Road surface condition estimation system, road surface condition estimation device, road surface condition estimation method, and road surface condition estimation program

Abstract

To provide a road surface state estimation system and the like which can estimate the state of a road surface while securing a detectable distance of an optical sensor.SOLUTION: A road surface state estimation system includes a processor and estimates the state of a road surface on which a host vehicle having an optical sensor for detecting reflection light according to the light irradiation travels. The processor is configured to execute acquisition of reflection light information on the travel direction rear side by the optical sensor. The processor is configured to execute recognition of a scattering state of a scattering substance swirled up from the road surface by the travel of a host vehicle on the basis of the reflection light information. The processor is configured to execute estimation of the state of the road surface on the basis of the scattering state.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present disclosure relates to a road surface condition estimation technology that estimates the condition of a road surface on which a host vehicle is traveling. [Background technology]

[0002] Patent Document 1 discloses a road surface condition estimation device that estimates the condition of a road surface on which a vehicle is traveling. This road surface condition estimation device uses a laser radar to irradiate a laser beam onto the road surface so as to scan the surface, and acquires a reflection intensity value corresponding to each irradiation point. The road surface condition estimation device includes a road surface condition classifier that is generated by machine learning and that uses the reflection intensity values ​​to classify the road surface condition. The road surface condition classifier receives as input a multi-point reflection intensity value set acquired for a road surface whose road surface condition is unknown, and determines and outputs the road surface condition. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2014-228300 A Summary of the Invention [Problem to be solved by the invention]

[0004] The technology of Patent Document 1 requires that the laser radar be installed so that the laser light scans the road surface, but in this case, the detectable distance of the laser radar becomes relatively short.

[0005] An object of the present disclosure is to provide a road surface condition estimation system that can estimate the condition of a road surface while ensuring the detectable distance of an optical sensor. Another object of the present disclosure is to provide a road surface condition estimation device. Yet another object of the present disclosure is to provide a road surface condition estimation method. Yet another object of the present disclosure is to provide a road surface condition estimation program. [Means for solving the problem]

[0006] The technical means of the present disclosure for solving the problems will be described below. Note that the claims and the reference symbols in parentheses in this section indicate the correspondence with the specific means described in the embodiments described later in detail, and do not limit the technical scope of the present disclosure.

[0007] A first aspect of the present disclosure is an optical sensor (10) having a processor (102) and detecting reflected light in response to light irradiation. An optical sensor having a sensing area behind a host vehicle (A) and an optical sensor having a sensing area in front of the host vehicle. Host vehicle equipped with Both A road surface condition estimation system that estimates the condition of a road surface on which a vehicle travels, The processor The sensing area is behind the host vehicle. Obtaining reflected light information from an optical sensor in the rear of the vehicle in the traveling direction; Recognizing the state of scattering of scattered matter kicked up from the road surface by the running of the host vehicle based on the reflected light information; Estimating the state of the road surface based on the scattering state; configured to run 、 Recognizing the scattering state includes recognizing the scattering state in a region of interest (R) within a distance range set behind the host vehicle; Estimating the road surface condition includes determining whether the road surface is in a bad road condition; The processor is further configured to, when it is estimated that the road surface is in a bad road state, perform a response process corresponding to the bad road state; Executing the response process includes, when it is estimated that the road surface is in a bad road condition, recognizing the point cloud on the front side as a leading vehicle (B) when there are point clouds before and after the driving direction in the reflected light information in the driving direction obtained by an optical sensor having a sensing area in front of the host vehicle. .

[0008] A second aspect of the present disclosure is an optical sensor (10) having a processor (102) and detecting reflected light in response to light irradiation. An optical sensor having a sensing area behind a host vehicle (A) and an optical sensor having a sensing area in front of the host vehicle. Host vehicle equipped with Both A road surface condition estimation device that estimates the condition of a road surface on which a vehicle travels, The processor The sensing area is behind the host vehicle. Obtaining reflected light information from an optical sensor in the rear of the vehicle in the traveling direction; Recognizing the state of scattering of scattered matter kicked up from the road surface by the running of the host vehicle based on the reflected light information; Estimating the state of the road surface based on the scattering state; configured to run 、 Recognizing the scattering state includes recognizing the scattering state in a region of interest (R) within a distance range set behind the host vehicle; Estimating the road surface condition includes determining whether the road surface is in a bad road condition; The processor is further configured to, when it is estimated that the road surface is in a bad road state, perform a response process corresponding to the bad road state; Executing the response process includes, when it is estimated that the road surface is in a bad road condition, recognizing the point cloud on the front side as a leading vehicle (B) when there are point clouds before and after the driving direction in the reflected light information in the driving direction obtained by an optical sensor having a sensing area in front of the host vehicle. .

