ROADWork Dataset: Learning to Recognize, Observe, Analyze and Drive Through Work Zones
<|reference_start|>ROADWork Dataset: Learning to Recognize, Observe, Analyze and Drive Through Work Zones: 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 %).<|reference_end|>
@article{ghosh2024roadwork,
    title={ROADWork Dataset: Learning to Recognize, Observe, Analyze and Drive
  Through Work Zones},
    author={Anurag Ghosh, Robert Tamburo, Shen Zheng, Juan R. Alvarez-Padilla,
  Hailiang Zhu, Michael Cardei, Nicholas Dunn, Christoph Mertz, Srinivasa G.
  Narasimhan},
    journal={arXiv preprint arXiv:2406.07661},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07661},
    primaryClass={cs.CV cs.RO}
}