A unique AI system that uses AI predictive capabilities to detect road accidents, as well as a collision warning system to convey timely warnings to drivers, to make several road safety-related improvements, is being developed in the City of Nagpur for the purpose. in a significant reduction in accidents. The ‘Smart Road Safety Solutions for Technology and Engineering’ (iRASTE) project in Nagpur will identify potential hazards while driving and inform drivers of similarities with the help of the Advance Driver Assistance System (ADAS).
Improved road infrastructure
The project will also identify ‘greyspots’, that is, through data analysis and continuous flow analysis by monitoring dynamic hazards across the road network. Greyspots are roadblocks, which are often overlooked and may be blackspots. The program also carries out regular road patrols and designs engineering repairs to repair existing black areas for safe maintenance and improved road infrastructure.
The iRASTE project is under the auspices of the Hub Foundation, IIIT Hyderabad, a Technology Innovation Hub (TIH) built on a technology base – Data Banks & Data Services supported by the Department of Science and Technology (DST) under its National Interdisciplinary Cyber ​​Mission Physical Systems (NM-ICPS) and INAI (Applied AI Research Institute). The project alliance includes CSIR-CRRI, with Nagpur Municipal Corporation, with Mahindra and Intel as industry partners.
Objectives is to prepare an important resource for future use
The institute works to integrate, integrate, and enhance the basic research used in data-driven technology and its distribution and translation across the country. One of the main objectives is to prepare an important resource for future use by researchers, beginners, and industries, especially in the areas of intelligent travel, health care and intellectual property.What makes the IRASTE project even more different is that AI and technology are used to create workable solutions, such as plans, in Indian environments. While the first release of IRASTE is in Nagpur, the final goal is to replicate the solution in other cities as well. Currently, negotiations are underway with the Telangana government to adopt more technology for highway buses. There are other plans to expand IRASTE to Goa and Gujarat as well.
The I-Hub Foundation also implemented techniques ranging from machine learning, computer vision and computer hearing in other data-driven technology solutions in the field of travel. One of those solutions is the India Driving Dataset (IDD), a set of road rage information in informal settlements taken from India’s roads, which highlights the deviations from global thinking of well-designed infrastructure such as routes, limited participants, low variability. on the object or in the rear view and adherence to the rules of the road.
5000 registered users of this database worldwide
The database, the first of its kind, contains 10,000 photographs, carefully described by 34 classes collected in 182 consecutive drives on Indian roads captured on a front-facing camera connected to a car driven in Hyderabad, Bangalore, and beyond. The database is issued on a public domain for use under a public license and becomes a defacto database for all analyzes in Indian street markets. Currently, there are more than 5000 registered users of this database worldwide.
Another database called Open World Object Detection on Road Scenes (ORDER) was also created using the India Driving Dataset which could be used for independent navigation systems in Indian driving environments for localization and segregation of road objects. Apart from this, the Mobility Car Data Platform (MCDP) is built with several sensors – cameras, LIDARs, with computers needed for anyone to download or process car data that can help researchers and beginners in India to check their car algorithms and navigation and research methods on Indian roads.
Framework , road quality , school is calculated
Lane Road Net (LRNet), a new integrated framework that analyzes rail and road boundaries using in-depth learning, is designed to address Indian road problems, with few obstacles, closed railway signs, broken dividers, cracks, potholes, etc. put drivers at high risk while driving. In this framework, road quality school is calculated with the help of a modular measurement function. The end result helps authorities monitor road quality and prioritize road maintenance programs to improve driving. To help local self-governing institutions adopt appropriate rehabilitation strategies for deforested roads, the Hub Foundation developed a framework for street tree identification, counting and monitoring using object finders and a similar calculation algorithm. Work has paved the way for a faster, more accurate, and cheaper way to see tree-lined roads.
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