Advanced Driver Assistance Systems (ADAS) in intelligent vehicles rely on accurate driver perception within the vehicle cabin, often leveraging a combination of sensing modalities. However, these modalities operate at varying rates, posing challenges for real-time, comprehensive driver state monitoring. This paper addresses the issue of missing data due to sensor frame rate mismatches, introducing a generative model approach to create synthetic yet realistic thermal imagery. We propose using conditional generative adversarial networks (cGANs), specifically comparing the pix2pix and CycleGAN architectures. Experimental results demonstrate that pix2pix outperforms CycleGAN, and utilizing multi-view input styles, especially stacked views, enhances the accuracy of thermal image generation. Moreover, the study evaluates the model's generalizability across different subjects, revealing the importance of individualized training for optimal performance. The findings suggest the potential of generative models in addressing missing frames, advancing driver state monitoring for intelligent vehicles, and underscoring the need for continued research in model generalization and customization.

通过使用生成模型方法，提出了使用有条件的生成对抗网络（cGANs），比较了pix2pix和CycleGAN两种结构，实验结果表明pix2pix的性能优于CycleGAN，并且利用多视角输入样式，特别是叠加视图，提高了热像生成的准确性。 此外，该研究评估了该模型在不同主体上的普适性，揭示了个性化训练对优化性能的重要性。 研究结果表明生成模型在解决缺失帧问题、推进智能车辆的驾驶状态监测以及强调对模型泛化和定制化的持续研究的潜力。

Learning to Find Missing Video Frames with Synthetic Data Augmentation:
  A General Framework and Application in Generating Thermal Images Using RGB
  Cameras

使用合成数据增强学习找到缺失的视频帧：通用框架及在使用RGB相机生成热像的应用