Autonomous Vehicle Data AI
Pipelines that turn petabytes of drive logs into the scenarios, labels, and edge cases that actually improve your driving stack.
An AV program's bottleneck is rarely collecting data, it is finding the fraction of it that matters. We build autonomous vehicle data AI: scenario mining that searches petabytes of drive logs for cut-ins, occlusions, and near-misses, auto-labeling pipelines that pre-annotate camera, lidar, and radar frames for human QA, and curation systems that decide which clips are worth keeping, labeling, and adding to training and simulation sets. Instead of engineers grepping logs and labeling vendors burning budget on redundant frames, your team queries the fleet's experience like a database and spends its labeling money on the edge cases that move disengagement metrics.
Index drive logs across camera, lidar, radar, and CAN signals so events and scenarios become searchable rather than buried in raw storage.
Mine the fleet's history for rare and safety-relevant scenarios using embeddings and rule-based triggers your safety team defines.
Auto-label selected clips with foundation-model pre-annotation, then route them through human QA at a fraction of full manual cost.
Feed curated scenarios into training sets and simulation, and track which additions actually improve model and system metrics.
What it does
Scenario mining
Search petabytes of logs for cut-ins, jaywalkers, sensor degradation, and near-misses by description, embedding similarity, or trigger rules.
Auto-labeling
Foundation-model pre-annotation for 2D and 3D data that cuts manual labeling cost while human QA keeps ground truth trustworthy.
Data curation
Deduplicates near-identical driving and prioritizes clips by novelty and safety relevance, so storage and labeling spend follow value.
Simulation feed
Converts mined real-world scenarios into structured formats your simulation stack can replay and permute for coverage testing.
One AV team cut per-scene labeling cost by roughly 60 percent and surfaced 4x more usable edge-case scenarios per petabyte of logs.
Questions, answered
It is built for multimodal, time-synchronized sensor data at petabyte scale: lidar and camera alignment, CAN context, and safety-relevant scenario semantics that generic data platforms do not understand.
Yes. Pre-annotation and curation sit in front of your vendors, so they receive fewer, better-chosen frames with labels to correct rather than create from scratch.
Yes. Drive logs are competitively sensitive and enormous, so the pipelines run in your cloud or on-prem clusters. Nothing leaves your control.
Bring autonomous vehicle data ai to your team
Book a free consultation and we'll map the fastest path to production.