Enterprise-Scale 3D HD Map Annotation & Validation for ADAS and Autonomous Driving

3D HD Map Annotation (Lane-Level & Regulatory Elements), Expert Review, AI-Assisted Labeling, Positional Accuracy Validation (IoU), Secure Client-Hosted Processing

The project focused on building and validating high-definition (HD) 3D maps to support ADAS and autonomous driving use cases across complex European urban environments. Mindy Support was engaged to provide large-scale 3D annotation and expert validation of road infrastructure elements within the client’s proprietary HD mapping platform. The engagement covered multi-region datasets, long production cycles, and continuous quality monitoring to ensure stable, production-ready outputs for downstream automotive applications.

Accurate HD maps are safety-critical for ADAS and autonomous driving systems, requiring consistent, centimeter-level precision across all annotated elements. The client faced challenges related to complex lane geometries, dense traffic environments, frequent occlusions, and temporary road changes, all of which increased annotation complexity and error risk. In addition, the project required a partner capable of scaling quickly without compromising quality, operating securely within a client-hosted environment, and maintaining strict delivery timelines aligned with automotive development cycles.

Mindy Support was selected for its proven ability to deliver high-precision data services for safety-critical AI systems at enterprise scale. With deep expertise in 3D annotation, ADAS workflows, and multi-level quality control, the team ensured consistent accuracy across complex urban environments and long production cycles. Mindy Support’s flexible engagement model, secure client-hosted operations, and strong focus on annotator training and expert validation made it a reliable long-term partner for the client’s HD mapping and autonomous driving programs.

• Delivered large-scale 3D HD map annotation covering 15,000+ road infrastructure objects, including lane boundaries, road topology, traffic signs, poles, and regulatory elements

• Deployed a dedicated team of 20+ trained 3D annotators and senior reviewers, ramped up in under 2 weeks

• Implemented AI-assisted labeling workflows, increasing annotation throughput by 40%+ during peak production phases

• Established multi-level quality control, combining annotator review, expert validation, and statistical sampling

• Applied IoU-based positional accuracy checks, achieving consistent centimeter-level precision across urban datasets

• Performed all operations securely within the client’s proprietary HD mapping environment, fully aligned with internal access and compliance requirements

• 95%+ positional accuracy (IoU) across core HD map features

• 90%+ error detection rate during expert review cycles

• 30% reduction in rework compared to previous internal benchmarks

• 40% increase in annotation throughput without quality degradation

• On-time delivery across all project milestones, supporting production-ready ADAS and autonomous driving use cases