IIT Jodhpur Develops Technology To Reduce Traffic Congestion And Road Accidents

The technology has the capability to assess driver stress levels through driving patterns.

Indian Institute of Technology Jodhpur (File Photo)

Source : IIT Jodhpur

Jodhpur: The Indian Institute of Technology Jodhpur researchers introduce a cutting-edge technology known as the Novel MAC-Based Authentication Scheme (NoMAS), poised to redefine the landscape of future transportation. With its potential to mitigate traffic congestion and reduce road accidents, this innovation marks a significant leap forward. IIT Jodhpur researchers have also developed a Federated learning-based driver recommendation for next-generation transportation systems. The remarkable feature of this system is its ability to gauge the stress level of a driver by analyzing the driving patterns.

The Novel MAC-Based Authentication Scheme (NoMAS) aims not only to enhance the security and intelligence of vehicles but also to address multifaceted challenges prevalent on Indian roads. It can contribute to road safety by enabling secure and real-time communication between vehicles, allowing them to share information about road conditions, accidents, and traffic jams. This information can be crucial for enhancing driver awareness and avoiding collisions. NoMAS can also assist in improving road safety by facilitating the exchange of data on road conditions among vehicles. This information can help drivers make informed decisions and avoid road hazards.

NoMAS can be used to facilitate the rapid dissemination of emergency alerts in the event of accidents or hazardous road conditions. This can help nearby vehicles take necessary precautions and alert emergency services promptly. The data collected and shared through the IoV network can also be analyzed to identify accident-prone areas, patterns of risky behavior, or road conditions that require improvement. Authorities can then take proactive measures to address these issues. NoMAS security measures can also help protect vehicles from theft and unauthorized use.

The proposed NoMAS MAC-based authentication scheme addresses key disclosure vulnerabilities and data leakage concerns in IoV networks. NoMAS significantly reduces computational and communication overhead compared to existing schemes, without compromising on performance, as verified through formal security proofs using different tools. This encryption not only ensures data confidentiality but also enhances performance significantly, reducing workload by up to 99.60% and cutting communication needs by approximately 81% compared to existing methods.

NoMAS can easily work with different types of vehicles in India, with the help of an On-board Unit (OBU), through this, they are able to communicate and create the IoV network. In IoV, communication between vehicles and other IoV components can be performed using Dedicated Short-Range Communication (DSRC) protocol and Wireless Access in Vehicular Environment (WAVE) protocol.

The research was published in IEEE Transactions on Intelligent Transportation Systems (DOI: 0.1109/TITS.2023.3242291) by Dr. Debasis Das, Associate Professor, and Ms. Himani Sikarwar, Ph.D. Student from the Department of Computer Science & Engineering, IIT Jodhpur.

While NoMAS plays a vital role in improving communication and sharing information among vehicles to help mitigate accidents, it's crucial to understand that it isn't a standalone solution for ensuring road safety in India. The overall safety scenario relies on various factors like road infrastructure, traffic management, driver conduct, law enforcement, and vehicle safety features.

This research was funded by the Global Innovation & Technology Alliance (GITA), Department of Science & Technology (DST), Science and Engineering Research Board (SERB), Ministry of Electronics and Information Technology (MeitY), and Duckietown, ETH Zurich and MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), USA.

Aligning with the same research, the IIT Jodhpur researchers have also developed a Federated learning-based driver recommendation for the next-generation transportation system. Driving a vehicle under stress poses a danger not only to the driver but also to others on the road. There are many factors which put drivers under stress. In this work we are concerned about driving-induced stress. This system gathers vehicle telematics data to identify different driving types and stress levels. Once the system detects that a person is driving under stress, it can be addressed through various interventions, including taking breaks between consecutive rides, displaying alert messages, and imposing penalties for stressed driving.

IIT Jodhpur researchers have developed a testbed called DD-Monitor (Driver's driving monitor) which is able to collect real-time driving data and identify stressed and abnormal driving behaviour. A run of DD-Monitor is already conducted on the roads of Jodhpur City which was successful. The uniqueness of DD-Monitor is that it is useful for every individual. Parents can use this for monitoring their children. Car rental companies can collect the rent based on how vehicles were driven.

The team for this research includes Dr. Debasis Das along with Mr. Jayant Vyas, Research Scholar, VANET Lab, Department of CSE, IIT Jodhpur, Ms. Bhumika, Research Scholar, VANET Lab, Department of CSE, IIT Jodhpur, and Prof. Santanu Chaudhury, Director, IIT Jodhpur. The paper was published in Expert Systems with Applications (DOI: https://doi.org/10.1016/j.eswa.2023.119951). This is collaborative research with Jodhpur City Knowledge and Innovation Foundation (JCKIF), IIT Jodhpur.

Talking about the importance of both these research to reduce the occurrence of accidents on Indian Road, Dr. Debasis Das, Associate Professor, Department of Computer Science & Engineering, IIT Jodhpur, said, “We are excited to introduce the NoMAS (MAC-based authentication scheme) as a groundbreaking solution to enhance the security and efficiency of the Internet of Vehicles. With NoMAS, we have strived to address critical challenges in data protection, communication overhead, and security vulnerabilities. Also, Our Federated learning-based driver recommendation for the next-generation transportation system helps detect driver stress and do the behavioural analysis. These technologies will reduce pollution, traffic congestion and will also reduce road accidents.”