Raw Data

Multi-Camera Footage

NATIX provides synchronized multi-camera driving footage captured across different geographies, road types, weather conditions, and driving cultures. Unlike traditional proprietary fleets operating in limited environments, NATIX data reflects the diversity and unpredictability of real-world driving behavior.

The raw data layer includes multi-camera video streams, GPS data, IMU data, and vehicle telemetry captured during real-world driving activity.

Edge Case and Long-Tail Scenarios

NATIX combines trip metadata, vehicle telemetry, AI-driven indexing, and Vision-Language Models (VLMs) to identify, rank, and retrieve edge cases and long-tail scenarios across large-scale video datasets.

Metadata signals such as GPS, IMU, and vehicle telemetry enable efficient detection of events, including harsh braking, sudden acceleration, sharp turns, and other unusual driving behaviors. Vision-Language Models complement these capabilities by identifying semantically rich scenarios that cannot be inferred from telemetry alone, such as emergency vehicles, construction zones, unusual road actors, complex pedestrian interactions, and other rare real-world situations.

This enables customers to efficiently discover safety-critical and behaviorally significant events for training, testing, benchmarking, and simulation workflows in Physical AI systems.

Supported Use Cases

Large-scale real-world visual data is becoming increasingly important for modern Physical AI systems, particularly for world models and end-to-end driving architectures. The raw data products NATIX provides form the foundation layer for modern Physical AI training, validation, and simulation workflows.

World Foundation Models (WFMs) are rapidly becoming a core component of autonomous driving and robotics systems. These models aim to understand, simulate, and predict how the physical world behaves. NATIX data helps enrich these models with diverse real-world motion, interactions, and environmental context captured across a wide range of driving environments, road systems, and traffic behaviors.

Similarly, end-to-end (E2E) driving systems rely on large-scale real-world driving footage rather than manually engineered driving rules. NATIX datasets support the training and validation of E2E models by providing diverse multi-camera footage across different jurisdictions, weather conditions, infrastructure styles, and traffic patterns, helping improve model generalization beyond a single fleet, geography, or driving culture.

Post-Processed Data Products

Beyond raw data collection, NATIX can transform real-world footage into structured outputs optimized for simulation and mapping workflows.

4D Scenario Generation

NATIX can generate structured driving scenarios for graphics-based simulators and validation systems. These scenarios can be organized into searchable libraries and exported into industry-standard simulation formats such as OpenSCENARIO and OpenDRIVE, enabling teams to recreate and test real-world interactions inside virtual environments.

Map Features

NATIX processes real-world visual data to generate structured maps and spatial intelligence outputs. This includes the extraction of road signs, lane markings, road geometry, and infrastructure assets such as poles, barriers, traffic systems, and curb features. The system can also identify points of interest and generate traffic and mobility insights such as flow, density, and behavioral patterns. These outputs support mapping platforms, navigation systems, and infrastructure monitoring workflows. They also enable broader spatial intelligence applications derived from continuously updated real-world visual data.