[0009] A third aspect of the present disclosure is an optical sensor (10) for detecting reflected light in response to light irradiation. An optical sensor having a sensing area behind a host vehicle (A) and an optical sensor having a sensing area in front of the host vehicle. Host vehicle equipped with Both A road surface condition estimation method executed by a processor (102) to estimate a condition of a road surface on which a vehicle is traveling, comprising: The sensing area is behind the host vehicle. Obtaining reflected light information from an optical sensor in the rear of the vehicle in the traveling direction; Recognizing the state of scattering of scattered matter kicked up from the road surface by the running of the host vehicle based on the reflected light information; Estimating the state of the road surface based on the scattering state; Including fruit, Recognizing the scattering state includes recognizing the scattering state in a region of interest (R) within a distance range set behind the host vehicle; Estimating the road surface condition includes determining whether the road surface is in a bad road condition; When it is estimated that the road surface is in a bad road state, a corresponding process is executed to correspond to the bad road state; Executing the response process includes, when it is estimated that the road surface is in a bad road condition, recognizing the point cloud on the front side as a leading vehicle (B) when there are point clouds before and after the driving direction in the reflected light information in the driving direction obtained by an optical sensor having a sensing area in front of the host vehicle. .

[0010] A fourth aspect of the present disclosure is an optical sensor (10) for detecting reflected light in response to light irradiation. An optical sensor having a sensing area behind a host vehicle (A) and an optical sensor having a sensing area in front of the host vehicle. Host vehicle equipped with Both A road surface condition estimation program stored in a storage medium (101) for estimating the condition of a road surface on which a vehicle is traveling and including instructions to be executed by a processor (102), The command is, The sensing area is behind the host vehicle. Acquiring reflected light information from the rear in the traveling direction by an optical sensor; Based on the reflected light information, the state of scattering of the scattered matter kicked up from the road surface by the running of the host vehicle is recognized; Estimating the state of the road surface based on the state of scattering; Including fruit, Recognizing the scattering state includes recognizing the scattering state in a region of interest (R) within a distance range set behind the host vehicle, Estimating the road surface condition includes determining whether the road surface is in a bad road condition, The instructions include executing a response process corresponding to the bad road condition when it is estimated that the road surface is in a bad road condition; Executing the response process includes, when it is estimated that the road surface is in a bad road condition, recognizing the point cloud on the front side as a leading vehicle (B) when there are point clouds before and after the driving direction in the reflected light information in the driving direction obtained by an optical sensor having a sensing area in front of the host vehicle. .

[0011] According to these first to fourth aspects, the road surface condition is estimated based on the scattering state of scattered material kicked up from the road surface by the host vehicle's travel using reflected light information from behind in the travel direction. Therefore, whether or not material kicked up by travel is present on the road surface can be indirectly estimated based on the scattering state of the scattered material. This reduces the need for the optical sensor to scan the road surface, making it easier to ensure the sensor's detectable distance. As a result, it may be possible to estimate the road surface condition while ensuring the optical sensor's detectable distance. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a block diagram showing the overall configuration of a first embodiment. [Figure 2] 1 is a schematic diagram showing a traveling environment of a host vehicle to which a first embodiment is applied; [Figure 3] 1 is a block diagram showing a functional configuration of a road surface condition estimation system according to a first embodiment. [Figure 4] 3 is a flowchart showing a road surface condition estimation method according to the first embodiment. [Figure 5] 5 is a flowchart showing detailed processing of FIG. 4. [Figure 6] FIG. 10 is a diagram showing a method for counting points. [Figure 7] 10 is a flowchart showing a road surface condition estimation method according to a second embodiment. [Figure 8] 10 is a flowchart showing a road surface condition estimation method according to a third embodiment. [Figure 9] FIG. 10 is a schematic diagram showing a difference in detection logic. [Figure 10] 10 is a flowchart showing a road surface condition estimation method according to a fourth embodiment. [Figure 11] 10 is a flowchart showing a road surface condition estimation method according to a fifth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, multiple embodiments of the present disclosure will be described with reference to the drawings. Note that corresponding components in each embodiment are designated by the same reference numerals, and redundant description may be omitted. Furthermore, when only a portion of the configuration is described in each embodiment, the configuration of another previously described embodiment may be applied to the remaining portions of the configuration. Furthermore, in addition to the combinations of configurations explicitly stated in the description of each embodiment, configurations of multiple embodiments may be partially combined together even if not explicitly stated, provided that there is no particular problem with the combination.

