Solutions · 02 · Autonomous Driving & World Models
The scenarios that matter are the ones that never happen.
End-to-end driving stacks and world models trained on fleet video rather than hand-written rules.
The problem
A single instrumented test vehicle can produce well over ten terabytes of sensor data in a day, and a fleet turns that into petabytes a week. Almost none of it is interesting. The frames that decide whether a release ships — the occluded pedestrian, the overturned load, the flooded intersection at dusk — appear a handful of times in a year of driving, if at all. Curation is where the compute goes, and generation is how you get the cases the fleet never happened to see.
How the loop runs here
Raw fleet video is filtered, captioned and scored at GPU speed, then world models extend the interesting fragments into scenario families: same geometry, different weather, different agent behaviour, different time of day.
Large-scale video training for end-to-end policies and world models, on multi-node clusters where the interconnect keeps the GPUs fed rather than waiting on the network.
Rollouts against held-out scenarios and regression suites, so a model that improves the average case but breaks a known edge case is caught before it reaches a vehicle.
What we provide
- Curation throughput that keeps up with what the fleet uploads
- Scenario generation for the long tail you cannot collect
- Training clusters with storage next to the compute, not across a network
- Regression evaluation on every candidate build
Industry context
One instrumented autonomous test vehicle has been measured generating 11 to 152 TB of data per day; a 200-car fleet puts that in the petabytes per day range.
Source: Tuxera
NVIDIA's pipeline curated 20 million hours of video in 14 days on Blackwell hardware — the same job runs over three years on a CPU-only pipeline.
Source: NVIDIA Newsroom