Autonomous AI Solutions for the Next Generation of Vehicles and Mobility

With a multitude of key features, Cartica’s visual intelligence platform provides the solution for many existing challenges in the automotive industry spanning far beyond the capabilities of deep learning.

 

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Self Learning artificial intelligence

  • As the system is capable of unsupervised learning, it does not require an annotated training set.

  • This unsupervised AI autonomously identifies commonalities among raw data to cluster footage together by concept and event.

  • The insights gathered from this process allow for full edge case coverage as well as a complete understanding of detailed scenarios.

 
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Minimal Power Consumption

  • Cartica’s low power consumption AI crosses existing technological barriers of safety per watt for ADAS and AV.

  • Power consumption 10 times lower than the best available solution in the market.

  • The flat architecture runs on existing hardware accelerators to minimize overall compute power

 
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Robust perception in all conditions

  • High accuracy visual perception in all weather and lighting conditions including fog, rain, snow, night, and more.

  • Programmatic coverage of longtail and edge cases

  • Robust concept coverage with recognition of thousands of object types (fine grain and broad)

 
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Predictive AI understands TRavel paths

  • The predictive driving module provides the platform with a clear real-time risk management analysis of the environmental model, allowing it to adapt concept prediction to the driving scenario.

 
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Knowledge Sharing (V2V, V2i, V2X)

  • Signature-based technology enables knowledge sharing between vehicles in an efficient way

  • Automatically updates local vehicles and system

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Sensor Fusion

  • The Cartica platform is able to fuse multiple sensor inputs into a single lightweight representation space. This allows for a full understanding of the environment in harsh scenarios (torrential downpour, heavy fog, etc.)

  • This signature fusion leverages the expressive benefits of any added sensor without the limitations of rule based fusion.

 
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Localization & Mapping

  • Utilizing any visual cue as a landmark, Autonomous AI maps visual features to high-dimensional, linear signatures for localization and easy mapping updates.

  • A hybrid cloud/local architecture holds a small subset of signatures in the car

  • Previously generated signatures are reused and matched with the local cached mapping signatures. Location is determined based upon signature matching.

  • Unmatched signatures are sent to the cloud for processing to update the global database