[0014] (First embodiment) The road surface condition estimation system 100 of the first embodiment shown in Fig. 1 estimates the condition of the road surface on which a host vehicle A shown in Fig. 2 is traveling. From a viewpoint centered on the host vehicle A, the host vehicle A can also be said to be an ego-vehicle. From a viewpoint centered on the host vehicle A, the target vehicle B can also be said to be another road user.

[0015] The host vehicle A is provided with an autonomous driving mode that is classified into levels according to the degree of manual intervention by the occupant in the driving task. The autonomous driving mode may be realized by autonomous driving control, such as conditional driving automation, high driving automation, or full driving automation, in which the system performs all driving tasks when activated. The autonomous driving mode may also be realized by advanced driving assistance control, such as driving assistance or partial driving automation, in which the occupant performs some or all driving tasks. The autonomous driving mode may be realized by either autonomous driving control or advanced driving assistance control, or by a combination of these, or by switching between them.

[0016] The host vehicle A is equipped with a LiDAR device 10, an internal sensor 20, a communication system 30, and a driving control system 40 shown in FIG.

[0017] The LiDAR device 10 is an optical sensor that measures the distance to a reflection point by detecting reflected light from the reflection point in response to illumination of light. For example, the host vehicle A is provided with a LiDAR device 10 whose sensing area is at least the rear of the host vehicle A. The host vehicle A may further be provided with a LiDAR device 10 whose sensing area is the front of the host vehicle A. That is, a plurality of LiDAR devices 10 may be provided on the host vehicle A. Alternatively, a LiDAR device 10 whose sensing area is substantially the entire periphery of the host vehicle A may be provided. The LiDAR device 10 includes a light emitter 11, a light receiver 12, a mirror, and a control circuit 14.

[0018] The light-emitting unit 11 is a semiconductor element, such as a laser diode, that emits directional laser light. The light-emitting unit 11 emits laser light in the form of an intermittent pulse beam toward the outside of the vehicle A. The light-receiving unit 12 is composed of light-receiving elements that are highly sensitive to light, such as a SPAD (Single Photon Avalanche Diode). A plurality of light-receiving elements are arranged in a two-dimensional array. A set of a plurality of adjacent light-receiving elements constitutes one light-receiving pixel (hereinafter also referred to as a pixel). The number of light-receiving elements that constitute one light-receiving pixel can be changed by the control circuit 14. The light-receiving elements are exposed to light that is incident from a sensing area, which is determined by the angle of view of the light-receiving unit 12, from the outside of the light-receiving unit 12.

[0019] The actuator 13 controls the reflection angle of a reflecting mirror that reflects the laser light emitted from the light emitting unit 11 onto the emission surface of the LiDAR device 10. The actuator 13 controls the reflection angle of the reflecting mirror, thereby scanning the laser light. The scanning direction may be horizontal or vertical. The actuator 13 may also scan the laser light by controlling the attitude angle of the housing of the LiDAR device 10 itself.

[0020] The control circuit 14 controls the light-emitting unit 11, the light-receiving unit 12, and the actuator 13. The control circuit 14 is a computer including at least one memory and one processor. The memory is at least one type of non-transitory tangible storage medium, such as a semiconductor memory, a magnetic medium, or an optical medium, that non-temporarily stores or stores computer-readable programs and data. The memory stores various programs executed by the processor.

[0021] The control circuit 14 controls exposure and scanning of multiple pixels in the light receiving unit 12, and processes and converts signals from the light receiving unit 12 into data. The control circuit 14 can perform reflected light detection, in which the light receiving unit 12 detects reflected light in response to light irradiation by the light emitting unit 11.

[0022] In reflected light detection, laser light emitted from the light-emitting unit 11 hits an object within the sensing area and is reflected. This reflected point becomes the reflection point of the laser light. The laser light reflected at the reflection point (hereinafter referred to as reflected light) enters the light-receiving unit 12 through the incident surface and causes exposure. At this time, the control circuit 14 scans multiple pixels of the light-receiving unit 12 to acquire reflected light at various angles within the angle of view. In this way, the control circuit 14 acquires a point cloud image of the reflecting object.

[0023] More specifically, the control circuit 14 integrates the intensity of reflected light obtained by scanning each pixel within a certain period of time, or a value obtained based on that intensity (hereinafter referred to as reflection intensity), for each distance obtained. This allows the control circuit 14 to obtain a histogram of distance and reflection intensity, such as that shown in FIG. 5. The control circuit 14 calculates the distance to the reflection point based on the reflection intensity of each bin in the histogram. Specifically, the control circuit 14 generates an approximation curve for bins that are equal to or greater than a predetermined threshold, and sets the extreme value of the approximation curve as the distance to the reflection point at that pixel. By performing the above-described process for all pixels, the control circuit 14 can generate a point cloud image containing distance information for each pixel. The point cloud image is an example of "reflected light information."

