JP7729166B2 - Road surface condition estimation system, road surface condition estimation device, road surface condition estimation method, and road surface condition estimation program - Google Patents

Description

This disclosure relates to road surface condition estimation technology that estimates the condition of the road surface on which a host vehicle is traveling.

Patent Document 1 discloses a road surface condition estimation device that estimates the condition of the road surface on which a vehicle is traveling. This road surface condition estimation device uses a laser radar to emit laser light to scan the road surface and obtain reflection intensity values corresponding to each irradiation point. The road surface condition estimation device is equipped with a road surface condition classifier that is generated by machine learning and uses the reflection intensity values to determine the road surface condition. The road surface condition classifier receives as input a multi-point reflection intensity value set obtained for a road surface with unknown road surface conditions, and determines and outputs the road surface condition.

JP 2014-228300 A

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

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. A further object of the present disclosure is to provide a road surface condition estimation method. A further object of the present disclosure is to provide a road surface condition estimation program.

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

A first aspect of the present disclosure is a road surface condition estimation system that includes a processor (102) and an optical sensor (10) that detects reflected light in response to light irradiation, the 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, and that estimates the condition of a road surface on which a host vehicle is traveling, the host vehicle being equipped with the optical sensor,
The processor
acquiring reflected light information behind the host vehicle in the traveling direction by an optical sensor having a sensing area behind the host vehicle ;
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 processing includes, when it is estimated that the road surface is in a bad condition, recognizing the point cloud in front as the vehicle ahead (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 an optical sensor whose sensing area is ahead of the host vehicle .

A second aspect of the present disclosure is a road surface condition estimation device that includes a processor (102) and an optical sensor (10) that detects reflected light in response to light irradiation, the 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, and that estimates the condition of a road surface on which a host vehicle is traveling, the host vehicle being equipped with the optical sensor,
The processor
acquiring reflected light information behind the host vehicle in the traveling direction by an optical sensor having a sensing area behind the host vehicle ;
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 processing includes, when it is estimated that the road surface is in a bad condition, recognizing the point cloud in front as the vehicle ahead (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 an optical sensor whose sensing area is ahead of the host vehicle .

A third aspect of the present disclosure is 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 an 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 an optical sensor having a sensing area behind the host vehicle ;
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,
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 processing includes, when it is estimated that the road surface is in a bad condition, recognizing the point cloud in front as the vehicle ahead (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 an optical sensor whose sensing area is ahead of the host vehicle .

A fourth aspect of the present disclosure is a road surface condition estimation program 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 host vehicle (A) having an optical sensor with a sensing area behind the host vehicle and an optical sensor with a sensing area ahead of the host vehicle, is traveling, the program including instructions to be executed by a processor (102),
The command is,
acquiring reflected light information behind the host vehicle in the traveling direction by an optical sensor having a sensing area behind the host vehicle ;
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,
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 processing includes, when it is estimated that the road surface is in a bad condition, recognizing the point cloud in front as the vehicle ahead (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 an optical sensor whose sensing area is ahead of the host vehicle .

According to these first to fourth aspects, the road surface condition is estimated based on the scattering state of airborne material kicked up from the road surface by the host vehicle's travel using reflected light information from behind in the direction of travel. 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 airborne 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 is possible to estimate the road surface condition while ensuring the optical sensor's detectable distance.

1 is a block diagram showing the overall configuration of a first embodiment; FIG. 2 is a schematic diagram showing a traveling environment of a host vehicle to which the first embodiment is applied. 1 is a block diagram showing a functional configuration of a road surface condition estimation system according to a first embodiment. FIG. 3 is a flowchart showing a road surface condition estimation method according to the first embodiment. 5 is a flowchart showing the detailed processing of FIG. 4; FIG. 10 is a diagram showing a method for counting points. 10 is a flowchart showing a road surface condition estimation method according to a second embodiment. 10 is a flowchart showing a road surface condition estimation method according to a third embodiment. FIG. 10 is a schematic diagram showing a difference in detection logic. 10 is a flowchart showing a road surface condition estimation method according to a fourth embodiment. 13 is a flowchart showing a road surface condition estimation method according to a fifth embodiment.

Below, several embodiments of the present disclosure will be described with reference to the drawings. Note that corresponding components in each embodiment will be given the same reference numerals, and duplicate descriptions 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 that configuration. Furthermore, in addition to the combinations of configurations explicitly stated in the description of each embodiment, configurations of multiple embodiments may also be partially combined together even if not explicitly stated, provided that there are no particular problems with the combination.

(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 perspective centered on the host vehicle A, the host vehicle A can also be said to be an ego-vehicle. From a perspective centered on the host vehicle A, the target vehicle B can also be said to be another road user.

Host vehicle A is provided with an autonomous driving mode that is divided 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.

Host vehicle A is equipped with a LiDAR device 10, an internal sensor 20, a communication system 30, and a driving control system 40, as shown in Figure 3.

