Study On On-board Image Monitoring System Based On Embedded Technique

With the increase of vehicle population, heavy traffic and too many traffic accidents make safety problems become increasingly prominent. Collision Avoidance Warning System, one of important components of Safety Driving Assist Systems, which alerts the vehicle in danger by using sensors to monitor driving environments, is one of effective measures to improve traffic safety. At present, most of Safety Driving Assist Systems have been studied using PCs which have high main frequency and processing speed as processing platforms. But redundant function, high cost and large size of PCs have restricted their application in on-board systems. Embedded systems for software and hardware provide practical technical means to study and develop Safety Driving Assist Systems, with their characters such as real-time performance, low resources use and small sizeResearch on Lane Departure Warning System and Minimum Safe Forward Distance Warning System by use of embedded techniques, is favorable for practicability and production of Collision Avoidance Warning System, which is of significance to improve active safety of vehicles, reduce traffic accidents and minimize losses brought by traffic accidents. This dissertation has also put forward methods of analyzing drivers'driving characters with data acquired in driving, which has important theoretical significance and engineering practical value to study more scientifically driving characters and strengthen training and supervision of drivers.This dissertation has discussed the recognition for road traffic marking lines and algorithm for lane departure warning, according to forward environment information obtained by a visual sensor. On basis of the location of forward vehicle, Minimum Safe Forward Distance Warning System is proposed to avoid automobile forward collision. A method of analysis and evaluation of Drivers' driving characters is provided in accordance of lateral distance of the vehicle in the road. Finally, Embedded On-board Image Monitoring System is designed and realized on the hardware platform based on DSP and ARM chips and Linux operating system.Utilizing computer vision theory and technique, an image method of detecting road traffic marking lines and rules of lane departure warning are studied. Road images are preprocessed by median filter, Sobel operator edge detection and threshold segmentation of maximum class variances. Standard Hough Transform is improved by Elementary Line Segments representation in order to identify and track road traffic marking lines extraction more quickly. Based on camera calibration of interior and exterior parameters, lane departure warning algorithm is put forward dependent on lateral distance and lateral deflection angle of the vehicle to the road traffic marking line. The experiments results show that the algorithm can consider the effects of the vehicle's lateral velocity and dynamic deflection angle to vehicle departure very well.By using monocular vision and projection transformation principle, minimum safe forward distance warning algorithm is proposed to avoid vehicle forward collision. Forward vehicle can be detected by means of conditional combination of shadow underneath a vehicle and grayscale symmetry of a vehicle. By use of Kalman Filtering principle, a vehicle's moving positions in serial images are forecasted to track it, which has appeased the conflict of accuracy and real-time performance of vehicle detection. On basis of recognized vehicle rectangle hemline, a method of calculating longitudinal distance between vehicles on structured roads is put forward. Minimum safe forward distance warning model is established according to braking distance calculation in the automobile theory. The experiment results prove that the method ease up the conflict between safe distance from theoretical calculation and habitual safe distance of drivers.By use of Knowledge Discovery in Database, research on driving characteristics which a driver shows in traveling are discussed according as detected road marking lines and lateral distance of a vehicle to road marking lines. Safety evaluation model for driving in lane is built to analyze statistically lateral distance data in lane and safety driving in lane is evaluated by fuzzy membership grades. Driving routes model for changing lane to overtake is established and driving curves are fitted in accordance of sample data. Information fusion technique is used to fuse similar driving curves so as that the algorithm has self-adaptability. Stability evaluation model for changing lane to overtake is brought forward to account out fuzzy membership grades for changing lane to overtake. Analysis of driving characteristics in the background of real traffic environment overcome subjectivity of questionnaire survey and limitation of measuring with instruments.Finally, software and hardware for Embedded On-board Image Monitoring System are designed and realized. On hardware platform of evaluation board based on Digital Signal Processing (DSP) and microprocessor ARM9 and in Linux embedded operating system, overall function design of On-board Image Monitoring System is carried out, and software design of some important function modules is also realized.