Patent Description:
During the last few years camera based driver assistance systems (DAS) have been entering the market; including lane departure warning (LDW), automatic high-beam control (AHC), traffic sign recognition (TSR) and forward collision warning (FCW).

Lane departure warning (LDW) systems are designed to give a warning in the case of unintentional lane departure. The warning is given when the vehicle crosses or is about to cross the lane marker. Driver intention is determined based on use of turn signals, change in steering wheel angle, vehicle speed and brake activation. There are various LDW systems available. One algorithm for lane departure warning (LDW) used by the Applicant (Mobileye Technologies Ltd. , Nicosia, Cyprus, hereinafter "Mobileye") of the present application is predictive in that it computes time-to-lane crossing (TLC) based on change in wheel-to-lane distance and warns when the time-to-lane crossing (TLC) is below a certain threshold. Other algorithms give a warning if the wheel is inside a certain zone around the lane marker. In either case, essential to a lane departure warning system is a lane marker detection algorithm. Typically, the lane markers are detected in the camera image and then, given the known camera geometry and camera location relative to the vehicle, the position of the vehicle relative to the lane is computed. The lane markers detected in the camera image are then collected over time, for instance using a Kalman filter. Wheel-to-lane marker distance may be given with an accuracy of better than <NUM> centimetres. With a forward looking camera, wheel-to-lane marker distance is not observed directly but is extrapolated from the forward view of the camera. The closer road markings are observed, less extrapolation is required for determining wheel-to-lane marker distance and more accurate estimates of wheel-to-lane marker distance are achieved especially on curves of the road. Due to the car bonnet (hood) and the location of the camera, the road is seldom visible closer than six meters in front of the wheels of the car. In some cars with longer bonnets, minimal distance to visible road in front of the car is even greater. Typically the lane departure warning system of Mobileye works on sharp curves (with radius down to <NUM>). With a horizontal field of view (FOV) of <NUM> degrees of the camera, the inner lane markers are still visible on curves with a radius down to <NUM> meters. In order to correctly perform lane assignment on curves, lane markings are detected at <NUM> meters and beyond. With a horizontal field of view (FOV) of <NUM> degrees for the camera, a lane mark of width <NUM> meters at <NUM> distance corresponds in the image plane to just under two pixels wide and can be detected accurately. The expectation from the lane departure warning systems is greater than <NUM>% availability when lane markings are visible. Expectation with <NUM>% availability is particularly challenging to achieve in low light conditions when the lane markings are not freshly painted (have low contrast with the road) and the only light source is the car halogen headlights. In low light conditions, the lane markings are only visible using the higher sensitivity of the clear pixels (i.e. using a monochrome sensor or a red/clear sensor). With the more powerful xenon high intensity discharge (HID) headlights it is possible to use a standard red green blue (RGB) sensor in most low light conditions.

The core technology behind forward collision warning (FCW) systems and headway distance monitoring is vehicle detection. Assume that reliable detection of vehicles in a single image a typical forward collision warning (FCW) system requires that a vehicle image be <NUM> pixels wide, then for a car of width <NUM>, a typical camera (640x480 resolution and <NUM> deg FOV) gives initial detection at <NUM> and multi-frame approval at <NUM>. A narrower horizontal field of view (FOV) for the camera gives a greater detection range however; the narrower horizontal field of view (FOV) will reduce the ability to detect passing and cutting-in vehicles. A horizontal field of view (FOV) of around <NUM> degrees was found by Mobileye to be almost optimal (in road tests conducted with a camera) given the image sensor resolution and dimensions. A key component of a typical forward collision warning (FCW) algorithm is the estimation of distance from a single camera and the estimation of scale change from the time-to-contact/collision (TTC) as disclosed for example in <CIT>.

A recent <CIT> states (column <NUM> lines <NUM>-<NUM>) that lane departure warning systems which are equipped with only one image-transmitting sensor are not capable of differentiating between edge-of-lane markings and a structural boundary at the edge of the lane (emphasis added). Consequently, <CIT> discloses a driver assistance system for warning a driver of a motor vehicle of a risk of departure from the lane. The disclosed system includes a camera for detecting edge-of-lane and/or lane markings in the area sensed by the camera, and in addition a distance sensor with which the distance from objects elevated with respect to the surface of the lane can be determined in the region of the edge of the lane, in particular of a structural boundary of the edge of the lane. Another example of relevant prior art is <CIT> (<NUM>-<NUM>-<NUM>).

