Many people die each year in the world in single vehicle roadway departure crashes caused by driver inattention, especially on the freeway. Lane Departure Warning System (LDWS) is a useful system to avoid those accident, in which, the lane detection is a key issue. In this paper, after a brief overview of existing methods, we present a robust lane detection algorithm based on geometrical model and Gabor filter. This algorithm is based on two assumptions: the road in front of vehicle is approximately planar and marked which are often correct on the highway and freeway where most lane departure accidents happen [1]. The lane geometrical model we build in this paper contains four parameters which are starting position, lane original orientation, lane width and lane curvature. The algorithm is composed of three stages: the first stage is called off-line calibration which just runs once after the camera is mounted and fixed in the vehicle. The parameters of camera used for lane detection is accurately estimated by the 2D calibration method [2]; The second stage is called lane model parameters estimation and lane model candidates construction, the first three parameters, starting position, lane original orientation and lane width will be estimated using dominant orientation estimation [3] and local Hough transform. Then the construction of lane model candidates is implemented for the final lane model matching; the third stage is model matching. The proposed lane module matching algorithm is implemented to match the best fitted lane model. The combination of these modules can overcome the universal lane detection problems due to inaccuracies in edge detection such as shadow of tree and passengers on the road. Experimental results on real road will be presented to prove the effectiveness of the proposed lane detection algorithm.

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Autonomous robotic navigation in forested environments is difficult because of the highly variable appearance and geometric properties of the terrain. In most navigation systems, researchers assume a priori knowledge of the terrain appearance properties, geometric properties, or both. In forest environments, vegetation such as trees, shrubs, and bushes has appearance and geometric properties that vary with change of seasons, vegetation age, and vegetation species. In addition, in forested environments the terrain surface is often rough, sloped, and/or covered with a surface layer of grass, vegetation, or snow. The complexity of the forest environment presents difficult challenges for autonomous navigation systems. In this paper, a self-supervised sensing approach is introduced that attempts to robustly identify a drivable terrain surface for robots operating in forested terrain. The sensing system employs both LIDAR and vision sensor data. There are three main stages in the system: feature learning, feature training, and terrain prediction. In the feature learning stage, 3D range points from LIDAR are analyzed to obtain an estimate of the ground surface location. In the feature training stage, the ground surface estimate is used to train a visual classifier to discriminate between ground and nonground regions of the image. In the prediction stage, the ground surface location can be estimated at high frequency solely from vision sensor data. C 2012 Wiley Periodicals, Inc.

Road detection is an important problem with application to driver assistance systems and autonomous, self-guided vehicles. The focus of this paper is on the problem of feature extraction and classification for front-view road detection. Specifically, we propose using Support Vector Machines (SVM) for road detection and effective approach for self-supervised online learning. The proposed road detection algorithm is capable of automatically updating the training data for online training which reduces the possibility of misclassifying road and non-road classes and improves the adaptability of the road detection algorithm. The algorithm presented here can also be seen as a novel framework for self-supervised online learning in the application of classification-based road detection algorithm on intelligent vehicle. R IEEE Intelligent Vehicles Symposium