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.

中文翻译：

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AIDE：自动驾驶中物体检测的自动数据引擎

自动驾驶汽车 (AV) 系统依靠强大的感知模型作为安全保证的基石。然而，路上遇到的物体表现出长尾分布，罕见或看不见的类别对部署的感知模型构成了挑战。这就需要一个昂贵的过程，需要花费大量的人力来不断地整理和注释数据。我们建议利用视觉语言和大型语言模型的最新进展来设计一个自动数据引擎（AIDE），它可以自动识别问题，有效地管理数据，通过自动标记改进模型，并通过生成不同的场景来验证模型。这个过程迭代运行，允许模型不断自我改进。我们进一步建立了 AV 数据集开放世界检测的基准，以全面评估各种学习范例，以较低的成本展示我们的方法的卓越性能。