A Concise Survey on Lane Topology Reasoning for HD Mapping

• URL: http://arxiv.org/abs/2504.01989v1
• Date: Mon, 31 Mar 2025 11:30:40 GMT
• Title: A Concise Survey on Lane Topology Reasoning for HD Mapping
• Authors: Yi Yao, Miao Fan, Shengtong Xu, Haoyi Xiong, Xiangzeng Liu, Wenbo Hu, Wenbing Huang,
• Abstract summary: Lane topology reasoning techniques play a crucial role in high-definition (HD) mapping and autonomous driving applications.<n>Recent years have witnessed significant advances in this field, but there has been limited effort to consolidate these works into a comprehensive overview.<n>This survey systematically reviews the evolution and current state of lane topology reasoning methods.
• Score: 30.73664953504888
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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