Research On Dangerous Driving Status Machine Vision Recognition System For Passenger Vehicle

With the rapid development of highway transportation industry, road traffic safetyproblems have been increasingly prominent. Generally highway passenger transportationaccidents are personnel and malignant, they not only caused huge economic losses totransport enterprise, but also had a bad social influence on local road transport administration.What’s more, they have become a new social unstable factor to some extent. So carrying outhighway passenger vehicle dangerous driving status machine vision recongition systemresearch, improving highway passenger safety and road passenger transport safetymanagement ability, providing some visual evidence after the traffic accident, have wideapplication future and market demand.Relying on the "11th five-year plan" national science and technology support plan keyprojects (2009BAG13A07) and the national natural science fund project (51278062), thispaper applies a combination of computer graphics, information engineering, vehicleengineering, traffic engineering and other multi-disciplinary theories, and computer visiontechnology for on-board CCD image sensor technology, embedded dure-core parallelhigh-speed DSP digital image processing technology, features of shape edge detection andanalysize technology, machine vision and pattern recognition technology. Through a largenumber of simulation tests, data analysis, theoretical modeling and programming, study thevisual recognition system which can collect real-time passenger vehicle driving state visualimage information, and online identify vehicle driving potential dangers that exist in theprocess of driving, warning and recording passenger vehicle drivers’ improper drivingbehavior.Aiming at visual perception problems of passenger vehicle driving status, movingtrajectory and road environment, applying the method of multi-objective characteristiccollection, study the recognition of road marking lines’ position and linear and vehicle lateralyaw warning technology. Through the road image gray equalization enhancement, rapidrestructuring of median filtering, Scharr filtering extracting edge information, searching ROIarea and bound quick scanning optimal threshold segmentation, excavate road edge’s profileinformation deeply. Combining constraint of joint seed point voting area with constraint of polar angle and constraint of boundary in chain code direction, improving the Houghtransform to achieve the goal of azimuth detection of road marking line; realize linearidentification by HSI color space segmentation and dynamic window search; introduce areaconstraint particle filter for dynamic tracking and improve the detection efficiency of roadmarking line and environment adaptability. According to inverse perspective projectiontransformation, rebuild the key information of road, estimate the driving track of vehiclewhich is in the lane plane, comprehensive consideration of the influence of yaw rate andlateral angle, quantitative risk within the space domain and time domainon, establish thelateral yawing warning model which is based on the parking posture and risk of time domain,improve the warning mechanism and the acceptable degree of the system.Aiming at the problems that too many interference factors difficulties lie in ruling out thecomplex background, limitations result from single feature representation that exist in theprocess of the front vehicle recognition, carries on the research on recognition technology ofthe front car in the same lane plane by the feature extraction method of multi-scale direction.Fully excavcating the image information of front vehicle, set target search area, reducing theprocessing amount of system calculation. Through the analysis of the pavement grayscaleaverage mutation characteristics, put forward the front vehicle existence assumption; extractsthe multi-scale direction characteristics of vehicle gray sample by using dual channel Gaborfilter, fuse the extracted features which is extracted by Adaboost classifier to learn, train andclassify, detect the position in the image of the front vehicle; verifies the existence assumptionof front vehicle on the ground of information entropy normalized symmetry measure, theneliminates the false targets; realizes the detection and location of front vehicle through themachine learning method of a combination of off-line training and on-line detection ofvehicles’ characteristic sample. Fusing the improved GM(1,1) gray forecasting model,dynamically predicts the moving track of front vehicle through only a small amount datainformation. Using the continuity of frame interval as clues, establishes a detection andtracking feedback working mechanism to defuse the target vehicle contradiction betweenreal-time and robustness.On the basis of image recognition and location of front vehicle, applyingdriver-vehicle-road multi-source information fusion method to further study the safety vehicle distance recognition and warning technology. Through the theoretical analysis of monocularvision range finding principle, establishes the monocular vision vertical distance measurementmodel which is base on the lane plane constraint on the basis of accurate calibration of CCDimage sensor key measure parameters, realized the precise measurement of the verticaldistances. Given full consideration to the driver’s cognitive response characteristics, vehicleresponse characteristics and road environment factors, using multi-sensor information fusiontechnology to get vehicle running status information of front vehicle and host vehicle,establish the safety distance model which is based on multi-source, such as driver, vehicle,road information fusion. Collaborating driver emergency response probability agent withrelative state agent of front vehicle and host vehicle and road environment constraints agent asarchitecture, establishes Multi-Agent system for safety distance warning model. Given fullconsideration to the impact of outside uncertainty factors, through fuzzy integral and fuzzymeasure as the warning decision, guarantees the driving safety as well as the capacity ofhighway traffic.Discussed the overall design and implementation of machine vision recognition systemfor passenger vehicles’ dangerous driving state, with embedded dual-core parallel high-speeddigital image signal processing of DSP and microprocessor MCU as hardware developmentplatform, completed the selection of key system components and design of overall functionmodule, optimized the memory allocation and transfer for the system image processing.