  Due to the poor illumination and the difficulty in annotating, nighttime
conditions pose a significant challenge for autonomous vehicle perception
systems. Unsupervised domain adaptation (UDA) has been widely applied to
semantic segmentation on such images to adapt models from normal conditions to
target nighttime-condition domains. Self-training (ST) is a paradigm in UDA,
where a momentum teacher is utilized for pseudo-label prediction, but a
confirmation bias issue exists. Because the one-directional knowledge transfer
from a single teacher is insufficient to adapt to a large domain shift. To
mitigate this issue, we propose to alleviate domain gap by incrementally
considering style influence and illumination change. Therefore, we introduce a
one-stage Dual-Teacher Bi-directional Self-training (DTBS) framework for smooth
knowledge transfer and feedback. Based on two teacher models, we present a
novel pipeline to respectively decouple style and illumination shift. In
addition, we propose a new Re-weight exponential moving average (EMA) to merge
the knowledge of style and illumination factors, and provide feedback to the
student model. In this way, our method can be embedded in other UDA methods to
enhance their performance. For example, the Cityscapes to ACDC night task
yielded 53.8 mIoU (\%), which corresponds to an improvement of +5\% over the
previous state-of-the-art. The code is available at
\url{https://github.com/hf618/DTBS}.
