CN102862574A - Method for realizing active safety of vehicle on the basis of smart phone - Google Patents
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Abstract
The invention discloses a method for realizing the active safety of a vehicle on the basis of a smart phone, which comprises the following steps: (1) obtaining a video image in front of a vehicle by the smart phone, and extracting a pavement gray scale parameter; (2) according to the extracted pavement gray scale parameter, carrying out lane line deviation detection; (3) according to the extracted pavement gray scale parameter, detecting a front obstacle; and (4) according to a lane line deviation detection result and a front obstacle detection result, carrying out risk early warning. According to the method for realizing the active safety of the vehicle on the basis of the smart phone, the smart phone is used as a hardware platform for processing data, the lane line extraction technology and the pavement obstacle extraction technology in the image processing technical field are combined to detect whether the vehicle is deviated from the lane line in real time, and whether an obstacle exists in the front is detected to guarantee the driving safety of a driver.
Description
Technical field
The present invention relates to the automobile active safety technology, be specifically related to realize based on smart mobile phone the method for vehicle active safety.
Background technology
Along with the development of society, the traffic safety problem in the city is day by day serious.According to statistics, the whole nation in 2011 relates to altogether 210812 of the road traffic accidents of personal casualty, causes 62387 people death, 237421 people injured, has caused huge economic loss and personal casualty.Traditional vehicle safety means as: bumper/spoiler, safety strap, safety air bag etc. all are passive types, are called vehicle passive security technology, can't avoid the generation of accident, the loss that can only the minimizing accident brings.Therefore, vehicle active safety technology more and more is applied in the vehicle design and production.
Existing vehicle active safety technology, as: the ACC(cruise system), LDWS (lane departure warning system) etc. needs extra sensor (such as laser radar) to come surrounding environment is carried out perception, these sensors all are that vehicle has been finished installation before dispatching from the factory, and the vehicle that does not install sensor additional then can't use vehicle active safety technology after dispatching from the factory.Therefore, existing vehicle active safety technology portability is relatively poor.If vehicle active safety technology can be implemented on other hardware platforms, break away from the bus of vehicle itself, so its portable can greatly enhancing.Intelligent mobile phone terminal has self independently sensor and treater, is a good hardware platform.
The advantages such as image processing techniques is large owing to its obtaining information amount, cost is low are widely used in the field of traffic, such as car plate identification, traffic information extraction etc.It equally also is applied in the vehicle active safety, such as lane mark deviation detection etc.Image processing techniques in the existing vehicle active safety technology all relies on the dedicated imbedded systems such as PC or DSP, and the arithmetic capability of these hardware platforms is powerful, can guarantee the real-time of certain complicated algorithm.Yet, the arithmetic capability of smart mobile phone relatively a little less than, and its treater also will be processed communication service, the simultaneously existing vehicle active safety technology of processing based on image is comparatively complicated again, if therefore prior art is grafted directly to intelligent mobile phone terminal, can cause the real-time of system greatly to reduce, practicality is also had a greatly reduced quality.As follows for the existing result for retrieval of lane detection technology that image processing techniques realizes and detection of obstacles technology that utilizes:
Patent publication No. CN 101608924A has announced the method for detecting lane lines of a kind of intensity-based estimation and Cascade H ough conversion.This invention is at first carried out gray scale to the sensitizing range of the vehicle front road conditions original image I that gathers and is estimated it is divided into vehicle shadow zone, the non-mark region in road surface and pavement marker zone (the vehicle body zone that contains front vehicles); Then adopt Mathematical Morphology Method from the zone that the road surface gray scale is estimated to divide, to obtain the boundary image in pavement marker zone; The boundary image of road pavement mark region carries out the Hough conversion to extract straight line characteristics of image wherein subsequently; At last, the search by road pavement sign edge vanishing point has realized the detection to lane mark.The defective of this invention is, when utilizing Hough change detection lane mark, can be subjected to the impact at other strong edges, and its operand is larger.
