Geo-Data-Driven HD Map Generation Workflow with Integrated Reference-Free Constraint-Based Verification
Ruidi He, Vaibhav Tiwari, Mohanad Al-Ghobari, Meng Zhang, Andreas Rausch

TL;DR
This paper introduces a geo-data-driven workflow for generating high-definition maps for automated driving, incorporating integrated verification to ensure quality without relying on external reference data.
Contribution
It presents a novel, modular workflow that transforms openly available geo-engineering datasets into detailed HD maps with built-in constraint-based verification.
Findings
Generated maps satisfy constraints in real-world scenarios.
Complete detection of injected defects without false positives.
Workflow reduces dependency on costly sensor data.
Abstract
High-definition (HD) maps are core artifacts for automated driving systems, but their generation commonly relies on sensor-intensive mobile mapping campaigns, while quality assessment often depends on high-precision reference data. These dependencies make HD map engineering costly and difficult to apply in settings where specialised measurement data or independently measured reference maps are unavailable. This paper presents an engineering-oriented geo-data-driven workflow for HD map generation with integrated representation-level verification. The workflow uses openly available geo-engineering datasets as the primary input source and transforms them into lane-level HD map representations of existing road environments through explicit intermediate representations and processing stages. To assess the generated representations without external reference maps, the workflow integrates…
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