Artiphysial provides an autonomous‑vehicle data platform that supplies curated, high‑quality datasets built from real‑world telematics and dash‑cam streams collected from luxury transportation fleets across major U.S. cities. The service includes end‑to‑end data collection, annotation, and ready‑to‑use domain‑specific datasets that capture edge‑case driving behaviors missed by traditional test programs, helping AV teams train and validate safer perception models.
Funding
Funding not disclosed
Founders
Product
Problem
Autonomous vehicle developers struggle to obtain large, diverse, and accurately annotated real‑world driving data, especially edge‑case scenarios that are rare in test‑track collections. This limits the ability to train and validate perception models for safe operation in complex traffic environments.
Solution
Artiphysial offers a data platform that continuously ingests telematics and dash‑cam video streams from distributed luxury transportation fleets operating in major U.S. cities. The platform applies AI‑powered pipelines to extract multi‑object detection, motion tracking, and intent prediction for vehicles, pedestrians, cyclists, lane markings, traffic signals, and hazards, each with confidence scores and precise spatial positioning. Curated, high‑quality annotations are delivered as ready‑to‑use, domain‑specific datasets that can be directly employed to train and validate autonomous driving perception stacks. By turning routine fleet miles into measurable training signals, Artiphysial enables AV teams to stress‑test their systems on a broad spectrum of real‑world driving behaviors and edge cases.
Target Audience
Primary customers are autonomous vehicle manufacturers, perception‑software providers, and research teams that require large‑scale, high‑quality real‑world driving data for model development and validation.
Features
- Continuous ingestion of telematics and dash‑cam feeds from a nationwide fleet of luxury vehicles
- Automated AI annotation pipeline delivering multi‑object detection, motion tracking, and intent prediction per video frame
- Confidence scoring and spatial positioning for each detected entity to support robust model training
- Edge‑case identification that surfaces rare scenarios missed by conventional test programs
- Ready‑to‑use, domain‑specific dataset packages formatted for common autonomous driving training pipelines