CN101894271A - Visual computing and prewarning method of deviation angle and distance of automobile from lane line - Google Patents
Visual computing and prewarning method of deviation angle and distance of automobile from lane line Download PDFInfo
- Publication number
- CN101894271A CN101894271A CN 201010238436 CN201010238436A CN101894271A CN 101894271 A CN101894271 A CN 101894271A CN 201010238436 CN201010238436 CN 201010238436 CN 201010238436 A CN201010238436 A CN 201010238436A CN 101894271 A CN101894271 A CN 101894271A
- Authority
- CN
- China
- Prior art keywords
- lane line
- automobile
- line
- angle
- lane
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Traffic Control Systems (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
Abstract
The invention relates to a visual computing and prewarning method of deviation angle and distance of an automobile from a lane line. The image processing and computer vision technologies are utilized, and the deviation angle and distance of the automobile from the lane line are computed in real time according to the road surface image acquired by a vehicle-mounted camera, thereby estimating the line crossing time for safety prewarning. The method comprises the following steps: detecting the lane lines of the road surface image to obtain a linear equation of partial lane lines; establishing a three-dimensional coordinate system by using the camera as the initial point, and recording the mounting height and depression angle of the camera; calibrating the focal length according to the lane detection result under the condition of a given deflection angle; computing the deflection angle and vertical distance of the automobile relative to the lane line according to a pinhole camera model; and estimating the deviation time from the lane according to the instantaneous running speed of the automobile, thereby obtaining the safety prewarning or intelligent control information of the running automobile.
Description
Technical field
The present invention relates to a kind of application image and handle and computer vision technique,, calculate the quick calculating and the safe early warning method of the angle and distance of automobile run-off-road line in real time according to the pavement image that the vehicle-mounted pick-up head obtains.At first the road pavement image carries out the lane line detection, obtains the straight-line equation of local lane line; With the camera is that initial point is set up three-dimensional system of coordinate, the setting height(from bottom) and the angle of depression of record camera; Its focal length is calibrated, calculated the deflection angle and the vertical range of the relative lane line of automobile then according to pinhole camera modeling, estimate time of sailing out of the track, for the safe early warning in the car steering or Based Intelligent Control provide effective information.
Background technology
In recent years, the develop rapidly of automobile autonomous driving technology, and make progress gradually.The autonomous driving system of each research institution development can be on structured road (highway) high speed autonomous driving, and possessed various intelligent functions.In huge and complicated Modern Traffic system, guarantee that traffic safety is a primary goal, the automobile active safety technology is exactly by the various parameters of vehicle operating being monitored, regulated and control the purpose that reaches driver assistance, and track sideslip warning system one of major technique wherein just.
The purpose of research track sideslip warning system is that the dangerous situation that vehicle is about to roll away from the track is provided alarm, and this unsafe condition causes owing to reasons such as driver's spirit is concentrated inadequately or sleepy, fatigues mostly, belongs to unconscious run-off-road.Therefore, the bad steering state that track sideslip warning system is fundamentally said so to the driver provides warning, and wherein the computer vision measurement method is because its directly perceived, easy-to-use and reliability becomes the research direction of a main flow.This method has merged people, car, 3 systems in road, by research car-road relation, and then the anti-state that obtains the people that pushes away.
Track sideslip warning system directly depend on automobile travel speed, direction and and lane line between distance.Wherein travel speed easily directly the electronic system from the car extract and travel direction and be difficult for obtaining with the distance of lane line.The single-point that the Guo Konghui professor of Jilin University proposes is taken aim at the optimal curvature model in advance, the interior equilibrium of forces of horizontal vertical plane was determined target steering angle and target roll angle respectively when Ackerman geometric relationship when utilizing Vehicular turn and stable state turned to, adopt ADAMS software to set up driver-vehicle closed loop kinetic model, and carrying out emulation by the two-track line and the two kinds of typical driving cycles that crawl, the pilot model of being set up is applicable to the dynamics simulation research of single-track vehicle people-car closed-loop control model.
Because the lane detection technology based on graphical analysis is very ripe at present, scholars have proposed multiple lane detection algorithm, even under complex environments such as urban road, lane detection all has good effect.Because in subrange, the track distributes can be approximately straight line, and the present invention utilizes image analysis technology to carry out lane detection, obtains the lane line equation, and is that initial point is set up three-dimensional system of coordinate with the camera; Spatial information in conjunction with the camera installation, calculate the deflection angle and the vertical range of the relative lane line of automobile according to pinhole camera modeling, closing with car-road is the early warning information that foundation obtains the automobile sideslip, becomes the important technical links of guarantee driving safety in intelligent transportation system and the autonomous driving.
Summary of the invention
At existing automobile sideslip method for early warning model complexity, calculated amount is big, environmental factor dependence is strong deficiency, the vision that the purpose of this invention is to provide a kind of automobile run-off-road line angle and distance is calculated and method for early warning, this method utilizes image analysis technology to finish lane detection, employing vision computing method obtain the locus and the travel direction of automobile phase road pavement lane line, as the early warning information of automobile sideslip in the intelligent driving.
