CN110298216A - Vehicle deviation warning method based on lane line gradient image adaptive threshold fuzziness - Google Patents

Vehicle deviation warning method based on lane line gradient image adaptive threshold fuzziness Download PDF

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CN110298216A
CN110298216A CN201810245281.5A CN201810245281A CN110298216A CN 110298216 A CN110298216 A CN 110298216A CN 201810245281 A CN201810245281 A CN 201810245281A CN 110298216 A CN110298216 A CN 110298216A
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lane line
pixel
lane
gradient
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CN110298216B (en
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华春生
马立永
何玉庆
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Shenyang Institute of Automation of CAS
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
    • B60Q9/00—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/588—Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20092—Interactive image processing based on input by user
    • G06T2207/20104—Interactive definition of region of interest [ROI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30248—Vehicle exterior or interior
    • G06T2207/30252—Vehicle exterior; Vicinity of vehicle
    • G06T2207/30256—Lane; Road marking

Abstract

The present invention relates to a kind of vehicle deviation warning methods based on lane line gradient image adaptive threshold fuzziness, acquire the original image of vehicle front road conditions；Road video image lane line close shot area is extracted as lane detection area-of-interest；It is converted into gray level image；Gaussian filtering is carried out, carries out convolution algorithm with the single order Sobel gradient operator of horizontal direction；Carry out non-maxima suppression；The gradient pixel in area-of-interest is extracted with unrestrained water filling algorithm；With maximum variance between clusters, adaptive optimal threshold value is sought, as segmentation threshold；Probability Hough transformation is carried out, lane line is extracted；The lane line obtained to extraction tracks.Area-of-interest of the present invention using the close shot area of fixed video image as lane detection, it avoids and determines that picture drop-out point calculates the loss of dynamic area-of-interest bring calculation amount in real time, the selection in close shot area can guarantee that the lane line in close shot area meets linear model, avoid the difficulty of corner lane detection.

Description

Vehicle deviation warning method based on lane line gradient image adaptive threshold fuzziness
Technical field
The present invention relates to pattern-recognitions and field of automation technology, specifically a kind of to be based on lane line gradient image certainly Adapt to the vehicle deviation warning method of Threshold segmentation.
Background technique
In intelligent vehicle research field, either deviation alarm is still prevented with identification for lane detection and vehicle The automobiles auxiliary security control loops such as road holding are all a key technologies.The premise of deviation alarm is that quick and precisely Detection can when vehicle deviates this effective driver area correctly to divide effective driver area of this vehicle with tracking lane line It provides warning note in time to guarantee traffic safety to the maximum extent, while also preparing for the distance and speed of measurement front truck, To cooperate the work of anti-collision driving system.
In technical research currently based on detection with the identification of lane and Lane Mark, the application of monocular vision is more general Time.Lane detection existing method based on monocular vision mainly passes through the images low layers such as color, shape, edge to lane line Secondary characteristic information extracts, and lane line is then determined with straight line or curve model, lane detection is converted at this time Determine the parameter of mathematical model.However existing algorithm is all based on specific simple single road environment design, only specific Just there is satisfied effect under simple structure road environment, once road environment changes, such as there is object shadow interference, week There are the classes straight line objects such as electric pole and street lamp to interfere for surrounding environment, road illumination condition changes etc., all by the detection to lane line Effect has an impact.For example, being influenced by environmental lighting conditions and lane line abrasive conditions, Threshold segmentation and side to lane line Edge detection is all difficult to carry out with fixed threshold.The lane detection based on mathematical model lacks generality, i.e., a kind of lane simultaneously Model is difficult to be suitable for a variety of different roads.In addition, the contradiction in terms of the accuracy of algorithm and the real-time of calculating, and limit An important factor for making existing lane detection algorithm application at present.
Summary of the invention
In view of the deficiencies of the prior art, the present invention provides a kind of vehicle based on lane line gradient image adaptive threshold fuzziness Deviation warning method solves existing lane detection algorithm and is difficult to solve shadow of object interference, is difficult to adapt to road illumination item The problem of part changes.
