| Summary: | 博士 === 國立交通大學 === 電機與控制工程系所 === 97 === The dissertation aims to explore techniques of image processing and computer vision applicable to driving assistance system, including lane detection, vehicle detection, estimation of the distance to the preceding car, error estimation, and dynamic calibration of cameras. The vision-based driving assistance system films the front road scenes with a camera equipped on the intelligent vehicle, computes lane positions and the distance to the preceding car by the lane and vehicle detection and then adopts the obtained information to improve driving safety. The dissertation mainly includes three sections. The first section is a brief introduction of the application of computer vision techniques to the driving assistance system. The second section presents analyses of the information obtained from lane detection and approaches for reducing errors. The third section proposes some algorithms and their application to the range estimation, error estimation, dynamic calibration, and detection of lanes and vehicles.
The dissertation presents several approaches to estimate the range between the preceding vehicle and the intelligent vehicle, to compute vehicle size and its projective size, and to dynamically calibrate cameras. First, a camera model is developed to transform coordinates from the ground plane onto the image plane to estimate the relative positions between the detected vehicle and the camera. Then, a new estimation method is proposed to estimate the actual and projective size of the preceding vehicle. This method can estimate the range between the preceding vehicle and the camera with the information of the contact points between vehicle tires and the ground and then estimate the actual size of the vehicle according to the positions of its vertexes in the image. Because the projective size of a vehicle varies with its distance to the camera, a simple and rapid method is presented to estimate the vehicle’s projective height, which allows a reduction of the computation time in the size estimation of the real-time systems.
Errors caused by the application of different camera parameters are also estimated and analyzed in this study. The estimation results are used to determine suitable parameters during camera installation to reduce estimation errors. Finally, to guarantee robustness of the detection system, a new efficient approach of dynamic calibration is presented to obtain accurate camera parameters, even when they are changed by camera vibration arising from on-road driving. Experimental results demonstrate that our approaches can provide accurate and robust estimation of range and size of the target vehicles.
In the dissertation, an approach for rapidly computing the projective lane width is presented to predict the projective lane positions and widths. Lane Marking Extraction (LME) Finite State Machine (FSM) is designed to extract points with features of lane markings in the image and a cubic B-spline is adopted to conduct curve fitting to reconstruct road geometry. A statistical search algorithm is also proposed to correctly and adaptively determine thresholds under various kinds of illumination. Furthermore, parameters of the camera in a moving car may change with vibration, so a dynamic calibration algorithm is applied to calibrate camera parameters and lane widths based on the information of lane projection. Besides, a fuzzy logic is used to discern the situation of occlusion. Finally, an ROI (Region of Interest) determination strategy is developed to narrow the search region and make the detection more robust with respect to the occlusion on the lane markings or complicated changes of curves and road boundaries.
The developed fuzzy-based vehicle detection method, Contour Size Similarity (CSS), performs the comparison between the projective vehicle sizes and the estimated ones by fuzzy logic. The aim of vehicle detection is to detect the closest preceding car in the same lane with the intelligent vehicle. Results of the experiments demonstrate that the proposed approach is effective in vehicle detection. Furthermore, the approach can rapidly adjust to the changes of detection targets when another car cuts in the lane of the intelligent vehicle. Finally, a conclusion and future works are also presented.
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