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While several datasets for autonomous navigation have become available inrecent years, they tend to focus on structured driving environments. Thisusually corresponds to well-delineated infrastructure such as lanes, a smallnumber of well-defined categories for traffic participants, low variation inobject or background appearance and strict adherence to traffic rules. Wepropose IDD, a novel dataset for road scene understanding in unstructuredenvironments where the above assumptions are largely not satisfied. It consistsof 10,004 images, finely annotated with 34 classes collected from 182 drivesequences on Indian roads. The label set is expanded in comparison to popularbenchmarks such as Cityscapes, to account for new classes. It also reflectslabel distributions of road scenes significantly different from existingdatasets, with most classes displaying greater within-class diversity.Consistent with real driving behaviours, it also identifies new classes such asdrivable areas besides the road. We propose a new four-level label hierarchy,which allows varying degrees of complexity and opens up possibilities for newtraining methods. Our empirical study provides an in-depth analysis of thelabel characteristics. State-of-the-art methods for semantic segmentationachieve much lower accuracies on our dataset, demonstrating its distinctioncompared to Cityscapes. Finally, we propose that our dataset is an idealopportunity for new problems such as domain adaptation, few-shot learning andbehaviour prediction in road scenes.

IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments