Autonomous vehicle training data. Sourced to the scenes that cause disengagements.
Your fleet logs have millions of ordinary miles. What they lack is the cyclist behind the van, the lane closure marked with three hand-placed cones, the wet asphalt at dusk. eeko's data practice sources driving footage, point clouds, and expert-reviewed labels against a written scenario list, with consent, masking, and signed provenance on every item your safety team will ask about.
What is autonomous vehicle training data?
Autonomous vehicle training data is the recorded and labeled driving experience a perception, prediction, or planning model learns from: camera video and lidar point clouds of real roads, the boxes, masks, and lane markings drawn over them, the intent tags that say what a pedestrian is about to do, and the scenario descriptions a planner is judged against. A driving stack is only as safe as the rarest scene in that set.
The economics are upside down. Common driving is nearly free to collect and nearly useless at the margin. The scenes that matter are rare in every fleet log, expensive to label consistently, and full of people who never agreed to be recorded. A dataset that solves all three does not come from a crawler or from more miles.
eeko's data practice sources it on purpose: footage from consenting contributors matched to a scenario list, annotation to your guideline by trained reviewers, and a verifiable record on every clip. The same governance habits run through our compliance and governance work for regulated enterprises. Start at the AI training data hub or see how verification works item by item.
Driving edge-case data and AV annotation, to a written scenario list.
Six things AV teams ask for most. Each starts from a spec and a pilot batch, evaluated on your side before volume.
- Scenario list written in scoping
- Match rate reported on the pilot
- Guideline piloted before production
- Label source recorded per object
- Crossing, yielding, merging, waiting
- Disagreement kept in the record
- 3D boxes and free-space masks
- Calibration files in the delivery
- What happened, what should have happened
- Graded against a written rubric
- Near-duplicate check against the registry
- Per-clip hash on the receipt
Four things a bigger fleet log still cannot give you.
Perception training data labeled by people who understand roads.
The reviewers behind a driving dataset
- Computer vision annotators — boxes, masks, lanes, and flags to your guideline, with throughput and agreement tracked per annotator.
- Driving instructors and traffic engineers — intent labels, scenario descriptions, and critiques of planner behavior from people who teach and design for real roads.
- 3D technicians and engineers — point cloud labeling, free-space masks, calibration checks, and the hard occlusion rulings. See mechanical engineering experts and teleoperation experts.
- Contributors — drivers and fleet operators who license footage through the eeko creator catalogue, each on a release with consent recorded.
Every reviewer is identity-verified before payment. Every label says who made it: a contributor, an AI suggestion, or an expert reviewer. You can filter on that when you train.
What a clip carries by the time it ships.
An illustrative clip lifecycle. Every clip in the batch goes through the same gates.
From disengagement report to delivered batch.
Fixed scope and a fixed price once the spec and guideline are signed. Nothing goes to volume until the pilot has been through your evaluation.
Self-driving dataset questions.
Can you source specific driving scenarios like night rain or construction zones?
Yes, and that is the normal way we work. In scoping we convert your disengagement reports or eval failures into a written scenario list: weather, light, road type, road users, and the event itself. Sourcing then targets that list instead of general mileage. The pilot batch shows how closely the footage matches before you commit to volume, and a standing order keeps extending coverage as the model improves and the list changes.
How are faces and license plates handled in driving footage?
Street footage contains people who never agreed to be in a dataset, so the rules are strict. A clip showing an identifiable person is released for training only with consent on record, and releases are tied to the item itself. Location and personal metadata are removed at intake. If your privacy review calls for blurring or boxing faces and plates in the imagery, we write that into the spec and apply it before delivery, not after.
What annotation types do you offer for autonomous vehicle data?
2D and 3D bounding boxes, semantic and instance segmentation, occlusion flags, truncation flags, lane geometry, sign labels, pedestrian and cyclist intent tags, and free-text scene descriptions. Everything is drawn to your annotation guideline, each label carries its source, and a second expert decides disputed frames. An unusual taxonomy gets piloted on a small batch first so the ambiguous cases surface while they are still cheap to resolve.
What formats do you deliver AV datasets in?
Images and captions in COCO, video as WebDataset shards, labels and metadata as JSON Lines or Parquet, point clouds as PLY, and Croissant metadata describing the whole set. If your pipeline expects a specific frame naming or calibration layout, the spec records it and the pilot batch arrives that way. Every delivery ships with per-item provenance records and a signed receipt that names every item by ID and hash.
How much does autonomous vehicle training data cost?
Four things set the price: how rare the scenario is, how dense the annotation is, how many label types you need, and the licence. Full segmentation of a rare night-rain intersection costs far more than boxes on a clear highway. Exclusive rights cost more than shared rights, and volume or a standing order brings the unit price down. We quote a fixed price once the spec and guideline are agreed.
Do you provide held-out test scenarios for regression testing?
Yes. A held-out set is sourced and labeled for you alone, hashed frame by frame at intake, checked against the registry for near-duplicates, and licensed exclusively, so it stays out of every training corpus including your own. The signed receipt lists each clip by hash, which lets your safety team confirm later that nothing in the test set ever crossed into training. It is the cleanest answer to a benchmark contamination question.
Training data for neighboring fields.
Solutions and experts AV teams use most.
Send us the scene your stack keeps missing.
A disengagement report, an eval failure, or a one-line description is enough to start. Inside one business day a data lead comes back with questions, a first scenario list, and a route to a pilot batch. No deck. A ranked list.
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Markets served.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: