Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review

Navdeep Bohra, Ashish Kumari, Vikash Kumar Mishra, Pramod Kumar Soni () and Vipin Balyan ()
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Navdeep Bohra: Department of CSE/IT, Maharaja Surajmal Institute of Technology, New Delhi 110058, India
Ashish Kumari: Department of CSE/IT, Maharaja Surajmal Institute of Technology, New Delhi 110058, India
Vikash Kumar Mishra: Department of Electrical Engineering, University of Cape Town, Rondebosch 7700, South Africa
Pramod Kumar Soni: Department of Computer Applications, Manipal University Jaipur, Jaipur 302007, India
Vipin Balyan: Department of Electrical, Electronics, and Computer Engineering, Cape Peninsula University of Technology, Cape Town 8000, South Africa

Future Internet, 2025, vol. 17, issue 2, 1-40

Abstract: Advancements in intelligent vehicular networks and computing systems have created new possibilities for innovative approaches that enhance traffic safety, comfort, and transportation performance. Machine Learning (ML) has become widely employed for boosting conventional data-driven methodologies in various scientific study domains. The integration of a Vehicle-to-Everything (V2X) system with ML enables the acquisition of knowledge from multiple places, enhances the operator’s awareness, and predicts future crashes to prevent them. The information serves multiple functions, such as determining the most efficient route, increasing the driver’s knowledge, forecasting movement strategy to avoid risky circumstances, and eventually improving user convenience, security, and overall highway experiences. This article thoroughly examines Artificial Intelligence (AI) and ML methods that are now investigated through different study endeavors in vehicular ad hoc networks (VANETs). Furthermore, it examines the benefits and drawbacks accompanying such intelligent methods in the context of the VANETs system and simulation tools. Ultimately, this study pinpoints prospective domains for vehicular network development that can utilize the capabilities of AI and ML.

Keywords: intelligent vehicular networks; Artificial Intelligence; Machine Learning; vehicular ad hoc networks; Vehicle-to-Everything (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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