Present invention technical solution used for the above purpose is:
A kind of vehicle deviation warning method based on lane line gradient image adaptive threshold fuzziness, including following procedure:
Step 1: the original image of acquisition vehicle front road conditions generates continuous video flowing；
Step 2: extracting road video image lane line close shot area as lane detection area-of-interest IROI；
Step 3: by lane detection area-of-interest IROIIt is converted into gray level image Igray；To obtained gray level image Igray After carrying out gaussian filtering, with the single order Sobel gradient operator G of horizontal directionxCarry out convolution algorithm, the gradient map after obtaining convolution Picture
Step 4: to the gradient image after convolutionCarry out non-maxima suppression, the gradient map after obtaining non-maxima suppression Picture
Step 5: to the gradient image after Threshold segmentationIt is further extracted in area-of-interest with unrestrained water filling algorithm Gradient pixel, obtain removal interference after gradient image
Step 6: to the gradient image after removal interferenceWith maximum variance between clusters, adaptive optimal threshold value T is sought, Bianry image I using the adaptive optimal threshold value T as segmentation threshold, after obtaining Threshold segmentationT；
Step 7: to the bianry image I after Threshold segmentationTProbability Hough transformation is carried out, lane line is extracted；
Step 8: being tracked according to the lane line that Kalman filter algorithm obtains extraction.
The lane detection area-of-interest IROIAre as follows:
Making headstock portion in video capture image, excluded pixel is expert at the bottom as area-of-interest just, sense Interest region arranges a height of original image and arranges high 3/11, and area-of-interest line width is equal to original image line width.
The single order Sobel gradient operator G with horizontal directionxCarrying out convolution algorithm includes:
F (i, j)=Gx* (2) I (i, j)
Wherein, GxFor the single order Sobel gradient operator of horizontal direction, (i, j) indicates pixel coordinate in picture.
The non-maxima suppression are as follows:
In gradient image, if the gray value (i.e. gradient value of the original image in the point) of current point is more of the row than its The gray value of left and right pixel is big, then retains the gray value of the point, otherwise inhibit, and sets 0 for the gray value of current point.
The maximum variance between clusters include:
Step 1: to the gradient image after removal interferenceCarry out normalization；
Step 2: for the gray level of the non-zero pixel value of gradient image after setting normalization as L, total non-zero pixel number is N, Non-zero pixel is divided into foreground pixel and background pixel, and T is the segmentation threshold of prospect and background, and foreground pixel is counted total non-zero of Zhan The ratio of pixel number N is w0, foreground pixel points average gray is u0；The ratio of background points Zhan total non-zero pixel number N For w1, background pixel points average gray is u1, the total average gray of the non-zero pixel of image is u, the side of foreground and background image Difference is g, then has:
U=w0×u0+w1×u1 (3)
G=w0×(u0-u)2+w1×(u1-u)2 (4)
Simultaneous above formula obtains:
When variance g maximum, foreground pixel point and background pixel point difference are maximum, and the gray threshold T obtained at this time is certainly Adapt to optimal threshold.
The normalization includes:
It seeksIn gray scale maximum value Pmax, thenIn each pixel multiplied by scale coefficient of dilatation (255/Pmax)； That is:
P (i, j)=P*(i, j) * (255/Pmax) (6)
Wherein, P*(i, j) is the gray value of (i, j) point in image before change of scale, and P (i, j) is image after change of scale In (i, j) gray value.
The bianry image I to after Threshold segmentationTProbability Hough transformation is carried out, lane line is extracted, comprising:
To the bianry image I after Threshold segmentationTProbability Hough transformation is carried out, two of detected straight line in bianry image are obtained A endpoint obtains the slope of detection straight line and the angle in image coordinate system, passes through the restriction of straight line and coordinate system horizontal axis angle Filter out undesirable line segment.
When extracting lane line, using the result of extract real-time as last lane detection result；
When not extracting lane line, examined using by previous frame image Kalman filter tracking prediction result as lane line Survey result.
