The article details Amap’s (Gaode) technical approach to building and deploying high‑precision maps for autonomous driving, covering accuracy requirements, data collection, point‑cloud alignment, AI‑driven perception and map‑update pipelines, and the challenges of scale, cost, and freshness.
This article presents the CITT framework, a three‑stage algorithm that automatically calibrates road‑intersection topology using massive GPS trajectory data, detailing preprocessing, core‑area detection via quadtree‑mean‑shift, influence‑zone calibration with direction‑weighted Frechet distance and DBSCAN, and demonstrating superior accuracy over existing methods.
Gaode leverages high‑resolution satellite imagery as an active discovery tool for map updates, extracting road, region and building elements through advanced semantic segmentation networks (U‑Net, ASPP, attention, non‑local) and instance‑segmentation pipelines, to accelerate accurate road‑network and building‑block data refreshes while addressing future scalability challenges.
