Design And Implementation Of Vehicle Obstacle Ahead Detection System Based On Radar And Computer Vision

With the development of automotive active safety technology, the environmental perception technology applied to complex urban becomes one of the hotspot research subjects in modern transportation technology field. Millimeter-wave radar owns good perception ability for depth information of the vehicle forward environment. Computer vision own good resolution capability for color and texture of objects, which has been extensively studied in object type recognition and lane detection. According to the typical characteristics of urban environment, this thesis does an in-depth theoretical and experimental research on obstacle detection and lane recognition, based on the research platform of Commercial Bus. The main research contents are summarized as follows:The overall framework of the system is analyzed according to the actual demand of obstacle detection system. Hardware, software structure and information flow are determined. The selection of on-board radar, image sensor and ultrasonic sensors were compared and discussed. Radar processing module and ultrasonic distance measuring module are designed, and CAN communication programme among modules has been written to realize distributed collection network. Vehicle detection system software is designed, which realizes data acquisition and processing, target graphic image display and information fusion.To meet the requirement of driver assistance systems, a method of target efficiency validation based on kalman filter and object decision-making methods based on lifecycle has been presented in the paper. A omnidirectional collision warning algorithm for large vehicles is designed, which is proved high detection and high effective alarm rate through experiments on Commercial bus.According to the definition of effective object front of vehicle in collision warning system, a method for lane line image recognition has been proposed, which can reduce the search scope of the effective target. Line in ROI(region of interest) was detected though Canny algorithm and Hough transform. Then, a selection was implemented according to line feature in image axis. The method has been verified to have excellent ability to recognize lane line under different light conditions and road conditions according to road experiments.To meet the accuracy requirement of In-vehicle obstacle detection system, a method based on millimeter-wave radar and computer vision has been present. The coordinate unification problem of multi-sensor data has been solved, which project radar data to image through data fusion model. The method improve the stability of ROI candidate. Meanwhile, it reduces the search scope of the effective target. Then, template matching method of statistical learning is used to detect vehicle in candidate region. Robustness and real-time of the algorithm are proved through experiments.