The present invention comprises following steps
A) with the CCD video frequency pick-up head along the automobile dead ahead to being installed in Che Nei or roof, adjust the angle of depression and focal length and make it, the setting height(from bottom) h and the angle of depression θ of record camera road surface blur-free imaging in the 50m of the place ahead;
B) by the camera road pavement carry out continuous shooting, collecting to the pavement image sequence, the digital video high-speed channel by DSP is realized data acquisition;
C) lane line in the road pavement image detects, and comprising:
The image edge detects.Utilize two 5 * 5 templates respectively computing is taken advantage of-added to image, obtain two width of cloth gradient images, obtain the edge image of former figure by this two width of cloth image corresponding to level and vertical direction.The edge image is the contour feature of saliency maps so picture, especially the side information of lane line;
The binaryzation of image.Adopt the Otsu algorithm computation to go out adaptive threshold to the edge image, image is carried out the black-and-white two color binary conversion treatment according to this threshold value;
3. remove horizontal profile: thus the subsequent treatment calculated amount reduced in order to reduce white point quantity, the characteristics that in picture, can not occur the level trend according to lane line, the horizontal edge point is merged, promptly the white point that horizontal direction is occurred continuously only keeps first white point of Far Left, thereby deletion horizontal edge, reduce white point quantity, the lane detection effect is not exerted an influence again simultaneously;
4. the application constraint condition is gone a little.Set constraint condition according to brightness, width and continuity Characteristics that actual lane line distributes, further remove noise spot;
5. obtain the lane line equation.Through after top a series of processing, effectively reduce white point quantity.The Hough conversion is a kind of straight-line detection means of extensive employing, and it determines most probable linear position by " ballot " mode, is used among the present invention realizing that lane line detects.In testing process, introduce constraint condition once more, and set minimum poll, when the straight line that satisfies the poll condition surpasses 4, keep 4 the highest straight lines of poll, finally obtain the straight-line equation of 0~4 lane line as track candidate's line;
D) the focal length parameter f of camera is demarcated.The vehicle that installs camera is parked on the direction that becomes known angle with lane line, discharges of the coke apart from f, be used for of the calculating of follow-up vehicle ' process deflection angle and vertical range according to detected lane line Equation for Calculating;
E) the deflection angle β and the vertical range d of the relative lane line of calculating automobile: in vehicle traveling process, lane line to every two field picture detects and obtains straight-line equation in real time, calculates deflection angle β and vertical range d according to the focal distance f that the height h of step a) record and angle of depression θ and step d) calibration obtain;
F) deflection angle β that obtains according to the instant travel speed and the step e) of automobile and distance parameter d calculate automobile and surmount the lane line required time, set alarm threshold value, if calculate more the line time then provide information warning less than threshold value, remind the driver in time to handle.
Patent of the present invention is addressed the operation result explanation of method:
(1) present lane detection method is nearly all used the line detection method of Hough conversion, because Hough transformation calculations amount is big, has influenced real-time.Owing to adopted multiple effective constraint condition in this method, make the point that participates in Hough conversion ballot greatly reduce, generally have only dozens of, improved speed.Experimental results show that and to satisfy real-time detection requirement fully;
(2) interference of having removed most of non-carriageway image information owing to effective constraint condition is so this method antijamming capability is strong.Experimental results show that and under the complex environment of urban road, agree to have satisfied reliability;
(3) mode of on-the-spot calibration has reduced because the influence that the error of camera itself causes has improved accuracy;
(4) angle computation method adopts the geometrical calculation form, has improved computing velocity.
In a word, the similar achievement in research that this method is present relatively, the characteristic in that oneself is all arranged aspect the environmental suitability of system, computing velocity, the reliability more helps satisfying practical application request.
Description of drawings
Fig. 1 be in the experimentation CCD camera clap road surface lane line realistic picture.
Fig. 2 is the lane detection result that step c) obtains.
Fig. 3 is the road surface schematic top view.
Fig. 4 is the road surface schematic side view.
Fig. 5 is the space coordinates illustraton of model that adopts in the algorithm.
Embodiment
Below in conjunction with a non-limiting example implementation process of the present invention is further described, referring to Fig. 1, Fig. 2, Fig. 3, Fig. 4, Fig. 5.
The present invention focuses on method and describes, and the experimental provision that adopts in the enforcement comprises that camera and DSP image processing board adopt common apparatus on the market to realize that the collection of image, size scaling also are popular technology, no longer are described in detail.Integrality for guaranteeing that implementation process is described can relate to some current techiques, does not have patent protection character, will offer some clarification in claims.
Embodiment of the present invention is as follows:
(a) along the automobile dead ahead to the CCD video frequency pick-up head is installed, adjust the angle of depression and focal length and make it road surface blur-free imaging in the 50m of the place ahead, the setting height(from bottom) h and the angle of depression θ of record camera see Fig. 4.
(b) lane line in the pavement image detects, and comprising:
The image edge detects: utilize two 5 * 5 templates of formula (1), (2) respectively computing is taken advantage of-added to image, obtain two width of cloth gradient images corresponding to level and vertical direction
With
, then by formula
Obtain the edge image
The edge image has kept the profile information of image, especially the side information of lane line:
To the edge binarization processing of images.Adopt the Otsu algorithm computation to go out adaptive threshold to the edge image, and image is carried out binary conversion treatment;
3. remove horizontal profile.Bianry image is carried out horizontal direction scanning, investigate continuous two points: if certain point is white point, and the front consecutive point are stain, then keep this white point, and the institute that does not satisfy this condition is become stain a little, are about to its gray-scale value clear 0.Can remove horizontal profile like this, reduce white point quantity greatly, and can eliminate the interference of other traffic sign, not influence lane line simultaneously and detect effect;
4. the application constraint condition is further eliminated white point.Characteristics according to actual track are set constraint condition, further remove noise spot.Used three constraint conditions among the present invention: the first, lane line is than road surface brightness height, on the lane line on gray values of pixel points and the next door road difference of gray-scale value be not less than 20; The second, the wide constraint of lane line, generally between 2~20 pixels, and there is above spaciousness (no white point) zone of 40 pixels at least in the right and left; The 3rd, the continuity restriction, white point should meet on distributing or approximate (left-right deviation the is no more than a pixel) linear feature that meets.The white point deletion of above-mentioned constraint condition will do not met;
5. obtain the lane line equation.Through after top a series of processing, effectively reduce white point quantity, adopt the Hough conversion to make straight-line detection then.In this process, introduce constraint condition again: the first, lane line quantity is no more than 4 in the qualification image; The second, according to the vision reason, the differential seat angle of two lane lines can not be less than 5--; The 3rd, lane line can not allow to occur intersecting below picture; The 4th, the ballot quantity of Hough conversion is limited, think interfere information less than 15 tickets.Through top constraint, selecting 0~4 maximum straight line of poll is lane line.With the picture centre is initial point, calculates the straight-line equation of every lane line in the plane of delineation
Wherein
Be horizontal ordinate,
Be ordinate,
Be slope,
Be intercept.See Fig. 1, Fig. 2.
