JP2002160598A - Outside car control device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a outside car control device which can accurately detect a dry condition on the road. SOLUTION: A road dry decision part 12c decides that the road is not dry when a road average brightness Br computed in a road confirmation part 11 is large or road surface data value I computed in a distance data count part 12a is large and also a brightness dispersion value VAR computed in a brightness dispersion computing part 12b is large. To the road which is decided as dry the road dry decision part 12c decides that the road is dry when a under road data value J computed in the distance data count part 12a is small. And also the road dry decision part 12c decides that the road is dry when a car line reliable value D computed in the road confirmation part 11 is high even when the under-road data value J is large.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an external monitoring system for detecting a road surface condition of a traveling road, particularly a dry road surface, based on a captured image.

[0002]

2. Description of the Related Art In recent years, attention has been paid to a vehicle exterior monitoring device using a vehicle-mounted camera having a solid-state imaging device such as a CCD.
This device recognizes a driving environment based on an image captured by a vehicle-mounted camera, calls attention to a driver, and performs vehicle control such as downshifting as necessary.

[0003] In this type of vehicle exterior monitoring device, there is one that detects a road surface condition based on a captured image and reflects the detected road surface condition in vehicle control or the like. For example, Japanese Patent Application No. 11-216713 filed by the present applicant discloses that on a road surface wet by rain or the like, a three-dimensional object is reflected on the road surface to detect the wet state of the road surface, and the wet state of the road surface is detected. In such a case, a technique for temporarily interrupting vehicle control or changing control parameters is disclosed. According to this technology, the distance data is calculated based on the parallax of the object in the pair of image data, and the three-dimensional shape of the road is recognized based on the image data and the distance data. Road surface wet condition (wet road surface) based on the number of distance data (the number of wet data) existing below
Recognize. In addition, for example, Japanese Patent Application No.
No. 216373 and Japanese Patent Application No. 11-216713 similarly disclose an outside monitor for detecting an uneven snowy road surface in which snow remains partially on the road surface or a snowy road surface in which the road surface is covered with snow from the image. An apparatus is disclosed. In such a vehicle exterior monitoring device, by detecting a wet road surface or a snowy road and recognizing that the road surface is a low μ road, changing the control parameters and the like, improving the safety of the vehicle when traveling. Can be.

[0004]

By the way, in the vehicle exterior monitoring device as described above, in addition to recognition of a wet road surface or a snowy road, a dry state (dry road surface) of the road surface is positively recognized to control the vehicle. It is desirable to reflect it. For example,
When the outside monitoring device mounted on the four-wheel drive vehicle recognizes a dry road surface, it determines that the road surface is a high μ road,
By preventing unnecessary deceleration control or the like or limiting the drive wheels to the front wheels or the rear wheels, fuel efficiency can be improved. However, in order to perform such control, it is necessary to accurately detect the dry state of the road surface.

The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a vehicle exterior monitoring device that can accurately detect a dry state of a road surface.

[0006]

According to a first aspect of the present invention, there is provided an out-of-vehicle monitoring device for detecting a road surface condition from a captured image based on the image. A road recognition unit that recognizes a road shape including lane detection; and a road surface state recognition unit that determines whether a road surface of the road recognized by the road recognition unit is dry. The means is dry when the reliability of the lane detected by the road recognition means is higher than a set threshold, even if the road is determined to be highly likely to be wet in the road surface. Is determined.

According to a second aspect of the present invention, there is provided a vehicle exterior monitoring apparatus according to the first aspect, wherein stereo image processing is performed on the basis of a pair of captured images to calculate distance data of an object on the images. Means, the road recognition means performs three-dimensional recognition of the road shape including lane detection based on the distance data and the luminance information of the image,
The road surface state recognizing means determines that the road surface is highly likely to be in a wet state when the number of distance data existing below the road surface position in the set area on the road is equal to or greater than a set threshold. It is characterized by the following.

According to a third aspect of the present invention, in the vehicle exterior monitoring device according to the first or second aspect, the road surface state recognizing means includes a threshold value for setting an average luminance of the road surface on the road. When it is larger than the threshold value, it is determined that the road surface is not in a dry state.

According to a fourth aspect of the present invention, in the vehicle outside monitoring apparatus according to any one of the first to third aspects, the road surface state recognizing means is provided in a setting area on the road. When the number of distance data existing on the surface of the road is larger than the set threshold and the variance of the luminance on the road is larger than the set threshold, it is determined that the road is not in a dry state. Features.