The present invention seeks to provide an improved driver assistance method and apparatus.

In particular, the preferred embodiments are able to provide a driver assistance system and corresponding method operable to perform and vertical structural barrier or guardrail detection along the edge of a road or a lane using a camera and without requiring use of an additional sensor for instance to detect distance to the guardrail or barrier.

According to an aspect of the present invention, there is provided a method of detecting a structural barrier extending along a road as specified in claim <NUM>.

According to another aspect of the present invention, there is provided a system corresponding to the above method, as recited in claim <NUM>.

Various methods are disclosed herein for detecting a structural barrier extending along a road. The methods are performable by a driver assistance system mountable in a host vehicle. The driver assistance system may include a camera operatively connected to a processor. Multiple image frames may be captured in the forward field of view of the camera. In the image frames, motion of images of the barrier are processed to detect the barrier. The camera may be a single camera. The motion of the images may be responsive to forward motion of the host vehicle and/or the motion of the images may be responsive to lateral motion of the host vehicle.

The structural barrier may include multiple posts. Multiple linear image structures are hypothesized in an image frame as projections of the barrier onto the road surface and multiple vertical image coordinates are obtained respectively from the linear image structures. The linear image structures may be image lines which run parallel to the image of the road and intersect the vanishing point of the image of the lane markers.

Multiple forward distances and corresponding lateral distances to the posts are computed based on the vertical image coordinates. Based on the known forward motion of the host vehicle and horizontal image coordinates of the linear image structures new horizontal image coordinates of the linear image structures are computed. The horizontal image coordinate in a second image frame of one of the images of the linear image structures is selected to align an image of one of the posts.

For each of the posts, forward distances from the host vehicle to the posts may be determined based on the motion of the images and the forward motion of the host vehicle. Lateral distances to the posts from the host vehicle may be determined from the forward distances and the horizontal image coordinates of the posts. Road plane lines at the lateral distances may be hypothesized to form multiple hypothesized road plane lines as projections of the vertical structural barrier onto the road surface. The hypothesized road plane lines at the lateral distances may be projected onto an image of the vertical structural barrier in an image frame. The correct road plane line is selected from the hypothesized road plane lines by aligning the correct road plane line with the image of the vertical structural barrier in the image frame.

For a barrier without substantial vertical image texture, an image patch may be located in one of the image frames on an image line intersecting the vanishing point in the image frame. The image patch may be warped based on a vertical surface model. The vertical structural barrier may be detected by ascertaining that the patch is an image of the vertical structural barrier when points in columns of the patch scale vertically with host vehicle motion. In another embodiment, the image patch may be warped based on a road surface model, and the patch may be an image of the road surface when points in rows of the patch scale horizontally with host vehicle motion.

Various driver assistance systems may be provided for detecting a structural barrier extending along a road, The driver assistance system may be mountable in a host vehicle. The camera may capture multiple image frames in the forward field of view of the camera. A processor may process motion of images of the barrier in the image frames. The camera may be a single camera. The camera may be configured to view in the direction of forward motion of the host vehicle. The motion of the images may be responsive to forward motion of the host vehicle and/or the motion of the images may be responsive to lateral motion of the host vehicle.

The motion of the images of the structural barrier may correlate with an image line in the direction of the vanishing point of the road, wherein the image line corresponds to a vertical projection of the structural barrier onto the road plane. The processor may be operable to hypothesize linear image structures as projections of the structural barrier onto the road plane to produce multiple hypotheses. Each of the hypotheses gives a lateral position of the barrier relative to the host vehicle. For each hypothesis, the lateral positions and host vehicle motion are used to predict image motion. The predicted image motion is compared to the actual image motion to verify the hypothesis and to derive the actual lateral position of the structural barrier relative to the host vehicle.

Motion of the host vehicle may have a lateral component relative to the road direction and the image motion is of an image line in the image that is above the linear image structure The image line may be that of the top of the barrier. Vertical motion or looming of the image line may be used to determine lateral distance between the host vehicle and the structural barrier to determine whether the image line is of the same lateral distance as the linear image structure (the barrier) or on the road surface farther away.