[0024] The control circuit 14 can also perform background light detection, in which background light is detected by the light receiving unit 12 while light irradiation by the light emitting unit 11 is stopped. Background light can also be called external light or ambient light.

[0025] The control circuit 14 can control the scanning speed of the light-emitting unit 11 and the light-receiving frequency of the light-receiving unit 12 during the above-described reflected light detection and background light detection. The control circuit 14 controls the actuator 13 to change the scanning speed.

[0026] The internal sensor 20 acquires internal information usable by the road surface condition estimation system 100 from the internal environment of the host vehicle A. The internal sensor 20 acquires the internal information by detecting a specific physical quantity of motion in the internal environment of the host vehicle A. The internal sensor 20 includes a vehicle speed sensor. The internal sensor 20 may further include at least one of an acceleration sensor, a gyro sensor, etc.

[0027] The communication system 30 acquires communication information usable by the road surface condition estimation system 100 via wireless communication. The communication system 30 may receive positioning signals from artificial satellites of a Global Navigation Satellite System (GNSS) present in the external world of the host vehicle A. The positioning type communication system 30 is, for example, a GNSS receiver. The communication system 30 may transmit and receive communication signals to and from a V2X system present in the external world of the host vehicle A. The V2X type communication system 30 is, for example, at least one of a Dedicated Short Range Communications (DSRC) communication device and a Cellular V2X (C-V2X) communication device. The communication system 30 may transmit and receive communication signals to and from a terminal present in the internal world of the host vehicle A. The terminal communication type communication system 30 is, for example, at least one of a Bluetooth (registered trademark) device, a Wi-Fi (registered trademark) device, an infrared communication device, etc.

[0028] The cruise control system 40 is configured to control the cruise of the host vehicle A. The cruise control system 40 includes a steering ECU that performs steering control, a power unit control ECU that performs acceleration / deceleration control, and a brake ECU. The cruise control system 40 acquires detection signals output from various sensors mounted on the host vehicle A, such as a steering angle sensor and a vehicle speed sensor, and outputs control signals to various cruise control devices, such as an electronically controlled throttle, a brake actuator, and an EPS (Electric Power Steering) motor. The cruise control system 40 acquires control instructions for the host vehicle A from the road surface condition estimation system 100, etc., and controls the various cruise control devices to achieve automatic or manual driving in accordance with the control instructions.

[0029] The road surface condition estimation system 100 is connected to the LiDAR device 10, the internal sensor 20, the communication system 30, and the driving control system 40 via at least one of, for example, a LAN (Local Area Network) line, a wire harness, an internal bus, and a wireless communication line. The road surface condition estimation system 100 is configured to include at least one dedicated computer.

[0030] The dedicated computer constituting road surface condition estimation system 100 may be a driving control ECU (Electronic Control Unit) that controls the driving of host vehicle A. The dedicated computer constituting road surface condition estimation system 100 may be a navigation ECU that navigates the driving route of host vehicle A. The dedicated computer constituting road surface condition estimation system 100 may be a locator ECU that estimates the host vehicle A's own state quantity. The dedicated computer constituting road surface condition estimation system 100 may be an actuator ECU that controls the driving actuator of host vehicle A. The dedicated computer constituting road surface condition estimation system 100 may be an HCU (Human Machine Interface (HMI) Control Unit) that controls information presentation in host vehicle A. The dedicated computer constituting road surface condition estimation system 100 may be a computer other than host vehicle A that constitutes an external center or mobile terminal capable of communication via, for example, a V2X type communication system 30.

[0031] The dedicated computer constituting the road surface condition estimation system 100 may be an integrated ECU (Electronic Control Unit) that integrates the driving control of the host vehicle A. The dedicated computer constituting the road surface condition estimation system 100 may be a determination ECU that determines a driving task in the driving control of the host vehicle A. The dedicated computer constituting the road surface condition estimation system 100 may be a monitoring ECU that monitors the driving control of the host vehicle A. The dedicated computer constituting the road surface condition estimation system 100 may be an evaluation ECU that evaluates the driving control of the host vehicle A.

[0032] The dedicated computer constituting the road surface condition estimation system 100 has at least one memory 101 and one processor 102. The memory 101 is at least one type of non-transitory tangible storage medium, such as a semiconductor memory, a magnetic medium, or an optical medium, that non-temporarily stores computer-readable programs, data, etc. The processor 102 includes at least one type of core, such as a central processing unit (CPU), a graphics processing unit (GPU), a reduced instruction set computer (RISC)-CPU, a data flow processor (DFP), or a graph streaming processor (GSP).

