V2X Network-Based Enhanced Cooperative Autonomous Driving for Urban Clusters in Real Time: A Model for Control, Optimization and Security

For the commercialization of connected vehicles and smart cities, extensive research is carried out on autonomous driving, Vehicle-to-Everything (V2X) communication, and platooning. However, limitations remain, such as restrictions to highway environments, and studies are conducted separately due to challenges in ensuring reliability and real-time performance under external influences. This paper proposes a cooperative autonomous driving system based on V2X network implemented in the CARLA simulator, which simulates an urban environment to optimize vehicle-embedded systems and ensure safety and real-time performance. First, the proposed Throttle–Steer–Brake (TSB) driving technique reduces the computational overhead for following vehicles by utilizing the control commands of a leading vehicle. Second, a V2X network is designed to support object perception, cluster escape, and joining. Third, an urban perception system is developed and validated for safety. Finally, pseudonymized vehicle identifiers, Advanced Encryption Standard (AES), and the Edwards-curve Digital Signature Algorithm (EdDSA) are employed for data reliability and security. The system is validated in processing time and accuracy, confirming feasibility for real-world application. TSB driving demonstrates a computation speed approximately 466 times faster than conventional waypoints-based driving. Accurate urban perception and V2X communication enable safe cluster escape and joining, establishing a foundation for cooperative autonomous driving with improved safety and real-time capabilities.