The processor may be operable to hypothesize multiple linear image structures in an image frame as projections of the barrier onto the road surface and obtain thereby multiple vertical image coordinates respectively from the linear image structures. The processor may be operable to compute multiple forward distances and corresponding lateral distances to the posts based on the vertical image coordinates. Based on the known forward motion of the host vehicle and horizontal image coordinates of the linear image structures, the processor may be operable to compute new horizontal image coordinates of the linear image structures to select the horizontal image coordinate in a second image frame of one of the images of the linear image structures and to align an image of one of the posts.

For each of the posts, the processor may be operable to determine forward distances from the host vehicle to the posts based on the motion of the images and the forward motion of the host vehicle. The processor may be operable to compute lateral distances to the posts from the host vehicle from the forward distance and horizontal image coordinates x of the posts. The processor may be operable to hypothesize road plane lines at the lateral distances, to form multiple hypothesized road plane lines as projections of the structural barrier onto the road surface; to project the hypothesized road plane lines at the lateral distances onto an image of the structural barrier in an image frame. The processor may be operable to select the correct road plane line from the hypothesized road plane lines by aligning the correct road plane line with the image of the structural barrier in the image frame.

For a barrier without substantial vertical image texture, the processor may be operable to locate in one of the image frames an image patch on an image line intersecting the vanishing point in an image frame, to warp said image patch based on a vertical surface model and to detect the structural barrier by ascertaining that the patch may be an image of the structural barrier when points in columns of the patch scale vertically with host vehicle motion. Or, the processor may be operable to ascertain that the patch may be an image of the road surface if or when points in rows of the patch scale horizontally with host vehicle motion.

It is to be understood that all of the different functionalities disclosed herein could be included in one method or apparatus as optional modes of functioning.

Embodiments of the present invention are described below, by way of example only, with reference to the accompanying drawings, in which:.

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. The embodiments are described below to explain the teachings herein by referring to the Figures.

By way of introduction, embodiments of the present invention may be directed to detection of guard rails and/or other generic structural barriers by image processing using a monochromatic camera which may be dedicated to multiple driver assistance systems or functions running simultaneously and/or in parallel in a host vehicle. The use of another sensor, (e.g. time-of-flight laser distance sensor or a second camera) other than a single camera may be avoided, to sense the presence of a structural barrier extending along the edge of the road. The camera may be mountable behind the windshield with the optical axis of the camera substantially parallel to the forward direction (Z) of motion of the host vehicle so that the camera may be forward viewing.

The term "structural barrier" as used herein is intended to refer to a road barrier installed and extending along a road at the side of a road, at the median of a divided highway or as a lane divider. The structural barrier may for example be a concrete barrier, Jersey barrier, a metal guard rail or a cable barrier or other barrier having similar features. The concrete barrier may include anti-glare slats on top as disclosed in <CIT>. The terms "structural barrier" and "vertical structural barrier" are used herein interchangeably.

The term "posts" as used herein refers to an imageable vertical texture in a structural barrier and may include any substantially vertical structure or surface texture, e.g. painted stripes, or the anti-glare slats. Hence, The terms "vertical texture" and "post" are used herein interchangeably.

Reference is now made to <FIG> which illustrate a system <NUM> including a camera or image sensor <NUM> mountable in a vehicle <NUM>, according to an embodiment of the present invention. Image sensor <NUM>, imaging a field of view in the forward direction typically delivers images in real time and the images may be captured in a time series of image frames <NUM>. An image processor <NUM> may be used to process image frames <NUM> simultaneously and/or in parallel to serve a number of driver assistance systems. Image sensor <NUM> is typically monochrome or black-white, i.e. without colour separation. By way of example in <FIG>, image frames <NUM> may be used to serve a warning system <NUM> which may include collision warning <NUM>, lane departure warning <NUM>, traffic sign recognition (TSR) <NUM> and barrier and guard rail detection <NUM> (BGD). Image processor <NUM> is used to process image frames <NUM> to detect barriers and/ or guardrails in the forward field of view of camera <NUM>. The terms "camera" and "image sensor" are used herein interchangeably. The detection of guard rails, structural barriers, e.g. concrete lane dividers is important for many driver assistance functions. Embodiments of the present invention may include exchange of information between barrier and/or guardrail detection <NUM> and other driver assistance functions and/or systems including but not limited by FCW <NUM> and LDW <NUM>. For example, a Lane Departure Warning (LDW) <NUM> as part of warning system <NUM>, may respond more strongly to a lane departure towards a guard rail or a barrier rather than a lane marker or even a white line. A Forward Collision Warning (FCW) system <NUM> may trigger sooner if the path to either side of in-path vehicle is blocked by a guard rail or another vehicle.