Patent publication No. CN 102156979A discloses a kind of lane mark method for quick based on GrowCut, and it comprises: acquisition monitoring video and the image of selecting at least one frame are as key frame; Cut Seed Points in described key frame images acceptance of the bid deckle fate; Utilizing the GrowCut algorithm to carry out the edge cuts apart; Adopt median filter that segmentation result is carried out smothing filtering; With the smothing filtering result, carry out edge extracting again; Edge extracts the result again, divides half to process; To minute half result, adopt the polynomial curve fitting method that lane mark is carried out match again, obtain final lane mark.The method is applicable to detect the static background image, and can't extract the lane mark in the dynamic background, is not suitable for vehicle active safety technology.
Patent publication No. CN 101447078A discloses a kind of method for obstacle segmentation and device.This inventive embodiment comprises: the current frame image in the sequence image and frame period are divided into identical some more than or equal to the consecutive frame image of 1 frame, correspondence position image block to described current frame image and described consecutive frame image is done difference, and the zone of the difference block in definite difference image; Calculate in the current frame image with described difference block zone in perpendicularity and the grain of corresponding each piecemeal; To be defined as the obstacle piecemeal with described difference block zone piecemeal corresponding and that have perpendicularity and grain in the current frame image; With described obstacle piecemeal according to being communicated with regular dyspoiesis object area.The shortcoming of this invention is, when cutting apart obstacle, needs the traversal entire image, and its operand is larger, can't realize at intelligent mobile phone terminal.
Patent publication No. CN 101419667A discloses the method for obstacle in a kind of recognition image, comprise: obtain present frame and the before image of N frame nearest with present frame, the every two field picture that obtains is divided the piece zone after all obtaining several and divide for every two field picture by identical mode; Calculate present frame and the before moving obstacle confidence level in N frame corresponding each piece zone nearest with present frame; According to described present frame and the before moving obstacle confidence level in described each the piece zone of N frame nearest with present frame, determine successively whether each piece zone is obstacle in the current frame image; According to the obstacle in each definite image in piece zone.The defective of the method is, its need to record with present frame nearest before the image of N frame, needed spatial cache is larger, is not suitable for intelligent mobile phone terminal.
Summary of the invention
The present invention is directed to existing vehicle active safety Technology Need based on the existing sensor of vehicle, portable relatively poor and operand is large, need the hardware platform of high request the problem such as to support, and a kind of method that realizes the vehicle active safety based on smart mobile phone is provided.The hardware platform that the method utilizes smart mobile phone to process as data, and the lane mark extractive technique in the combining image processing technology field and road obstacle extractive technique, can detect in real time whether run-off-road line of vehicle, and whether have obstacle, guarantee the traffic safety of chaufeur if detecting the place ahead.
In order to achieve the above object, the present invention adopts following technical scheme:
Realize the method for vehicle active safety based on smart mobile phone, the method comprises the steps:
(1) utilizes smart mobile phone to obtain the vehicle front video image, and extract the road surface grey parameter;
(2) carry out the lane mark deviation detection according to the road surface grey parameter that extracts;
(3) carry out the place ahead detection of obstacles according to the road surface grey parameter that extracts;
(4) result according to lane mark deviation detection and the place ahead detection of obstacles carries out danger early warning.
In preferred embodiment of the present invention, the process of extracting the road surface grey parameter in the described step (1) is as follows:
(11) extract the zone in the zone of going one of restriction in the zone to belong to the road surface that the vehicle front video image that obtains shows as the road surface grey parameter and be used for extracting corresponding road surface grey parameter;
(13) extraction road surface grey parameter extracts the road surface average gray in the zone;
(14) the road surface gray scale maximum of extracting in the grey parameter extraction zone, road surface allows the fluctuation difference;
(15) extraction road surface grey parameter extracts the road surface gray scale gradual change compensating factor in the zone.
