Automatically choosing data samples for annotation

Abstract

Among other things, we describe techniques for automatically selecting data samples for annotation. The techniques use bounding box prediction based on a bounding box score distribution, spatial probability density determined from bounding box sizes and positions and an ensemble score variance determined from outputs of multiple machine learning models to select data samples for annotation. In an embodiment, temporal inconsistency cues are used to select data samples for annotation. In an embodiment, digital map constraints or other map-based data are used to exclude data samples from annotation. In an exemplary application, the annotated data samples are used to train a machine learning model that outputs perception data for an autonomous vehicle application.