EU-NATIVE ACQUISITION // BUILT FOR AI ACT ART. 10

CAPTURING THE EDGE CASES
THAT BREAK YOUR STACK.

Raw sensor fusion data for perception model training // NL · DE · BE · FR · AT · CH

RT-Fusion delivers raw sensor fusion data — RGB video, 200Hz IMU, and human gaze — for robotics, AVs, and foundation models. The specific adversarial edge cases your simulators cannot render: glare, rain, and unpredictable pedestrian behavior.

Operational Capacity: 4h+ continuous World-View (GoPro 5.3K) combined with Event-Triggered Intent-View (Ray-Ban Meta) for high-entropy interactions.

SCENE: HEAVY RAIN / OCCLUSION

Resolution

5.3K

Telemetry

200Hz IMU

Scenario

Adversarial Weather

IMU Frequency 200 Hz TRACKING Color Depth 10-BIT GP-LOG Telemetry GPMF LOCK Pipeline NATIVE INGESTION Scenarios 7 ODDs ACTIVE Sensor Pairs DUAL-STREAM SYNC EU Coverage 6 COUNTRIES ACTIVE Deployment 48H NOTICE ON-DEMAND IMU Frequency 200 Hz TRACKING Color Depth 10-BIT GP-LOG Telemetry GPMF LOCK Pipeline NATIVE INGESTION Scenarios 7 ODDs ACTIVE Sensor Pairs DUAL-STREAM SYNC EU Coverage 6 COUNTRIES ACTIVE Deployment 48H NOTICE ON-DEMAND

/// THE MISSING LAYER IN YOUR STACK

Whether you rely on LiDAR, Radar, or pure Vision, your model needs real-world context and observable intent. RT-Fusion deploys on-demand to acquire the specific European failure modes your simulators cannot render: sensor degradation in rain, glare-induced false negatives, and unpredictable VRU behavior at uncontrolled crossings.

Adversarial Weather

The Problem: Lidar absorbs water; RGB cameras blind in glare.
Acquisition Focus: Heavy rain, dirty windshields, low-sun lens flare, and wet road reflections. Train your model to survive sensor degradation.

Human Intent Capture (Gaze & Head-Pose)

The Problem: Bounding boxes don't show intent.
Acquisition Focus: Head-locked First Person View (FPV) captures the operator's visual saliency. Teach your model what a human focuses on before they act.

Complex Infrastructure

The Problem: US-centric datasets fail in dense European cities.
Acquisition Focus: Narrow Dutch streets, complex roundabouts, and high-density cyclist interactions (VRU). Rapidly deployable across DE, BE, FR, AT, and CH for domain-specific ground truth.

/// THE PROXY STACK

HARDWARE_ID: EU_V2.5

Chest-mounted GoPro (world-view) + head-worn Ray-Ban Meta (gaze-view), running in parallel with audio-synced timestamps.

[ WORLD SENSOR ]

KINEMATICS & CONTEXT

HARDWARE: GOPRO HERO 13 (CUSTOM ACQUISITION RIG)

  • RESOLUTION 4K (CONFIG A) / 5.3K (CONFIG B)
  • COLOR SCIENCE GP-LOG (10-BIT)
  • TELEMETRY GPS + ACCEL + GYRO
  • POWER WEATHERPROOF CONTINUOUS MAG-LINK
  • MOUNTING VIBRATION-ISOLATED

Captures the "World Model." High dynamic range handles the "Tunnel Exit" blinding light problem. Rolling shutter stress-tests VIO pipelines against vibration artifacts.

[ GAZE SENSOR ]

INTENT & GAZE

HARDWARE: RAY-BAN META GEN 2

  • DATA STREAM 3K (CONFIG C1) / 1080p (CONFIG C2)
  • AUDIO BINAURAL SPATIAL ARRAY
  • POV ALIGNMENT TRUE HUMAN EYE-LEVEL
  • SOCIAL CUES MICRO-GESTURE CAPTURE
  • TARGETING CENTER-FRAME GAZE LOCK

Captures the 'Agent Model.' Solves the High-Density VRU problem by recording the eye-contact negotiation and intent signaling that LiDAR cannot see.

