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Announcing NVIDIA DRIVE Map: Scalable, Multi-Modal Mapping Engine Accelerates Deployment of Level 3 and Level 4 Autonomy

By Eric Van Rees - 31st March 2022 - 15:51

With a detailed knowledge of the world and everything in it, maps provide the foresight AI uses to make advanced and safe driving decisions.

At his GTC keynote, NVIDIA founder and CEO Jensen Huang introduced NVIDIA DRIVE Map, a multimodal mapping platform designed to enable the highest levels of autonomy while improving safety. It combines the accuracy of DeepMap survey mapping with the freshness and scale of AI-based crowdsourced mapping.

With three localization layers — camera, lidar and radar — DRIVE Map provides the redundancy and versatility required by the most advanced AI drivers.

DRIVE Map will provide survey-level ground truth mapping coverage to 500,000 kilometers of roadway in North America, Europe and Asia by 2024. This map will be continuously updated and expanded with millions of passenger vehicles.

NVIDIA DRIVE Map is available to the entire autonomous vehicle industry.


DRIVE Map contains multiple localization layers of data for use with camera, radar and lidar modalities. The AI driver can localize to each layer of the map independently, providing the diversity and redundancy required for the highest levels of autonomy.

The camera localization layer consists of map attributes such as lane dividers, road markings, road boundaries, traffic lights, signs and poles.

The radar localization layer is an aggregate point cloud of radar returns. It’s particularly useful in poor lighting conditions, which are challenging for cameras, and in poor weather conditions, which are challenging for cameras and lidars.

Radar localization is also useful in suburban areas where typical map attributes aren’t available, enabling the AI driver to localize based on surrounding objects that generate a radar return.

The lidar voxel layer provides the most precise and reliable representation of the environment. It builds a 3D representation of the world at 5-centimeter resolution — accuracy impossible to achieve with camera and radar.

Once localized to the map, the AI can use the detailed semantic information provided by the map to plan ahead and safely perform driving decisions.

Read More: Digital Mapping Transport & Logistics

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