Enhancing Road Safety: Machine Learning-Driven Vehicle Speed Monitoring and Alerting in VANET Environments-A Review

Abstract

Inexperienced and fast driving poses a significant threat to the safety of innocent people, resulting in severe automobile accidents. Presently, most efforts have been made in detecting the driver behavior, as traditional methods show limited success the researchers have delved into the machine learning and the deep learning methods for predicting the vehicle speed and as well as altering. This review explores at the manner in which machine learning and the deep learning can be used to improve road safety using Vehicle Ad-hoc Networks. The primary objective revolves around a Machine Learning-Driven Vehicle Speed Monitoring and Alerting System, which is intended to reduce the dangers associated with variable speeds in VANET environments. The paper reviews the existing research, approaches, and breakthroughs in the use of machine learning algorithms for real-time vehicle speed monitoring. This analysis intends to provide insights into the emerging environment of intelligent transportation systems, with a focus on the role of artificial intelligence in identifying and responding to potential risks. It presents an in-depth review of the challenges, opportunities, and future prospects for using machine learning to improve road safety within the VANETs.