Abstract:

In order to enhance the accuracy and stability of lane detection, a lane detection algorithm based on bi-directional sliding window technology is proposed. Firstly, a combination of EDF(Edge Distribution Function) and Hough transform is used to obtain the linear models of lane boundaries. Secondly, bi-directional sliding window feature extraction technique is applied to detect real lane markings. Finally, a linear-hyperbolic lane model is used: in the near vision field, a linear model is used to obtain robust information about lane orientation; in the far vision field, a hyperbolic function is used, the curved parts of the road can be efficiently detected.Experimental results indicate that the proposed method can enhance the adaptability to deal with the random and
dynamic environment of road scenes, such as curved lane markings, sparse shadows, object occlusions and bad conditions of road painting.

Key words: lane detection, bi-directional sliding window feature extraction, hough transform, lane model