JP2023059629A - Road surface state estimation system, road surface state estimation device, road surface state estimation method and road surface state estimation program - Google Patents

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

The present disclosure relates to a road surface condition estimation technique for estimating the condition of a road surface on which a host vehicle travels.

Patent Document 1 discloses a road surface state estimation device that estimates the state of a road surface on which a vehicle travels. This road surface condition estimation device irradiates a laser beam so as to scan the road surface with a laser radar, and obtains a reflection intensity value corresponding to each irradiation point. The road surface condition estimation device includes a road surface condition discriminator which is generated by machine learning and discriminates the road surface condition using the reflection intensity value. The road surface condition discriminator receives as input a set of multi-point reflection intensity values obtained for a road surface with an unknown road surface condition, and discriminates and outputs the road surface condition.

Japanese Patent Application Laid-Open No. 2014-228300

In the technique of Patent Document 1, it is necessary to install the laser radar so that the laser beam scans the road surface. However, in that 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 capable of estimating the condition of the road surface while ensuring the detectable distance of the 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 state estimation method. Yet another object of the present disclosure is to provide a road surface state estimation program.

Technical means of the present disclosure for solving the problems will be described below. It should be noted that the symbols in parentheses described in the claims and this column indicate the correspondence with specific means described in the embodiments described in detail later, and limit the technical scope of the present disclosure. not something to do.

A first aspect of the present disclosure has a processor (102) and is equipped with an optical sensor (10) that detects reflected light according to light irradiation. An estimation system,
The processor
Acquiring reflected light information behind the traveling direction by an optical sensor;
Recognizing the state of scattering of scattered substances that have been lifted 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

A second aspect of the present disclosure has a processor (102) and is equipped with an optical sensor (10) that detects reflected light according to light irradiation. An estimating device,
The processor
Acquiring reflected light information behind the traveling direction by an optical sensor;
Recognizing the state of scattering of scattered substances that have been lifted 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

A third aspect of the present disclosure is executed by a processor (102) to estimate 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. A road surface state estimation method comprising:
Acquiring reflected light information behind the traveling direction by an optical sensor;
Recognizing the state of scattering of scattered substances that have been lifted 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.

A fourth aspect of the present disclosure is stored in a storage medium (101) for estimating the state of a road surface on which a host vehicle (A) equipped with an optical sensor (10) that detects reflected light according to light irradiation. , a road surface condition estimation program including instructions to be executed by a processor (102),
the instruction is
Acquiring reflected light information behind the traveling direction by an optical sensor;
Recognizing the state of scattering of scattered substances that have been lifted 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.

According to the first to fourth aspects, the state of the road surface can be estimated based on the state of scattering of the scattered substances that have been lifted up from the road surface by the running of the host vehicle, based on the reflected light information behind the vehicle in the running direction. Therefore, it is possible to indirectly estimate whether or not there is a substance on the road surface that is picked up by running, based on the scattering state of the flying substance. This reduces the need for the optical sensor to scan the road surface, making it easier to secure a detectable distance for the sensor. As described above, it is possible to estimate the state of the road surface while ensuring the detectable distance of the optical sensor.

It is a block diagram showing the whole composition of a 1st embodiment. FIG. 2 is a schematic diagram showing a running environment of a host vehicle to which the first embodiment is applied; 1 is a block diagram showing the functional configuration of a road surface condition estimation system according to a first embodiment; FIG. 4 is a flowchart showing a road surface state estimation method according to the first embodiment; 5 is a flowchart showing detailed processing of FIG. 4; It is a figure which shows the counting method of a score. 8 is a flowchart showing a road surface state estimation method according to the second embodiment; 9 is a flowchart showing a road surface state estimation method according to the third embodiment; It is a schematic diagram which shows the difference of a detection logic. 14 is a flowchart showing a road surface state estimation method according to the fourth embodiment; 14 is a flowchart showing a road surface state estimation method according to the fifth embodiment;

A plurality of embodiments of the present disclosure will be described below based on the drawings. Note that redundant description may be omitted by assigning the same reference numerals to corresponding components in each embodiment. Moreover, when only a part of the configuration is described in each embodiment, the configurations of the other embodiments previously described can be applied to the other portions of the configuration. Furthermore, not only the combinations of the configurations explicitly specified in the description of each embodiment, but also the configurations of the multiple embodiments can be partially combined even if they are not explicitly specified unless there is a particular problem 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 the host vehicle A shown in FIG. 2 travels. From a viewpoint centering on the host vehicle A, the host vehicle A can also be said to be an ego-vehicle. From the viewpoint centered on the host vehicle A, the target vehicle B can also be said to be a user on another road.

