In this paper a novel spline-based multi-lane detection and tracking system is proposed. Reliable lane detection and tracking is an important component of lane departure warning systems, lane keeping support systems or lane change assistance systems. The major novelty of the proposed approach is the usage of the so-called Catmull-Rom spline in combination with the extended Kalman filter tracking. The new spline-based model enables an accurate and flexible modeling of the lane markings. At the same time the application of the extended Kalman filter contributes significantly to the system robustness and stability. There is no assumption about the parallelism or the shapes of the lane markings in our method. The number of lane markings is also not restrained, instead each lane marking is separately modeled and tracked. The system runs on a standard PC in real time (i.e. 30 fps) with WVGA image resolution (752 × 480).The test vehicle has been driven on the roads with challenging scenarios, like worn out lane markings, construction sites, narrow corners, exits and entries of the highways, etc., and good performance has been demonstrated. The quantitative evaluation has been performed using manually annotated video sequences.
Intelligent Driver Assistance Systems, such as Lane Departure Warning, extract 3D information of the road geometry from a camera. Therefore, the transformation between the image and the ground plane has to be determined with a very high accuracy. Conventional calibration methods are usually a compromise between the accuracy and a preferably small effort for the calibration set-up. In this paper, we present an efficient and robust method for an accurate estimation of the extrinsic parameters based on minimizing an error function. The idea is to avoid the difficult and timeconsuming measurement of marker positions in the 3D world coordinate system which is fixed with respect to the vehicle. A pattern of circles is placed on the ground plane in front of the car. For our approach, it is only necessary to measure the relative distances between the centers of the circles to each other. A nonlinear-optimization algorithm minimizes the squared difference between the distances of the backprojected circles segmented in the images on the ground plane and of the measurement in the real world.
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