# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
###### 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. intelligent vehicular networks; Artificial Intelligence; machine learning; vehicular ad hoc networks; Vehicle-to-Everything +
Footnote †: journal: Future Internet  
0000-0001-6000-0001-6000][PERSON]  
0000-0001-6000-0001-6000][PERSON]  
0000-0001-6000-0000][PERSON]  
0000-0001-6000-0000][PERSON]  
0000-0001-6000-0000][PERSON]  
0000-0001-of Vehicles, are now growing, beginning with active and passive highway security, forecasting congestion [6], and road safety and extending to driver assistance [7] and traffic optimization [8]. As shown in Figure 1, the fundamental basis of the vehicular network involves carrying data through the utilization of the following types: Vehicle-to-Everything (V2X), vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), Vehicle-to-Network (V2N), Vehicle-to-Pedestrian (V2P), and vehicle-to-roadside units (V2R). Within VANETs, the relay of signals across vehicular networks can be facilitated using V2V communications. This network mode enables vehicles to transmit data on highway situations, including mishaps and congestion. An RSU, made up of a stationary infrastructure device linked to the web and capable of facilitating communication with moving vehicles and other RSUs, receives data transmissions from nearby RSUs. RSUs guarantee data transmission's dependability and are beneficial in numerous critical incidents [9; 10]. In VANETs [11], a V2V connection is deemed obligatory while RSUs are not near vehicles. The V2N communication model relies on 3G/4G/5G cellular networks utilized for vehicular surveillance because they offer extensive reachability and facilitate fast data sharing [12]. The V2P connection model involves sharing information among vehicles and pedestrians. An invable communications model involves interaction within vehicles to identify and evaluate automobiles to reduce mishaps caused by driver sleepiness or vehicle problems [13]. The dynamicity of traffic flows, physical structures, and transmission within vehicular communications (V2X) is quite intense. Furthermore, the increasing prevalence of mobile  
Figure 1: Types of VANET communications. applications, including inside vehicle information technology systems, is putting wireless connectivity under unprecedented load. Consequently, Mobile Network Operators (MNOs) will be obligated to amass a tremendous volume of diverse information to oversee network efficiency and deliver improved amenities. Considering the present network methodologies, ensuring the functionality while adhering to various Service Level Agreements (SLAs) will progressively escalate in complexity. This issue can be resolved by implementing additional intelligence in the network [7].