The term "object" as used herein refers to an object in real space being viewed by a camera. A guard rail along the edge of a road and a lane marker in the road are examples of objects. The term "image" refers to the image of one or more objects in image space at the focal plane of camera <NUM>. Image coordinates (x,y) in small letters refer to image space and may be in arbitrary units or numbers of picture elements in the horizontal and vertical directions with the pixel dimensions assumed. The term "image motion" refers to motion of an image of an object in image space. From image frame <NUM> to a subsequent image frame <NUM> the points of the image of the object may map from one set of coordinates (x1,y1) to a different set of coordinates (x2,y2). The term "image motion" refers to the mapping of coordinates of an image from image frame to image frame or a function of the mapping. The term "projection" or "projecting" as used herein refers to camera or perspective projection unless otherwise indicated by the context.

<FIG> illustrates a simplified generalized method, according to an embodiment of the present invention. The term "capture" as used herein refers to the real time recording and/or storage of image frames <NUM> in memory, for example volatile and/or non-volatile memory accessible by processor <NUM>. In step <NUM>, image frames are captured by camera <NUM> and in step <NUM> the image motion of guardrails and/or structural barriers along the edge of the road may be processed while host vehicle <NUM> is moving on the road.

Reference is now made to <FIG> which shows an image frame <NUM> of a road scene as viewed through the windshield of host vehicle <NUM> by camera <NUM>, according to embodiments of the present invention. Real space coordinates (X,Y,Z) usually in meters are indicated using capital letters. Distance Z from camera <NUM> or from the front of vehicle <NUM> is in the forward direction of motion of vehicle <NUM>. Lateral distance X is in the plane of the road perpendicular to forward direction. Host vehicle <NUM> may be fast approaching an in-path target vehicle <NUM> ahead. The lane on the right of vehicle <NUM> may be blocked by another vehicle <NUM>. The exact location of road divider or barrier <NUM> on the left may be important to determine if there is an open path on the left. Image frame <NUM> shows two examples of structural barriers; mixed concrete and metal barrier <NUM> on the left and a solid concrete barrier <NUM> on the far right. A dominant texture on concrete barrier <NUM> is parallel to the road so it may be a challenge in image <NUM> to distinguish between structural barrier <NUM>, a marking on the road or a change of road surface colour. A relevant issue with respect to the dominant texture on concrete barrier <NUM>, would be to assess if there is a free emergency lane or shoulder to the right of vehicle <NUM> or does barrier <NUM> start already on or near white line <NUM>. An observation of barrier <NUM> shows a vertical texture or posts 30a. The repetitive pattern of the vertical texture may be detected. The vertical texture may be distinguished from the road surface, however there still may be a challenge to estimate a lateral distance to barrier <NUM> because the bottoms of posts 30a in barrier <NUM> do not necessarily reach the road surface. Estimating the lateral distance X between vehicle <NUM> and barrier <NUM> based on the assumption that posts 30a do reach the road may lead to a significant error in measurement of the lateral distance to barrier <NUM>. For example, if the bottom of post 30a touches the road at circle A, distance Z of <NUM> meter is given from host vehicle <NUM> and then lateral distance X to barrier <NUM> is determined to be <NUM> meter to host vehicle <NUM>. If the bottom of post 30a touches the road at circle B, a distance Z of <NUM> is given and the barrier is <NUM> lateral distance X to the left of vehicle <NUM>. Additionally, motion aliasing, due to the repetitive pattern of the vertical texture may result in an ambiguous computation of lateral position X of barrier <NUM>. <FIG> show motion aliasing or how post 30a in <FIG> may be matched to a number of similar posts 30a in a second image frame shown in <FIG>. Each possible post 30a in the second image translates to a different lateral distance X.

Reference is now made to <FIG> which show: <MAT>.

<MAT> <MAT> <MAT> X2 = X1; new lateral distance X2 <MAT>.