After detection obtains lane line position information, vehicle deviation distance is resolved, obtains vehicle deviation distance L, If vehicle deviation distance L is greater than setting threshold value of warning LTWhen, it issues and deviates alarm.
It is described that vehicle deviation distance is resolved, obtain vehicle deviation distance L, comprising the following steps:
1) calibration vehicle foreside bodywork width midpoint；
2) linear equation of left and right lane line is calculated according to the lane line endpoints and slope that acquire；
3) it calculates vehicle body midpoint to be expert at and two lane line intersection point A and B, and seeks the midpoint M of two intersection point AB；
4) the difference DELTA d of the column coordinate of body width midpoint C and point M is calculated；
5) left-hand lane line outer edge and vehicle body midpoint intersection point A ' of the row and AA ' distance are calculated；
6) according to the ratio and Δ d of AA ' distance and lane line developed width, vehicle deviation distance L is obtained.
If when Δ d is timing vehicle right avertence, vehicle left avertence when Δ d is negative；
The invention has the following beneficial effects and advantage:
1. the present invention using the close shot area of fixed video image as the area-of-interest of lane detection, avoids in real time Determine that picture drop-out point calculates the huge calculation amount loss of dynamic area-of-interest bring, while the selection in close shot area can be maximum Guarantee to limit that the lane line in close shot area meets linear model, avoids the difficulty of corner lane detection；
2. the present invention is by the inspiration of Canny Edge check thought, only using single order horizontal direction Sobel gradient operator to vehicle Diatom detects carry out convolutional calculation interested, and obtained edge gradient image describes left and right lane line edge picture to greatest extent The variation of plain gradient reduces the influence of the noise signal with other gradient directions；
3. the present invention seeks edge gradient adaptive optimal threshold value with maximum variance between clusters on edge gradient image, Allow to adaptively seek optimal lane line drawing threshold value according to present road light conditions and road surface shadow interference situation, avoid Gray threshold is directly sought on gray level image to carry out Threshold segmentation or carry out edge detection etc. vulnerable to illumination with fixed threshold The factors such as uneven, shadow of object interference or background complexity influence and caused by segmentation effect it is poor, edge detection bad adaptability lacks Point；
4. the present invention dexterously devises a kind of according to the ratio such as lane line width transformation solution vehicle deviation in video image The method of distance, method is simple and effective, avoids that resolve calculation method when vehicle deviates by Camera extrinsic in the past complicated, outside camera Ginseng vulnerable to sideways inclined condition and vehicle attitude influence and caused by unfavorable factor influence；
5. the present invention can satisfy to a certain extent, there are object shadow interference, surrounding enviroment, there are electric poles and street lamp Etc. the interference of classes straight line object, road illumination condition the lane detection under complicated road environment such as change, and algorithm calculation amount is few, Substantially it can satisfy requirement of real-time.
Detailed description of the invention
Fig. 1 is system flow schematic diagram
Fig. 2 is that image close shot area extracts schematic diagram
Fig. 3 is the gradient image after horizontal direction single order Sobel gradient operator convolution
Fig. 4 is that non-maxima suppression refines the gradient image behind edge
Fig. 5 is that unrestrained water fills exposure mask template
Fig. 6 is removal interference result gradient image
Fig. 7 is to the bianry image obtained after Fig. 6 Threshold segmentation.
Fig. 8 is lane line straight line model analysis diagram.
Fig. 9 is to resolve vehicle deviation distance schematic diagram than transformation idea according to lane line width etc..
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and embodiments.
A kind of vehicle departure warning system based on lane line gradient image Threshold sementation, realization are broadly divided into two It is most of: 1) detection of driving vehicle or so lane line；2) resolving and alarm of vehicle deviation distance.
(1) detection of driving vehicle or so lane line
Wherein, the detection of driving vehicle or so lane line can be divided into 8 steps and carry out:
Step 1: using the original image of camera acquisition vehicle front road conditions, generating continuous video flowing.
