  Lane detection for autonomous vehicles is an important concept, yet it is a
challenging issue of driver assistance systems in modern vehicles. The
emergence of deep learning leads to significant progress in self-driving cars.
Conventional deep learning-based methods handle lane detection problems as a
binary segmentation task and determine whether a pixel belongs to a line. These
methods rely on the assumption of a fixed number of lanes, which does not
always work. This study aims to develop an optimal structure for the lane
detection problem, offering a promising solution for driver assistance features
in modern vehicles by utilizing a machine learning method consisting of binary
segmentation and Affinity Fields that can manage varying numbers of lanes and
lane change scenarios. In this approach, the Convolutional Neural Network
(CNN), is selected as a feature extractor, and the final output is obtained
through clustering of the semantic segmentation and Affinity Field outputs. Our
method uses less complex CNN architecture than existing ones. Experiments on
the TuSimple dataset support the effectiveness of the proposed method.
