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Review

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

by
Navdeep Bohra
1,
Ashish Kumari
1,
Vikash Kumar Mishra
2,
Pramod Kumar Soni
3,* and
Vipin Balyan
4,*
1
Department of CSE/IT, Maharaja Surajmal Institute of Technology, New Delhi 110058, India
2
Department of Electrical Engineering, University of Cape Town, Rondebosch 7700, South Africa
3
Department of Computer Applications, Manipal University Jaipur, Jaipur 302007, India
4
Department of Electrical, Electronics, and Computer Engineering, Cape Peninsula University of Technology, Cape Town 8000, South Africa
*
Authors to whom correspondence should be addressed.
Future Internet 2025, 17(2), 79; https://doi.org/10.3390/fi17020079
Submission received: 18 December 2024 / Revised: 26 January 2025 / Accepted: 5 February 2025 / Published: 10 February 2025

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 intelligent vehicular networks; Artificial Intelligence; Machine Learning; vehicular ad hoc networks; Vehicle-to-Everything

Share and Cite

MDPI and ACS Style

Bohra, N.; Kumari, A.; Mishra, V.K.; Soni, P.K.; Balyan, V. Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review. Future Internet 2025, 17, 79. https://doi.org/10.3390/fi17020079

AMA Style

Bohra N, Kumari A, Mishra VK, Soni PK, Balyan V. Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review. Future Internet. 2025; 17(2):79. https://doi.org/10.3390/fi17020079

Chicago/Turabian Style

Bohra, Navdeep, Ashish Kumari, Vikash Kumar Mishra, Pramod Kumar Soni, and Vipin Balyan. 2025. "Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review" Future Internet 17, no. 2: 79. https://doi.org/10.3390/fi17020079

APA Style

Bohra, N., Kumari, A., Mishra, V. K., Soni, P. K., & Balyan, V. (2025). Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review. Future Internet, 17(2), 79. https://doi.org/10.3390/fi17020079

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