CenterLineFormer: Road Centerlines Graph Generation with Single Onboard Camera

As autonomous driving systems advance rapidly, there is a surge in demand for high-definition (HD) maps that provide accurate and dependable prior information on static environments around vehicles. As one of the main high-level elements in HD maps, the road lane centerline is essential for downstream tasks such as autonomous navigation and planning. Considering the complex topology and significant overlap concerns of road centerlines, previous studies have rarely examined the centerline HD map mapping problem. Recent learning-based pipelines take heuristic post-processing predictions to generate a structured centerline output without instance information. To ameliorate this situation, we propose a novel, end-to-end road centerlines vectorized graph generation pipeline, termed CenterLineFormer. CenterLineFormer takes a single onboard camera image as input and predicts a directed graph representing the lane-layer map in the bird’s-eye view (BEV). We propose a strategy for better view transformation that uses a cross-attention mechanism to generate a dense BEV feature map. With our pipeline, we can describe the connection relationship between different centerlines and generate structured lane graphs for downstream modules as planning and control. Qualitatively, our experiments emphasize that our pipeline achieves a superior performance against previous baselines on nuScenes dataset. We also show that CenterLineFormer can generate accurate centerline graph topologies on night driving and complex traffic intersection scenes.