[0033] In the road surface condition estimation system 100, a processor 102 executes a plurality of instructions included in a road surface condition estimation program stored in a memory 101 in order to estimate the condition of the road surface on which the host vehicle A is traveling. In this way, the road surface condition estimation system 100 constructs a plurality of functional blocks for estimating the condition of the road surface on which the host vehicle A is traveling. The plurality of functional blocks constructed in the road surface condition estimation system 100 include an acquisition block 110, a recognition block 120, an estimation block 130, and a correspondence block 140, as shown in FIG. 3 .

[0034] The flow of the road surface condition estimation method (hereinafter referred to as the road surface condition estimation flow) in which the road surface condition estimation system 100 estimates the condition of the road surface on which the host vehicle A is traveling through the cooperation of these blocks 110, 120, 130, and 140 will be described below with reference to Figures 4 and 5. This processing flow is repeatedly executed while the host vehicle A is running. Note that each "S" in this processing flow represents a plurality of steps executed by a plurality of commands included in the road surface condition estimation program.

[0035] First, in S10 of Fig. 4, the recognition block 120 determines whether the vehicle speed of the host vehicle A has reached a determination range. For example, the determination range is a range in which the vehicle speed is equal to or greater than a threshold value (e.g., 50 km / h). If it is determined that the vehicle speed has not reached the determination range, this flow is ended. On the other hand, if it is determined that the vehicle speed has reached the determination range, a determination of the road surface condition is performed in S10.

[0036] In the determination process at S10, the road surface condition is estimated based on the state of scattered matter kicked up from the road surface by the travel of host vehicle A. The scattered matter is, for example, water-related matter such as water droplets or snow. The road surface condition here refers to whether or not water-related matter is present on the road surface in excess of an allowable limit. A state in which water-related matter is present on the road surface in excess of an allowable limit can also be said to be a wet state or a snow-covered state. Such a state can also be said to be a rough road. The scattered matter may be sand, soil, etc. In this case, a rough road surface means that the road surface is in a so-called off-road state.

[0037] 5, the detailed processing of S10 will be explained. First, in S100, the acquisition block 110 acquires reflected light information of the current frame detected by the LiDAR device 10, which has a sensing area behind in the direction of travel. Next, in S110, the recognition block 120 counts the number of reflection points (hereinafter, "points") Nk within the attention area R in the reflected light information of the current frame. The number of points within the attention area R becomes a parameter related to the scattering state of the airborne material in the frame.

[0038] The attention area R is defined by the angle of view of the LiDAR device 10 and a set distance l, for example, as shown in FIG. 2. In FIG. 2, the x direction is the longitudinal direction (driving direction) of the host vehicle A, the y direction is the lateral direction of the host vehicle A, and the z direction is the vertical direction of the host vehicle A. The set distance l is the distance range in the x direction that defines the attention area R. The set distance l is the distance obtained by subtracting a margin from the x direction distance from the origin to the intersection of the lower range of the angle of view and the road surface. The margin may be set taking into consideration, for example, unevenness of the road surface and sinking of the vehicle body. The lateral range of the attention area R may be defined based on the angle of view of the LiDAR device 10 in the lateral direction.

[0039] Therefore, if multiple reflection points included in the point cloud information are defined as in the following formula (1), the recognition block 120 counts reflection points that satisfy both formulas (2) and (3) as reflection points within the attention area R. Through this processing, the recognition block 120 recognizes the reflection points within the attention area R as reflection points of scattered material (see black dots in Figure 2) kicked up by the running of the host vehicle A.

number

number

number

[0040] In the next step S120, the recognition block 120 accumulates the scores counted for each of the multiple frames in the time series. The recognition block 120 accumulates the scores for each frame within the time window ΔT set as shown in FIG. 6 to calculate a total score M. That is, the total score M calculated by the recognition block 120 can be expressed by the following formula (4). This total score M is the recognition result for the state of airborne matter within the region of interest R.

number

[0041] In the next step S130, the estimation block 130 determines whether the total number of points M exceeds the first threshold value M1. If it is determined that the total number of points M exceeds the first threshold value M1, the flow proceeds to step S140. In step S140, the estimation block 130 sets the rough road flag to ON. The total number of points M exceeding the first threshold value M1 corresponds to the degree of dispersion of the airborne matter reaching the rough road determination range. In other words, the total number of points M is an example of the degree of dispersion of the airborne matter based on the number of reflection points of the airborne matter recognized within the attention area R.

[0042] On the other hand, if it is determined in S130 that the total score M is equal to or less than the first threshold M1, the flow proceeds to S150. In S150, the estimation block 130 determines whether the total score M is less than the second threshold M2. The second threshold M2 is a threshold smaller than the first threshold M1. If it is determined that the total score M is equal to or greater than the second threshold M2, the flow ends with the previous setting of the bad road flag maintained.

