Towards V2X HD Mapping for Autonomous Driving: A Concise Review

High-definition (HD) maps are fundamental components to autonomous driving systems, providing essential centimeter-level accuracy and lane-level semantic information. While traditional HD mapping methods have evolved into online learning approaches, current solutions face significant challenges due to sensor limitations and environmental constraints. This paper presents a systematic review of HD map construction methods, tracking their evolution from conventional techniques to advanced Vehicle-to-Everything (V2X) cooperative mapping enabled by edge computing and communication technologies. Through comprehensive analysis of methodologies, algorithms, and datasets, we identify critical challenges in current HD mapping systems. Our review encompasses three key domains: traditional mapping methods, online learning approaches, and V2X cooperative construction of HD maps. We evaluate existing solutions against standardized metrics, compare their effective-ness, and outline promising directions for future research. This work provides researchers and practitioners with a structured understanding of the HD mapping landscape and highlights opportunities for advancing autonomous driving systems.