[0010]

Embodiments of the present invention will be described below with reference to the drawings. The drawings relate to an embodiment of the present invention, and FIG. 1 is a functional block diagram of a stereo exterior monitoring device.
FIG. 2 is an explanatory diagram showing a lane detection area on an image, and FIG.
4 is an explanatory diagram of a lane model, FIG. 4 is an explanatory diagram showing a distance data monitoring area, FIG. 5 is a flowchart showing a dry road determination routine, FIG. 6 is an explanatory diagram showing an example of an image when driving on a dry road surface, and FIG. FIG. 8 is an explanatory diagram illustrating an example of an image when traveling on a road surface, and FIG. 8 is an explanatory diagram illustrating an example of an image when traveling on a dry road surface at night.

In FIG. 1, a pair of cameras 1 and 2 having a built-in image sensor such as a CCD are attached at predetermined intervals in a vehicle width direction of a vehicle such as an automobile, and capture an image of a scene in front of the vehicle. The main camera 1 captures a reference image (right image) necessary for performing stereo processing, and the sub camera 2 captures a comparison image (left image) in this processing. In a state where the cameras 1 and 2 are synchronized with each other,
Each of the analog images output from 2 is converted into a digital image of a predetermined luminance gradation (for example, 256 gray scales) by A / D converters 3 and 4. The digitized image is subjected to luminance correction, geometric conversion of the image, and the like in the image correction unit 5. Usually, since the mounting positions of the pair of cameras 1 and 2 have an error to some extent, a shift caused by the error exists in the left and right images. In order to correct this displacement, geometric transformation such as rotation or translation of the image is performed using affine transformation or the like. The reference image and the comparative image corrected in this way are stored in the original image memory 8.

On the other hand, a stereo image processing section 6 as a stereo image processing means uses the reference image and the comparison image corrected by the image correction section 5 to convert the three-dimensional position of the same object in the image (from the own vehicle to the object). Is calculated). This distance can be calculated based on the principle of triangulation from the relative displacement of the position of the same object in the left and right images. The distance information of the image thus calculated is stored in the distance data memory 7.

The microcomputer 9 has a three-dimensional object recognizing unit 10, a road recognizing unit 11 as a road recognizing unit, and a road surface state recognizing unit 12 as a road surface recognizing unit. 8 and distance data memory 7
Based on each piece of information stored in the vehicle, recognition of a three-dimensional object such as a traveling vehicle ahead of the vehicle, recognition of a road ahead of the vehicle including detection of a lane (white line, etc.), recognition of a road surface state on the recognized road, and the like are performed. Here, as the road surface state recognition performed by the road surface state recognition unit 12, it is determined whether or not the road surface is in a dry state (asphalt dry road surface, hereinafter also simply referred to as dry road surface). Then, these recognition results are input to the processing unit 13, and based on each input information, the processing unit 13 alerts the driver with an alarm device 19 such as a monitor speaker when an alarm is required, Alternatively, the control units 14 to 18 are controlled as needed. For example, it instructs an AT (automatic transmission) control unit 14 to execute downshifting. Further, it may instruct the engine control unit 18 to reduce the engine output. In addition,
Anti-lock brake system (ABS) control unit 15,
Traction control system (TCS) controller 1
6, or an appropriate vehicle control can be instructed to the vehicle behavior control unit 17 that controls the torque distribution and the rotation speed of each wheel.

The road recognition unit 11 recognizes a road shape by a method described in Japanese Patent Application No. 11-269578 filed by the present applicant, for example. That is, the road recognizing unit 11 sets a reference image having a 512 × 200 pixel area within a search range that is set in advance or variably set according to the pitching state of the host vehicle (search start line js to Lane detection is performed for each horizontal line of the search end line je (see FIG. 2), and based on the detected lane position and distance information from the distance data memory 7, lane position recognition in real space (that is, road recognition). I do.

More specifically, the road recognizing unit 11 first calculates a road surface luminance Aroad for each search line j. The road surface luminance Aroad is obtained in the left and right regions excluding the central portion of the image. In the search start line js, the search start line js and four pre-horizontal lines jpre before the search start line js are obtained. Is performed based on the luminance information which has already been detected in the subsequent search lines.

