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Software Architecture

Imagination & Reasoning

We go beyond the "End-to-End" reflex models used by Tesla and others. Our vehicles don't just predict the next second – they imagine "What if I make this maneuver?" in a virtual world within milliseconds.

"Latent Space Simulation & Reasoning"
Comparison

World Models vs End-to-End

Traditional Approach

End-to-End Models

Maps sensor input directly to control outputs. Learns to mimic human driving patterns without understanding why those decisions were made.

Limitation: Cannot handle novel situations not seen in training data.

Our Approach

World Models

Builds an internal model of the world. Can simulate future scenarios, understand cause and effect, and reason about novel situations.

Advantage: True understanding enables safe handling of any scenario.
Process

How It Works

1. Perceive
Sensor data encoded to latent space
2. Imagine
Simulate possible futures
3. Reason
Evaluate outcomes for safety
4. Act
Execute safest action

Explore Our Perception System

See how we fuse multiple sensor types for super-human perception.

Sensor Fusion