[0043] Furthermore, if it is determined in S150 that the total score M is less than the second threshold value M2, the flow proceeds to S160. In S160, the estimation block 130 sets the bad road flag to OFF. The estimation block 130 may change the threshold values ​​M1 and M2 according to the vehicle speed. For example, the estimation block 130 may lower the threshold values ​​M1 and M2 as the vehicle speed decreases. Furthermore, instead of determining whether the total score M exceeds the first threshold value M1, the estimation block 130 may determine whether the total score M is equal to or greater than the first threshold value M1. Similarly, instead of determining whether the total score M is less than the second threshold value M2, the estimation block 130 may determine whether the total score M is below the second threshold value M2.

[0044] Returning to Fig. 4, in S20, the corresponding block 140 determines whether the bad road flag is set to ON. If the bad road flag is set to OFF, the process returns to S10. By repeatedly executing the detailed processing S100 to S160 of S10, the road surface condition during travel is successively determined.

[0045] On the other hand, if it is determined that the bad road flag is set to ON, the flow proceeds to S30. In S30, the corresponding block 140 executes processing (bad road processing) corresponding to the setting of the bad road flag to ON.

[0046] As an example of the rough road handling process, the corresponding block 140 suspends autonomous driving of the host vehicle A that is currently operating autonomously. In this suspension process, the corresponding block 140 may terminate the autonomous driving by making an evacuation run and stopping the vehicle. This process is executed, for example, in the host vehicle A that operates autonomously without a driver on board. Alternatively, in the suspension process, the corresponding block 140 may transition from autonomous driving to manual driving. This process is executed, for example, in the host vehicle A that operates autonomously with a driver on board.

[0047] Through the above processing, the road surface condition estimation system 100 estimates whether the road surface condition is bad or not, and if it is bad, suspends autonomous driving. Note that the road surface condition estimation system 100 may continue to repeatedly determine the road surface condition even after suspending autonomous driving. In this case, when the bad road flag is set to OFF after suspending autonomous driving, the corresponding block 140 may execute processing to resume autonomous driving.

[0048] According to the first embodiment described above, the road surface condition is estimated based on the scattering state of scattered material kicked up from the road surface by the traveling of the host vehicle A using reflected light information from the rear in the traveling direction. Therefore, whether or not material kicked up by traveling is present on the road surface can be indirectly estimated based on the scattering state of the scattered material. Therefore, the need for the LiDAR device 10 to scan the road surface is reduced, making it easier to ensure the detectable distance of the LiDAR device 10. As described above, it may be possible to estimate the road surface condition while ensuring the detectable distance of the LiDAR device 10.

[0049] (Second embodiment) As shown in FIG. 7, the second embodiment is a modified example of the first embodiment.

[0050] The corresponding block 140 of the second embodiment performs restricted driving to restrict the behavior of the host vehicle A during autonomous driving as a rough road response process (see S41 in FIG. 7). Specifically, the corresponding block 140 reduces the upper limit value for at least one of the magnitude of acceleration and the magnitude of speed compared to when the rough road flag is set to off. In this way, the corresponding block 140 sets at least one of the acceleration profile and the speed profile in a direction that restricts the behavior, and then continues autonomous driving.

[0051] (Third embodiment) As shown in FIGS. 8 and 9, the third embodiment is a modified example of the first embodiment.

[0052] As a rough road handling process, the corresponding block 140 of the second embodiment changes the detection logic in the LiDAR device 10 for the target vehicle B, which is a leading vehicle traveling ahead of the host vehicle A. Specifically, when multiple echoes including a near-side echo and a far-side echo are detected, the corresponding block 140 detects the far-side echo as an echo corresponding to the target vehicle B.

[0053] More specifically, when the rough road flag is set to off, the corresponding block 140 sets the point cloud detection logic so that the near side echo is detected as the rear end of the target vehicle B (see the upper frame in FIG. 9). If this detection logic is maintained, when multiple echoes are detected due to scattering material being kicked up by the target vehicle B, the echo of the water-related material will be recognized as the rear end of the target vehicle B (see the center frame in FIG. 9).

[0054] Therefore, when the bad road flag is set to ON, the corresponding block 140 changes the detection logic so as to recognize the distant echo as the echo from the target vehicle B (see S42 in FIG. 8). This prevents the corresponding block 140 from recognizing the scattered material stirred up by the target vehicle B as the echo from the target vehicle B (see the lower frame in FIG. 9).

[0055] (Fourth embodiment) As shown in FIG. 10, the fourth embodiment is a modification of the first embodiment.