The road recognizing unit 11 performs lane detection for each target line j based on a luminance determination threshold calculated from the road surface luminance Aroad, image luminance information, distance information, and the like. That is, the road recognition unit 11 determines the lane start point and the end point based on the edge strength (luminance differential value), and determines the lane start point and the end point based on the comparison between the luminance determination threshold and the luminance. Extract candidates. Then, based on the distance information, by verifying whether or not the lane candidate is on the road surface, only the lane is extracted from the lane candidate points. Here, the lane start point refers to a lane point corresponding to the inner boundary between the lane and the asphalt, and the lane end point refers to a lane point corresponding to the inner boundary between the lane and the asphalt.

Further, the road recognizing section 11 calculates the lane point P (i, j) obtained by the lane detection and the parallax d at that point.
(I, j, d) are obtained for all lane points P, and the positions (X, Y, Z) of the left and right lanes in the real space are uniquely calculated based on the camera specifications and the like. Then, in the road recognizing unit 11, a function representing the road shape (road model)
By setting each of the parameters so as to match the road shape, the predicted travel line L (see FIG. 3) is calculated. That is, in the example of FIG. 3, the recognition range is set to a predetermined distance (Z1 to Z1).
Z7) is divided into seven sections, and the lane point P in each section is linearly approximated by the least squares method. (Lane model) Horizontal shape model Left lane X = aL · Z + bL Right lane X = aR · Z + bR Road height model Left lane Y = cL · Z + dL Right lane Y = cR · Z + dR And the left and right sides calculated in this way The predicted travel line L can be calculated from the line.

Further, the road recognizing unit 11 considers the presence / absence (number) of the detected lane points P and the reliability of the left and right lanes in consideration of the continuity with the lane points P detected in the previous frame. Calculate D. In this case, each lane reliability D is
It becomes high when the lane point P is continuously detected.

The road recognizing unit 11 calculates an average road surface luminance Br on the recognized road. Here, it is desirable that the road surface area when calculating the road surface average luminance is set to be a horizontally long area in order to reduce the influence of the luminance change in the front-rear direction. In addition, in consideration of running at night, it is desirable that the road surface area is an area that is within the irradiation range of the own vehicle light and is less affected by disturbance due to other illumination light or the like. Therefore, in the present embodiment, the setting is made such that the road surface average luminance Br is calculated for the portion of the road detected in the second region (see FIG. 3) of the road model. In this case, the calculation of the road surface average brightness Br is performed on the inner road surface of the detected left and right lanes in order to reduce the influence of the roadside object.

The road surface condition recognizing unit 12 detects distance data existing on the road surface of the recognized road (on-road data) and distance data existing below the road surface (under-road data). A distance data counting unit 12a that counts the number I of data on the road surface and the number J of data below the road surface, a luminance variance value calculation unit 12b that calculates a luminance variance value VAR of the recognized road on the road surface, A dry road determination unit 12c that determines a dry state of the road surface based on the number I, the number J of data under the road surface, the luminance variance value VAR, the lane reliability D, the road surface average luminance Br, and the like.

The distance data counting section 12a counts the number I of data on the road surface and the number J of data under the road surface by a method described in detail in Japanese Patent Application No. 11-216713 filed by the present applicant.

That is, in the distance data counting unit 12a, first, the predicted travel line L is inputted from the road recognition unit 11, and as a reference of the predicted travel line L, for example, 40 m in the forward direction of the own vehicle in the corresponding real space. 0 ≦ Z ≦ 4
0), and a distance data monitoring region R of 2 m (-2 ≦ X ≦ 2) is set in each of the left and right width directions (see FIG. 4).

Then, the distance data counting unit 12a specifies effective distance data from the distance data existing in the distance data monitoring region R. Here, the effective distance data is calculated, for example, in units of 4 × 4 pixel blocks, and refers to distance data relating to a pixel block having a predetermined number or more of luminance edges in the horizontal direction (horizontal direction) of an image.

Further, the distance data counting section 12a classifies each of the specified effective distance data into three types of three-dimensional object data, on-road data, and under-road data, and counts the number of each data. Here, the three-dimensional object data is distance data calculated due to a three-dimensional object such as a preceding vehicle,
Distance data of Y> 0.3 (unit: m) is classified as three-dimensional object data. The road surface data is distance data calculated based on the road surface (lane, road surface rut, gravel, etc.) of the traveling road, and the distance data of −0.4 ≦ Y ≦ 0.3 is calculated on the road surface. Classified as surface data. The under-road data is distance data calculated due to reflection of a three-dimensional object on a road surface wet by rain, and Y <
The distance data of 0.4 is classified as under-road data.

