Parallel Heterogeneous Networks With Adaptive Routing for Online Video Lane Detection

Abstract

Lane detection plays a critical role in the field of autonomous driving. Most previous lane detection methods focus exclusively on analyzing individual images and overlook the inter-frame dynamics, while car-mounted cameras capture continuous streams amenable to leveraging intra-video context. In challenging scenes with blur, occlusion, or illumination variations, considering visible lanes from previous frames can aid current frame interpretation. To tackle above challenges, we introduce a parallel heterogeneous framework, called PHNet, for video lane detection. First, a novel router automatically analyzes multi-level features to determine the inference route of candidates with different visual cues. Then, a cross-frame attention mechanism leverages relationships between current candidates and positive embeddings from past frame to aggregate contextual cues. Unlike existing offline video lane detection methods, which face limitations in handling long video sequences and real-time video streams due to computational constraints, our proposed method operates as an online framework capable of processing clips of any length. Our method demonstrates robust lane detection through temporally association modeling and efficient online inference. The extensive experiments on public benchmark show that PHNet has superior performance versus state-of-the-art video lane detection methods.