24-h Lane Line Detection via Parallel Scene Information Collaboration

Lane detection is a critical technology for autonomous driving, but current deep learning-based methods face significant challenges due to the lack of diverse datasets, especially for nighttime conditions. Most datasets are predominantly composed of daytime images, making it difficult to develop models that perform reliably around the clock. Inspired by the parallel system theory, we explore a novel approach to generate comprehensive 24-hour datasets from daytime images alone. In this paper, we propose the Parallel Scene Information Collaboration (PSIC) framework, designed to enhance 24-hour lane detection using only daytime data. The PSIC framework consists of three key components: artificial scene generation, information collaboration, and lane line detection. First, we address the limitations of existing datasets by proposing two generators—one that transforms daytime images into realistic nighttime scenes, and another that refines nighttime images by adding daytime characteristics. Next, to mitigate noise in the generated scenes, we propose a Multi-Spatial Feature Fusion (MSFF) module that effectively integrates features from both real and artificial scenes through spatial collaboration. Finally, the combined information is used by an anchor-based detection head to accurately identify lane positions. Our experiments on the TuSimple, Night TuSimple, and CULane datasets demonstrate that our method achieves state-of-the-art performance in 24-hour lane line detection, significantly improving reliability and robustness across varying conditions.