The LiDAR device 10 is an optical sensor that measures the distance to a reflection point by detecting light reflected from the reflection point in response to light irradiation. 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 also be provided with a LiDAR device 10 whose sensing area is the front of the host vehicle A. That is, multiple 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.

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 vehicle A. The light-receiving unit 12 is composed of highly sensitive light-receiving elements, such as a SPAD (Single Photon Avalanche Diode). Multiple light-receiving elements are arranged in a two-dimensional array. A set of multiple 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 enters from a sensing area, determined by the angle of view of the light-receiving unit 12, from the outside of the light-receiving unit 12.

The actuator 13 controls the reflection angle of the reflector that reflects the laser light emitted from the light emitter 11 onto the emission surface of the LiDAR device 10. The laser light is scanned by the actuator 13 controlling the reflection angle of the reflector. 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.

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 that includes at least one memory and one processor. The memory is at least one type of non-transitory tangible storage medium, such as semiconductor memory, magnetic media, or optical media, that non-temporarily stores or stores computer-readable programs and data. The memory stores various programs executed by the processor.

The control circuit 14 controls the exposure and scanning of multiple pixels in the light receiving unit 12, and processes signals from the light receiving unit 12 to convert them into data. The control circuit 14 can also perform reflected light detection, in which the light receiving unit 12 detects reflected light from light emitted by the light emitting unit 11.

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 from 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.

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 processing for all pixels, the control circuit 14 can generate a point cloud image containing distance information for each pixel. A point cloud image is an example of "reflected light information."

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

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 when detecting reflected light and background light. The control circuit 14 changes the scanning speed by controlling the actuator 13.

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 internal information by detecting specific physical quantities 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.

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 GNSS (Global Navigation Satellite System) satellites located outside the host vehicle A. A 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 located outside the host vehicle A. A V2X-type communication system 30 is, for example, at least one of a DSRC (Dedicated Short Range Communications) 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 located inside the host vehicle A. A 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.

The driving control system 40 is configured to control the driving of the host vehicle A. The driving control system 40 includes a steering ECU that controls steering, a power unit control ECU that controls acceleration and deceleration, and a brake ECU. The driving 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 driving control devices, such as an electronically controlled throttle, brake actuator, and EPS (Electric Power Steering) motor. The driving control system 40 acquires control instructions for the host vehicle A from the road surface condition estimation system 100, etc., and controls each driving control device to achieve automatic driving or manual driving in accordance with the control instructions.

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

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

The dedicated computer that constitutes 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 that constitutes the road surface condition estimation system 100 may be a judgment ECU that determines the driving task in the driving control of the host vehicle A. The dedicated computer that constitutes 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 that constitutes the road surface condition estimation system 100 may be an evaluation ECU that evaluates the driving control of the host vehicle A.

The dedicated computer that constitutes 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 semiconductor memory, magnetic media, or optical media, that non-temporarily stores computer-readable programs and data. The processor 102 includes at least one type of core, such as a CPU (Central Processing Unit), GPU (Graphics Processing Unit), RISC (Reduced Instruction Set Computer)-CPU, DFP (Data Flow Processor), or GSP (Graph Streaming Processor).

In the road surface condition estimation system 100, the processor 102 executes multiple instructions contained in a road surface condition estimation program stored in the memory 101 to estimate the condition of the road surface on which the host vehicle A is traveling. As a result, the road surface condition estimation system 100 constructs multiple functional blocks for estimating the condition of the road surface on which the host vehicle A is traveling. The multiple 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.

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, using the cooperation of these blocks 110, 120, 130, and 140, is described below with reference to Figures 4 and 5. This processing flow is executed repeatedly while the host vehicle A is running. Note that each "S" in this processing flow represents multiple steps executed by multiple commands included in the road surface condition estimation program.

First, in S10 of Figure 4, the recognition block 120 determines whether the vehicle speed of host vehicle A has reached the determination range. For example, the determination range is the 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 ends. 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 made in S10.

In the determination process at S10, the road surface condition is estimated based on the state of airborne matter kicked up from the road surface by the travel of host vehicle A. Airborne 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 the allowable limit. A state in which water-related matter is present on the road surface in excess of the allowable limit can also be said to be a wet state or a state of snow accumulation. Such a state can also be said to be a rough road surface. Note that airborne 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.

The detailed processing of S10 will be explained with reference to Figure 5. First, in S100, the acquisition block 110 acquires reflected light information for the current frame detected by the LiDAR device 10, whose sensing area is the rear in the direction of travel. Next, in S110, the recognition block 120 counts the number of reflection points (hereinafter referred to as points) Nk within the region of interest R in the reflected light information for the current frame. The number of points within the region of interest R becomes a parameter related to the state of dispersion of airborne matter in that frame.

As shown in FIG. 2, the region of interest R is defined by the angle of view of the LiDAR device 10 and a set distance l. 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 region of interest R. The set distance l is the distance in the x direction from the origin to the intersection of the lower range of the angle of view and the road surface, minus a margin. The margin may be set, for example, taking into account unevenness in the road surface and sinking of the vehicle body. The left-right range of the region of interest R may be defined based on the left-right angle of view of the LiDAR device 10.

