In the rapidly evolving field of Intelligent Transport Systems (ITS) in the Autonomous driving context, introducing Vehicle-to-everything (V2X) communication is an enormous change towards enhanced road safety and traffic efficiency. This thesis comprehensively analyzes the V2X ecosystem, describing its evolution from simple Day 1 applications to complex Day 3 use cases. The research explores various state-of-the-art and currently being researched trust algorithms with the complexities of networked mobility, intelligent transportation networks, and autonomous driving functionalities. After rigorous research, a trust algorithm was created from various algorithms that are currently being researched. Using a modular approach, we developed a hybrid trust algorithm using Weighted sum and the recommendation-based method and evaluated it using various close-to-real-life scenarios for an accident event. By using Veins and INET frameworks within the OMNet++ development environment, V2X communication was established. Using SUMO and Open street maps, an Local Dynamic Map (LDM) is created for better visualization demonstrating a real-life traffic flow for our scenarios. The study is mainly about exchanging external data, which is important in creating a secure and productive environment within the autonomous driving domain. It clarifies how integrating a trust algorithm using various external data sources can improve decision-making by promoting adaptability, and risk reduction in a fully autonomous scenario. As V2X communication develops, the research aims to provide new approaches using multiple trust algorithm techniques to raise vehicle safety standards, and interoperability mainly to resolve various road safety concerns. The result of this research emphasizes, how important it is to use external data with a secured trust algorithm that can handle the complexities and unknowns of the mobility ecosystem with various automated driving services to ensure road safety and better traffic efficiency.