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This paper proposes a novel approach for lane detection fusion by visual and vehicle localization system. To estimate accurate camera's gesture as well as road parameters, model-driven approach was introduced in our approach, which was the most popular way for lane detection so far. Be different from traditional processing way relying on visual recognition results, our approach develops a fusion method...
In this paper, we describe a precise road model for vehicle localization and map matching for the purpose of driver assistance. This model utilizes the prevalent digital road map and road points ('IP' points) such as: intersection points and supporting points with orientation variation, to estimate road curvature in two-dimensional alignment. In our work, road geometries such as clothoid curve and...
This paper presents a fast and robust lane detection method designed to track 3D road shape and estimate accurate vehicle location in real-time. This method is based upon road curvature calculated from digital road network. This curvature gives cues for detection in some difficult situation like road lane markers are occupied by other vehicles, dirt and shadow. Moreover, to stabilize estimated road...
This paper presents a novel approach of real-time vehicle's localization (position and orientation) estimation. Fusion of GPS, gyroscope, speedometer and visual data is employed here to provide real time and accurate localization information. Global probability density function(PDF) is adopted to be the blending factor instead of general Kalman gain, which allows our approach to be robust and accurate...
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