The host vehicle A is given an automatic driving mode that is classified according to the degree of manual intervention of the driver in the driving task. Autonomous driving modes may be achieved by autonomous cruise control, such as conditional driving automation, advanced driving automation, or full driving automation, in which the system performs all driving tasks when activated. Autonomous driving modes may be provided by advanced driving assistance controls, such as driving assistance or partial driving automation, in which the occupant performs some or all driving tasks. The automatic driving mode may be realized by either one, combination, or switching of the autonomous driving control and advanced driving support control.

The host vehicle A is equipped with the LiDAR device 10, the internal sensor 20, the communication system 30, and the travel control system 40 shown in FIG.

The LiDAR device 10 is an optical sensor that measures the distance to a reflection point by detecting reflected light from the reflection point with respect to irradiation of light. The host vehicle A is provided with a LiDAR device 10 having a sensing area at least behind the host vehicle A, for example. The host vehicle A may further be provided with a LiDAR device 10 having a sensing area in front of the host vehicle A. FIG. That is, the host vehicle A may be provided with a plurality of LiDAR devices 10 . Alternatively, the LiDAR device 10 having a sensing area substantially all around the host vehicle A may be provided. The LiDAR device 10 includes a light emitting section 11, a light receiving section 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 irradiates laser light toward the outside of the vehicle A in the form of an intermittent pulse beam. The light receiving unit 12 is configured by a light receiving element having high sensitivity to light, such as SPAD (Single Photon Avalanche Diode). A plurality of light receiving elements are arranged in an array in a two-dimensional direction. A set of adjacent light-receiving elements constitutes one light-receiving pixel (hereinafter also referred to as pixel). It should be noted that the number of light-receiving elements forming one light-receiving pixel can be changed by the control circuit 14 . The light-receiving element is exposed to the light incident from the sensing area determined by the angle of view of the light-receiving unit 12 in the external world of the light-receiving unit 12 .

The actuator 13 controls the reflection angle of a reflector that reflects the laser light emitted from the light emitting unit 11 to the emission surface of the LiDAR device 10 . The laser beam is scanned by the actuator 13 controlling the angle of reflection of the reflecting mirror. The scanning direction may be horizontal or vertical. The actuator 13 may 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 section 11 , the light receiving section 12 and the actuator 13 . The control circuit 14 is a computer including at least one memory and at least one processor. Memory is at least one type of non-transitory tangible storage medium such as semiconductor memory, magnetic medium, optical medium, etc., for non-transitory storage or storage of computer-readable programs and data. medium). The memory stores various programs that are executed by the processor.

The control circuit 14 controls exposure and scanning of a plurality of pixels in the light receiving section 12, and processes signals from the light receiving section 12 into data. The control circuit 14 can perform reflected light detection in which the light receiving section 12 detects the reflected light of the light emitted from the light emitting section 11 .

In reflected light detection, the laser light emitted from the light emitting unit 11 hits an object within the sensing area and is reflected. This reflected portion becomes a reflection point of the laser beam. The laser light reflected at the reflection point (hereinafter referred to as reflected light) is incident on the light receiving section 12 through the incident surface and exposed. At this time, the control circuit 14 scans a plurality of pixels of the light receiving unit 12 to obtain reflected light at various angles within the angle of view. Thereby, the control circuit 14 acquires a point cloud image of the reflecting object.

Specifically, the control circuit 14 obtains the intensity of the reflected light obtained by scanning each pixel within a certain period of time, or a value obtained based on the intensity (hereinafter referred to as reflection intensity). Accumulated for each distance. Thereby, the control circuit 14 obtains a histogram of distance and reflection intensity as shown in FIG. The control circuit 14 calculates the distance to the reflection point based on the reflection intensity of each bin of the histogram. Specifically, the control circuit 14 generates an approximated curve for bins equal to or greater than a predetermined threshold value, and defines the extreme value of the approximated curve as the distance to the reflection point in that pixel. The control circuit 14 can generate a point cloud image including distance information for each pixel by performing the above-described processing for all pixels. A point cloud image is an example of "reflected light information."

