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

被引：0
作者：
Bohra, Navdeep [1 ]
Kumari, Ashish [1 ]
Mishra, Vikash Kumar [2 ]
Soni, Pramod Kumar [3 ]
Balyan, Vipin [4 ]
机构：
[1] Maharaja Surajmal Inst Technol, Dept CSE IT, New Delhi 110058, India
[2] Univ Cape Town, Dept Elect Engn, ZA-7700 Rondebosch, South Africa
[3] Manipal Univ Jaipur, Dept Comp Applicat, Jaipur 302007, India
[4] Cape Peninsula Univ Technol, Dept Elect Elect & Comp Engn, ZA-8000 Cape Town, South Africa
来源：
FUTURE INTERNET | 2025年 / 17卷 / 02期
关键词：
intelligent vehicular networks; Artificial Intelligence; Machine Learning; vehicular ad hoc networks; Vehicle-to-Everything; AD-HOC NETWORKS; VEHICULAR NETWORKS; ARTIFICIAL-INTELLIGENCE; PERFORMANCE EVALUATION; MOBILITY MANAGEMENT; ROUTING PROTOCOLS; NEURAL-NETWORKS; OPTIMIZATION; PREDICTION; 5G;
D O I：
10.3390/fi17020079
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
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页数：40
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