(c) the focal length parameter of camera is demarcated: referring to Fig. 3, with the vehicle that installs camera be parked in one with the known direction of lane line angulation β on, according to detected lane line equation
, together with the θ value of step a) record, calculate focal distance f (illustrate: formula (3) is derived by β=45--) according to formula (3), be used for of the calculating of follow-up vehicle ' process to deflection angle and distance:
(d) the deflection angle β and the vertical range d of the relative lane line of calculating automobile see Fig. 3.In vehicle traveling process, the lane line of every two field picture is detected and obtains straight-line equation in real time
, the focal distance f according to the height h of step a) record and angle of depression θ and step d) calibration obtain by space coordinates shown in Figure 5, adopts pinhole camera modeling can derive the computing formula of deflection angle β and vertical range d, sees formula (4), (5):
(4)
If many lane lines occur, can calculate the automobile deflection angle and the vertical range of every lane line relatively.
(e) according to the instant travel speed of automobile
And the β that obtains of step e) and d calculate automobile and surmount lane line required time t, sees formula (6)
Setting T is a time of fire alarming, and T is relevant with driver's reaction velocity and automobile brake effect, when t<T, provides information warning, thereby ensures the driving safety of automobile.
Claims (4)
1. the vision of an automobile run-off-road line angle and distance is calculated and method for early warning, may further comprise the steps:
A) with the CCD video frequency pick-up head along the automobile dead ahead to being installed in Che Nei or roof, adjust the angle of depression and focal length and make it, the setting height(from bottom) h and the angle of depression θ of record camera road surface blur-free imaging in the 50m of the place ahead;
B) by the camera road pavement carry out continuous shooting, collecting to the pavement image sequence, the digital video high-speed channel by DSP is realized data acquisition;
C) lane line in the road pavement image detects, and comprising:
The image edge detects: utilize two 5 * 5 templates respectively computing is taken advantage of-added to image, obtain two width of cloth gradient images corresponding to level and vertical direction, obtained the side information of edge image, the especially lane line of former figure by this two width of cloth image;
The binaryzation of image: adopt the Otsu algorithm computation to go out adaptive threshold to the edge image, image is carried out the black-and-white two color binary conversion treatment according to this threshold value;
3. remove horizontal profile: thus the subsequent treatment calculated amount reduced in order to reduce white point quantity, the characteristics that in picture, can not occur the level trend according to lane line, the horizontal edge point is merged, promptly the white point that horizontal direction is occurred continuously only keeps first white point of Far Left, thereby deletion horizontal edge, reduce white point quantity, the lane detection effect is not exerted an influence again simultaneously;
4. the application constraint condition is gone a little: set constraint condition according to brightness, width and continuity Characteristics that actual lane line distributes, further remove noise spot;
5. obtain the lane line equation: adopt the Hough conversion to determine most probable linear position, finally obtain the straight-line equation of 0~4 lane line;
D) the focal length parameter f of camera is demarcated: the vehicle that will install camera is parked on the direction that becomes known angle with lane line, discharge of the coke apart from f according to detected lane line Equation for Calculating, be used for of the calculating of follow-up vehicle ' process deflection angle and vertical range;
E) the deflection angle β and the vertical range d of the relative lane line of calculating automobile: in vehicle traveling process, lane line to every two field picture detects and obtains straight-line equation in real time, calculates deflection angle β and vertical range d according to the focal distance f that the height h of step a) record and angle of depression θ and step d) calibration obtain according to following formula:
F) deflection angle β and the distance parameter d that obtains according to the instant travel speed and the step e) of automobile is according to formula
Calculate automobile and surmount lane line required time t, set alarm threshold value, if calculate to such an extent that the line time then provides information warning less than threshold value more.
2. the vision of the described automobile run-off-road of claim 1 line angle and distance is calculated and the pre-police, it is characterized in that: step c) 3. in for reducing white point quantity, the edge bianry image has been removed horizontal profile, bianry image is carried out horizontal direction scanning, investigate continuous two points: if certain point is a white point, and the front consecutive point are stain, then keep this white point, and the institute that does not satisfy this condition is become stain a little.
3. the vision of the described automobile run-off-road of claim 1 line angle and distance is calculated and the pre-police, it is characterized in that: step c) 4. in for reducing white point quantity, adopt constraint condition to remove white point, adopted three constraint conditions: the first, on the lane line on gray values of pixel points and the next door road difference of gray-scale value be not less than 20; The second, the pixel of lane line along continuous straight runs is between 2~20, and there is a stain zone that is no less than 40 pixels at least in the outer the right and left of line; The 3rd, white point should meet on distributing or approximate meeting is the linear feature that left-right deviation is no more than a pixel; The white point deletion of above-mentioned constraint condition will do not met.
