  Test-time adaptation (TTA) intends to adapt the pretrained model to test
distributions with only unlabeled test data streams. Most of the previous TTA
methods have achieved great success on simple test data streams such as
independently sampled data from single or multiple distributions. However,
these attempts may fail in dynamic scenarios of real-world applications like
autonomous driving, where the environments gradually change and the test data
is sampled correlatively over time. In this work, we explore such practical
test data streams to deploy the model on the fly, namely practical test-time
adaptation (PTTA). To do so, we elaborate a Robust Test-Time Adaptation (RoTTA)
method against the complex data stream in PTTA. More specifically, we present a
robust batch normalization scheme to estimate the normalization statistics.
Meanwhile, a memory bank is utilized to sample category-balanced data with
consideration of timeliness and uncertainty. Further, to stabilize the training
procedure, we develop a time-aware reweighting strategy with a teacher-student
model. Extensive experiments prove that RoTTA enables continual testtime
adaptation on the correlatively sampled data streams. Our method is easy to
implement, making it a good choice for rapid deployment. The code is publicly
available at https://github.com/BIT-DA/RoTTA
