  Autonomous driving is rapidly advancing, and Level 2 functions are becoming a
standard feature. One of the foremost outstanding hurdles is to obtain robust
visual perception in harsh weather and low light conditions where accuracy
degradation is severe. It is critical to have a weather classification model to
decrease visual perception confidence during these scenarios. Thus, we have
built a new dataset for weather (fog, rain, and snow) classification and light
level (bright, moderate, and low) classification. Furthermore, we provide
street type (asphalt, grass, and cobblestone) classification, leading to 9
labels. Each image has three labels corresponding to weather, light level, and
street type. We recorded the data utilizing an industrial front camera of RCCC
(red/clear) format with a resolution of $1024\times1084$. We collected 15k
video sequences and sampled 60k images. We implement an active learning
framework to reduce the dataset's redundancy and find the optimal set of frames
for training a model. We distilled the 60k images further to 1.1k images, which
will be shared publicly after privacy anonymization. There is no public dataset
for weather and light level classification focused on autonomous driving to the
best of our knowledge. The baseline ResNet18 network used for weather
classification achieves state-of-the-art results in two non-automotive weather
classification public datasets but significantly lower accuracy on our proposed
dataset, demonstrating it is not saturated and needs further research.
