  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.