4. the vision of the described automobile run-off-road of claim 1 line angle and distance is calculated and the pre-police, it is characterized in that: step c) retrains the quantity and the distribution of lane line in 5.: the first, limit in the image no more than 4 of lane line quantity; The second, according to the vision reason, the differential seat angle of two lane lines can not be less than 5 degree; The 3rd, lane line can not allow to occur intersecting below picture; The 4th, the ballot quantity of Hough conversion is limited, abandon being less than the testing result of 15 tickets; Calculate the straight-line equation of every lane line in the plane of delineation at last.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN2010102384366A CN101894271B (en) | 2010-07-28 | 2010-07-28 | Visual computing and prewarning method of deviation angle and distance of automobile from lane line |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN2010102384366A CN101894271B (en) | 2010-07-28 | 2010-07-28 | Visual computing and prewarning method of deviation angle and distance of automobile from lane line |
Publications (2)
Publication Number | Publication Date |
---|---|
CN101894271A true CN101894271A (en) | 2010-11-24 |
CN101894271B CN101894271B (en) | 2012-11-07 |
Family
ID=43103459
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN2010102384366A Expired - Fee Related CN101894271B (en) | 2010-07-28 | 2010-07-28 | Visual computing and prewarning method of deviation angle and distance of automobile from lane line |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN101894271B (en) |
Cited By (64)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102288121A (en) * | 2011-05-12 | 2011-12-21 | 电子科技大学 | Method for measuring and pre-warning lane departure distance based on monocular vision |
CN102509418A (en) * | 2011-10-11 | 2012-06-20 | 东华大学 | Fatigue driving estimation and early-warning method and device of multi-sensor information fusion |
CN102542635A (en) * | 2012-02-09 | 2012-07-04 | 重庆长安汽车股份有限公司 | Car recorder combined with lane deviation alarming system |
CN102589434A (en) * | 2012-02-27 | 2012-07-18 | 西北工业大学 | Method for detecting vehicle sideslip movement by image hub mark |
CN103213579A (en) * | 2013-04-07 | 2013-07-24 | 杭州电子科技大学 | Lane departure early warning method independent of camera parameters and vehicle system |
CN103500328A (en) * | 2013-10-16 | 2014-01-08 | 北京航空航天大学 | Method for automatically detecting deflection fault of railway wagon locking plate |
CN103496367A (en) * | 2013-10-23 | 2014-01-08 | 惠州华阳通用电子有限公司 | Method and device for detecting mistaken alarm of lane departure alarming |
CN103587529A (en) * | 2013-10-12 | 2014-02-19 | 长安大学 | Prediction system and prediction method for line cross moment in lane changing process of straight road |
CN103587528A (en) * | 2013-10-12 | 2014-02-19 | 长安大学 | Lane change process crossing moment prediction device and method |
CN103647947A (en) * | 2013-12-04 | 2014-03-19 | 广东好帮手电子科技股份有限公司 | Driving pathway intelligent monitor system and realization method thereof |
CN103954292A (en) * | 2012-05-30 | 2014-07-30 | 常州市新科汽车电子有限公司 | Navigator-based method for matching main road and side road of road according to traffic lane line |
CN103968837A (en) * | 2014-04-25 | 2014-08-06 | 惠州华阳通用电子有限公司 | Method and device for correcting calibration factor of gyroscope in inertial navigation system |
CN103985131A (en) * | 2014-05-28 | 2014-08-13 | 大连理工大学 | Camera fast-calibration method for highway lane departure warning system |
CN103991410A (en) * | 2014-04-22 | 2014-08-20 | 国通道路交通管理工程技术研究中心有限公司 | Method and system for preventing line pressing unlawful act of important transport vehicle |
CN104029680A (en) * | 2014-01-02 | 2014-09-10 | 上海大学 | Lane departure warning system and method based on monocular camera |
CN104048663A (en) * | 2014-04-25 | 2014-09-17 | 惠州华阳通用电子有限公司 | Vehicular inertial navigation system and navigation method |
CN104063691A (en) * | 2014-06-27 | 2014-09-24 | 广东工业大学 | Lane line fast detection method based on improved Hough transform |
CN104126196A (en) * | 2012-02-29 | 2014-10-29 | 株式会社电装 | Driving assistance device and driving assistance method |
CN104210493A (en) * | 2014-09-16 | 2014-12-17 | 成都衔石科技有限公司 | Linear array image sensor based following vehicle road lane line detection device |
CN104296761A (en) * | 2012-05-30 | 2015-01-21 | 常州市新科汽车电子有限公司 | Method for matching main and side roads by navigator with high real-time performance |
CN104715473A (en) * | 2013-12-11 | 2015-06-17 | 鹦鹉股份有限公司 | Method for angle calibration of the position of a video camera on board an automotive vehicle |
CN105069859A (en) * | 2015-07-24 | 2015-11-18 | 深圳市佳信捷技术股份有限公司 | Vehicle driving state monitoring method and apparatus thereof |
CN105758790A (en) * | 2016-04-08 | 2016-07-13 | 重庆交通大学 | Accelerating loading experimental system for highway pavement |
CN105758751A (en) * | 2016-04-08 | 2016-07-13 | 重庆交通大学 | Automobile traveling track positioning and adjusting system |
CN105806352A (en) * | 2012-05-30 | 2016-07-27 | 常州市新科汽车电子有限公司 | Camera-based navigating instrument operation method high in real-time performance and accuracy |
CN105882515A (en) * | 2015-11-11 | 2016-08-24 | 乐卡汽车智能科技(北京)有限公司 | Information processing method and device applied to automobile data recorder and automobile data recorder |
CN106203267A (en) * | 2016-06-28 | 2016-12-07 | 成都之达科技有限公司 | Vehicle collision avoidance method based on machine vision |
CN106447862A (en) * | 2016-10-13 | 2017-02-22 | 凌美芯(北京)科技有限责任公司 | Intelligent gate ticket-checking method based on computer vision technique |
WO2017075984A1 (en) * | 2015-11-02 | 2017-05-11 | 乐视控股(北京)有限公司 | Method for controlling depression angle of panorama camera on vehicle, and vehicle-mounted device |