Further, the process of lane mark deviation detection is as follows in the described step (2):
(21) set up white, yellow, grey color model: refer to according to the road surface grey parameter that extracts, dialogue, Huang, three kinds of colors of ash are carried out modeling, if the rgb value of a certain pixel meets a certain model, then think the color of this pixel for this model sign;
(22) search lane mark border initial point: begin the upwards border initial point of search vehicle left and right sides lane mark from the video image bottom that obtains;
(23) search next lane mark boundary point: set up the estimation range according to the last lane mark boundary point that searches, then next lane mark boundary point of search in this estimation range; Carry out iteration and carry out, until in the estimation range, can't find the lane mark boundary point;
(24) lane mark position analysis: calculate its coefficient of deviation according to the lane mark information that detects.
Further, carry out the process of the place ahead detection of obstacles in the described step (3) as follows:
(32) zone, road surface start line is set; In the zone, road surface on video image demonstration road surface, a cut-off rule is set, in order to zone, road surface and the front cover in the segmented video image;
(32) zone, search road surface: according to the road surface grey parameter, begin upwards to search for the zone, road surface every row from zone, road surface start line, if run into the zone of non-road surface color, then stop search, and this point is designated as the road surface region separation point of these row;
(33) the place ahead obstacle probe: according to the road surface region separation point that searches, detect whether there is sunk area, if sunk area is arranged, it meets certain requirement, and then this thinks that there is obstacle in this zone.
Further, the specific implementation process of described step (4) is as follows: if the lane mark coefficient of deviation that detects surpasses a certain scope, think that then vehicle departs from, utilize smart mobile phone to send voice suggestion; If when detecting the place ahead obstacle and being in the set danger early warning zone of user, then think and need to keep away the barrier operation, utilize smart mobile phone to send voice suggestion.
Technical scheme provided by the present invention, it carries out full figure not being traveled through when image is processed, and operand is little, can well be applied to intelligent mobile phone terminal.
This programme is realized based on common smart mobile phone, need not to load on the vehicle any sensor, the vehicle active safety technology that realizes thus can be implanted into any vehicle very easily, can prevent to a certain extent that lane mark from departing from and bump against with the place ahead obstacle, wide application, practical, with low cost.
Moreover, because the employed image processing techniques operand of this programme is little, can guarantee the real-time of the system of embodiment.
Description of drawings
Further specify the present invention below in conjunction with the drawings and specific embodiments.
Fig. 1 is the principle flow chart that the present invention realizes;
Fig. 2 is that the present invention carries out the schematic flow sheet that the road surface grey parameter extracts;
The mobile video image showed schematic diagram when Fig. 3 was the invention process;
Fig. 4 is the schematic flow sheet that the present invention carries out the lane mark deviation detection;
Fig. 5 is the schematic flow sheet that the present invention carries out the place ahead detection of obstacles;
Fig. 6 is the schematic flow sheet that the present invention carries out the zone, road surface;
Fig. 7 is the schematic flow sheet that the present invention carries out danger early warning.
The specific embodiment
For technological means, creation characteristic that the present invention is realized, reach purpose and effect is easy to understand, below in conjunction with concrete diagram, further set forth the present invention.
Referring to Fig. 1, the present invention is based on the method that smart mobile phone is realized the vehicle active safety, its specific implementation process is as follows:
Step 101: extract the road surface grey parameter.
Concrete, at first obtain the vehicle front video image, then in the shown zone of image, set the road surface grey parameter and extract the zone, and from this zone, extract the road surface grey parameter, comprise road surface average gray, road surface gray scale fluctuation difference and road surface gray scale gradual change compensating factor.
Step 102: the road surface grey parameter according to extracting, carry out the lane mark deviation detection.
Specifically, at first according to the road surface grey parameter that extracts, definition is white, yellow, the grey color model, then seeks lane mark border initial point, then seeks next lane mark boundary point, is searching for the laggard moving traffic lane line position analysis of all lane mark boundary points.
