Attention based network for real-time road drivable area, lane line detection and scene identification

Abstract

The detection of road drivable areas and lane lines is considered a fundamental component of autonomous driving systems. However, most existing approaches handle these tasks independently, and multi-task networks frequently neglect the inherent correlation between them while failing to differentiate various lane line types. In practice, the delineation of drivable regions is strongly influenced by both lane line characteristics and contextual street scenes. To address these limitations, a novel multi-task network—Real-time Road Drivable Area, Lane Line Detection, and Scene Identification Network (RLSNet)—is proposed. This network is designed to perform simultaneous segmentation of drivable areas, detection of lane lines, and classification of road scenes. Drivable area estimation is optimized through the integration of lane and scene cues, guided by traffic regulations. A Residual Network (ResNet)-based backbone is employed, enhanced with Bidirectional Fusion Attention (BFA) for feature encoding. This is followed by a decoder incorporating a Feature Aggregation Module (FAM) to enable effective semantic–spatial fusion. Lane line detection is further refined using a Bilateral Up-Sampling Decoder (BUSD), while scene understanding is enhanced via a Scene Classification Module (SCM). Extensive experiments conducted on the challenging Berkeley DeepDrive 100K(BDD100K) dataset have demonstrated that RLSNet achieves high accuracy in both drivable area and lane line detection by leveraging the mutual guidance of lane and scene information. Furthermore, the network maintains real-time inference speed at 93 frames per second (FPS), striking a practical balance between semantic fidelity and computational efficiency for real-world deployment. The implementation code has been made publicly available at: https://github.com/033186ZSY/RLSNet-master.