Therefore, if the multiple reflection points included in the point cloud information are defined as in the following formula (1), the recognition block 120 counts the 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 the black dots in Figure 2) kicked up by the running of the host vehicle A.

In the next step S120, the recognition block 120 adds up the scores counted for each of the multiple frames in the time series. The recognition block 120 calculates a total score M by adding up the scores for each frame within the time window ΔT set as shown in FIG. 6. That is, the total score M calculated by the recognition block 120 can be expressed by the following equation (4). This total score M is the recognition result for the airborne state of the airborne material within the region of interest R.

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

On the other hand, if it is determined in S130 that the total score M is less than or equal to 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 that is smaller than the first threshold M1. If it is determined that the total score M is greater than or equal to the second threshold M2, the flow ends with the previous setting of the bad road flag maintained.

Also, 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 rough road flag to OFF. Note that the estimation block 130 may change the threshold values M1 and M2 depending on the vehicle speed. For example, the estimation block 130 may lower the threshold values M1 and M2 as the vehicle speed decreases. Also, 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.

Returning to Figure 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 of S10, S100 to S160, the road surface conditions during travel are successively determined.

On the other hand, if it is determined that the rough road flag is set to ON, the flow proceeds to S30. In S30, the corresponding block 140 executes processing corresponding to the rough road flag being set to ON (rough road response processing).

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

Through the above processing, the road surface condition estimation system 100 estimates whether the road surface condition is bad, and if so, suspends autonomous driving. The road surface condition estimation system 100 may continue to repeatedly determine the road surface condition even after suspending autonomous driving. In this case, if the bad road flag is set to off after suspending autonomous driving, the corresponding block 140 may execute processing to resume autonomous driving.

According to the first embodiment described above, the road surface condition is estimated based on the scattering state of airborne material kicked up from the road surface by the movement of host vehicle A using reflected light information from behind in the direction of travel. Therefore, whether or not material kicked up by the movement is present on the road surface can be indirectly estimated based on the scattering state of the airborne material. This reduces the need for the LiDAR device 10 to scan the road surface, making it easier to ensure the detectable distance of the LiDAR device 10. As a result, it is possible to estimate the road surface condition while ensuring the detectable distance of the LiDAR device 10.

Second Embodiment
As shown in FIG. 7, the second embodiment is a modification of the first embodiment.

In the second embodiment, the corresponding block 140 performs restricted driving, which restricts the behavior of the host vehicle A during autonomous driving, as a rough road response process (see S41 in Figure 7). Specifically, the corresponding block 140 reduces the upper limit value for at least one of the acceleration magnitude and the speed magnitude 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.

(Third embodiment)
As shown in FIGS. 8 and 9, the third embodiment is a modification of the first embodiment.

In the second embodiment, the correspondence block 140 performs rough road handling processing by changing the detection logic of 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 echo and a far echo, are detected, the correspondence block 140 detects the far echo as the echo corresponding to the target vehicle B.

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

Therefore, when the rough road flag is set to ON, the corresponding block 140 changes the detection logic so that the distant echo is recognized as target vehicle B (see S42 in Figure 8). This prevents the corresponding block 140 from recognizing the scattered material kicked up by target vehicle B as an echo from target vehicle B (see the bottom frame in Figure 9).

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

In the fourth embodiment, the correspondence block 140 transmits road surface conditions to the outside as part of the rough road response process (see S43 in Figure 10). Specifically, the correspondence block 140 notifies the center of information that the road surface on which the vehicle is currently traveling is in a rough road condition.

Fifth Embodiment
As shown in FIG. 11, the fifth embodiment is a modification of the first embodiment.

In the fifth embodiment, the correspondence block 140 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 location, 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 acceptable range. In other words, the correspondence block 140 determines the driving position of the host vehicle A to be the position where the number of detected points is equal to or less than a predetermined value.

(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.

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

In a modified example, the response block 140 may perform multiple processes that can be combined from among the multiple response processes described in the above embodiment.

In a modified example, the recognition block 120 may perform processing to exclude 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 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 exclude from the points within the attention area R based on weather information acquired from a center, etc.

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, a digital circuit is at least one of the following: an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), an SOC (System on a Chip), a PGA (Programmable Gate Array), and a CPLD (Complex Programmable Logic Device). Furthermore, such a digital circuit may have memory that stores a program.

In addition to the embodiments described above, the road surface condition estimation system 100 according to the above-described embodiments and variations may be implemented as a road surface condition estimation device that is a processing device (e.g., a processing ECU, etc.) mounted on the host vehicle A. The above-described embodiments and variations may also be implemented as a semiconductor device (e.g., a semiconductor chip, etc.) that has at least one processor 102 and one memory 101 of the road surface condition estimation system 100.

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

Claims (10)

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