Project iRASTE aims to re-imagine Road Safety with the predictive power of AI. For the first time, AI will act as a force multiplier and transform Road Safety Engineering. Predictive insights generated via AI can prevent accidents even before they happen. Project iRASTE applies a Safe Systems approach to aspects of Vehicle Safety, Mobility Analysis & Infrastructure Safety.
40,000 images with bounding box annotations; released 2018.
The main aim of this project is to deploy faster & better method for COVID testing as well as develop risk stratification algorithms.
While several datasets for autonomous navigation have become available in recent years, they have tended to focus on structured driving environments. The dataset consists of images obtained from a front facing camera attached to a car. IDD is a novel dataset for road scene understanding in unstructured environments. 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 have the following benchmarks involving domain adaptation from around 20k samples of Mapillary, Cityscapes (fine annotations only), Berkeley Deep Drive, and GTA as the source dataset (S) to the IDD as target dataset (T).