The luminance variance value calculator 12b calculates the luminance variance value VAR by a method described in detail in, for example, Japanese Patent Application No. 11-216915 filed by the present applicant. In this case, the brightness variance value calculation unit 12b does not calculate the variance value for the brightness over the entire image, but calculates the brightness variance value VAR limited to the recognized area on the road.

That is, in the luminance variance value calculation unit 12b,
A rectangular image area corresponding to the road recognized by the road recognition unit 11 is set on the original image, and the image area is divided into predetermined pixels in the horizontal direction and monitored in a rectangular shape (extending in the vertical direction of the image). An area Ni (1 ≦ i ≦ n) is set.

The luminance variance value calculator 12b calculates the luminance sum A for each monitoring area N. The luminance sum Ai in a certain monitoring region Ni can be calculated as a value obtained by extracting a plurality of pixels uniformly distributed in the region as a sample and adding their luminance values (or their average value). .

Then, the brightness variance value calculation unit 12b calculates the variance value VAR of the brightness distribution characteristics of the image area based on the following equation. Here, n is the number of monitoring areas, and Aave is the average value of the luminance sum.

The dry road determining unit 12c receives the lane reliability D and the average road surface brightness Br calculated by the road recognizing unit 11, and inputs the number I of data on the road surface counted by the distance data counting unit 12a, The lower data number J and the luminance variance value VAR calculated by the luminance variance value calculation unit 12b are input, and a dry road surface is determined based on these pieces of information. The dry road determination unit 12c turns on the DRY road surface determination flag F when it is determined that the vehicle is on a dry road surface.
Otherwise, the DRY road surface determination flag F is turned off.
I do. Here, in addition to the above information, the own vehicle speed from a vehicle speed sensor (not shown) and information on the preceding vehicle from the three-dimensional object recognition unit 10 are input to the dry road determination unit 12c.
Prior to the dry road surface determination, it is determined whether or not the dry road surface determination is currently possible.

Hereinafter, the dry road surface determination processing will be described in detail with reference to the flowchart of FIG. The dry road surface determination processing is to determine a dry road surface by determining a road surface state (one snow surface, uneven snow road surface, wet road surface, or the like) that is not a dry road surface by each determination described later.

This routine is executed at set time intervals. In step S101, first, it is determined whether or not the vehicle speed is equal to or higher than a set speed V1 set in advance.
Here, the set speed V1 is set to a predetermined low speed that the host vehicle will take when traveling on a narrow road or a parking lot. That is, in narrow roads, parking lots, and the like, it is often difficult to sufficiently obtain road information based on images because of a wall in front or no lane, and the like. Judgment of dry road surface becomes difficult.

Therefore, when the vehicle speed is lower than the set speed V1 in step S101, it is determined that it is difficult to determine an appropriate dry road surface, and the process proceeds to step S115, where the DRY determination flag F is turned off. Exit.

On the other hand, if it is determined in step S101 that the own vehicle speed is equal to or higher than the set speed V1,
Proceed to step S102.

In step S102, the three-dimensional object recognition unit 10
Is determined on the basis of the information from the vehicle to determine whether the inter-vehicle distance between the host vehicle and the immediately preceding vehicle is equal to or greater than a preset distance l. Here, the set distance 1 is a minimum inter-vehicle distance so that a road surface area necessary for determining a dry road surface can be imaged without being covered by a preceding vehicle, and is a distance set in advance by an experiment or the like. That is, in step S102,
By determining whether or not the preceding inter-vehicle distance is equal to or greater than the set distance l, it is determined whether or not appropriate dry road surface determination is possible.

If the preceding inter-vehicle distance is smaller than the set distance 1 in step S102, it is determined that it is difficult to image a necessary road surface area and it is difficult to appropriately determine a dry road surface, and the process proceeds to step S115. After turning off the DRY road surface determination flag F, the routine exits.

On the other hand, if it is determined in step S102 that the preceding inter-vehicle distance is equal to or greater than the set distance l, the process proceeds to step S103.

