Using real road testing to optimize autonomous driving algorithms is time-consuming and capital-intensive. To solve this problem, we propose a GAN-based model that is capable of generating high-quality images across different domains. We further leverage Contrastive Learning to train the model in a self-supervised way using image data acquired in the real world using real sensors and simulated images from 3D games. In this paper, we also apply an Attention Mechanism module to emphasize features that contain more information about the source domain according to their measurement of significance. Finally, the generated images are used as datasets to train neural networks to perform a variety of downstream tasks to verify that the approach can fill in the gaps between the virtual and real worlds.

翻译：利用真实道路测试优化自动驾驶算法耗时且成本高昂。为解决这一问题，我们提出了一种基于生成对抗网络的模型，能够生成跨域高质量图像。我们进一步利用对比学习，通过真实传感器采集的真实世界图像数据与三维游戏生成的模拟图像，以自监督方式训练模型。本文还引入了注意力机制模块，根据特征的重要性度量，突出包含源域更丰富信息的特征。最后，将生成的图像作为数据集训练神经网络执行多种下游任务，以验证该方法能够弥合虚拟世界与真实世界之间的差异。