Technology · Perception
Perception inside a single model.
The classical perception stack is a cascade: per-sensor detectors, per-modality trackers, and a late fusion step that tries to reconcile them. AREN replaces all of it with a single multimodal transformer that ingests camera, LiDAR, radar, and CAN jointly.
Per-sensor pipelines were structurally right when each sensor demanded its own compute budget and its own specialized network. They are structurally awkward now. The interfaces between the detector, the tracker, and the fusion layer discard information the downstream components would benefit from: uncertainty, temporal context, low-confidence candidates, and the raw shape of the sensor signal itself.
AREN does not assemble a scene object by object. It produces a dense, temporally-consistent scene representation directly from the sensor tensor stream. Downstream heads for occupancy, motion, and semantics read from that shared representation rather than re-learning it.
We will publish measured comparisons against reference pipelines on our research page as they pass internal review. Until a number is measured in a reproducible configuration, it is left as an em dash. That is the editorial standard for this site.