KEYWORDS: Data modeling, RGB color model, Image segmentation, Sensors, Binary data, Visual process modeling, Robots, Systems modeling, Transparency, Environmental sensing
As society becomes increasingly reliant on autonomous vehicles, it becomes necessary for these vehicles to have the ability to navigate new environments. Environmental data is expensive to label especially because it comes from many different sensors, and it can be difficult to interpret how the underlying models works. Therefore, an adequate machine learning model for multi-modal, unsupervised domain adaptation (UDA) that is accurate and explainable is necessary. We aim to improve xMUDA, a state-of-the-art multi-modal UDA model by incorporating a multi-step binary classification algorithm, which allows us to prioritize certain data labels, and alongside human evaluation, we report the mIoU and accuracy of the final output.
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