Step 103: the road surface grey parameter according to extracting, carry out the place ahead detection of obstacles.
Concrete, at first set zone, road surface start line, then according to the road surface grey parameter that extracts, begin upwards to search for the zone, road surface from zone, road surface start line, then carry out the road surface regional analysis and obtain the place ahead obstacle information.
Step 104: according to lane mark deviation detection and the place ahead detection of obstacles, carry out danger early warning.
Concrete, if after carrying out the lane mark deviation detection, find the automotive run-off-road line, then send alarm sound by smart mobile phone and carry out danger early warning.Same, if after carrying out the place ahead detection of obstacles, finding has obstacle in the place ahead is closely, then sends alarm sound by smart mobile phone and carries out danger early warning.
Based on above-mentioned principle, specific implementation process of the present invention is as follows:
The first step: extract the road surface grey parameter
Referring to Fig. 2, it is as follows that this example carries out the leaching process of road surface grey parameter:
Step 201: obtain the vehicle front video image.
Vehicle front video image acquisition equipment is the post-positioned pick-up head of smart mobile phone, and then the video image that the post-positioned pick-up head of smart mobile phone can the real time shooting vehicle front carries out subsequent treatment with this image transmitting to the core processor of smart mobile phone.
Step 202: set the road surface grey parameter and extract the zone.
As shown in Figure 3,202a territory, grey parameter extraction district, road surface refers to and most possibly be a zone on road surface in the regional 202c that image shows, generally above front cover 202b, the centre of two lane mark 202f, this zone is not affected by front cover, be not subject to the front vehicles impact yet, can reflect preferably the intensity profile situation on road surface.This regional width is 1/2nd of picture traverse, highly is 1/8th of picture altitude, and this zone is placed in the middle about in image, and upper-lower position is in middle the latter half of image.
Step 203: extract the road surface average gray.
The road surface average gray can reflect the average gray value on road surface.Its concrete extracting method is: extract in the zone at the road surface grey parameter, extraction is in the gray value of the pixel of even number line and even column, and gray value carried out descending sort, the eliminating gray value is front 15% and rear 15% pixel, then the gray value addition with residual pixel point can obtain the road surface average gray divided by the number of remaining pixel again, easy for narrating, it is defined as I
Aver
Step 204: extract road surface gray scale fluctuation difference.
Road surface gray scale fluctuation difference has reflected the maximum fluctuation amplitude of the road surface gray value that system allows, if the gray value of a certain pixel has surpassed this amplitude, can think that then this pixel does not belong to the zone, road surface.Its concrete extracting method is: in the time of will extracting the road surface average gray pixel value of remaining pixel and road surface average gray do poor, calculate its sum of squares, then divided by the number of remaining pixel, open again the standard deviation that obtains gray value behind the root, at last with the twice of this standard deviation as road surface gray scale fluctuation difference, easy for narrating, it is defined as I
DIts computing formula is as follows:
Step 205: extract road surface gray scale gradual change compensating factor.
Because the impact of light, the gray value on the road surface in the vehicle front video image that camera collects not is invariable, and being has certain gradual change trend, and that its trend is generally with the distance of car is far away, its gray value is larger, and can be similar to and think linear change.Will cause certain error if all adopt identical road surface average gray to weigh the road surface gray scale to the overall situation, therefore need road surface gray scale gradual change compensating factor to compensate the error that gradual change brings.Its concrete extracting method is: calculate respectively the gray value aviation value that the road surface grey parameter extracts top in the zone and lowermost end delegation, top aviation value and low side aviation value are subtracted each other, extract regional height divided by the road surface grey parameter again and just obtained road surface gray scale gradual change compensating factor, easy for narrating, it is defined as α.Its computing formula is as follows:
The compensation formula of road surface average gray is:
I '
Aver=I
Aver+ (pixel ordinate-bottom row ordinate) * α.
