Research Area:  Vehicular Ad Hoc Networks

Abstract:

Low latency in communication among the vehicles and RSUs, smooth traffic flow, and road safety are the major concerns of the Intelligent Transportation Systems. Vehicular Ad hoc Network (VANET) has gained attention from various research communities for such a matters. These systems need constant monitoring for proper functioning, opening the doors to apply Machine Learning algorithms on enormous data generated from different applications in VANET (for example, crowdsourcing, pollution control, environment monitoring, etc.). Machine Learning is an approach where the system automatically learns and improves itself based on previously processed data. These algorithms provide efficient supervised and unsupervised learning of these collected data, which effectively implements VANET’s objective. We highlighted the safety, communication, and traffic-related issues in VANET systems and their implementation in-feasibility and explored how machine learning algorithms can overcome these issues. Finally, we discussed future direction and challenges, along with a case study depicting a VANET based scenario.

Keywords:  

Author(s) Name:  Sahil Khatri, Hrishikesh Vachhani, Shalin Shah, Jitendra Bhatia, Manish Chaturvedi, Sudeep Tanwar & Neeraj Kumar

Publisher name:  SPRINGER

DOI:  10.1007/s12083-020-00993-4

Volume Information:  volume 14, pages 1778–1805 (2021

Paper Link:   https://link.springer.com/article/10.1007/s12083-020-00993-4