INAI’s goal in this domain is to advance state-of-AI research in the area of road safety that aligns with VISION ZERO goal of the government to reduce road accidents & fatalities in the country. Towards this goal, INAI put together the world’s first driving dataset (IDD) of Indian driving conditions.
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.
The driving conditions in India are quite diverse and the traffic behaviour is highly unstructured compared with 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 an immense opportunity for possible technical innovations in AI/ML systems for better road safety.
Project iRASTE Nagpur: This project uses AI & ADAS as a force multiplier to transform Road Safety and reduce Road accidents. Focus is on Urban Environment in Nagpur.
IDD is a novel dataset for road scene understanding in unstructured environments. It consists of 10,000 images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads.The dataset consists of images obtained from a front facing camera attached to a car driven in Hyderabad and Bengaluru.
IDD is a novel dataset for road scene understanding in unstructured environments. It consists of 10,000 images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads.The dataset consists of images obtained from a front facing camera attached to a car driven in Hyderabad and Bengaluru.
Project iRASTE Nagpur: This project uses AI & ADAS as a force multiplier to transform Road Safety and reduce Road accidents. Focus is on Urban Environment in Nagpur.
Project TS-20 was conducted in the city of Hyderabad, Telangana, using Collision Avoidance Systems (CAS) installed in a fleet of 20 vehicles. The objective of this project was to study the impact of collision alert systems on driver behavior. The vehicles in this project operated on the roads of Hyderabad and were observed over a period of six months. It was observed that the presence of a CAS system changed the behavior of 54% of drivers on average to adopt safer driving behavior, and among those drivers who changed behavior, the average improvement in driving behavior was about 34%. From the analysis and results on various aspects of the study, it was concluded that the use of a collision avoidance system leads to a change in behavior – improved safer driving among drivers.
Introducing Ihub-data's mobility solutions, a suite of cutting-edge systems designed to improve safety and efficiency on the roads. Innovative traffic violation detection system, designed to identify and flag instances of riders without helmets and triple riding in real-time, making it a valuable tool for law enforcement and traffic safety organizations. Revolutionary tree detection and counting system, A valuable tool for environmental agencies, municipalities, and researchers. An efficient solution for monitoring and understanding the health and distribution of tree populations.A cutting-edge solution for detecting potholes on roads, designed to automatically identify, locate and plot potholes in real-time, making it an essential tool for municipalities and road maintenance crews.