Second step: lane mark deviation detection
Referring to Fig. 4, the implementation process that this example carries out the lane mark deviation detection is as follows:
Step 301: definition is white, yellow, the grey color model.
Lane mark on the road surface has two kinds of colors, and is white and yellow, will carry out lane detection according to the feature of distribution of color, therefore needs to define first white, yellow, grey color model.
Shown in the color model of white is defined as follows:
Wherein R, G, B are respectively each channel value of RGB of certain pixel, and thresold1 is each interchannel relevance threshold of RGB, and I is the gray value of certain pixel, I '
AverBe the road surface average gray value of this pixel position, I
DBe the road surface gray scale fluctuation difference of extracting before.If certain pixel satisfies above-listed inequality, think that then this pixel belongs to white lane mark.
Shown in the color model of yellow is defined as follows:
Wherein thresold3 is the minimal difference threshold value between R, G and B channel value.If certain pixel satisfies all above-listed inequality, think that then this pixel belongs to yellow lane mark.
Shown in the color model of grey is defined as follows:
If certain pixel satisfies above-listed inequality, think that then this pixel belongs to grey.
Step 302: seek lane mark border initial point.
At first search for the border initial point of left-lane line.The initial point of search is the bottom intermediate point of image, and the direction of search is from the centre left, makes progress from the bottom, and the pixel of search need meet the following conditions: (1) this pixel and its left pixel point all satisfy white or yellow line color model; (2) these pixel right side two pixels all satisfy the grey color model.If search the pixel that satisfies this condition, then with it as left-hand lane line boundary initial point; If search for to half place of image and also do not search the pixel that satisfies above condition, think that then the left side is without Road.
Then search for the border initial point of right lane line.The initial point of search is the bottom intermediate point of image, and the direction of search is from the centre to the right, makes progress from the bottom, and the pixel of search need meet the following conditions: (1) this pixel and its right pixel point all satisfy white or yellow line color model; (2) these pixel left side two pixels all satisfy the grey color model.If search the pixel that satisfies this condition, then with it as right-hand lane line boundary initial point; If search for to half place of image and also do not search the pixel that satisfies above condition, think that then the right side is without Road.
Step 303: seek next lane mark boundary point.
Next boundary point estimation range of model, this zone is:
X wherein
I-1, y
I-1Be respectively the horizontal stroke, the ordinate that search a lane mark boundary point, and thresold1, thresold2 have characterized estimation range, if thresold1 is less, thresold2 is larger, operand will reduce so, but the not situation in estimation range of next boundary point might occur, h is the height of vehicle front video image.The slope of considering lane mark is within the specific limits, if therefore satisfy thresold1 〉=thresold2*2, next lane mark boundary point is bound in the estimation range so.Because the first half of image is sky, therefore need not in this zone, to search for, we will search for limitation in height at 3/4 place of picture altitude here, to reduce calculated amount.
After setting up next boundary point estimation range, can in this zone, search for next boundary point, its searching method is with step 302.If find the lane mark boundary point in the estimation range, then repeated execution of steps 303; If in the estimation range, do not find the lane mark boundary point, think so and finish detection to this lane mark.This part can use the mode of iteration to realize.
Step 304: lane mark position analysis.
About the testing result of two lane maries following four kinds of situations may appear: (1) two lane mark all exists; (2) only there is one in left side; (3) only there is one on right side; (4) do not exist.Do not consider for situation (4) whether it departs from, and for other three kinds of situations, need to calculate respectively its coefficient of deviation.
For situation (1), about two lane maries get some boundary point pairs, i.e. two boundary points that ordinate is identical, then calculate the center-point abscissa of each boundary point pair, then calculate the average of each center-point abscissa, this numerical representation method the center abscissa of Road, be designated as W ', its computing formula is as follows:
For situation (2), only need to get some boundary points at its detected left-hand lane line, the intermediate value of then calculating its abscissa and image right side boundary can obtain W ', value, its computing formula is as follows:
Wherein w is the width of vehicle front video image.
