  Perceiving and navigating through work zones is challenging and
under-explored, even with major strides in self-driving research. An important
reason is the lack of open datasets for developing new algorithms to address
this long-tailed scenario. We propose the ROADWork dataset to learn how to
recognize, observe and analyze and drive through work zones. We find that
state-of-the-art foundation models perform poorly on work zones. With our
dataset, we improve upon detecting work zone objects (+26.2 AP), while
discovering work zones with higher precision (+32.5%) at a much higher
discovery rate (12.8 times), significantly improve detecting (+23.9 AP) and
reading (+14.2% 1-NED) work zone signs and describing work zones (+36.7 SPICE).
We also compute drivable paths from work zone navigation videos and show that
it is possible to predict navigational goals and pathways such that 53.6% goals
have angular error (AE) < 0.5 degrees (+9.9 %) and 75.3% pathways have AE < 0.5
degrees (+8.1 %).
