This presentation introduces a new approach to industrializing perception AI: a standardized manufacturing framework designed to transform raw sensor data into deployable intelligence through automated and reproducible processes. Instead of treating perception development as a one-off research effort, this approach structures the workflow into an end-to-end production pipeline-spanning data collection, automated annotation, model training, optimization, and deployment—so that organizations can reliably scale perception capabilities across applications.
A key technical focus will be on automation strategies that significantly reduce manual labeling overhead while improving consistency across large datasets. By leveraging large-scale AI models to assist in data preparation and adaptation, the pipeline enables faster
iteration cycles and supports rapid deployment to diverse hardware and environmental configurations. This method also emphasizes reproducibility and transparency, ensuring that performance can be maintained as operational conditions evolve.