For situation (3), only need to get some boundary points at its detected right-hand lane line, the intermediate value of then calculating its abscissa and image left border can obtain W ' value, and its computing formula is as follows:
Calculate at last coefficient of deviation, coefficient of deviation β can use following formula to calculate:
The 3rd step, the place ahead detection of obstacles
Referring to Fig. 5, the implementation process that this example carries out the place ahead detection of obstacles is as follows:
Step 401: zone, road surface start line is set.
Referring to Fig. 3, because the picture of seeing among vehicle front video Figure 20 2c can be divided into front cover 202b and zone, road surface 202d two parts, therefore for the place ahead detection of obstacles, there is no need to begin search from the image bottom, and should begin search from zone, road surface 202d, zone, road surface start line 202e can be regarded as the cut-off rule of front cover zone 202b and zone, road surface 202d.The user needs by the angle that changes the mobile phone placement this line to be in the zone, road surface in use, and in setting up procedure, the ordinate of this line is set to the ordinate that the road surface grey parameter extracts bottom, zone horizontal line.
Step 402: begin upwards to search for the zone, road surface from zone, road surface start line.
As shown in Figure 6, the basic thought that this example is searched for the zone, road surface is, a certain row for image, begin upwards search from zone, road surface start line, if run into the pixel of non-road surface color, then stop search, and the coordinate of record when stopping to detect, and this point is called road surface region separation point.Because the first half of image mostly is sky, therefore there is no need to search for, therefore, also do not find non-road surface colored pixels point if search for to 3/4 o'clock of picture altitude, then stop search.Road surface color herein refers to the Huang of definition in step 301, white, three kinds of colors of ash.In order to reduce operand, this example is not all searched for each row pixel of image, but several row in interval are once searched for.If the interval is larger, that operand is less, but some less obstacle may be left in the basket.
Step 403: carry out the road surface regional analysis and obtain the place ahead obstacle information.
Zone, road surface under normal circumstances concave shape can not occur, if find to have concave shape after carrying out the road surface regional analysis, then thinks to detect obstacle.The method of the detection of concave shape is: begin to search is arranged from the road surface region separation point in image left side, if the difference of the height of the height of certain road surface region separation point and next road surface region separation point is greater than a certain threshold value, think that then this road surface region separation point is depression left side initial point, if the difference of the height of the height of certain road surface region separation point and a upper road surface region separation point, thinks then that this road surface region separation point is depression right side initial point greater than a certain threshold value.If depression left side initial point is adjacent with depression right side initial point, and this coordinate of 2 satisfies following condition, then thinks to have depression between these 2.
X wherein
LWith x
RBe respectively the abscissa of the left and right initial point of depression, y
LWith y
RBe respectively the ordinate of the left and right initial point of depression, thresold1 is used for the minimum width of restriction depression, can get rid of the impact of little noise spot, and thresold2 is used for the diff-H of restriction left and right sides initial point, if surpass this threshold value, think that then two initial points do not consist of depression about this.
The 4th step, dangerous forecast
Referring to Fig. 7, the implementation process that this example carries out danger early warning is as follows:
Step 501: judge whether the run-off-road line.
Coefficient of deviation β should satisfy-1＜β＜1, if satisfy
Think that then vehicles failed departs from, otherwise think that vehicle departs from, concrete, if
Think that then vehicle departs to the right, utilize smart mobile phone to send voice suggestion, the prompting chaufeur travels left; If
Think that then vehicle departs from left, utilize smart mobile phone to send voice suggestion, the prompting chaufeur is to right travel.
Step 502: judge whether to keep away barrier.
Because the obstacle that detects not necessarily needs to keep away barrier, therefore, chaufeur can be according to the driving habit of oneself, at smart mobile phone the danger early warning zone is set, if when obstacle appears in this zone, utilize smart mobile phone to send voice suggestion, the prompting chaufeur is kept away the barrier operation.
