Vehicle Road Cooperation Systems (VRCS) use next-generation Internet technologies, including 5G, edge computing, and artificial intelligence to improve mobility, comfort, and travel efficiency. Autonomous Vehicles (AV) ecosystem serves as the technological backbone for VRCS by enabling seamless communication and data exchange between vehicles, infrastructure, and traffic management centers. This enables real-time, high-speed communication, efficient data processing, and enhanced security, fostering the development of autonomous driving, smart traffic management, and seamless connectivity within the VRCS ecosystem. On the other hand, modeling TI is a challenging task due to the limited labels available for different cyber threat sources. Second, most of the available designs requires a large investment of resources and use hand-crafted features, making the entire process error prone and time-consuming. In order to address these issues, this project suggests TIMIF, a threat intelligence modeling and identification framework for Intelligent AV that is based on deep learning and consists of three main modules: first, the proposed TIMIF adopts an Automated Pattern Extractor (APE) module to extract hidden patterns from AV networks. Employing its output, design a TI-Based Detection (TIBD) module to detect abnormal behavior and TI-Attack Type Identification (TIATI) module to identify attack types. Extensive experiments are carried out on three different publicly intrusion data sources namely ToN-IoT to illustrate the utility of TIMIF framework over some commonly used baselines and state-of-the-art techniques.