How rare episodes are made
Platform
A training data generation platform that creates rare episodes inside the real scenes in your data.
Rare episodes
Controlled, world-model-generated events set in observed scenes: agents, behaviours, failures, and conditions that the source capture never contained.
Example
Turn one hour of sunny daytime driving into hundreds of capture-grounded episodes: a pedestrian stepping out between parked cars, a wrong-way vehicle, a tyre shredding ahead, the same at night and in fog and snow, while preserving the original route, scene structure, and task semantics.
The system reconstructs a shared, calibrated representation of the real scenes in your existing data. Every episode is generated from that same world state:
- Cameras World models create photorealistic camera variations.
- Other sensors Physics-based renderers generate LiDAR, radar, event-camera, IMU, GNSS, and other measurement-sensitive data.
- Ground truth Geometry and labels remain outside the generative model.
- Validation A strict quality gate rejects attractive-looking but physically incorrect samples.
What you are buying is the massive infrastructure around it: a scenario compiler, a model-routing layer, a validation system, lineage, and measured improvement on your real test set.
Accepted inputs
We accept your native datasets: ROS bags, MCAP logs, video, point clouds, calibration files, maps, and labels.
What stays anchored
- The source capture remains visually anchored
- Scene structure
- Task semantics
What can vary
- Agents, their paths, and their behaviour
- Events: cut-ins, crossings, obstacles, failures
- Weather, smoke, fog, and clouds
- Time of day and lighting
- Season and surface appearance
- Compatible vehicle types
- Vegetation and field types
- Buildings and roadside objects