Deep Lane Detection Based on Kullback-Leibler Divergence.

Lane detection is a fundamental perception technology in autonomous driving cars that utilizes image or 3D information acquired through sensors attached to the vehicle to recognize lanes in the surrounding area. The versatility of lane detection technology is vast as it is utilized in various processes such as path planning or constructing high definition maps for autonomous driving cars. In this paper, we propose an image-based lane detection technology that applies Kullback-Leibler Divergence to the objective function of the existing ordinal classification-based lane detection technique. By quantitatively defining the neural network's logit distribution and label distribution, the proposed method induces the lane detection neural network to extract and represent various global features from the input image. To validate the performance of the proposed method, we conducted experiments using several public benchmarks and demonstrated that our method outperforms existing lane detection methods, achieving high accuracy and precision.