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

Publication Date: 3/26/2024



Authors: Mingfu Liang, NEC Laboratories America, Inc., Northwestern University; Jong-Chyi Su, NEC Laboratories America, Inc.; Samuel Schulter, NEC Laboratories America, Inc.; Sparsh Garg, NEC Laboratories America, Inc.; Shiyu Zhao, NEC Laboratories America, Inc., Rutgers University; Ying Wu, Northwestern University; Manmohan Chandraker, NEC Laboratories America, Inc., UC San Diego

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.

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