TLSD: Breaking the Limit of Topological Lane Mapping with Graph Knowledge and Distance Awareness

Abstract

High-Definition (HD) maps are essential for both Advanced Driver-Assistance Systems (ADAS) and autonomous driving. However, offline HD map construction remains costly and challenging to maintain due to the dynamic nature of real-world environments. Consequently, online HD map generation using onboard sensors has become a key area of research. Despite recent advancements, existing deep learning-based methods often provide inaccurate output even using computationally heavy architectures, limiting their practicality for real-world applications. We introduce TLSD, an efficient end-to-end neural network that generates HD maps, incorporating both topological and geometric road information. To enhance both accuracy and efficiency, we introduce four key innovations: (1) an iterative refinement scheme within the decoder to progressively improve map predictions, (2) a group-wise one-to-many assignment strategy that accelerates training convergence, (3) a graph neural network (GNN) module that integrates lane segment coordinates for improved spatial reasoning, and (4) a distance-aware topological post-processing method that enhances the quality of connectivity outputs. We performed extensive experiments