The control circuit 14 can also detect background light 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.

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 detecting background light. The control circuit 14 changes the scan speed by controlling the actuator 13 .

The inner world sensor 20 acquires inner world information that can be used by the road surface state estimation system 100 from the inner world that is the internal environment of the host vehicle A. FIG. The inner world sensor 20 acquires inner world information by detecting a specific kinematic physical quantity in the inner world of the host vehicle A. FIG. 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, and the like.

The communication system 30 acquires communication information that can be used by the road surface condition estimation system 100 through wireless communication. The communication system 30 may receive positioning signals from artificial satellites of GNSS (Global Navigation Satellite System) existing outside the host vehicle A. The positioning type communication system 30 is, for example, a GNSS receiver or the like. The communication system 30 may transmit and receive communication signals to and from a V2X system existing outside the host vehicle A. The V2X type communication system 30 is, for example, at least one of a DSRC (Dedicated Short Range Communications) communication device, a cellular V2X (C-V2X) communication device, and the like. The communication system 30 may transmit and receive communication signals to and from terminals existing in the inner world of the host vehicle A. The terminal communication type communication system 30 is, for example, at least one of Bluetooth (registered trademark) equipment, Wi-Fi (registered trademark) equipment, infrared communication equipment, and the like.

The travel control system 40 is configured to perform travel control of the host vehicle A. FIG. The traveling control system 40 includes a steering ECU that performs steering control, a power unit control ECU that performs acceleration/deceleration control, a brake ECU, and the like. The travel control system 40 acquires detection signals output from sensors such as a steering angle sensor and a vehicle speed sensor mounted on the host vehicle A, and outputs signals from an electronically controlled throttle, a brake actuator, an EPS (Electric Power Steering) motor, and the like. Outputs control signals to the running control device. The cruise control system 40 acquires a control instruction for the host vehicle A from the road surface state estimation system 100 or the like, and controls each cruise control device so as to realize automatic driving or manual driving according to the control instruction.

The road surface condition estimation system 100 communicates with the LiDAR device 10, the internal sensor 20, the communication system 30, and the vehicle through at least one of, for example, a LAN (Local Area Network) line, a wire harness, an internal bus, a wireless communication line, and the like. It is connected to the control system 40 . The road surface state estimation system 100 includes at least one dedicated computer.

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

The dedicated computer that constitutes the road surface state estimation system 100 may be an integrated ECU (Electronic Control Unit) that integrates the operation control of the host vehicle A. The dedicated computer that constitutes the road surface condition estimation system 100 may be a judgment ECU that judges the driving task in the driving control of the host vehicle A. FIG. A dedicated computer that constitutes the road surface state estimation system 100 may be a monitoring ECU that monitors the operation control of the host vehicle A. FIG. The dedicated computer that constitutes the road surface state estimation system 100 may be an evaluation ECU that evaluates the driving control of the host vehicle A.

A dedicated computer that constitutes the road surface state estimation system 100 has at least one memory 101 and at least one processor 102 . The memory 101 stores computer-readable programs, data, etc., non-temporarily, and includes at least one type of non-transitory storage medium such as a semiconductor memory, a magnetic medium, and an optical medium. tangible storage medium). The processor 102 is, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a RISC (Reduced Instruction Set Computer)-CPU, a DFP (Data Flow Processor), a GSP (Graph Streaming Processor), or the like. as a core.

In the road surface condition estimation system 100, the processor 102 executes a plurality of instructions contained in the road surface condition estimation program stored in the memory 101 in order to estimate the condition of the road surface on which the host vehicle A travels. Thus, 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 travels. A 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.

The flow of the road surface condition estimation method for estimating the condition of the road surface on which the host vehicle A travels by the road surface condition estimation system 100 (hereinafter referred to as the road surface condition estimation flow) is shown in FIG. 4 and 5 will be described below. This processing flow is repeatedly executed while the host vehicle A is running. Each "S" in this processing flow means a plurality of steps executed by a plurality of instructions included in the road surface condition estimation program.

