A model driven 3D lane detection system using stereovision

This paper presents a new method for the detection and 3D reconstruction of the road lane using onboard stereovision. The proposed algorithm makes it possible to overcome the assumptions commonly used in most of the detection systems using monocular vision such as: flat road, constant pitch angle or absence of roll angle. The proposed method of detection and 3D reconstruction is based on two modules. The first one is aimed at detecting the road markings of the lane in each image of the stereoscopic pair. It is based on a recognition algorithm driven by a statistical model of the road lane: the initial state of the model is obtained after a learning stage; it is updated using the results of a feature extraction stage. Our model takes into account the intrinsic links between the projections of the road in the two images of the stereoscopic pair, so that its update from a feature extracted in one of two images drives the detection of the features in the other image. No disparity map is required since the matching of the road features is directly obtained as the result of the model update. The second module is aimed at 3D reconstruction and relative localization of the vehicle with respect to its lane. The parameters of a 3D surface including the horizontal and vertical profiles of the road lane are estimated from the road borders previously detected. The robustness of the algorithm is evaluated from synthetic and real images