# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 5 V2V Aids toward Sustainable Transportation Systems
### V2V AI-Driven Dynamic Network Contributions in ITSs  
V2V AI installations in cutting-edge, dynamic networks represent decisive progress in urban traffic and completely change the way vehicles relate to and interact in the ITSs. Through AI deployment, the network can self-learn and dynamically adjust transmission lines and protocols in real-time based on information such as traffic flow, weather, and network load [79]. With such dynamic automation, not only does the V2V communication system become more efficient and reliable, but it can also provide emergency response faster and quicker, as the V2V system can help coordinate collision avoidance and emergency response. Furthermore, AI-dependent networks are capable of intelligently routing bandwidth resources, which are then prioritized for important data transmissions by minimizing network congestion and latency [80]. Concurrently, the AI algorithms access the data from the vehicles and the sensors that are used for traffic management, for example, to provide actionable insights. These insights include urban planning, environmental monitoring, and environmental monitoring, all of which lead to efficient and sustainable urban transport systems. In addition, intelligent AI-based networks are inherently dynamic, which implies they can constantly learn and improve as they keep up with the shifting needs of smart cities. In a nutshell, AI-driven V2V dynamic networks are playing a key role in the safe, smart, and sustainable urban mobility of ITSs. As a result of the survey, the following features make different V2V communication architectures and their uses in ITSs different from one another: For example, it was determined that decentralized mesh networks proved optimal for disruption tolerance and adaptability in large, complex environments but struggled with issues of connectivity and interference in rural settings. On the other hand, V2V infrastructures developed under edge computing-based models offer low-latency communication that is essential to real-time business operations while also providing voluminous infrastructure support. Based on the system characterization results, we identified several valuable findings concerning the functionality and connectivity of V2V communications [81]. One such insight is the reliability that the implemented BlockChain-based V2V network can provide in terms of security and transparency of data exchange, which goes a long way in achieving the goals of safety and security in communication streams for the self-driving car fleet. Furthermore, the analysis of AI-enabled dynamic networks demonstrated their ability to address traffic congestion problems and improve the prospects for smart city development [82]. Thus, the applied significance of our results is significant for the further advancement and usage of V2V communication systems. In this way, by applying different patterns of architecture, it is possible to understand their strengths and weaknesses and choose which of the technologies would be suitable for implementation in conditions affected by specific tasks or characteristics of the environment. For example, it is possible for city planners and transportation authorities to implement dynamic networks through artificial intelligence with the goal of augmenting traffic control systems, thereby increasing mobility levels in urban areas and decreasing emission levels. Furthermore, the identification of security threats and suggested improvements to the protocols, such as IEEE 802.11p and DSRC, provide beneficial directions to developers and policymakers on how to create secure and reliable V2V communication structures [83]. The incorporation of blockchain presents a strategy for protected and confirmable exchanges of data that are beneficial for autonomous vehicle frameworks and their dependability.