In this paper, an advanced and reliable road-lanes detection and tracking technique is proposed and implemented. The proposed technique is well suited for use in Advanced Driving Assistance Systems (ADAS) applications or Self-Driving Cars (SDC). The main emphasis of the proposed technique is the precision and the predictability in identifying the driving lane boundaries (linear or curved) and tracking it throughout the drive. Moreover, the technique provides fast enough computation to be embedded in CPUs with affordable GPUs that are employed by ADAS systems. The proposed technique is mainly a pipeline of reliable computer vision algorithms that augment each other and take in raw RGB images to produce the required lane boundaries, which represent the front driving space for the car. Moreover, some of the employed algorithms are working in parallel to strengthen each other in order to produce a sophisticated output. 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. © 2019 Institution of Engineering and Technology. All rights reserved.