Abstract
Lane detection is a crucial issue for advanced driving assistance systems (ADAS) as well as for autonomous vehicle guidance functions and it is required to have high real-time performance and reliability to avoid lane departure. Taking the curve road as the main interest field, this paper presents a 3D curve lane model to detect, track and reconstruct real roads based on stereovision. The stereovision algorithm defines the 3D curve lane as the vertical and horizontal clothoid, which allows elimination of the common assumption: rectilinear lane, flat road or constant pitch angle. To accurately detect and match the curve lane, this paper proposes an improved real-time ROI setup algorithm. The successive detection results are used to estimate the lane model parameters through Kalman filtering. The experiments show that the algorithm proposed has good real-time performance and robustness. Key word: Stereovision; 3D curve lane parameter; ROI setup; Lane detection.
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