[0056] The corresponding block 140 of the fourth embodiment transmits the road surface condition to the outside as a rough road handling process (see S43 in FIG. 10). Specifically, the corresponding block 140 notifies the center of information that the road surface on which the vehicle is currently traveling is in a rough road condition.

[0057] (Fifth embodiment) As shown in FIG. 11, the fifth embodiment is a modification of the first embodiment.

[0058] The correspondence block 140 of the fifth embodiment executes a process for dealing with ruts on the road surface (rut response process) as a rough road response process (see S43 in FIG. 11). Specifically, in the rut response process, the correspondence block 140 determines that the greater the number of detected points and the greater the amount of scattered material, the greater the rut depth at that driving position. The rut depth is an example of the rut "occurrence state." Furthermore, in the rut response process, the correspondence block 140 may transmit the determined rut state to an external device such as a center. Alternatively, in the rut response process, the correspondence block 140 may change the driving position of the host vehicle A depending on the rut state. For example, the correspondence block 140 controls the host vehicle A to travel in a left-right position where the rut depth is within an allowable range. In other words, the correspondence block 140 determines the driving position of the host vehicle A to be a position where the number of detected points is equal to or less than a predetermined value.

[0059] (Other embodiments) Although multiple embodiments have been described above, the present disclosure should not be construed as being limited to those embodiments, and can be applied to various embodiments and combinations within the scope that does not deviate from the gist of the present disclosure.

[0060] In a modified example, the estimation block 130 may estimate the degree of the road condition based on the total score M in addition to whether the road condition is bad or not.

[0061] In a modified example, the response block 140 may perform a plurality of processes that can be combined from among the plurality of response processes described in the above embodiment.

[0062] In a modified example, the recognition block 120 may perform a process of excluding raindrops during rainfall or snowflakes during snowfall from the points counted within the attention area R. For example, the recognition block 120 may acquire reflected light information while the host vehicle A is stopped, and subtract the points within the attention area R while the host vehicle A is stopped from the points within the attention area R while the vehicle is moving. Alternatively, the recognition block 120 may determine the points to be excluded from the points within the attention area R based on weather information acquired from a center or the like.

[0063] In a modified example, the dedicated computer constituting the road surface condition estimation system 100 may have at least one of a digital circuit and an analog circuit as a processor. Here, the digital circuit is at least one of an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a system on a chip (SOC), a programmable gate array (PGA), and a complex programmable logic device (CPLD). Furthermore, such a digital circuit may have a memory that stores a program.

[0064] In addition to the forms described above, the road surface condition estimation system 100 according to the above-described embodiments and modifications may be implemented as a road surface condition estimation device that is a processing device (e.g., a processing ECU) mounted on the host vehicle A. Furthermore, the above-described embodiments and modifications may be implemented as a semiconductor device (e.g., a semiconductor chip) having at least one processor 102 and one memory 101 of the road surface condition estimation system 100. [Explanation of symbols]

[0065] 10: LiDAR device (optical sensor), 100: Road surface condition estimation system, 101: Memory (storage medium), 102: Processor, A: Host vehicle, B: Target vehicle (foreground vehicle), R: Region of interest

Claims

1. A road surface condition estimation system having a processor (102), and estimating the condition of a road surface on which a host vehicle (A) is traveling, the system being equipped with an optical sensor (10) that detects reflected light in response to light irradiation, the optical sensor having a sensing area behind the host vehicle and the optical sensor having a sensing area in front of the host vehicle, The processor: acquiring reflected light information behind the host vehicle in the traveling direction by the optical sensor, the sensing area of ​​which is behind the host vehicle; Recognizing the state of scattering of the scattered matter kicked up from the road surface by the running of the host vehicle based on the reflected light information; estimating the state of the road surface based on the scattering state; configured to run Recognizing the scattering state includes recognizing the scattering state in a region of interest (R) within a distance range set behind the host vehicle, estimating the road surface condition includes determining whether the road surface is in a bad road condition; The processor is further configured to, when it is estimated that the road surface is in the bad road state, execute a response process corresponding to the bad road state; A road surface condition estimation system in which executing the response processing includes, when the road surface is estimated to be in the bad road condition, recognizing the point cloud on the forward side as a preceding vehicle (B) in the case where there are point clouds ahead in the driving direction in the reflected light information ahead in the driving direction obtained by the optical sensor, which has the front of the host vehicle as its sensing area.

2. The road surface condition estimation system of claim 1, wherein estimating the road surface condition includes estimating that the road surface is in the bad road state when the degree of dispersion of the scattered material based on the number of reflection points of the scattered material recognized within the area of ​​interest reaches the bad road judgment range.

