AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving
This addresses the problem of expensive manual data curation for rare objects in autonomous vehicles, offering an incremental improvement through automation.
The paper tackles the challenge of long-tailed object distributions in autonomous driving perception by proposing AIDE, an automatic data engine that iteratively identifies issues, curates data, auto-labels, and verifies models, achieving superior performance on a new benchmark at reduced cost.
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.