CN106679633A (en) * | 2016-12-07 | 2017-05-17 | 东华大学 | Vehicle-mounted distance measuring system and vehicle-mounted distance measuring method |
CN106828489A (en) * | 2017-02-14 | 2017-06-13 | 中国科学院自动化研究所 | A kind of vehicle travel control method and device |
CN107301776A (en) * | 2016-10-09 | 2017-10-27 | 上海炬宏信息技术有限公司 | Track road conditions processing and dissemination method based on video detection technology |
CN107351802A (en) * | 2017-06-22 | 2017-11-17 | 天津交通职业学院 | A kind of automotive rear-view video imaging and early warning system and method for early warning |
CN107491722A (en) * | 2017-06-16 | 2017-12-19 | 南京栎树交通互联科技有限公司 | One kind realizes that driver fatigue sentences method for distinguishing based on lane line image procossing |
CN107826109A (en) * | 2017-09-28 | 2018-03-23 | 奇瑞汽车股份有限公司 | Track keeping method and device |
CN108052921A (en) * | 2017-12-27 | 2018-05-18 | 海信集团有限公司 | A kind of method for detecting lane lines, device and terminal |
CN108297867A (en) * | 2018-02-11 | 2018-07-20 | 江苏金羿智芯科技有限公司 | A kind of lane departure warning method and system based on artificial intelligence |
CN108875603A (en) * | 2018-05-31 | 2018-11-23 | 上海商汤智能科技有限公司 | Intelligent driving control method and device, electronic equipment based on lane line |
CN109147368A (en) * | 2018-08-22 | 2019-01-04 | 北京市商汤科技开发有限公司 | Intelligent driving control method device and electronic equipment based on lane line |
CN109211260A (en) * | 2018-10-30 | 2019-01-15 | 奇瑞汽车股份有限公司 | The driving path method and device for planning of intelligent vehicle, intelligent vehicle |
CN109344704A (en) * | 2018-08-24 | 2019-02-15 | 南京邮电大学 | A kind of vehicle lane change behavioral value method based on direction of traffic Yu lane line angle |
CN109747529A (en) * | 2017-11-02 | 2019-05-14 | 郭宇铮 | A kind of lane line prior-warning device |
CN110243357A (en) * | 2018-03-07 | 2019-09-17 | 杭州海康机器人技术有限公司 | A kind of unmanned plane localization method, device, unmanned plane and storage medium |
CN110533945A (en) * | 2019-08-28 | 2019-12-03 | 肇庆小鹏汽车有限公司 | Method for early warning, system, vehicle and the storage medium of traffic lights |
CN110697373A (en) * | 2019-07-31 | 2020-01-17 | 湖北凯瑞知行智能装备有限公司 | Conveying belt deviation fault detection method based on image recognition technology |
CN110733416A (en) * | 2019-09-16 | 2020-01-31 | 江苏大学 | lane departure early warning method based on inverse perspective transformation |
CN111137287A (en) * | 2019-12-26 | 2020-05-12 | 联创汽车电子有限公司 | Lane departure early warning method and early warning system |
CN111324616A (en) * | 2020-02-07 | 2020-06-23 | 北京百度网讯科技有限公司 | Method, device and equipment for detecting lane line change information |
CN111619584A (en) * | 2020-05-27 | 2020-09-04 | 北京经纬恒润科技有限公司 | State supervision method and device for unmanned automobile |
CN111862231A (en) * | 2020-06-15 | 2020-10-30 | 南方科技大学 | Camera calibration method, lane departure early warning method and system |
CN111874003A (en) * | 2020-06-23 | 2020-11-03 | 安徽信息工程学院 | Vehicle driving deviation early warning method and system |
CN112020461A (en) * | 2018-04-27 | 2020-12-01 | 图森有限公司 | System and method for determining a distance from a vehicle to a lane |
CN112017249A (en) * | 2020-08-18 | 2020-12-01 | 东莞正扬电子机械有限公司 | Vehicle-mounted camera roll angle obtaining and mounting angle correcting method and device |
CN112183226A (en) * | 2020-09-08 | 2021-01-05 | 昆明理工大学 | Large transport vehicle auxiliary positioning method based on deep learning |
CN112184754A (en) * | 2020-09-21 | 2021-01-05 | 浙江华消科技有限公司 | Method and device for determining deviation of moving track |
CN112257539A (en) * | 2020-10-16 | 2021-01-22 | 广州大学 | Method, system and storage medium for detecting position relation between vehicle and lane line |
CN112382068A (en) * | 2020-11-02 | 2021-02-19 | 陈松山 | Station waiting line crossing detection system based on BIM and DNN |
CN112406884A (en) * | 2019-08-20 | 2021-02-26 | 北京地平线机器人技术研发有限公司 | Vehicle driving state recognition method and device, storage medium and electronic equipment |
CN112562406A (en) * | 2020-11-27 | 2021-03-26 | 众安在线财产保险股份有限公司 | Method and device for identifying off-line driving |
CN112590670A (en) * | 2020-12-07 | 2021-04-02 | 安徽江淮汽车集团股份有限公司 | Three-lane environment display method, device, equipment and storage medium |
CN113033441A (en) * | 2021-03-31 | 2021-06-25 | 广州敏视数码科技有限公司 | Pedestrian collision early warning method based on wide-angle imaging |
CN114445335A (en) * | 2021-12-22 | 2022-05-06 | 武汉易思达科技有限公司 | Vehicle running state monitoring method and system based on binocular machine vision |
CN115115607A (en) * | 2022-07-19 | 2022-09-27 | 重庆大学 | Image shape feature extraction and recognition method based on image analysis |
CN115410362A (en) * | 2022-08-17 | 2022-11-29 | 咪咕音乐有限公司 | Blind road passing guiding method, blind road passing guiding equipment, storage medium and device |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP0431861A2 (en) * | 1989-12-05 | 1991-06-12 | Sony Corporation | Visual point position control apparatus |
EP1234452B1 (en) * | 1999-09-29 | 2008-08-13 | Rockwell Scientific Licensing LLC | Dynamic visual registration of a 3-d object with a graphical model |
CN101251381A (en) * | 2007-12-29 | 2008-08-27 | 武汉理工大学 | Dual container positioning system based on machine vision |
CN101702233A (en) * | 2009-10-16 | 2010-05-05 | 电子科技大学 | Three-dimension locating method based on three-point collineation marker in video frame |
CN101727671A (en) * | 2009-12-01 | 2010-06-09 | 湖南大学 | Single camera calibration method based on road surface collinear three points and parallel line thereof |
-
2010
- 2010-07-28 CN CN2010102384366A patent/CN101894271B/en not_active Expired - Fee Related
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP0431861A2 (en) * | 1989-12-05 | 1991-06-12 | Sony Corporation | Visual point position control apparatus |
