While several datasets for autonomous navigation have become available in recent years, they have tended to focus on structured driving environments. This usually corresponds to well-delineated infrastructure such as lanes, a small number of well-defined categories for traffic participants, low variation in object or background appearance and strong adherence to traffic rules. We propose a novel dataset for road scene understanding in unstructured environments where the above assumptions are largely not satisfied. It consists of 20,000 images, finely annotated with 34 classes collected over 200 drive sequences on Indian roads. The label set is expanded in comparison to popular benchmarks such as Cityscapes, to account for new classes.

The challenge will feature
  • Datasets for segmentation, localization.
  • More than 20,000 annotated images hand picked from unstructured environments and occurrence of rare events.
  • More associated data including nearby frames, LIDAR data, GPS which can be used for specific challenges.
  • Challenges for resource constrained models with specific runtime budgets.

If you are interested in participating, please register here: Click Here


  • Challenge Opening: Datasets for Localization and evaluation scripts released. Datasets for Segmentation have been released. See benchmarks for more info.
  • Challenge Final Submission: October 20, 2019 (Extended)
  • Result Announcement: October 23, 2019


Intel is sponsoring prize money of 1000USD for the winners of each of the 4 challenges.