Enhancing Road Safety through Dynamic Threat Detection in Vehicular Ad Hoc Networks

Abstract

This paper presents a comprehensive literature survey focused on the critical topic of enhancing road safety through dynamic threat detection in Vehicular Ad Hoc Networks (VANETs). As our world becomes increasingly interconnected, road users’ safety is paramount, and VANETs have emerged as a promising solution for improving road safety by enabling real-time communication among vehicles and infrastructure. Our survey delves into the existing body of knowledge, summarizing key findings, methodologies, and advancements in the field of dynamic threat detection within VANETs. We analyze various approaches, including sensor-based systems, machine learning algorithms, and communication protocols, that have been proposed and evaluated in the literature. Furthermore, this survey explores the challenges and open issues in VANET-based road safety enhancement, such as privacy concerns, scalability, and the need for standardized communication protocols. We highlight the significance of adapting dynamic threat detection techniques to the unique characteristics of VANETs, where network conditions and the threat landscape can change rapidly. By synthesizing insights from existing research, this survey provides a valuable resource for researchers, practitioners, and policymakers seeking to understand the state of the art in VANET-based road safety and identify promising directions for future investigations. It underscores the importance of continued research to make our roads safer for all.