In this paper, we address the problem of lane detection and lane tracking. A linear model is used to approximate lane boundaries in the first frame of a video sequence, using a combination of the edge distribution function and the Hough Transform. A new linear-parabolic model is used in the subsequent frames: the linear part of the model is used to fit the near vision field, while the parabolic model fits the far field. The proposed technique demands low computational power and memory requirements, and showed to be robust in the presence of noise, shadows, lack of lane painting and change of illumination conditions.