Step 2: extracting road video image lane line close shot area as lane detection area-of-interest IROI.By video Making headstock portion in acquisition image, excluded pixel is expert at the bottom as area-of-interest just, area-of-interest column height High 3/11 is arranged for original image, area-of-interest line width is equal to original image line width.The signal of lane line region of interesting extraction For figure as shown in Fig. 2, wherein W is picture traverse, H is picture altitude.
Step 3: the lane detection area-of-interest I that step 2 is extractedROI, it is converted into gray level image Igray；To The gray level image I arrivedgrayThe single order Sobel gradient operator G of horizontal direction is used after progress gaussian filteringxConvolution algorithm is carried out, is obtained Gradient image after convolutionAs shown in Figure 3.Gradient operator kernel GxAs shown in formula (1), convolutional calculation formula such as formula (2) shown in, wherein i, j indicate pixel coordinate in picture.
F (i, j)=Gx* (2) I (i, j)
Step 4: the gradient image after the application level direction Sobel gradient operator convolution acquired to step 3It carries out non- Maximum inhibits the gradient image to refine edge, after obtaining non-maxima suppressionAs shown in Figure 4.Non-maxima suppression Algorithm are as follows: 1) compare the gradient intensity of current point and the gradient intensity of the be expert at left and right pixel gradient direction point of current point.2) If it is maximum that the gradient intensity of current point compares with the gradient intensity of left and right pixel of the row, retain its value.Otherwise Inhibit, that is, is set as 0.Formula is as follows:
Step 5: to the gradient image after non-maxima suppressionRegion of interest is further extracted with unrestrained water filling algorithm Gradient pixel in domain, the gradient map to remove the influence of ambient enviroment and non-lane markings interested, after obtaining removal interference PictureUnrestrained water filling exposure mask template is as shown in figure 5, remove the gradient image after ambient enviroment and non-Lane Mark interested As shown in Figure 6.
Step 6: the gradient image after the removal interference that step 5 is obtainedMaximum kind is used for non-zero gray-value pixel Between variance method (OTSU) seek adaptive optimal threshold value T, the Threshold segmentation of edge gradient image is realized, after obtaining Threshold segmentation Bianry image IT, as shown in Figure 7.The gradient image that step 4 is obtained first before seeking optimal thresholdNormalization is carried out, Method is to seekIn gray scale maximum value Pmax, thenIn each pixel multiplied by scale coefficient of dilatation (255/Pmax).Such as Under:
P (i, j=P (i, j) * (255/Pmax) (4)
The method of adaptive optimal threshold value T is sought such as with maximum variance between clusters (OTSU) for non-zero gray-value pixel Under: the gray level of the non-zero pixel value of gradient image after setting normalization is L, and total non-zero pixel number is N, these non-zero pixels It is divided into foreground pixel and background pixel, T is the segmentation threshold of prospect and background, and prospect counts Zhan total non-zero pixel number N's Ratio is w0, average gray u0；The ratio of background points Zhan total non-zero pixel number N is w1, average gray u1, image is non- The total average gray of 0 pixel is u, and the variance of foreground and background image is g, then has:
U=w0×u0+w1×u1 (5)
G=w0×(u0-u)2+w1×(u1-u)2 (6)
Simultaneous (5) (6) formula obtains:
When variance g maximum, it is believed that foreground pixel point and background pixel point difference are maximum at this time, gray scale threshold at this time Value T is optimal threshold.Using optimal threshold T as segmentation threshold, the Threshold segmentation of edge gradient image, after obtaining Threshold segmentation Bianry image IT。
Step 7: the bianry image I that step 6 is obtainedT, applied probability Hough transformation, extraction lane line.Suddenly by probability Husband converts two endpoints that can obtain detected straight line in bianry image, and then obtains the slope of detection straight line and sit in image The angle for marking system filters out undesirable line segment by the restriction of straight line and coordinate system horizontal axis angle.As shown in figure 8, (xi, yi) i=1 ... 4, the endpoint of the lane line respectively detected.Left and right lane line and image coordinate system horizontal axis angle are respectively θ1 And θ2, by a large amount of actual test, the restriction range that we obtain them meets formula (8).Finally we, which choose, meets limit On the left of the figure area-of-interest middle line of fixed condition slope be negative near submedial straight line as the left-hand lane detected Line, on the right side of middle line slope be positive near submedial straight line as the left-hand lane line detected.
