that exists on the road surface and that may be scattered by the passage of a vehicle, its distribution on the road surface, and the amount of the debris; A driving assistance method executed by a driving assistance control device.

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