With the development of the IoV(Internet of Vehicles), vehicles are embedded with various advanced wireless communications and sensor technologies, allowing vehicles to collect massive information on surroundings such as other vehicles, roads, traffic lights and pedestrians through V2X (Vehicle-to-Everything) communication. Therefore, V2X communication plays an important role in ITS(Intelligent Transportation Systems) and is enhancing road safety and traffic efficiency. Currently, the main wireless V2X communication technologies include 802.11p, LTE-V2X and NR-V2X. As the number of connected vehicles increases, the demand for reliable and efficient V2X communication grows. However, each V2X technology is not perfect and has inherent trade-offs in different aspects, such as latency, coverage, and reliability. Basically, IEEE 802.11p suffers from limited scalability and high congestion in dense traffic scenarios. LTE-V2X and NR-V2X offer improved network control and reliability but require cellular infrastructure and effective resource allocation strategies. It is essential to determine the optimal communication technology for different vehicular environments and propose improvements. 

We will first present the main technical aspects related to 802.11p, LTE-V2X, and NR-V2X technologies and perform a detailed theoretical comparison of them. Then we will evaluate the key performance metrics such as response time, reliability, scalability, and resource allocation. After that, we will Identify possible optimization and hybrid approaches to improve V2X communications. Finally, we will analyze how new technologies such as artificial intelligence-based resource allocation can improve performance and list what is the drawback of each technology.

This approach will provide comprehensive information about V2X technology, its limitations, and optimization opportunities.

Through comparative analysis, we aim to:

1. Identify the optimal V2X communication technology for different vehicular environments.

2. Analyze trade-offs between the three technologies, providing insights into their practical applications.

3. Explore potential optimization and hybrid approaches to improve V2X communication efficiency.

4. Investigate how AI-driven resource allocation can enhance V2X performance.