In step S103, it is determined whether or not the average road surface brightness Br calculated by the road recognition unit 11 is equal to or less than a predetermined threshold value Br1. Here, threshold B
r1 is a threshold value for distinguishing a road surface covered with one-sided snow (hereinafter, a one-sided snowy road surface) from another road surface condition. That is,
On a one-sided snowy road surface, the average road surface luminance is higher than in other road surface conditions. Therefore, the threshold value Br1 is determined based on the average road surface luminance and the like in each road surface state obtained by experiments and the like.
Is appropriately set and compared with the calculated average road surface brightness Br, it is possible to distinguish between the one-side snowy road surface and other road surface conditions.

In step S103, when the average road surface luminance Br is larger than the threshold value Br1, it is determined that the road is a snowy road and not a dry road, and the process proceeds to step S110.

On the other hand, if the average road surface brightness Br is equal to or less than the threshold value Br1 in step S103, it is determined that the vehicle is in a road surface state other than a single snow road surface, and the flow advances to step S104.

In step S104, it is determined whether or not the number I of data on the road surface counted by the distance data counting unit 12a is equal to or less than a predetermined threshold I1. Here, the threshold value I1 is a threshold value for distinguishing an uneven snowy road surface in a state where snow partially remains on the road surface from other road surface conditions. That is, on an uneven snowy road surface, a mottled pattern due to the distribution of snow is detected as distance data (on-road surface data) on the road surface.
Becomes larger. Therefore, the threshold value I is determined based on the number of data on the road surface in each road surface condition obtained in advance by experiments or the like.
By appropriately setting 1 and comparing it with the calculated number I of data on the road surface, it is possible to distinguish the uneven snow road surface from other road surface conditions.

In step S104, when the number I of data on the road surface is equal to or smaller than the threshold value I1, the road surface is determined to be in a road surface state other than the uneven snow road surface, and the flow advances to step S106.

On the other hand, if the number I of data on the road surface is larger than the threshold value I1 in step 104, it is determined that the road surface is highly likely to be an uneven snow road surface, and step S10 is performed.
Go to 5.

In step S105, it is determined whether or not the luminance variance value VAR calculated by the luminance variance value calculator 12b is equal to or less than a predetermined threshold value VAR1. Here, the threshold value VAR1 is a threshold value for extracting and classifying, in the above step S104, those erroneously determined to be highly likely to be uneven snowy road surfaces. That is, on the road surface, the number I of data on the road surface may be exceptionally large even when there are many dirt and construction traces in addition to the uneven snow road surface, and in such a case, the road surface condition such as the dry road surface Even in such a case, it may be determined that the possibility of the uneven snow road surface is high. By the way, the road surface with many dirts and construction traces has smaller luminance dispersion than the uneven snow road surface, and the two are different in this point. Therefore, by appropriately setting the threshold value VAR1 based on the luminance variance value in each road surface state obtained in advance by an experiment or the like and comparing the threshold VAR1 with the calculated luminance variance value VAR, the possibility of the uneven snow road surface is high. Another road surface state can be extracted from the road surface determined to be classified.

In step S105, the luminance variance value V
If the AR is equal to or less than the threshold value VAR1, the road surface is determined to be in a road surface state other than the uneven snow road surface, and the process proceeds to step S106.

On the other hand, in step S105, when the luminance variance VAR is larger than the threshold VAR1 (when the number I of data on the road surface is larger than the threshold I1 and the luminance variance VA
If R is greater than the threshold value VAR1, the road surface is determined to be an uneven snow road surface and not a dry road surface, and the process proceeds to step S110.

In step S106, it is determined whether or not the number J of data under the road counted by the distance data counting unit 12a is equal to or less than a predetermined threshold value J1. Here, the threshold value J1 is a threshold value for distinguishing a wet road surface (wet road surface) from a dry road surface. That is, as shown in FIG. 7, a three-dimensional object is reflected on the wet road surface, and this point is different from the dry road surface (see FIG. 6). Then, on a wet road surface, due to the reflection of a three-dimensional object on the road surface, the number of distance data below the road surface (the number J of data under the road surface) becomes larger than that on a dry road surface.
Therefore, the threshold value J1 is appropriately set based on the number of data under the road surface in each road surface state obtained by experiments and the like,
The wet road surface and the dry road surface can be distinguished by comparing with the calculated number J of data under the road surface.

In step S106, when the number J of data under the road is equal to or smaller than the threshold value J1, it is determined that the road is a dry road, and the flow advances to step S108.