Above demonstration and described groundwork of the present invention, principal character and advantage of the present invention.The technical personnel of the industry should be understood; the present invention is not restricted to the described embodiments; that describes in above-described embodiment and the specification sheets just illustrates principle of the present invention; without departing from the spirit and scope of the present invention; the present invention also has various changes and modifications, and these changes and improvements all fall in the claimed scope of the invention.The claimed scope of the present invention is defined by appending claims and equivalent thereof.
Claims (5)
1. realize the method for vehicle active safety based on smart mobile phone, it is characterized in that, described method comprises the steps:
(1) utilizes smart mobile phone to obtain the vehicle front video image, and extract the road surface grey parameter;
(2) carry out the lane mark deviation detection according to the road surface grey parameter that extracts;
(3) carry out the place ahead detection of obstacles according to the road surface grey parameter that extracts;
(4) result according to lane mark deviation detection and the place ahead detection of obstacles carries out danger early warning.
2. the method that realizes the vehicle active safety based on smart mobile phone according to claim 1 is characterized in that, the process of extracting the road surface grey parameter in the described step (1) is as follows:
(11) extract the zone in the zone of going one of restriction in the zone to belong to the road surface that the vehicle front video image that obtains shows as the road surface grey parameter and be used for extracting corresponding road surface grey parameter;
(13) extraction road surface grey parameter extracts the road surface average gray in the zone;
(14) the road surface gray scale maximum of extracting in the grey parameter extraction zone, road surface allows the fluctuation difference;
(15) extraction road surface grey parameter extracts the road surface gray scale gradual change compensating factor in the zone.
3. the method that realizes the vehicle active safety based on smart mobile phone according to claim 1 is characterized in that, the process of lane mark deviation detection is as follows in the described step (2):
(21) set up white, yellow, grey color model: refer to according to the road surface grey parameter that extracts, dialogue, Huang, three kinds of colors of ash are carried out modeling, if the rgb value of a certain pixel meets a certain model, then think the color of this pixel for this model sign;
(22) search lane mark border initial point: begin the upwards border initial point of search vehicle left and right sides lane mark from the video image bottom that obtains;
(23) search next lane mark boundary point: set up the estimation range according to the last lane mark boundary point that searches, then next lane mark boundary point of search in this estimation range; Carry out iteration and carry out, until in the estimation range, can't find the lane mark boundary point;
(24) lane mark position analysis: calculate its coefficient of deviation according to the lane mark information that detects.
4. the method that realizes the vehicle active safety based on smart mobile phone according to claim 1 is characterized in that, the process of carrying out the place ahead detection of obstacles in the described step (3) is as follows:
(32) zone, road surface start line is set; In the zone, road surface on video image demonstration road surface, a cut-off rule is set, in order to zone, road surface and the front cover in the segmented video image;
(32) zone, search road surface: according to the road surface grey parameter, begin if run into the zone of non-road surface color, then to stop search, and this point is designated as the road surface region separation point of these row every being listed as upwards zone, search search road surface from zone, road surface start line;
(33) the place ahead obstacle probe: according to the road surface region separation point that searches, detect whether there is sunk area, if sunk area is arranged, it meets certain requirement, and then this thinks that there is obstacle in this zone.
5. the method that realizes the vehicle active safety based on smart mobile phone according to claim 1, it is characterized in that, the specific implementation process of described step (4) is as follows: if the lane mark coefficient of deviation that detects surpasses a certain scope, think that then vehicle departs from, utilize smart mobile phone to send voice suggestion; If when detecting the place ahead obstacle and being in the set danger early warning zone of user, then think and need to keep away the barrier operation, utilize smart mobile phone to send voice suggestion.
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| CN103617412A (en) * | 2013-10-31 | 2014-03-05 | 电子科技大学 | Real-time lane line detection method |
| CN103871243A (en) * | 2014-04-16 | 2014-06-18 | 武汉欧普威科技有限公司 | Wireless vehicle management system and method based on active safety platform |
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