First, in S10 of FIG. 4, the recognition block 120 determines whether the vehicle speed of the host vehicle A has reached the determination range. For example, the determination range is a range in which the vehicle speed is equal to or greater than a threshold value (for example, 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, the road surface condition is determined at S10.

In the determination process in S10, the state of the road surface is estimated based on the scattering state of the scattering substances that have been lifted up from the road surface as the host vehicle A travels. Scattered substances are, for example, water-related substances such as water droplets and snow. Moreover, the state of the road surface here means whether or not water-related substances are present on the road surface in excess of the allowable limit. The state in which water-related substances are present on the road surface in excess of the permissible limit can also be said to be a wet state or a snow-covered state of the road surface. Such a state can also be said that the road surface is rough. Incidentally, the scattering substance may be sand, soil, or the like. In this case, the fact that the road surface is rough means that the road surface is in a so-called off-road state.

The detailed processing of S10 will be explained according to FIG. In subsequent S110, the recognition block 120 counts the number of reflection points (hereinafter referred to as the number of points) Nk within the attention area R in the reflected light information of the current frame. The number of points in the region of interest R becomes a parameter related to the scattering state of the scattering substance in the frame.

The region of interest R is defined by, for example, the angle of view of the LiDAR device 10 and the set distance l, as shown in FIG. In FIG. 2, the x direction is the longitudinal direction (running 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. As shown in FIG. The set distance l is a distance range in the x direction that defines the region of interest R. The set distance l is a 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, for example, in consideration of unevenness of the road surface and subsidence of the vehicle body. Note that the range of the attention area R in the horizontal direction may be defined based on the angle of view of the LiDAR device 10 in the horizontal direction.

Therefore, when a plurality of reflection points included in the point cloud information are defined as in the following formula (1), the recognition block 120 detects reflection points that satisfy both formulas (2) and (3) in the region of interest R count as reflection points. Through such processing, the recognition block 120 recognizes the reflection points in the region of interest R as reflection points of scattered substances (see black dots in FIG. 2) that have been picked up by the running host vehicle A.

In subsequent S120, the recognition block 120 adds up the points counted in each of the plurality of time-series frames. The recognition block 120 calculates the total score M by accumulating the scores in each frame within the time window ΔT set as shown in FIG. That is, the total score M calculated by the recognition block 120 can be represented by the following formula (4). This total score M is the recognition result of the scattering state of the scattering substance in the region of interest R. FIG.

In subsequent S130, the estimation block 130 determines whether or not the total score M exceeds the first threshold M1. If it is determined that it exceeds, the flow shifts to S140. In S140, the estimation block 130 sets the rough road flag to ON. The fact that the total score M exceeds the first threshold value M1 corresponds to the fact that the scattering degree of the flying substance has reached the rough road determination range. That is, the total score M is an example of the scattering degree of the scattered substance based on the number of reflection points of the scattered substance recognized within the attention area R.

On the other hand, if it is determined in S130 that the total score M is equal to or less than the first threshold value 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 value M2, this flow ends with the rough road flag set just before.

Further, when it is determined in S150 that the total score M is less than the second threshold value M2, the flow shifts to S160. At S160, the estimation block 130 sets the rough road flag to OFF. Note that the estimation block 130 may change the thresholds M1 and M2 according to the vehicle speed. For example, the estimation block 130 may decrease the thresholds M1 and M2 as the vehicle speed decreases. Also, the estimation block 130 may determine whether the total score M is greater than or equal to the first threshold M1 instead of determining whether the total score M exceeds the first threshold M1. Similarly, instead of determining whether the total score M is less than the second threshold M2, the estimation block 130 may determine whether the total score M is less than the second threshold.

Returning to FIG. 4, in S20, the corresponding block 140 determines whether or not the rough road flag is set to ON. If the rough road flag is set to OFF, the process returns to S10. By repeatedly executing the detailed processes S100 to S160 of S10, the road surface condition during running is 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 setting of the rough road flag (rough road processing).

