Civil Maps’ artificial intelligence software aggregates raw 3D data from LiDAR (high-resolution laser imaging), camera and other sensors onboard autonomous vehicles and organizes the information into machine-readable maps. Civil Maps’ technology hashes each location in a grid framework that corrects distortion errors and relines the object’s position to its true mean.

In an interview with Geoinformatics published earlier this year, Civil Maps CEO Sravan Puttagunta explained:

Raw 3D data look like a giant Excel spreadsheet consisting of XYZ and density RGB. A computer doesn’t understand context from the sequence of coordinates, so we have to teach computers to contextualize raw 3D data into metadata which is meaningful information.

The information is more actionable than information in today’s mapping systems and requires a fraction of the data storage and transmission for existing technologies.

Because of this light data footprint, Civil Maps’ spatial information is less costly to transmit over cellular networks, enabling it more easily to crowdsource, update and share road data in real time—a major improvement over the lengthy processes that require human annotation in current use.

As a result, the company says it can quickly generate and maintain maps that give fully autonomous vehicles the comprehensive and precise knowledge to autonomous operate safely and smoothly on all roads.

The company will use the seed investment to accelerate product development and deployment with a number of leading automotive companies and technology partners.

Autonomous vehicles require a totally new kind of map. Civil Maps’ scalable map generation process enables fully autonomous vehicles to drive like humans do—identifying on-road and off-road features even when they might be missing, deteriorated or hidden from view and letting a car know what it can expect along its route. We are honored to work with Ford and the rest of our investor team to pave the way for fully autonomous vehicles at continental scale.

—Sravan Puttagunta

Going forward, Civil Maps will continue to focus on different ways to improve the parameters of localization and with the use of a crowdsourcing method to generate scalable 3D maps for the autonomous vehicle market.