Step 8: being tracked using the lane line that Kalman filter algorithm obtains extraction.Track the state packet chosen It includes: the endpoint and slope of left and right lane line.But since there are two variables of abscissa and ordinate for each endpoint, so invisible Between increase tracking mode quantity.In order to reduce algorithm complexity lane line segment is extended here, obtain The intersection point of it and ROI picture, and the row coordinate of these intersection points be it is fixed, just converted for the prediction of endpoint ranks coordinate in this way For the prediction for endpoint column coordinate, the quantity for needing predicted state is greatly reduced.When step 7 extract real-time obtains lane line When, using the result of extract real-time as last lane detection as a result, when step 7 does not extract lane line using by preceding One frame image Kalman filter tracking prediction result is as lane detection result.
(2) resolving and alarm of vehicle deviation distance
According to the left and right lane line position that detection obtains, the thought of transformation is compared according to lane line width etc. in video image, Solve vehicle deviation distance L.Its basic principle is as follows: calibration vehicle foreside bodywork width midpoint first, as shown in C point in Fig. 9, Then the linear equation of left and right lane line is calculated according to the lane line endpoints and slope that acquire.Vehicle body midpoint is calculated to be expert at With two lane line intersection point A and B as shown in horizontal line in figure, and the midpoint M of two intersection point AB is sought.Calculate body width midpoint C and The difference DELTA d of the column coordinate of point M, i.e. C point range coordinate subtract M point range coordinate.Calculate left-hand lane line outer edge and vehicle body midpoint Intersection point A ' of the row, and AA ' distance.It can be resolved according to the ratio and Δ d of AA ' distance and lane line developed width and work as vehicle Deviation distance L, shown in specific formula for calculation such as formula (9), it is known that lane line developed width W is measured in advance.As the big Mr. Yu of L One threshold value LTWhen, it issues and deviates alarm.The offset direction of vehicle can be acquired by the positive and negative of Δ d, and Δ d is timing vehicle right avertence, Vehicle left avertence when Δ d is negative.

Claims (10)

1. a kind of vehicle deviation warning method based on lane line gradient image adaptive threshold fuzziness, it is characterised in that: including Following procedure:
Step 1: the original image of acquisition vehicle front road conditions generates continuous video flowing；
Step 2: extracting road video image lane line close shot area as lane detection area-of-interest IROI；
Step 3: by lane detection area-of-interest IROIIt is converted into gray level image Igray；To obtained gray level image IgrayIt carries out After gaussian filtering, with the single order Sobel gradient operator G of horizontal directionxCarry out convolution algorithm, the gradient image after obtaining convolution
Step 4: to the gradient image after convolutionCarry out non-maxima suppression, the gradient image after obtaining non-maxima suppression
Step 5: to the gradient image after Threshold segmentationThe ladder in area-of-interest is further extracted with unrestrained water filling algorithm Pixel is spent, the gradient image after obtaining removal interference
Step 6: to the gradient image after removal interferenceWith maximum variance between clusters, adaptive optimal threshold value T is sought, with this Bianry image I of the adaptive optimal threshold value T as segmentation threshold, after obtaining Threshold segmentationT；
Step 7: to the bianry image I after Threshold segmentationTProbability Hough transformation is carried out, lane line is extracted；
Step 8: being tracked according to the lane line that Kalman filter algorithm obtains extraction.
2. the vehicle deviation warning method according to claim 1 based on lane line gradient image adaptive threshold fuzziness, It is characterized by: the lane detection area-of-interest IROIAre as follows:
Making headstock portion in video capture image, excluded pixel is expert at the bottom as area-of-interest just, interested Region arranges a height of original image and arranges high 3/11, and area-of-interest line width is equal to original image line width.