3. The road surface condition estimation system according to claim 1 or 2, wherein executing the response processing includes stopping the host vehicle, which is in autonomous driving, when the road surface is estimated to be in the bad road condition.

4. 3. The road surface condition estimation system according to claim 1, wherein executing the response processing includes switching the host vehicle, which is currently in automatic driving, to manual driving when the road surface is estimated to be in the bad road condition.

5. 3. The road surface condition estimation system of claim 1 or claim 2, wherein executing the response processing includes performing restricted driving to reduce the upper limit value of at least one of the speed and acceleration magnitude of the host vehicle during autonomous driving when the road surface is estimated to be in the bad road condition.

6. The road surface condition estimation system according to claim 1 , wherein the execution of the response process includes notifying an external device of the host vehicle that the road surface is in the bad road condition.

7. 7. The road surface condition estimation system according to claim 1, wherein executing the response processing further includes determining a rut occurrence state on the road surface when the road surface is estimated to be in the bad road condition.

8. A road surface condition estimation device having a processor (102), and estimating the condition of a road surface on which a host vehicle (A) is traveling, the device being equipped with an optical sensor (10) that detects reflected light in response to light irradiation, the optical sensor having a sensing area behind the host vehicle and the optical sensor having a sensing area in front of the host vehicle, The processor: acquiring reflected light information behind the host vehicle in the traveling direction by the optical sensor, the sensing area of ​​which is behind the host vehicle; Recognizing the state of scattering of the scattered matter kicked up from the road surface by the running of the host vehicle based on the reflected light information; estimating the state of the road surface based on the scattering state; configured to run Recognizing the scattering state includes recognizing the scattering state in a region of interest (R) within a distance range set behind the host vehicle, estimating the road surface condition includes determining whether the road surface is in a bad road condition; The processor is further configured to, when it is estimated that the road surface is in the bad road state, execute a response process corresponding to the bad road state; Executing the response processing includes, when the road surface is estimated to be in the bad road condition, recognizing the point cloud on the forward side as a preceding vehicle (B) in the case where there are point clouds ahead in the driving direction in the reflected light information ahead in the driving direction obtained by the optical sensor, which has the front of the host vehicle as its sensing area.

9. A road surface condition estimation method executed by a processor (102) to estimate the condition of a road surface on which a host vehicle (A) is traveling, the host vehicle being equipped with an optical sensor (10) that detects reflected light in response to light irradiation, the optical sensor having a sensing area behind the host vehicle and the optical sensor having a sensing area in front of the host vehicle, the method comprising: acquiring reflected light information behind the host vehicle in the traveling direction by the optical sensor, the sensing area of ​​which is behind the host vehicle; Recognizing the state of scattering of the scattered matter kicked up from the road surface by the running of the host vehicle based on the reflected light information; estimating the state of the road surface based on the scattering state; Including, Recognizing the scattering state includes recognizing the scattering state in a region of interest (R) within a distance range set behind the host vehicle, estimating the road surface condition includes determining whether the road surface is in a bad road condition; When it is estimated that the road surface is in the bad road state, a corresponding process is executed to correspond to the bad road state; The road surface condition estimation method includes, when the road surface is estimated to be in the bad road condition, performing the response processing, and when there are point clouds ahead in the driving direction, recognizing the point cloud on the forward side as a preceding vehicle (B) in terms of reflected light information ahead in the driving direction obtained by the optical sensor, which has the front of the host vehicle as its sensing area.

10. A road surface condition estimation program is stored in a storage medium (101) for estimating the condition of a road surface on which a host vehicle (A) equipped with an optical sensor (10) that detects reflected light in response to light irradiation, the optical sensor having a sensing area behind the host vehicle and the optical sensor having a sensing area in front of the host vehicle, the program including instructions to be executed by a processor (102), The instruction: acquiring reflected light information behind the host vehicle in the traveling direction by the optical sensor, the sensing area of ​​which is behind the host vehicle; Based on the reflected light information, a scattering state of the scattered matter kicked up from the road surface by the running of the host vehicle is recognized; Estimating the state of the road surface based on the scattering state; Including, Recognizing the scattering state includes recognizing the scattering state in a region of interest (R) within a distance range set behind the host vehicle, estimating the road surface condition includes determining whether the road surface is in a bad road condition, the instruction includes, when it is estimated that the road surface is in the bad road state, executing a response process corresponding to the bad road state; Executing the response processing is a road surface condition estimation program that includes, when the road surface is estimated to be in the bad road condition, recognizing the point cloud in front of the host vehicle (B) as a preceding vehicle (B) when there are point clouds ahead of and ahead in the driving direction, regarding reflected light information ahead of the driving direction obtained by the optical sensor, which has the front of the host vehicle as its sensing area.

Images