A Concise Survey on Lane Topology Reasoning for HD Mapping
It provides a comprehensive overview for researchers and practitioners in autonomous driving, addressing a gap in consolidated literature but is incremental as it reviews existing works without new results.
This survey consolidates recent advances in lane topology reasoning for HD mapping, categorizing methods into three paradigms and analyzing progression from rule-based to learning-based approaches, with performance comparisons on benchmarks like OpenLane-V2 using metrics such as APLS and DET scores.
Lane topology reasoning techniques play a crucial role in high-definition (HD) mapping and autonomous driving applications. While recent years have witnessed significant advances in this field, there has been limited effort to consolidate these works into a comprehensive overview. This survey systematically reviews the evolution and current state of lane topology reasoning methods, categorizing them into three major paradigms: procedural modeling-based methods, aerial imagery-based methods, and onboard sensors-based methods. We analyze the progression from early rule-based approaches to modern learning-based solutions utilizing transformers, graph neural networks (GNNs), and other deep learning architectures. The paper examines standardized evaluation metrics, including road-level measures (APLS and TLTS score), and lane-level metrics (DET and TOP score), along with performance comparisons on benchmark datasets such as OpenLane-V2. We identify key technical challenges, including dataset availability and model efficiency, and outline promising directions for future research. This comprehensive review provides researchers and practitioners with insights into the theoretical frameworks, practical implementations, and emerging trends in lane topology reasoning for HD mapping applications.