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.
@article{arxiv.2403.17373,
title = {AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving},
author = {Mingfu Liang and Jong-Chyi Su and Samuel Schulter and Sparsh Garg and Shiyu Zhao and Ying Wu and Manmohan Chandraker},
journal= {arXiv preprint arXiv:2403.17373},
year = {2024}
}