Deep neural network (DNN) based perception models are indispensable in the development of autonomous vehicles (AVs). However, their reliance on large-scale, high-quality data is broadly recognized as a burdensome necessity due to the substantial cost of data acquisition and labeling. Further, the issue is not a one-time concern, as AVs might need a new dataset if they are to be deployed to another region (real-target domain) that the in-hand dataset within the real-source domain cannot incorporate. To mitigate this burden, we propose leveraging synthetic environments as an auxiliary domain where the characteristics of real domains are reproduced. This approach could enable indirect experience about the real-target domain in a time- and cost-effective manner. As a practical demonstration of our methodology, nuScenes and South Korea are employed to represent real-source and real-target domains, respectively. That means we construct digital twins for several regions of South Korea, and the data-acquisition framework of nuScenes is reproduced. Blending the aforementioned components within a simulator allows us to obtain a synthetic-fusion domain in which we forge our novel driving dataset, MORDA: Mixture Of Real-domain characteristics for synthetic-data-assisted Domain Adaptation. To verify the value of synthetic features that MORDA provides in learning about driving environments of South Korea, 2D/3D detectors are trained solely on a combination of nuScenes and MORDA. Afterward, their performance is evaluated on the unforeseen real-world dataset (AI-Hub) collected in South Korea. Our experiments present that MORDA can significantly improve mean Average Precision (mAP) on AI-Hub dataset while that on nuScenes is retained or slightly enhanced.

本研究针对自动驾驶汽车在不同区域部署时对新数据集的需求，提出了一种利用合成环境作为辅助领域的方法，以降低数据获取和标注的成本。通过构建MORDA数据集，可以实现对韩国驾驶环境的有效学习，从而在未见真实世界数据集上的检测性能显著提升，同时保持在源数据集上的表现。

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors
  to Unseen Real-target Domain While Preserving Performance on Real-source
  Domain

MORDA：一个促进物体检测器适应未知真实目标领域的数据集，同时保持在真实源领域的性能