# High Definition Map Mapping and Update: A General Overview and Future Directions

arXiv: https://arxiv.org/abs/2409.09726 · HTML: https://mlsanity.com/paper/2409.09726/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2024.

## Built on by (most cited first, 7 of 7)

- [RTMap: Real-Time Recursive Mapping with Change Detection and Localization](https://mlsanity.com/paper/2507.00980/) (2025)
- [The Era of End-to-End Autonomy: Transitioning from Rule-Based Driving to Large Driving Models](https://mlsanity.com/paper/2603.16050/) (2026)
- [FlexMap: Robust HD Map Construction under Flexible Camera Configurations](https://mlsanity.com/paper/2601.22376/) (2026)
- [SEF-MAP: Subspace-Decomposed Expert Fusion for Robust Multimodal HD Map Prediction](https://mlsanity.com/paper/2602.21589/) (2026)
- [Maps for Autonomous Driving: Full-process Survey and Frontiers](https://mlsanity.com/paper/2509.12632/) (2025)
- [SceneEdited: A City-Scale Benchmark for 3D HD Map Updating via Image-Guided Change Detection](https://mlsanity.com/paper/2511.15153/) (2025)
- [Scene Reconstruction as Mapping Priors for 3D Detection](https://mlsanity.com/paper/2605.22997/) (2026)

## Abstract (arXiv)

Along with the rapid growth of autonomous vehicles (AVs), more and more demands are required for environment perception technology. Among others, HD mapping has become one of the more prominent roles in helping the vehicle realize essential tasks such as localization and path planning. While increasing research efforts have been directed toward HD Map development. However, a comprehensive overview of the overall HD map mapping and update framework is still lacking. This article introduces the development and current state of the algorithm involved in creating HD map mapping and its maintenance. As part of this study, the primary data preprocessing approach of processing raw data to information ready to feed for mapping and update purposes, semantic segmentation, and localization are also briefly reviewed. Moreover, the map taxonomy, ontology, and quality assessment are extensively discussed, the map data's general representation method is presented, and the mapping algorithm ranging from SLAM to transformers learning-based approaches are also discussed. The development of the HD map update algorithm, from change detection to the update methods, is also presented. Finally, the authors discuss possible future developments and the remaining challenges in HD map mapping and update technology. This paper simultaneously serves as a position paper and tutorial to those new to HD map mapping and update domains.