On the other hand, if the number J of data under the road surface is larger than the threshold value J1 in step S106, it is determined that there is a high possibility that the vehicle is on a wet road surface, and the flow advances to step S110.

In step S107, it is determined whether the lane reliability D of at least one of the left and right lanes detected by the road recognition unit 11 is equal to or greater than a predetermined threshold D1. Here, the threshold value D1 is a threshold value for extracting and classifying, in step S106, a road surface determined to be highly likely to be a wet road surface despite being a dry road surface. That is, on a dry road surface, as shown in FIG. 8, for example, when the road surface is illuminated at night by headlights of an oncoming vehicle or the like, the number J of data under the road surface may be exceptionally large. In this case, even on a dry road surface, it may be determined that the possibility of a wet road surface is high. By the way, on a dry road surface, a luminance difference and an edge strength (a luminance change amount) between the lane and the road surface are generally larger than on a wet road surface, and it is easy to continuously detect the lane. The reliability D increases. Therefore, by appropriately setting the threshold value D1 based on the lane reliability in each road surface condition obtained in advance through experiments and the like, and comparing the threshold value D1 with the calculated lane reliability D, it is determined that the possibility of the wet road surface is high. A dry road surface can be extracted and divided from the road surfaces thus set.

In step S107, if both the left and right lane reliability values D are smaller than the threshold value D1, it is determined that the vehicle is on a wet road surface, and the flow advances to step S110. In addition,
As shown in FIG. 8, even on a dry road surface, it may be temporarily difficult to detect the lane due to the headlight light of the oncoming vehicle, but the influence of the headlight light and the like is partial. It is unlikely that a decrease in lane reliability caused by such light or the like will occur simultaneously in the left and right lanes. Therefore, in step S107, only when both the left and right lane reliability D is smaller than the threshold value D1, step S110
Proceed to.

On the other hand, if the lane reliability D of at least one of the left and right lanes is equal to or greater than the threshold value D1 in step S107, it is determined that the vehicle is on a dry road surface, and the flow advances to step S108.

When the process proceeds from step S106 or S107 to step S108, in step S108, the DR
It is determined whether the Y road counter Cdry is smaller than the counter maximum value Cmax. If the DRY road counter Cdry is smaller than the counter maximum value Cmax, the process proceeds to step S109, and the DRY road counter Cdry is incremented (Cd).
After ry ← Cdry + 1), the process proceeds to step S112.

On the other hand, in step S108, DRY
When the road surface counter Cdry is the counter maximum value Cmax, the process proceeds to step S112.

When the process proceeds from step S103, S105 or S107 to step S110, it is checked in step S110 whether or not the DRY road surface counter Cdry is larger than a preset counter minimum value Cmin. Is larger than the counter minimum value Cmin, the process proceeds to step S111, and the DRY road surface counter Cdry is decremented (Cdry ← Cdry−1).
After that, the process proceeds to step S112.

On the other hand, in step S110, DRY
If the road surface counter Cdry is the counter minimum value Cmin, the process proceeds to step S112.

In step S112, the DRY road surface counter Cdry is set to a predetermined threshold value Cdry1 (Cmin <Cdry1 <
Cdry) or not, and if the DRY road surface counter Cdry is greater than or equal to the threshold value Cdry1, step S11.
Proceed to 3. Then, in step S113, the DRY determination flag F indicating that the road surface is a dry road surface is turned on.
After (F ← 1), the routine exits.

On the other hand, in step S112, DRY
When the road surface counter Cdry is smaller than the threshold value Cdry1, the process proceeds to step S114.

In step S114, the DRY road surface counter Cdry is set to a predetermined threshold value Cdry2 (Cmin <Cdry2 <
It is checked whether or not Cdry1 <Cmax or less. If the DRY road surface counter Cdry is equal to or less than the threshold Cdry2, the process proceeds to step S115, and the DRY determination flag F is turned off (F ←
After 0), exit the routine.

On the other hand, in step S114, DRY
If the road surface counter Cdry is larger than the threshold value Cdry2, the routine exits from the routine.

In such an embodiment, recognition of the road shape including lane detection is performed based on the captured image, and even if it is determined that there is a high possibility that the vehicle is on a wet road surface, the detected lane of the detected lane may be detected. When the reliability is higher than the set threshold, it is determined that the vehicle is on a dry road surface, so that the dry state of the road surface can be accurately detected.

