Many global automotive companies have been putting efforts to reduce traffic accidents by developing advanced driver assistance system (ADAS) as well as autonomous vehicles. Lane detection is essential for both autonomous driving and ADAS because the vehicle must follow the lane. However, existing lane detection algorithms have been struggling in achieving robust performance under real-world road conditions where poor road markings, surrounding obstacles, and guardrails are present. Therefore, in this paper, we propose a multi-lane detection algorithm that is robust to the challenging road conditions. To solve the above problems, we introduce three key technologies. First, an adaptive threshold is applied to extract strong lane features from images with obstacles and barely visible lanes. Next, since erroneous lane features can be extracted, an improved RANdom SAmple Consensus algorithm is introduced by using the feedback from lane edge angles and the curvature of lane history to prevent false lane detection. Finally, the lane detection performance is greatly improved by selecting only the lanes that are verified through the lane classification algorithm. The proposed algorithm is evaluated on our dataset that captures challenging road conditions. The proposed method performs better than the state-of-the-art method, showing 3% higher True Positive Rate and 2% lower False Positive Rate performance.