As an example of the bad road handling process, the handling block 140 suspends the automatic driving of the host vehicle A, which is currently being driven automatically. In this suspension process, the corresponding block 140 may terminate the automatic driving by evacuating and stopping the vehicle. This processing is executed, for example, in the host vehicle A that operates automatically without a driver on board. Alternatively, in the abort process, the response block 140 may execute a transition from automatic to manual operation. This process is executed, for example, in the host vehicle A that runs automatically with a driver on board.

Through the above processing, the road surface condition estimation system 100 estimates whether or not the road surface condition is rough, and if the road is rough, stops the automatic driving. Note that the road surface state estimation system 100 may continue to repeat determination of the road surface state even after stopping the automatic driving. In this case, when the bad road flag is set to OFF after the automatic driving is stopped, the corresponding block 140 may execute the process of restarting the automatic driving.

According to the above-described first embodiment, the state of the road surface is estimated based on the state of scattering of the scattering material that is lifted up from the road surface by the host vehicle A running, based on the reflected light information behind the vehicle in the running direction. Therefore, it is possible to indirectly estimate whether or not there is a substance on the road surface that is picked up by running, based on the scattering state of the flying substance. 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 described above, it is possible to estimate the state of the road surface 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.

The countermeasure block 140 of the second embodiment executes restricted travel that restricts the behavior of the host vehicle A in automatic driving as rough road countermeasure processing (see S41 in FIG. 7). Specifically, the corresponding block 140 sets the upper limit value of at least one of the magnitude of acceleration and the magnitude of velocity smaller than 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 the direction of restricting behavior, and then continues automatic driving.

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

The correspondence block 140 of the second embodiment changes the detection logic in the LiDAR device 10 for the target vehicle B, which is the preceding vehicle traveling in front of the host vehicle A, as rough road handling processing. Specifically, the corresponding block 140 detects the echo on the far side as the echo corresponding to the target vehicle B when the multi-echo of the echo on the near side and the echo on the far side is detected.

Specifically, if the bad road flag is set to off, the corresponding block 140 sets the point cloud detection logic to detect the near-side echo as the trailing edge of the target vehicle B (FIG. 9). (see upper frame of ). Supposing this detection logic is maintained, if multi-echoes are detected due to the scattered substances being blown up by the target vehicle B, the echoes of the water-related substances are recognized as the rear end of the target vehicle B (center frame in FIG. 9). reference).

Therefore, when the rough road flag is set to ON, the corresponding block 140 changes the detection logic so that the echo on the far side is recognized as the target vehicle B (see S42 in FIG. 8). As a result, the corresponding block 140 avoids recognizing the flying material blown up by the target vehicle B as an echo from the target vehicle B (see the lower frame in FIG. 9).

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

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

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

The correspondence block 140 of the fifth embodiment executes a process for dealing with ruts on the road surface (rut handling process) as the rough road handling process (see S43 in FIG. 11). Specifically, as the rut handling process, the handling block 140 determines that the greater the number of detected points and the greater the amount of scattered matter, the greater the depth of the rut at that travel position. The size of the depth of the rut is an example of the "state of occurrence" of the rut. Further, as a rut handling process, the handling block 140 may transmit the determined rut state to an external device such as a center. Alternatively, the response block 140 may change the running position of the host vehicle A according to the rut state as the rut response process. For example, the corresponding block 140 controls the host vehicle A so that it travels in the left and right positions where the rut depth is within the allowable range. In other words, the corresponding block 140 determines the travel position of the host vehicle A as the position where the detected score is less than or equal to the predetermined value.

(Other embodiments)
Although a plurality of embodiments have been described above, the present disclosure is not to be construed as being limited to those embodiments, and can be applied to various embodiments and combinations within the scope of the present disclosure. can be done.

In a modified example, the estimation block 130 may estimate the degree of rough road in the rough road state based on the total score M, in addition to whether the road is rough.

In a modified example, the correspondence block 140 may perform a plurality of combinable processes out of the plurality of correspondence processes described in the above embodiments.

In a modified example, the recognition block 120 may perform a process of excluding raindrops during rain or snow during snowfall from the points counted within the region of interest R. FIG. For example, the recognition block 120 may acquire the reflected light information of the host vehicle A while it is stopped, and may remove the points within the attention area R while the host vehicle A is stopped from the points within the attention area R while the host vehicle is running. Alternatively, the recognition block 120 may determine points to be excluded from the points within the attention area R based on weather information obtained from a center or the like.