EP1234452B1 (en) * | 1999-09-29 | 2008-08-13 | Rockwell Scientific Licensing LLC | Dynamic visual registration of a 3-d object with a graphical model |
CN101251381A (en) * | 2007-12-29 | 2008-08-27 | 武汉理工大学 | Dual container positioning system based on machine vision |
CN101702233A (en) * | 2009-10-16 | 2010-05-05 | 电子科技大学 | Three-dimension locating method based on three-point collineation marker in video frame |
CN101727671A (en) * | 2009-12-01 | 2010-06-09 | 湖南大学 | Single camera calibration method based on road surface collinear three points and parallel line thereof |
Cited By (98)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102288121B (en) * | 2011-05-12 | 2012-11-07 | 电子科技大学 | Method for measuring and pre-warning lane departure distance based on monocular vision |
CN102288121A (en) * | 2011-05-12 | 2011-12-21 | 电子科技大学 | Method for measuring and pre-warning lane departure distance based on monocular vision |
CN102509418A (en) * | 2011-10-11 | 2012-06-20 | 东华大学 | Fatigue driving estimation and early-warning method and device of multi-sensor information fusion |
CN102542635A (en) * | 2012-02-09 | 2012-07-04 | 重庆长安汽车股份有限公司 | Car recorder combined with lane deviation alarming system |
CN102589434B (en) * | 2012-02-27 | 2013-12-25 | 西北工业大学 | Method for detecting vehicle sideslip movement by image hub mark |
CN102589434A (en) * | 2012-02-27 | 2012-07-18 | 西北工业大学 | Method for detecting vehicle sideslip movement by image hub mark |
CN104126196A (en) * | 2012-02-29 | 2014-10-29 | 株式会社电装 | Driving assistance device and driving assistance method |
CN105806352B (en) * | 2012-05-30 | 2018-09-07 | 常州市新科汽车电子有限公司 | The working method of based on camera, real-time and the higher navigator of accuracy |
CN104296761B (en) * | 2012-05-30 | 2017-04-19 | 常州市新科汽车电子有限公司 | Method for matching main and side roads by navigator with high real-time performance |
CN103954293B (en) * | 2012-05-30 | 2016-10-05 | 常州市新科汽车电子有限公司 | The method of work of navigator |
CN105806352A (en) * | 2012-05-30 | 2016-07-27 | 常州市新科汽车电子有限公司 | Camera-based navigating instrument operation method high in real-time performance and accuracy |
CN103954292A (en) * | 2012-05-30 | 2014-07-30 | 常州市新科汽车电子有限公司 | Navigator-based method for matching main road and side road of road according to traffic lane line |
CN104296761A (en) * | 2012-05-30 | 2015-01-21 | 常州市新科汽车电子有限公司 | Method for matching main and side roads by navigator with high real-time performance |
CN103213579A (en) * | 2013-04-07 | 2013-07-24 | 杭州电子科技大学 | Lane departure early warning method independent of camera parameters and vehicle system |
CN103213579B (en) * | 2013-04-07 | 2015-08-19 | 杭州电子科技大学 | The irrelevant deviation of a kind of camera parameter and Vehicular system gives warning in advance method |
CN103587528A (en) * | 2013-10-12 | 2014-02-19 | 长安大学 | Lane change process crossing moment prediction device and method |
CN103587529B (en) * | 2013-10-12 | 2018-03-06 | 长安大学 | A kind of straight way section lane-change process gets over line moment forecasting system and Forecasting Methodology |
CN103587529A (en) * | 2013-10-12 | 2014-02-19 | 长安大学 | Prediction system and prediction method for line cross moment in lane changing process of straight road |
CN103500328A (en) * | 2013-10-16 | 2014-01-08 | 北京航空航天大学 | Method for automatically detecting deflection fault of railway wagon locking plate |
CN103500328B (en) * | 2013-10-16 | 2017-02-01 | 北京航空航天大学 | Method for automatically detecting deflection fault of railway wagon locking plate |
CN103496367A (en) * | 2013-10-23 | 2014-01-08 | 惠州华阳通用电子有限公司 | Method and device for detecting mistaken alarm of lane departure alarming |
CN103647947A (en) * | 2013-12-04 | 2014-03-19 | 广东好帮手电子科技股份有限公司 | Driving pathway intelligent monitor system and realization method thereof |
CN104715473A (en) * | 2013-12-11 | 2015-06-17 | 鹦鹉股份有限公司 | Method for angle calibration of the position of a video camera on board an automotive vehicle |
CN104715473B (en) * | 2013-12-11 | 2018-12-25 | 鹦鹉汽车股份有限公司 | The method that position for the video camera vehicle-mounted to automotive vehicle carries out angle calibration system |
CN104029680A (en) * | 2014-01-02 | 2014-09-10 | 上海大学 | Lane departure warning system and method based on monocular camera |
CN104029680B (en) * | 2014-01-02 | 2016-12-07 | 上海大学 | Lane Departure Warning System based on monocular cam and method |
CN103991410B (en) * | 2014-04-22 | 2016-03-02 | 国通道路交通管理工程技术研究中心有限公司 | A kind of method and system of preventing emphasis transport vehicle line ball mal-practice |
CN103991410A (en) * | 2014-04-22 | 2014-08-20 | 国通道路交通管理工程技术研究中心有限公司 | Method and system for preventing line pressing unlawful act of important transport vehicle |
CN104048663A (en) * | 2014-04-25 | 2014-09-17 | 惠州华阳通用电子有限公司 | Vehicular inertial navigation system and navigation method |
CN103968837A (en) * | 2014-04-25 | 2014-08-06 | 惠州华阳通用电子有限公司 | Method and device for correcting calibration factor of gyroscope in inertial navigation system |
CN103985131A (en) * | 2014-05-28 | 2014-08-13 | 大连理工大学 | Camera fast-calibration method for highway lane departure warning system |
CN104063691B (en) * | 2014-06-27 | 2017-08-25 | 广东工业大学 | Lane line quick determination method based on improved Hough transform |
CN104063691A (en) * | 2014-06-27 | 2014-09-24 | 广东工业大学 | Lane line fast detection method based on improved Hough transform |
CN104210493A (en) * | 2014-09-16 | 2014-12-17 | 成都衔石科技有限公司 | Linear array image sensor based following vehicle road lane line detection device |
CN105069859B (en) * | 2015-07-24 | 2018-01-30 | 深圳市佳信捷技术股份有限公司 | Vehicle running state monitoring method and device |
CN105069859A (en) * | 2015-07-24 | 2015-11-18 | 深圳市佳信捷技术股份有限公司 | Vehicle driving state monitoring method and apparatus thereof |
