— Autonomous Vehicles
Real-world perception and edge-case data for autonomous systems.
Scene understanding, object detection, edge-case capture, and environment-specific perception datasets.
Edge-case capture
— Common Robotic Tasks
Use cases we capture for this domain.
Object detection
Scene segmentation
Pedestrian behavior
Road condition variation
Low-light scenarios
Edge-case capture
Planning evaluation
Sim-to-reality testing
— Data Types
What a typical dataset includes.
Model development
Pre-training corpora, fine-tuning sets, and held-out evaluation data scoped to your tasks and environments.
Delivery formats
COCO, YOLO, Pascal VOC, JSON, CSV, or a custom schema mapped directly to your pipeline.
Quality validation
Coverage, precision, and traceability scoring on every batch before it reaches your training runs.
— Compliance & Trust
Compliant by design, across every dataset.
From first consent form to final delivery, dexset workflows are built to meet data protection, security, and labor standards across the regions where we capture and the regions where our customers operate.
Informed consent
Every participant is briefed and signs a release before capture begins. Consent records attach to each clip, and withdrawal requests are honored across all delivered versions.
Privacy protection
Face blurring, anonymization, and exclusion zones are applied wherever required. We minimize personal data by design and never collect more than the task brief demands.
Data security
Footage is encrypted in transit and at rest, with role-based access controls, signed delivery URLs, and per-recipient transfer records on every dataset.
Regional compliance
Capture and delivery workflows are designed to align with GDPR and UK GDPR, CCPA/CPRA, PIPL, APPI, and LGPD requirements, with regional data-residency options where needed.
Traceability & audit
Source, consent, capture, environment, and annotation records persist for every dataset, supporting audits that run from raw footage to the exported training file.
Responsible sourcing
Capture operators, demonstrators, and annotators are fairly paid and work under documented, safe conditions — quality data should never come from exploitative labor.
— What Teams Say
Trusted by robotics teams.
★★★★★
“dexset captured 400 hours of bin-picking demonstrations across three warehouses. The failure taxonomy alone cut our triage time in half.”
Lena Ortiz
Head of Robot Learning, AI Robotics Company
★★★★★
“From brief to delivered COCO export in three weeks. Generic annotation vendors quoted us three months for lower quality.”
David Park
Founding Engineer, Independent Robotics Vendor
★★★★★
“Coverage scoring meant we knew exactly what variation we were missing before training, not after burning a GPU budget on it.”
Sofia Almeida
Perception Engineer, AI Robotics Company
— FAQ
Frequently asked questions.
What data does dexset capture for autonomous vehicles?
Scene understanding, object detection, edge-case capture, and environment-specific perception datasets. Typical deliveries include multi-camera footage, scene segmentation masks, edge-case clips, environment variation data, annotated and validated to your task brief.
Which tasks can you cover in this domain?
Current programs cover object detection, scene segmentation, pedestrian behavior, road condition variation, low-light scenarios, edge-case capture and related workflows. Task lists are defined per brief, so coverage extends to the specific workflows your robot must learn.
Can capture run in our own autonomous vehicles environments?
Yes. On-site programs run under your access, safety, and compliance requirements using dexset capture protocols. Where on-site capture is not possible, comparable or controlled environments reproduce your task conditions.
How is the data delivered?
As annotated, quality-scored datasets in COCO, YOLO, Pascal VOC, JSON, CSV, or a custom schema — versioned and delivered through signed APIs or cloud transfer, ready for training, evaluation, and deployment improvement.