3. the vehicle deviation warning method according to claim 1 based on lane line gradient image adaptive threshold fuzziness, It is characterized by: the single order Sobel gradient operator G with horizontal directionxCarrying out convolution algorithm includes:
F (i, j)=Gx* (2) I (i, j)
Wherein, GxFor the single order Sobel gradient operator of horizontal direction, (i, j) indicates pixel coordinate in picture.
4. the vehicle deviation warning method according to claim 1 based on lane line gradient image adaptive threshold fuzziness, It is characterized by: the non-maxima suppression are as follows:
In gradient image, if the gray value of the gray value of current point left and right pixel more of the row than its is big, retaining should The gray value of point, otherwise inhibits, sets 0 for the gray value of current point.
5. the vehicle deviation warning method according to claim 1 based on lane line gradient image adaptive threshold fuzziness, It is characterized by: the maximum variance between clusters include:
Step 1: to the gradient image after removal interferenceCarry out normalization；
Step 2: the gray level of the non-zero pixel value of gradient image after setting normalization is L, and total non-zero pixel number is N, non-zero picture Element is divided into foreground pixel and background pixel, and T is the segmentation threshold of prospect and background, foreground pixel points Zhan total non-zero pixel The ratio of points N is w0, foreground pixel points average gray is u0；The ratio of background points Zhan total non-zero pixel number N is w1, Background pixel counts average gray as u1, the total average gray of the non-zero pixel of image is u, and the variance of foreground and background image is G then has:
U=w0×u0+w1×u1 (3)
G=w0×(u0-u)2+W1×(u1-u)2 (4)
Simultaneous above formula obtains:
When variance g maximum, foreground pixel point and background pixel point difference are maximum, and the gray threshold T obtained at this time is adaptive Optimal threshold.
6. the vehicle deviation warning method according to claim 5 based on lane line gradient image adaptive threshold fuzziness, It is characterized by: the normalization includes:
It seeksIn gray scale maximum value Pmax, thenIn each pixel multiplied by scale coefficient of dilatation (255/Pmax)；That is:
P (i, j)=P*(i, j) * (255/Pmax) (6)
Wherein, P*(i, j) is the gray value of (i, j) point in image before change of scale, P (i, j) be after change of scale in image (i, J) gray value.
7. the vehicle deviation warning method according to claim 1 based on lane line gradient image adaptive threshold fuzziness, The bianry image I to after Threshold segmentationTProbability Hough transformation is carried out, lane line is extracted, comprising:
To the bianry image I after Threshold segmentationTProbability Hough transformation is carried out, two ends of detected straight line in bianry image are obtained Point obtains the slope of detection straight line and the angle in image coordinate system, is filtered out by the restriction of straight line and coordinate system horizontal axis angle Undesirable line segment.
8. the vehicle deviation warning method according to claim 1 based on lane line gradient image adaptive threshold fuzziness, When extracting lane line, using the result of extract real-time as last lane detection result；
When not extracting lane line, using by previous frame image Kalman filter tracking prediction result as lane detection knot Fruit.
9. the vehicle deviation warning method according to claim 1 based on lane line gradient image adaptive threshold fuzziness, After detection obtains lane line position information, vehicle deviation distance is resolved, vehicle deviation distance L is obtained, if vehicle Deviation distance L is greater than setting threshold value of warning LTWhen, it issues and deviates alarm.
10. the vehicle deviation warning method according to claim 9 based on lane line gradient image adaptive threshold fuzziness, It is described that vehicle deviation distance is resolved, obtain vehicle deviation distance L, comprising the following steps:
1) calibration vehicle foreside bodywork width midpoint；
2) linear equation of left and right lane line is calculated according to the lane line endpoints and slope that acquire；
3) it calculates vehicle body midpoint to be expert at and two lane line intersection point A and B, and seeks the midpoint M of two intersection point AB；
4) the difference DELTA d of the column coordinate of body width midpoint C and point M is calculated；
5) left-hand lane line outer edge and vehicle body midpoint intersection point A ' of the row and AA ' distance are calculated；
6) according to the ratio and Δ d of AA ' distance and lane line developed width, vehicle deviation distance L is obtained.
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