Why GeoMate’s HD Maps Are the Future of Autonomous Driving
Explore GeoMate’s role in autonomous driving with advanced HD mapping technology, vital for the safety and future of self-driving vehicles.
GeoMate: AI-Powered Mapping for Smart Mobility
Compact HD Maps
For Safer & More Reliable AD & ADAS
Our Compact HD Maps provide precise localization, optimize path planning, and enhance sensor perception
Autonomous vehicles need precise, up-to-date HD maps to safely navigate our world’s roadways.
ROADWAY
STRUCTURE & LAYOUT
Improve routing and safe travel through detailed roadway and traffic schematics
TRAFFIC
CONTROL & REGULATIONS
Optimize navigation with lane specific routing while ensuring safety with detailed traffic sign information
ENVIROMENTAL
& URBAN
FEATURES
Prioritize vulnerable user safety with comprehensive mapping of urban environments and pedestrian routes
CONNECTIVITY
& DATA INTELLIGENCE
Enhance route optimization by utilizing dynamic, interconnected roadways and up-to-date environmental data
PRECISE
DATA
Up to 10 cm accuracy. We achieve up to 10 cm of accuracy in our Compact HD maps through advanced georeferencing techniques, employing high-resolution aerial imagery and robust quality assurance processes.
REGULAR
UPDATES
Maps are updated on weekly basis. We update our maps weekly using change detection models that analyze aerial imagery to identify changes. This efficient process ensures maps are current, focusing updates only on necessary areas.
ENHANCED COMPUTATIONAL EFFICIENCY
Megabytes Data size. Our compact HD maps are created with data sizes in megabytes (MB), significantly smaller than the gigabytes (GB) typical of LiDAR-based point cloud maps. This smaller data footprint enables quicker processing and reduces storage requirements.
FLEXIBLE
INTEGRATION
GeoJSON, OSM, ASAM OpenDrive, LaneLet2 Data Formats. We provide our maps in multiple data formats, including GeoJSON, OSM (OpenStreetMap), ASAM OpenDrive, and LaneLet2, catering to various industry needs and software compatibility requirements.
Data Acquisition
& Preparation
Our process starts with acquiring high-resolution geospatial imagery from our partners, followed by preparing the data for input into our AI systems.
Dectection,
Segmentation,
& Fusion
Our AI systems proceed with detecting and segmenting relevant features from the imagery. This information is then fused to create a cohesive and detailed map layer that enhances the accuracy of our models.
Semantic
& Traffic Rules
This layer adds context about road types, lane boundaries, traffic signs, and other regulatory elements, ensuring the maps support safe and rule-compliant navigation.
Real World
Construction
This comprehensive representation synthesizes all collected data and insights, enabling precise and reliable simulations for autonomous driving applications.
How it Works: HD Mapping with Aerial Imagery
Think of it like this: aerial mapping is comparable to adding a sweater to your online cart and clicking ‘buy’; lidar mapping is like driving out to a local farm, shearing a sheep, processing the wool, and then knitting a sweater by hand.
Safety of Autonomous Vehicles: Innovation from Risk to Reward
Self-driving cars are a technological marvel with serious technical hurdles that are causing declines in consumer confidence. But they’re the transportation of the future: so how can the industry solve this?