Referring now to method <NUM> illustrated in <FIG>, for each post 30a in image 60a, hypothesize (step <NUM>) that a post 30a is at the horizontal projection of barrier <NUM> onto the road surface. Each hypothesis corresponding to one of linear image structures (H,I,J,K) translates to a different image vertical coordinate y1 (step <NUM>) which can then be used to compute a forward distance Z1 and lateral distance X1 (step <NUM>). Given the known forward motion of host vehicle <NUM>, horizontal image coordinate of a post 30a can be used to compute (step <NUM>) the new horizontal image coordinate of the post (x2) in image 60b. Only one hypothesis (J) gives a projection x2 that correctly aligns with one post 30a in second image 60b. Linear mark J is therefore a correct hypothesis for the true projection of barrier 30a onto the road plane (step <NUM>). A forward distances Z to post 30a may be determined from host vehicle <NUM> based on the image motion and the forward motion of host vehicle <NUM>. The lateral distances X from the host vehicle to post 30a may be computed from the forward distance and the horizontal image coordinate of post 30a.

Reference is now also made to <FIG>, which illustrates another method <NUM>. For multiple vertical structures or posts 30a, forward distance Z is computed (step <NUM>) and from the forward distance and horizontal image coordinate, the lateral distances X are computed (step <NUM>) for posts 30a. Linear marks or linear image structure (H,I,J,K) may be assumed (step <NUM>) to be hypothetical road plane lines in the road plane at lateral distances X. However, only one of linear marks (H,I,J,K) is actually in the road plane. The hypothesized road plane lines at different lateral distances X may be projected (step <NUM>) onto an image of structural barrier <NUM> in image frame 60a. In image frame 60b, the correct linear mark (H,I,J,K) of the hypothesized road plane lines is selected (step <NUM>) by aligning with the image of one of posts 30a in image frame 60b, according to the image motion prediction responsive to the motion of vehicle <NUM>.

In other words, the image motion of an image patch suspected to be the image of part of a road barrier and the host vehicle motion are used to compute the longitudinal distance (Z) and lateral distance (X) of that patch from host vehicle <NUM>. The X,Yc,Z location is projected into the image point p(x,y), where Yc is taken to be the height of the road plane relative to the camera <NUM>. The patch is verified to be on barrier <NUM> by corresponding p(x,y) to nearby linear image structures (H,I,J,K).

Posts 30a are tracked sometimes with multiple possible image motions for the same post due to aliasing. Each motion gives a X,Z world coordinates for post 30a. Post 30a is projected onto the ground plane (by setting the Y coordinate to Yc). We now have X,Y,Z coordinates to project into the camera image point p(x,y). Then it is verified if point p(x,y) falls on or close to a linear image structure (H,I,J,K).

Reference is now made to <FIG> which shows two images 70a and 70b and <FIG> which shows a method <NUM> according to an embodiment of the present invention. In images 70a and 70b, the image of barrier <NUM> has an absence of a clearly detectable vertical structure unlike the image provided by barrier <NUM> and posts 30a (shown in <FIG>).

The principle of method <NUM>, is to ascertain if a patch in image (schematically shown by ellipse 76a), on or bounded by a line intersecting the lane vanishing point, is a patch on the road surface or an upright (substantially vertical) portion of road barrier <NUM>. However, in image 70a it is a challenge to decide if perhaps patch 76a is road surface. A motion of texture in the patch as host vehicle <NUM> moves forward may uniquely determine whether the surface of the patch is upright and a part of barrier <NUM> or the road patch is on the road surface. If the patch is upright, all the points along columns in the patch move together but scale vertically as shown by patch (schematically shown ellipse 76b) in image frame 70b. If the patch is on the road surface, then all points in rows of the patch will move together and scale horizontally. The motion of texture in the patch is typically large and the shape of the patch may change significantly.

Referring now also to method <NUM>, patch 76a is located (step <NUM>). The known vehicle motion and the lateral distance (X) may be used to warp (step <NUM>) image patch 76a using two motion models. One motion model assumes a vertical surface and the other motion model assumes a road surface. A matching score to next image 70b using warped image 70a is then computed (step <NUM>), allowing for some fine alignment to compensate for inexact host vehicle <NUM> motion. The matching score can be Sum of Square Distance (SDD) or edge based (e.g. Hauussdorf distance). The best matching score in decision step <NUM> determines whether, the image patch is of a concrete barrier <NUM> at the lateral distance (X) given by the lane mark (step <NUM>), or if the image patch is of road texture (step <NUM>).