WO2017075984A1 (en) * | 2015-11-02 | 2017-05-11 | 乐视控股(北京)有限公司 | Method for controlling depression angle of panorama camera on vehicle, and vehicle-mounted device |
CN105882515A (en) * | 2015-11-11 | 2016-08-24 | 乐卡汽车智能科技(北京)有限公司 | Information processing method and device applied to automobile data recorder and automobile data recorder |
CN105758751A (en) * | 2016-04-08 | 2016-07-13 | 重庆交通大学 | Automobile traveling track positioning and adjusting system |
CN105758790A (en) * | 2016-04-08 | 2016-07-13 | 重庆交通大学 | Accelerating loading experimental system for highway pavement |
CN106203267A (en) * | 2016-06-28 | 2016-12-07 | 成都之达科技有限公司 | Vehicle collision avoidance method based on machine vision |
CN107301776A (en) * | 2016-10-09 | 2017-10-27 | 上海炬宏信息技术有限公司 | Track road conditions processing and dissemination method based on video detection technology |
CN106447862B (en) * | 2016-10-13 | 2018-08-24 | 凌美芯(北京)科技有限责任公司 | A kind of intelligent gate ticket checking method based on computer vision technique |
CN106447862A (en) * | 2016-10-13 | 2017-02-22 | 凌美芯(北京)科技有限责任公司 | Intelligent gate ticket-checking method based on computer vision technique |
CN106679633A (en) * | 2016-12-07 | 2017-05-17 | 东华大学 | Vehicle-mounted distance measuring system and vehicle-mounted distance measuring method |
CN106679633B (en) * | 2016-12-07 | 2019-06-04 | 东华大学 | A kind of vehicle-mounted distance-finding system base and method |
CN106828489B (en) * | 2017-02-14 | 2019-04-26 | 中国科学院自动化研究所 | A kind of vehicle travel control method and device |
CN106828489A (en) * | 2017-02-14 | 2017-06-13 | 中国科学院自动化研究所 | A kind of vehicle travel control method and device |
CN107491722A (en) * | 2017-06-16 | 2017-12-19 | 南京栎树交通互联科技有限公司 | One kind realizes that driver fatigue sentences method for distinguishing based on lane line image procossing |
CN107351802A (en) * | 2017-06-22 | 2017-11-17 | 天津交通职业学院 | A kind of automotive rear-view video imaging and early warning system and method for early warning |
CN107826109A (en) * | 2017-09-28 | 2018-03-23 | 奇瑞汽车股份有限公司 | Track keeping method and device |
CN109747529A (en) * | 2017-11-02 | 2019-05-14 | 郭宇铮 | A kind of lane line prior-warning device |
CN108052921A (en) * | 2017-12-27 | 2018-05-18 | 海信集团有限公司 | A kind of method for detecting lane lines, device and terminal |
CN108297867A (en) * | 2018-02-11 | 2018-07-20 | 江苏金羿智芯科技有限公司 | A kind of lane departure warning method and system based on artificial intelligence |
CN108297867B (en) * | 2018-02-11 | 2019-12-03 | 江苏金羿智芯科技有限公司 | A kind of lane departure warning method and system based on artificial intelligence |
CN110243357A (en) * | 2018-03-07 | 2019-09-17 | 杭州海康机器人技术有限公司 | A kind of unmanned plane localization method, device, unmanned plane and storage medium |
US12073724B2 (en) | 2018-04-27 | 2024-08-27 | Tusimple, Inc. | System and method for determining car to lane distance |
CN112020461A (en) * | 2018-04-27 | 2020-12-01 | 图森有限公司 | System and method for determining a distance from a vehicle to a lane |
CN112020461B (en) * | 2018-04-27 | 2024-02-27 | 图森有限公司 | System and method for determining a distance from a vehicle to a lane |
WO2019228211A1 (en) * | 2018-05-31 | 2019-12-05 | 上海商汤智能科技有限公司 | Lane-line-based intelligent driving control method and apparatus, and electronic device |
US11314973B2 (en) | 2018-05-31 | 2022-04-26 | Shanghai Sensetime Intelligent Technology Co., Ltd. | Lane line-based intelligent driving control method and apparatus, and electronic device |
CN108875603A (en) * | 2018-05-31 | 2018-11-23 | 上海商汤智能科技有限公司 | Intelligent driving control method and device, electronic equipment based on lane line |
CN108875603B (en) * | 2018-05-31 | 2021-06-04 | 上海商汤智能科技有限公司 | Intelligent driving control method and device based on lane line and electronic equipment |
CN109147368A (en) * | 2018-08-22 | 2019-01-04 | 北京市商汤科技开发有限公司 | Intelligent driving control method device and electronic equipment based on lane line |
CN109344704A (en) * | 2018-08-24 | 2019-02-15 | 南京邮电大学 | A kind of vehicle lane change behavioral value method based on direction of traffic Yu lane line angle |
CN109344704B (en) * | 2018-08-24 | 2021-09-14 | 南京邮电大学 | Vehicle lane change behavior detection method based on included angle between driving direction and lane line |
CN109211260B (en) * | 2018-10-30 | 2022-04-08 | 奇瑞汽车股份有限公司 | Intelligent vehicle driving path planning method and device and intelligent vehicle |
CN109211260A (en) * | 2018-10-30 | 2019-01-15 | 奇瑞汽车股份有限公司 | The driving path method and device for planning of intelligent vehicle, intelligent vehicle |
CN110697373A (en) * | 2019-07-31 | 2020-01-17 | 湖北凯瑞知行智能装备有限公司 | Conveying belt deviation fault detection method based on image recognition technology |
CN112406884A (en) * | 2019-08-20 | 2021-02-26 | 北京地平线机器人技术研发有限公司 | Vehicle driving state recognition method and device, storage medium and electronic equipment |
CN110533945A (en) * | 2019-08-28 | 2019-12-03 | 肇庆小鹏汽车有限公司 | Method for early warning, system, vehicle and the storage medium of traffic lights |
CN110733416A (en) * | 2019-09-16 | 2020-01-31 | 江苏大学 | lane departure early warning method based on inverse perspective transformation |
CN110733416B (en) * | 2019-09-16 | 2022-09-16 | 江苏大学 | Lane departure early warning method based on inverse perspective transformation |
CN111137287A (en) * | 2019-12-26 | 2020-05-12 | 联创汽车电子有限公司 | Lane departure early warning method and early warning system |
CN111324616A (en) * | 2020-02-07 | 2020-06-23 | 北京百度网讯科技有限公司 | Method, device and equipment for detecting lane line change information |
CN111324616B (en) * | 2020-02-07 | 2023-08-25 | 北京百度网讯科技有限公司 | Method, device and equipment for detecting lane change information |
CN111619584A (en) * | 2020-05-27 | 2020-09-04 | 北京经纬恒润科技有限公司 | State supervision method and device for unmanned automobile |
CN111619584B (en) * | 2020-05-27 | 2021-09-21 | 北京经纬恒润科技股份有限公司 | State supervision method and device for unmanned automobile |
