Evaluation of deep-learning based lane detection under low-light environments

Abstract

Lane detection is an important technology in autonomous driving. Currently, the mainstream lane detection methods are based on deep learning. However, most of deep learning-based lane detection models are trained on images captured on sunny days with enough light. It is concerned about their performance when being used to detect lane lines in low-light environments. In this study, images of lane lines captured in the evening and on rainy days are used to evaluate the performance of a representative LaneNet model. The result shows that the model performed badly when detecting lane lines on rainy days or in the evening. When there is enough lamplight in the evening, the performance of the model is better with part of lane lines being detected, but it still cannot detect correctly as it does on sunny days. Two potential future directions are also proposed in this study.