In a modified example, a dedicated computer that constitutes 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 includes, for example, ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), SOC (System on a Chip), PGA (Programmable Gate Array), and CPLD (Complex Programmable Logic Device). , at least one Such digital circuits may also have a memory that stores the program.

In addition to the embodiments 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 (eg, a processing ECU, etc.) mounted on the host vehicle A. . Also, the above-described embodiments and variations may be implemented as a semiconductor device (such as a semiconductor chip) having at least one processor 102 and at least 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 (forward vehicle), R: attention area

Claims (13)

A road surface condition estimation system that has a processor (102) and is equipped with an optical sensor (10) that detects reflected light according to light irradiation and estimates the condition of a road surface on which a host vehicle (A) travels,
The processor
Acquiring reflected light information behind the traveling direction by the optical sensor;
Recognizing the scattering state of the scattering material that is lifted 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;
A road condition estimation system configured to perform a Recognizing the scattering state includes recognizing the scattering state in a region of interest (R) within a distance range set behind the host vehicle;
2. The road surface condition estimation system according to claim 1, wherein estimating the condition of the road surface includes determining whether the road surface is in a rough condition. Estimating the state of the road surface is performed when the degree of scattering of the flying substance based on the number of reflection points of the flying substance recognized within the attention area reaches a rough road determination range. 3. A road surface condition estimation system according to claim 2, comprising estimating a road condition. 4. The road surface according to claim 2 or 3, further configured to execute a corresponding process corresponding to the rough road condition when the road surface is estimated to be in the rough road condition. State estimation system. 5. The road surface condition estimation system according to claim 4, wherein executing the corresponding process includes stopping the host vehicle that is being automatically driven when the road surface is estimated to be in the rough road condition. 6. Execution of the handling process includes switching the host vehicle in automatic operation to manual operation when the road surface is estimated to be in the rough road condition. Road surface state estimation system according to. Executing the countermeasure processing reduces an upper limit value of at least one of the magnitude of speed and acceleration of the host vehicle during automatic driving when the road surface is estimated to be in the rough road state. 7. The road surface condition estimation system according to any one of claims 4 to 6, comprising executing traveling. When the road surface is estimated to be in the rough road state, the execution of the corresponding processing is performed when the reflected light information in front of the host vehicle includes a group of points moving forward and backward in the traveling direction. 8. The road surface state estimation system according to any one of claims 4 to 7, further comprising recognizing the point cloud on the side as the preceding vehicle (B). 9. The road surface condition estimation system according to any one of claims 4 to 8, wherein executing the corresponding process includes notifying the outside of the host vehicle that the road surface is in the rough road condition. . 10. The method according to any one of claims 4 to 9, wherein, when the road surface is estimated to be in the rough road condition, executing the countermeasure processing further includes determining a rut generation state on the road surface. The road surface condition estimation system according to the paragraph. A road surface condition estimating device that has a processor (102) and is equipped with an optical sensor (10) that detects reflected light according to light irradiation and estimates the condition of a road surface on which a host vehicle (A) travels,
The processor
Acquiring reflected light information behind the traveling direction by the optical sensor;
Recognizing the scattering state of the scattering material that is lifted 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;
A road surface condition estimation device configured to perform A road surface condition estimation method executed by a processor (102) for estimating the condition of a road surface on which a host vehicle (A), which is equipped with an optical sensor (10) for detecting reflected light according to light irradiation, travels, comprising: ,
Acquiring reflected light information behind the traveling direction by the optical sensor;
Recognizing the scattering state of the scattering material that is lifted 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;
Road surface condition estimation method including. Stored in a storage medium (101) and executed by a processor (102) for estimating the state of a road surface on which a host vehicle (A), which is equipped with an optical sensor (10) for detecting reflected light according to light irradiation, travels A road surface condition estimation program comprising instructions,
Said instruction
Acquiring reflected light information behind the traveling direction by the optical sensor;
Recognizing the scattering state of the scattering substance that is blown 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;
Road condition estimation program including.

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