IIT Jodhpur develops technology that aims to reduce traffic congestion, road accidents

The researchers have developed a federated learning-based driver recommendation system for next-generation transportation.

The technology is developed by the institute's computer science and engineering department. (Image: official website)The technology is developed by the institute's computer science and engineering department. (Image: official website)

Press Trust of India | January 26, 2024 | 05:27 PM IST

NEW DELHI: The IIT Jodhpur has developed cutting-edge technology that it claims can reduce traffic congestion and road accidents. The Novel MAC-based Authentication Scheme (NoMAS) -- developed by the institute's computer science and engineering department -- aims not only to enhance vehicle security and intelligence but also to address multifaceted challenges prevalent on Indian roads.

It can contribute to road safety by enabling a secure and real-time exchange of data on road conditions, accidents and traffic jams among vehicles. This information can help drivers make informed decisions and avoid hazards, according to a professor associated with the technology's development. The data collected and shared through the Internet of Vehicles network can also be analysed to identify accident-prone areas, patterns of risky behaviour or road conditions that require improvement -- leading the authorities to take proactive measures to address these.

Also readIIT Kanpur's research on binary fluids to help food processing, pharmaceutical industries

This technology is a groundbreaking solution to enhance the security and the efficiency of the Internet of Vehicles, said Debasis Das, an associate professor in the computer science and engineering department. "With this technology, we have strived to address critical challenges in data protection, communication overhead and security vulnerabilities," he said, adding that NoMAS can also help protect vehicles from theft and unauthorised use. The researchers have also developed a federated learning-based driver recommendation system for next-generation transportation. Its salient feature is its ability to gauge the stress level of a driver by analysing driving patterns.

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