New Delhi: The Centre on Tuesday said it is testing an Artificial Intelligence-based system in Nagpur which will alert the drivers ahead of a collision and make Indian roads safer, according to an official release. The project ‘Intelligent Solutions for Road Safety through Technology and Engineering’ (iRASTE) is being tested at Nagpur will identify potential accident-causing scenarios while driving a vehicle and alert drivers about the same with the help of the Advanced Driver Assistance System (ADAS), a statement from the ministry of science and technology said.
“A unique approach that uses the predictive power of AI to identify risks on the road, and a collision alert system to communicate timely alerts to drivers, to make several improvements related to road safety, is being implemented in Nagpur to bring significant reduction in the number of road accidents,” said an official.
The project will also identify ‘grey spots’ through data analysis and mobility analysis by continuously monitoring dynamic risks on the entire road network. Grey spots are locations on roads, which left unaddressed could become blackspots or locations with fatal accidents. The system also conducts continuous monitoring of roads and designs engineering fixes to correct existing road blackspots for preventive maintenance and improved road infrastructure, said an official.
According to the ministry of science and technology, the iRASTE project is under the I-Hub Foundation, IIIT Hyderabad, which is a Technology Innovation Hub (TIH) set up in the Data Banks and Data Services vertical supported by the Centre’s National Mission on Interdisciplinary Cyber-Physical Systems (NM-ICPS) along with INAI (Applied AI Research Institute). The project consortium includes CSIR-CRRI and the Nagpur Municipal Corporation and has companies like Mahindra and Mahindra and Intel as industry partners.
Read: AI at par with specialists in diagnosing prostate cancer: Study
“The hub is working to coordinate, integrate, and amplify basic and applied research in broad data-driven technologies as well as its dissemination and translation across the country. One of the primary aims is to prepare a critical resource for future use by researchers, start-ups, and industry, mainly in the areas of smart mobility, healthcare along with smart buildings,” said an official.
The aim is to replicate the AI-based solution in other cities too. Towards this, talks are on with the Telangana government to adopt the technology in a fleet of buses that ply on the highways. The project may be extended to Goa and Gujarat, said an official.
I-Hub Foundation used techniques ranging from machine learning, computer vision and computational sensing for several other data-driven technological solutions in the mobility sector. One such solution is the India Driving Dataset (IDD) for road scene understanding in unstructured environments captured from Indian roads.
The Indian data stands out as it deviates from the worldwide assumptions of well-delineated infrastructure such as lanes, limited traffic participants, low variation in object or background appearance and strong adherence to traffic rules. The LaneRoadNet (LRNet), a new framework with an integrated mechanism considering lane and road parameters using deep learning, has been designed to address problems of Indian roads, which have several obstacles, occluded lane markings, broken dividers, cracks, potholes, etc. that put the drivers at significant risk while driving.
In this framework, a road quality score is calculated with the help of a modular scoring function. The final score helps the authorities to assess road quality and prioritize maintenance schedules of the road so as to improve the drivability. To help local bodies employ suitable rejuvenation methods on tree-starved streets, I-Hub Foundation has designed a framework for street tree detection, counting and visualization that uses object detectors and a matching counting algorithm. The work paved the way for a quick, accurate, and inexpensive way to recognize tree-starved streets, an official added.
