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Industry — Autonomous Systems

Sensor Data for
Autonomous AI

Self-driving vehicles, drones, and industrial robots fail in real-world conditions because their training data was collected in controlled environments. DATALENT captures from actual operational deployments.

Datasets

What We Offer for Autonomous Systems

Autonomous Driving
Multi-City HD Drive Data
Synchronized LIDAR, camera, and radar from 12 global cities. 3D object tracks and HD semantic maps.
📦 8M frames🏘 12 cities
  • LIDAR + RGB + radar + GPS synchronized
  • 3D bounding boxes with object tracks
  • HD lane and drivable surface maps
  • Rare events: construction, accidents, unusual obstacles
Drone & UAV
Aerial Navigation Data
Aerial LIDAR, RGB, and multispectral imagery from real UAV deployments across urban and industrial environments.
📦 2.4M frames🚁 UAV
  • Aerial LIDAR point clouds
  • Obstacle and no-fly zone annotations
  • Safe landing zone labels
Industrial Robotics
Robotic Manipulation Data
Real robotic manipulation from factory environments: force-torque signals, depth imagery, motion trajectories.
📦 1.2M sequences🤖 Manipulation
  • Force-torque + depth + RGB synchronized
  • Success/failure labels with reasoning
  • 20+ manipulation task categories

Train on Data from Real Deployments

Your autonomous system needs data from the real world, not a simulation.

Talk to Our Team →Browse Datasets
DATALENT autonomous driving dataset: LIDAR point cloud bird's eye view with 3D object detection boxes alongside camera feed showing detected vehicles and pedestrians
Real deployment data: LIDAR + camera synchronized capture, Tokyo urban environment
Sensor Fusion

LIDAR + Camera + Radar, Synchronized

Our autonomous driving datasets capture all sensor modalities simultaneously at 10Hz — LIDAR point clouds, RGB camera, radar returns, and GPS — with sub-millisecond synchronization. Each frame includes 3D bounding boxes, object tracks across frames, and HD semantic lane maps.

Critically, our 12-city deployment includes the long-tail events that controlled test tracks never produce: construction zones, unusual vehicle types, adverse weather, and the unpredictable interactions between pedestrians, cyclists, and traffic that cause real-world failures.

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FAQ

Autonomous Data Questions

What sensor types are included in the autonomous driving dataset?
Our primary autonomous driving dataset includes: 64-channel LIDAR point clouds (Velodyne HDL-64E equivalent), 4-camera RGB (front, left, right, rear at 1080p), 4D radar returns, and GPS/IMU data. All sensors are synchronized at 10Hz with sub-millisecond precision. Camera and LIDAR are calibrated — we provide the calibration matrices for each recording session.
Do you include edge cases and rare scenarios?
This is where real-world data outperforms simulation most significantly. Our datasets include oversampled rare events: construction zones and detour scenarios, unusual vehicle types (three-wheelers, oversized vehicles, emergency vehicles), adverse weather conditions (rain, fog, low sun angle), nighttime driving, and complex multi-agent scenarios at intersections. These edge cases are tagged so you can weight them appropriately in training.