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

Mingfu Liang · Jong-Chyi Su · Samuel Schulter · Sparsh Garg · Shiyu Zhao · Ying Wu · Manmohan Chandraker

Arch 4A-E Poster #8
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Thu 20 Jun 5 p.m. PDT — 6:30 p.m. PDT


Autonomous driving (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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