CN111862231B (en) * | 2020-06-15 | 2024-04-12 | 南方科技大学 | Camera calibration method, lane departure early warning method and system |
CN111862231A (en) * | 2020-06-15 | 2020-10-30 | 南方科技大学 | Camera calibration method, lane departure early warning method and system |
CN111874003B (en) * | 2020-06-23 | 2021-07-20 | 安徽信息工程学院 | Vehicle driving deviation early warning method and system |
CN111874003A (en) * | 2020-06-23 | 2020-11-03 | 安徽信息工程学院 | Vehicle driving deviation early warning method and system |
CN112017249A (en) * | 2020-08-18 | 2020-12-01 | 东莞正扬电子机械有限公司 | Vehicle-mounted camera roll angle obtaining and mounting angle correcting method and device |
CN112183226A (en) * | 2020-09-08 | 2021-01-05 | 昆明理工大学 | Large transport vehicle auxiliary positioning method based on deep learning |
CN112184754A (en) * | 2020-09-21 | 2021-01-05 | 浙江华消科技有限公司 | Method and device for determining deviation of moving track |
CN112257539B (en) * | 2020-10-16 | 2024-06-14 | 广州大学 | Method, system and storage medium for detecting position relationship between vehicle and lane line |
CN112257539A (en) * | 2020-10-16 | 2021-01-22 | 广州大学 | Method, system and storage medium for detecting position relation between vehicle and lane line |
CN112382068A (en) * | 2020-11-02 | 2021-02-19 | 陈松山 | Station waiting line crossing detection system based on BIM and DNN |
CN112562406B (en) * | 2020-11-27 | 2022-08-16 | 众安在线财产保险股份有限公司 | Method and device for identifying off-line driving |
CN112562406A (en) * | 2020-11-27 | 2021-03-26 | 众安在线财产保险股份有限公司 | Method and device for identifying off-line driving |
CN112590670A (en) * | 2020-12-07 | 2021-04-02 | 安徽江淮汽车集团股份有限公司 | Three-lane environment display method, device, equipment and storage medium |
CN113033441A (en) * | 2021-03-31 | 2021-06-25 | 广州敏视数码科技有限公司 | Pedestrian collision early warning method based on wide-angle imaging |
CN113033441B (en) * | 2021-03-31 | 2024-05-10 | 广州敏视数码科技有限公司 | Pedestrian collision early warning method based on wide-angle imaging |
CN114445335A (en) * | 2021-12-22 | 2022-05-06 | 武汉易思达科技有限公司 | Vehicle running state monitoring method and system based on binocular machine vision |
CN114445335B (en) * | 2021-12-22 | 2024-04-12 | 武汉易思达科技有限公司 | Vehicle running state monitoring method based on binocular machine vision |
CN115115607A (en) * | 2022-07-19 | 2022-09-27 | 重庆大学 | Image shape feature extraction and recognition method based on image analysis |
CN115115607B (en) * | 2022-07-19 | 2024-06-07 | 重庆大学 | Image shape feature extraction and recognition method based on image analysis |
CN115410362A (en) * | 2022-08-17 | 2022-11-29 | 咪咕音乐有限公司 | Blind road passing guiding method, blind road passing guiding equipment, storage medium and device |
Also Published As
Publication number | Publication date |
---|---|
CN101894271B (en) | 2012-11-07 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN101894271B (en) | Visual computing and prewarning method of deviation angle and distance of automobile from lane line | |
Zhang et al. | Autonomous path tracking control of intelligent electric vehicles based on lane detection and optimal preview method | |
US10956756B2 (en) | Hazard detection from a camera in a scene with moving shadows | |
CN107730520B (en) | Lane line detection method and system | |
US10147002B2 (en) | Method and apparatus for determining a road condition | |
CN104129389A (en) | Method for effectively judging and recognizing vehicle travelling conditions and device thereof | |
CN101135558B (en) | Vehicle anti-collision early warning method and apparatus based on machine vision | |
Wu et al. | Applying a functional neurofuzzy network to real-time lane detection and front-vehicle distance measurement | |
US9064418B2 (en) | Vehicle-mounted environment recognition apparatus and vehicle-mounted environment recognition system | |
US8311283B2 (en) | Method for detecting lane departure and apparatus thereof | |
CN102270301B (en) | Method for detecting unstructured road boundary by combining support vector machine (SVM) and laser radar | |
CN112241007A (en) | Calibration method and arrangement structure of automatic driving environment perception sensor and vehicle | |
US9665781B2 (en) | Moving body detection device and moving body detection method | |
US20030195704A1 (en) | Vehicle surroundings monitoring apparatus and vehicle traveling control system incorporating the apparatus | |
US20070285217A1 (en) | Field recognition apparatus, method for field recognition and program for the same | |
CN104077756A (en) | Direction filtering method based on lane line confidence | |
CN107229906A (en) | A kind of automobile overtaking's method for early warning based on units of variance model algorithm | |
CN107415951A (en) | A kind of road curvature method of estimation based on this car motion state and environmental information | |
WO2015015939A1 (en) | Vehicle position/bearing estimation device and vehicle position/bearing estimation method | |
CN103192758B (en) | Front lamp following turning control method based on machine vision | |
CN107284455A (en) | A kind of ADAS systems based on image procossing | |
CN108470142A (en) | Lane location method based on inverse perspective projection and track distance restraint | |
CN103204104A (en) | Vehicle full-view driving monitoring system and method | |
CN103699899B (en) | Method for detecting lane lines based on equidistant curve model | |
CN202911633U (en) | Dynamic detection device based on multi-information fusion for hybrid electric vehicle lane identification lines |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
C14 | Grant of patent or utility model | ||
GR01 | Patent grant | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20121107 Termination date: 20150728 |
|
EXPY | Termination of patent right or utility model |