/// GROUND TRUTH EVIDENCE

Reference captures demonstrating acquisition methodology and output quality. Raw sensor output. No stabilization. No grading. Pure entropy.

SCENARIO A: HIGH-ENTROPY ENVIRONMENT // VRU INTENT
REC ISO: 200
VRU_DENSITY: CRITICAL GPS: 52.3676° N, 4.9041° E
SCENARIO B: VERTICAL GEOMETRY // 6DOF SLAM
REC SHUTTER: 1/1250
VIO_STRESS: HIGH Z-AXIS: UNSTABLE

/// ENGINEERED FOR INTEGRATION

RT-Fusion delivers structured, time-synchronized assets. Every frame is mapped to IMU telemetry and operator head-pose, enabling direct ingestion into standard machine learning and robotics pipelines.

World Sensor GoPro Hero 13 (5.3K)
Gaze Sensor Ray-Ban Meta Gen 2 (Raw)
Telemetry GPMF (GPS + IMU)
Sync Protocol Audio/IMU Event Alignment
metadata_sample.json

{
  "timestamp_utc": "2026-02-11T09:14:22.045Z",
  "frame_id": 4920,
  "environment": {
      "location": "NL_Amsterdam_Canal_District",
      "weather": "overcast_diffuse",
      "surface": "asphalt_bike_lane"
  },
  "telemetry": {
      "imu_accel_x_y_z": [0.02, -0.81, 0.15],
      "speed_mps": 5.8
  },
  "sensors": {
      "world_cam_file": "GH010492.MP4",
      "attention_cam_file": "RM010492.MP4",
      "head_pose_proxy": true
  }
}
                    

All assets delivered as time-stamped MP4 + GPMF telemetry, directly ingestible via ROS 2 bag conversion or PyTorch DataLoader.

Optimized For Standard Engineering Pipelines

ROS 2
ISAAC
PyTorch
OpenCV

/// THE OPERATOR

CREDENTIALS // METHODOLOGY

ARTY ZUEV

10+ years in professional media production — camera systems, color science, lighting, and post-production — across commercial, documentary, and marketing projects in the EU. When the industry shifted from language models to real-world perception, I identified a critical gap: companies building autonomous systems in Europe had no dedicated, on-demand source for the specific adversarial edge cases that break production stacks. RT-Fusion was built to close that gap — applying professional acquisition methodology to capture the high-entropy sensor data that simulators cannot render and US-centric datasets do not contain.

Based In Venlo / Amsterdam, NL
Coverage NL (Active) · DE · BE · FR · AT · CH (Deployable)
Acquisition Capacity 4h+ continuous dual-sensor // Rapid deployment, 48h notice
Arty Zuev — RT-Fusion operator with dual-sensor capture rig on location in the Netherlands

/// A COMPLETE DELIVERY

FROM BRIEF TO PIPELINE-READY DATASET

STEP 01

Deployment Brief

You specify target failure modes, locations, and environmental conditions. Campaign scoped per acquisition day.

STEP 02

On-Location Capture

Dual-sensor rig deploys to target location. GoPro 5.3K World-View + Ray-Ban Meta Gaze-View running in parallel. 4h+ continuous acquisition.

STEP 03

Structured Delivery

Time-stamped MP4 + GPMF telemetry, paired with JSON metadata per scene. All clips indexed by failure mode category and sensor config.

STEP 04

Pipeline Ingestion

Convert directly to ROS 2 bag via rosbag2, or load into a PyTorch DataLoader. GPMF telemetry parsed with gopro2gpx. Zero custom tooling required.

/// DIRECT ENGINEERING FEED

ESTABLISH UPLINK

Direct line to Engineering. No sales agents.

Prefer async? [email protected]

// Pricing: €1,500–€2,500 per acquisition day. Multi-day campaigns and recurring field deployments quoted per project. All deliverables include time-stamped MP4 + GPMF telemetry + JSON metadata.

— or submit a full brief below:

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