In some cases, methods <NUM> and <NUM> may not give a reliable result every image frame <NUM>/<NUM>. However, the road structure and presence of a barrier <NUM>, <NUM> persists over time. Therefore, a model for location of a barrier <NUM>, <NUM> may be accumulated over many image frames. Multiple hypotheses may be maintained with only the hypotheses above a certain confidence affecting a warning and/ or control system <NUM>. Different warning systems may require different levels of confidence: automatic lane change would require almost <NUM>% confidence that there is no barrier <NUM>, <NUM>. While initiating early braking for collision mitigation might require a moderately high (<NUM>%) confidence that there is a barrier <NUM>, <NUM>. Early triggering of lane departure warning (LDW) <NUM> may require a lower confidence.

Multiframe analysis may allow and/or an exchange of information to/from other driver assistance systems LDW <NUM>, FCW <NUM> allows for the integration of additional cues that might be less frequent. For example, a car passing on the left is a clear indication that there is no barrier <NUM>,<NUM> on the left side. In method <NUM>, lateral motion of host vehicle <NUM> in the lane of travel may produce a looming effect. The looming effect may be used to determine if the an upper line bounding patch <NUM> is in fact at the same lateral distance as a lower line bounding patch <NUM> indicating that patch <NUM> is part of a barrier <NUM> or if patch <NUM> is an image of an object farther away such as in the road or ground surface.

The indefinite articles "a" and "an" is used herein, such as "a patch", "an image " have the meaning of "one or more" that is "one or more patches" or "one or more images".

In one non-claimed alternative of the invention, there is a method of detecting a structural barrier extending along a road, the method performed by a driver assistance system mountable in a host vehicle, wherein the driver assistance system includes a camera operatively connectible to a processor, the method including the steps of:.

The vertical structural barrier may include a plurality of posts, the method including the steps of:.

The structural barrier may include a plurality of posts, the method including, for each of the posts:.

In another embodiment, there is a driver assistance system for detecting a vertical structural barrier extending along a road, wherein the driver assistance system is mountable in a host vehicle, the driver assistance system including:.

Motion of the images may be responsive to forward motion of the host vehicle.

Motion of the images may be responsive to lateral motion of the host vehicle.

Motion of the images of the structural barrier may correlate with an image line in the direction of the vanishing point of the road, wherein said image line corresponds to a vertical projection of the structural barrier onto the road plane.

The processor may be operable to hypothesize linear image structures as projections of the structural barrier onto the road plane to produce a plurality of hypotheses; wherein each said hypothesis gives a lateral position of the barrier relative to the host vehicle; wherein for each said hypothesis, the lateral positions and host vehicle motion are used to predict image motion and wherein the predicted image motion is compared to the actual image motion to verify the hypothesis and to derive the actual lateral position of the vertical structural barrier relative to the host vehicle.

Motion of the host vehicle may have a lateral component relative to the road direction and the image motion is of an image line in the image that is above the linear image structure, wherein vertical motion of the image line is used to determine lateral distance between the host vehicle and the structural barrier.

For a structural barrier which includes a plurality of posts the processor may be operable:.

For a structural barrier which includes a plurality of posts, for each of the posts the processor may be operable to:.

Claim 1:
An image processing method (<NUM>), comprising:
obtaining, via a camera (<NUM>) located in a moving vehicle, a first image frame (70a);
locating (<NUM>), in the first image frame, a first image patch (76a) comprising an image line intersecting a vanishing point in the first image frame (70a);
obtaining, via the camera (<NUM>) located in the moving vehicle, a second image frame (70b);
locating, in the second image frame (70b), a second image patch corresponding to the first image patch;
warping (<NUM>) the first image patch based on a vertical surface model, lateral distance of the first image patch from the vehicle and known vehicle motion to provide a first warped image patch;
warping (<NUM>) the first image patch based on a horizontal surface model to provide a second warped image patch, and
determining a first matching score for a match between the second image patch and the first warped image patch and a second matching score for a match between the second image patch and the second warped image patch; and,
determining the best of the first and second matching scores, wherein if the best matching score is the first score, determining the first image patch is of an upright barrier and if the best matching score is the second score, determining the first image patch is of road texture.