In this paper, a fast and reliable lane-lines detection and tracking technique is proposed. The proposed technique is well suited to be used in Advanced Driving Assistance Systems (ADAS) or self-driving cars. The main emphasis of the proposed technique in on simplicity and fast computation capability so that it can be embedded in affordable CPUs that are employed by ADAS systems. The proposed technique is mainly a pipeline of computer vision algorithms that augment each other and take in raw RGB images to produce the required lane-line segments that represent the boundary of the road for the car. Each used algorithm is described in details, implemented and its performance is evaluated using actual road images and videos captured by the front mounted camera of the car. The whole pipeline performance is also tested and evaluated on real videos. The evaluation of the proposed technique shows that it reliably detects and tracks road boundaries under various conditions. The usefulness and the shortcomings of the proposed technique are also discussed in details. © 2018 IEEE.

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