A vision-based car navigation system (CNS) gives drivers more precise and realistic traffic data than a traditional 2D-CNS. As part of the vision-based CNS, the ability to detect lane markings can provide significant warnings which increase traffic safety and convenience. Meanwhile, accurate lane classification results can indicate the current/approaching road conditions in this system. This paper concentrates on two kernels: lane marking detection and lane type identification. The lane detection part uses IPM and histogram sampling and the lane marking type classification step utilizes spatial and frequency sampling for different types of lane markings.

Lane markings can provide drivers significant warning instruction about current/ approaching road condition, in that case precise lane marking detection results are important assistance for safety issue in the driving assistance system (DAS). In this paper, we mainly focus on urban lane marking detection. We used sampling peaks from IPM image for lane markings' extraction and description. The proposed method is applied to various video images from black box, and is verified to be robust.

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