# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 2 Background
### Vehicular Networks and ML  
The accelerated advancement of computational innovations in the past decade has enabled the extensive implementation of AI techniques in a variety of sectors, such as production, medical fields, technological development, and healthcare, among others. The primary goal of VANET protocols is to enhance the performance and safety of vehicular networks through the facilitation of sharing information among vehicles, pedestrians, and RSUs. To facilitate services including congestion control, roadway security, and entertainment, VANETs collect information from a variety of locations. VANETs routinely collect knowledge from a variety of sources, including V2V and V2I interaction (velocity, position, guidance, signage, signals, and toll booths), sensors (vehicle inside sensor systems, road sensors, and roadside structures (traffic conditions, mishaps, weather current information), all contributing to this pool of information [3,4]. Data are gathered in VANETs through various methodologies. An example of a networking protocol that is tailored for VANETs facilitates communication between V2V and V2I nodes. Additionally, data transmission among automobiles and infrastructure is accomplished via cellular networks, particularly for distant or non-real interaction. The storage and processing of VANET data on cloud-based systems facilitate central administration and analysis in real time. Nonetheless, there are obstacles to data capture in VANETs, including issues of dependability, confidentiality and safety, connections to networks, adaptability, integration, and so on. An amalgamation of innovative technology, reliable communication, trustworthy and effective systems for managing data, and group collaboration--including that of infrastructure service providers, government agencies, and transportation manufacturers--is required to tackle these issues. Notwithstanding these obstacles, the prospective advantages of VANETs in enhancing motorist accessibility, highway effectiveness, and crash prevention render it an auspicious domain for scientific investigation and advancement. It is necessary to equip vehicles with sensors and navigational equipment like GPS, media gadgets, and wireless components to participate in VANET. To prevent mishaps or abrupt stops, automobiles can utilize sensors and media gadgets to perceive their surroundings and identify items like other moving nodes, obstructions, and people. Alternatively, wireless transmission units offer several kinds [24] of network linkages which may be classified relative to the communicating units as shown in Figure 4.  
The data gathered in VANETs play a substantial role in enabling streamlined routing, improving driver consciousness, and predicting variations in movement [25]. In addition to safety-relevant data such as incident warnings and highway risks, immediate-time traffic situations, accident details, and highway congestion notifications facilitate dynamic route planning and optimization. Historical and real-time mobility data aid in the prediction of upcoming requirements and trends in transportation. To accomplish these objectives, particular methodologies and models are implemented. The dynamic routing methods choose routes in response to circumstances such as traffic jams and mishap warnings. Users are promptly notified through context-aware messaging systems, while appropriate data are superimposed over the vehicle's range of sight via heads-up displays (HUDs) and augmented reality. By analyzing previous information, time series evaluation and ML prediction algorithms discover movements and predict future patterns. V2V and V2I networks facilitate the sharing of information instantaneously, which is utilized for optimum traffic movement and navigation. In 1959, ML was initially proposed by [PERSON]. ML is a subfield of AI that enables computers to acquire new computational abilities and improve their performance over time without the need for human interaction [26].