AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving

Abstract

Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of safety assurance. However objects encountered on the road exhibit a long-tailed distribution with rare or unseen categories posing challenges to a deployed perception model. This necessitates an expensive process of continuously curating and annotating data with significant human effort. We propose to leverage recent advances in vision-language and large language models to design an Automatic Data Engine (AIDE) that automatically identifies issues efficiently curates data improves the model through auto-labeling and verifies the model through generation of diverse scenarios. This process operates iteratively allowing for continuous self-improvement of the model. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms demonstrating our method's superior performance at a reduced cost.