ROADWork Dataset: Learning to Recognize, Observe, Analyze and Drive Through Work Zones
arxiv(2024)
Abstract
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
discovery rate (12.8 times), significantly improve detecting (+23.9 AP) and
reading (+14.2
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
have angular error (AE) < 0.5 degrees (+9.9
degrees (+8.1
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