The international Autonomous Vehicle (AV) market is estimated to reach around two thousand billion by 2030. Millions of lives are lost yearly in road accidents, and traffic violations cause a significant percentage of AV accidents. The traffic signs are generally installed at the side of the road to control traffic flow or pass information about the road environment to Vulnerable Road Users (VRUs). Often, the information is also available in the form of cues present in the context around the traffic signs in the cues away from it, which we refer to as contextual cues.

Autonomous driving has recently emerged as a keystone problem for computer vision and machine learning, with significant interest in both academia and industry. Besides being a rich source of research problems for visual perception, learning, mapping and planning, it is also poised to have an immense societal and economic impact.

Driving conditions in India are highly unstructured and diverse, with interesting behaviors of traffic participants, compared to the rest of the world. These driving conditions pose unique challenges that are yet unsolved, for research in artificial intelligence (AI) and machine learning (ML) systems, and hence offer immense opportunities for possible technical innovations in AI/ML systems for better road safety.

Segmentation and detection challenge as part of AutoNUE 2019 which is being organized in conjunction with ICCV 2019.

Autonomous driving has recently emerged as a keystone problem for computer vision and machine learning, with significant interest in both academia and industry. Besides being a rich source of research problems for visual perception, learning, mapping and planning, it is also poised to have immense societal and economic impact. The workshop adopts a broad view of what is entailed by driving in unconstrained environments. Some aspects such as changes in weather, time of day or imaging conditions are already being studied by the community, for instance, under the purview of domain adaptation.

Segmentation challenge on a novel dataset for road scene understanding in unstructured environments where the above assumptions are largely not satisfied. It consists of 10,000 images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads. The label set is expanded in comparison to popular benchmarks such as Cityscapes, to account for new classes..

Autonomous driving has recently emerged as a keystone problem for computer vision and machine learning, with significant interest in both academia and industry. Besides being a rich source of research problems for visual perception, learning, mapping and planning, it is also poised to have immense societal and economic impact. The workshop adopts a broad view of what is entailed by driving in unconstrained environments. Some aspects such as changes in weather, time of day or imaging conditions are already being studied by the community, for instance, under the purview of domain adaptation.

A hundred years back we witnessed a revolution that brought about a massive shift from old horse wagons to the newer cars and bikes. We stand at the brink of a similar breakthrough today where we will soon see autonomous vehicles taking over everywhere.

Autonomous navigation is a prime computer vision problem that is getting closer to reality. However existing datasets and challenges are tailored towards structred driving conditions observed in western countries. On the other hand, indian driving conditions are highly unstructured with many vehicles that are not observed in the western setting. Autorikshaws is a major example of this.