Every vehicle generates 80 TB a day. You can't store it all. You shouldn't store any of it.
RNDA encodes at the sensor and discards before transmission. The storage problem and the privacy problem — solved at the same step.
Request an AV POC →The Problem
AV systems generate terabytes of LiDAR, camera, and radar data per vehicle per day. Storing it creates a surveillance liability, a security target, and an infrastructure cost that scales with fleet size.
How RNDA Solves It
7,460x LiDAR compression on real KITTI data
Real Velodyne frames from 8 KITTI driving sequences encoded to 256-byte signatures. Proven on 1,913 real-world frames.
Scene similarity at 23ms
Submit any sensor frame and find the most similar driving scenarios from your entire history. Edge cases, near-misses, rare conditions — all queryable without raw data retention.
Fleet-scale without fleet-scale storage
Encode on the vehicle, discard raw data before transmission. Only signatures reach the cloud. Storage costs scale with fleet size, not data volume.
How RNDA Applies
Storage Elimination
A 1,000-vehicle fleet generates 29.2 million TB/year of sensor data. At 6,366x compression, that collapses to under 4,600 TB — eliminating edge storage hardware from vehicles entirely and making fleet-scale sensor archiving financially viable for the first time.
Privacy Protection
Bystander faces, license plates, and private property captured by vehicle cameras are encoded at ingestion and permanently discarded. GDPR and CCPA obligations are satisfied by architecture — the data cannot be breached because it no longer exists.
Compliance Management
NHTSA and EU AI Act requirements for autonomous vehicle incident reconstruction are met via RNDA's immutable audit trail. Certified evidence is available without raw personal data exposure — compliance without surveillance retention.
Intelligent Retrieval
Submit any sensor frame and find the most similar driving scenarios from your entire fleet history in 25ms. Proven on 300 real synchronized AV sensor frames — 6 cameras + LiDAR + 5 radar units fused. Discrimination gap 1.09 — the best of any domain tested.
Collaborative Intelligence
OEMs, tier-1 suppliers, and regulators access fleet intelligence for safety audits and model training without transferring raw footage or violating data residency rules. Scoped access to compressed fleet intelligence without raw data transfer.
Storage Impact
Industry stat: A fully autonomous vehicle generates ~4 TB of sensor data per hour — roughly 80 TB per vehicle per day (AutoDriveAI / Samsung Semiconductor)
29,200,000 TB × 20% × $276/TB ÷ 6,366x compression (1,000-vehicle fleet, 1 year)
1,000-vehicle AV fleet saves ~$8B/year in storage — raw cost $8.06B, post-RNDA cost ~$253K
Proof of Concept Results
Real data. Measured numbers. No synthetic results.
Source: Real synchronized AV sensor data — 6 cameras + LiDAR + 5 radar units
What Becomes Possible
"A vehicle encounters an unusual intersection scenario. The LiDAR frame is encoded in real time, the raw data discarded, and the signature transmitted. In 23ms, the fleet's entire driving history is queried for similar scenarios."
Ready to see it on your data?
Every number on this page came from a real POC. Yours will be built the same way — against your actual data type, measured compression, real query latency.
Request an AV POC →