  Unsupervised Domain Adaptation (UDA) has shown significant advancements in
object detection under well-lit conditions; however, its performance degrades
notably in low-visibility scenarios, especially at night, posing challenges not
only for its adaptability in low signal-to-noise ratio (SNR) conditions but
also for the reliability and efficiency of automated vehicles. To address this
problem, we propose a \textbf{Co}operative \textbf{S}tudents (\textbf{CoS})
framework that innovatively employs global-local transformations (GLT) and a
proxy-based target consistency (PTC) mechanism to capture the spatial
consistency in day- and night-time scenarios effectively, and thus bridge the
significant domain shift across contexts. Building upon this, we further devise
an adaptive IoU-informed thresholding (AIT) module to gradually avoid
overlooking potential true positives and enrich the latent information in the
target domain. Comprehensive experiments show that CoS essentially enhanced UDA
performance in low-visibility conditions and surpasses current state-of-the-art
techniques, achieving an increase in mAP of 3.0\%, 1.9\%, and 2.5\% on BDD100K,
SHIFT, and ACDC datasets, respectively. Code is available at
https://github.com/jichengyuan/Cooperitive_Students.
