In order to gain higher efficiency in obstacle avoidance task in autonomous vehicles from the aspect of processing cost and operating in real-time, it`s critical to find a region of interest (ROI) which obstacles are more possible to appear and degrade the obstacle`s search zone to it. In this paper we propose novel methods to find this ROI using computer vision technologies. The road scenes are acquired with a monocular camera. Current lane of autonomous vehicle is recognized by detection of lane markings. Adjacent lanes are also estimated based on some geometric calculations. A novel lane matching mechanism is suggested to validate detected lane markings. Finally a method for lane departure warning is proposed. The experimental results show that the proposed algorithms correctly find lanes region with high accuracy in real-time, are robust to noise and shadows, testing on Hemmat highway in Tehran and another dataset in the daytime.