That is, in general, on a dry road surface, it is easier to continuously detect lanes due to an increase in luminance difference and edge strength between the lane and the road surface than on a wet road surface. Focusing on the fact that the reliability of the lane becomes high, the road surface is determined to be a dry road surface if the lane reliability is high even if it is once determined that the road surface is likely to be wet, so the road surface is dry. The state can be accurately detected.

In particular, in the method of calculating the number of distance data under the road surface (the number of data under the road surface) and determining that the road surface is likely to be a wet road surface when the number of data under the road surface is large, the method of determining whether the road surface is a wet road surface at night may be used. In some cases, such as when the headlight light of an oncoming vehicle is reflected on the road surface, it is erroneously determined that there is a high possibility that the vehicle is on a wet road surface even though it is a dry road surface. Even when it is determined that there is a high possibility of a road surface, if the reliability of the lane is high, it is determined that the vehicle is a dry road surface, so that the detection accuracy of the dry road surface can be improved.

Further, the average luminance of the road surface is calculated, and when the average luminance of the road surface is large, the detection accuracy of the dry road surface can be improved by determining that the road surface is a single snow road surface and not a dry road surface. .

Further, the number of distance data on the road surface (the number of data on the road surface) is calculated and the luminance variance value on the road is calculated. When the number of data on the road surface is large and the luminance variance value on the road is large, By determining that the road surface is an uneven snow road surface and not a dry road surface, the detection accuracy of the dry road surface can be improved.

In such a vehicle exterior monitoring device, fuel efficiency can be effectively improved by accurately recognizing a dry road surface and reflecting it on vehicle control and the like.

[0066]

As described above, according to the present invention, the shape of a road including lane detection is recognized, and even if it is determined that the road surface is highly likely to be wet, the detected lane is detected. Is higher than the set threshold,
By determining that the road surface is in a dry state, the dry state of the road surface can be accurately detected.

[Brief description of the drawings]

FIG. 1 is a functional block diagram of a stereo exterior monitoring device.

FIG. 2 is an explanatory diagram showing a lane detection area on an image.

FIG. 3 is an explanatory diagram of a lane model.

FIG. 4 is an explanatory diagram showing a distance data monitoring area.

FIG. 5 is a flowchart showing a dry road determination routine.

FIG. 6 is an explanatory diagram showing an example of an image when traveling on a dry road surface.

FIG. 7 is an explanatory diagram showing an example of an image when traveling on a wet road surface.

FIG. 8 is an explanatory diagram showing an example of an image when driving on a dry road surface at night.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Main camera 2 Sub camera 5 Image correction part 6 Stereo image processing part (stereo image processing means) 7 Distance data memory 8 Original image memory 9 Microcomputer 10 Solid object recognition part 11 Road recognition part (road recognition means) 12 Road surface state recognition Unit (road surface state recognition means) 12a distance data counting unit 12b luminance variance value calculation unit 12c dry road determination unit

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Claims (4)

[Claims] An exterior monitoring device for detecting a road surface condition of a road from a captured image, comprising: a road recognizing means for recognizing a road shape including lane detection based on the image; Road surface state recognizing means for determining whether or not the road surface of the road is in a dry state, wherein the road surface state recognizing means is a road surface in which it is determined that there is a high possibility that the road surface is in a wet state. When the reliability of the lane detected by the road recognizing means is higher than a set threshold value, it is determined that the road surface is dry. 2. A stereo image processing means for calculating distance data of an object on an image based on a pair of captured images, wherein the road recognizing means calculates the distance data and luminance information of the image. Based on the three-dimensional recognition of the road shape including lane detection, the road surface state recognizing means sets the number of distance data present below the road surface position within the set area on the road. The outside monitoring apparatus according to claim 1, wherein it is determined that the possibility that the road surface is wet is high when the road surface is equal to or more than the threshold value. 3. The road surface condition recognizing means determines that the road surface is not in a dry state when the average luminance of the road surface on the road is larger than a set threshold value. The vehicle exterior monitoring device according to claim 1. 4. The road surface state recognizing means, wherein the number of distance data present on the road surface in the set area on the road is larger than a set threshold value, and the variance value of the luminance on the road is provided. The vehicle outside monitoring device according to any one of claims 1 to 3, wherein it is determined that the road surface is not in a dry state when is larger than a set threshold value.