# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 2 Background
### Vehicular Networks and ML
Although the literature contains numerous definitions of ML, two leading experts in the field, \"[PERSON] and [PERSON]\", offered succinct explanations of the terminology. [PERSON]  
Figure 4: Wireless communication links. fines ML as the process by which machines acquire the ability to execute predetermined operations despite the need for direct programming. ML, according to [PERSON], is the development of computer applications that enhance independently with practice. The notion that \"a computer program learns from experience about certain types of tasks and performance measurement\" was utilized to substantiate his definition. \"The performance measured concerning the task is enhanced with increasing experience [27]\". In doing so, ML helps systems make accurate data-based predictions. ML models are capable of pattern recognition and decision-making. ML models acquire knowledge from their past analysis states and independently collect new information, make decisions, and provide outputs. Pattern recognition, text recognition, speech recognition, vision, and robotics [28] are just a few of the numerous areas it helps tackle. ML is a huge area with many different classification schemes. A broad categorization classifies ML methods into four distinct groups based on the type of learning executed: Supervised, Unsupervised, Semi-supervised, and Reinforcement Learning (with an emphasis on deep Reinforcement Learning). Machine-based algorithms consist of entities that interpret their surroundings and modify their behavior via learning to accomplish a particular goal. ML is a fundamental subsection of AI concerned with the investigation of how entities' perceptions can be enhanced through knowledge or learning. To identify latent trends within datasets, ML algorithms approximate an intended expression that generates predictions on information that has not been observed before. The capacity of ML algorithms to extrapolate their insights from observed test data to unobserved instances is a critical component [29]. DL has emerged as a result of developments in Graphics Processing Units (GPUs) and the enhanced accessibility of information in recent decades. DL is a critical subfield of ML that exhibits superior accuracy compared to traditional ML methods when confronted with substantial data volumes. DL is built upon Artificial Neural Networks (ANNs), a well-known ML technique that draws inspiration from the learning process occurring in our bodies. In light of the above, machine-based systems may facilitate the implementation of sophisticated vehicular network cases, especially emergency braking, collaboration, and crossroad situations. These types of cases necessitate the training of a learning agent with vast quantities of knowledge by conditions obtained by the fundamental use case. Subsequently, the agents will execute actions while adjusting themselves to accommodate dynamic highway and communication situations. It is critical to maintain constant interaction among automobiles, networks, and pedestrians, irrespective of the route and network congestion, to facilitate these transport use cases. For V2X communications to continue without interruption, the underlying control and management processes have to be operational and enable the transmission and reception of consciousness alerts. This article showcases the various AI and ML applications made possible by emerging communication networks in several fields, as seen in Table 1.  
Peer-reviewed articles have led to significant advancements in various fields and have made valuable contributions to the existing body of literature. Reference [30] provides a focused analysis of ML applications in the context of IoT, specifically highlighting their utilization in a particular area. The major focus of reference [31] is to examine the uses of Convolutional Neural Networks (CNNs) in a particular area, along with its associated smaller issues. The researchers of the investigation offer a comprehensive analysis of CNNs' computational views in the given field.