The advances in highly automated and autonomous transportation systems over the last decade have generated great interest in topics in the safe navigation of land vehicles. With distributed control strategies employed in the majority of applications of autonomous vehicles, such as traffic and formation control, the much-required resilience takes the form of fault-tolerance with respect to information corruption, especially, when such information is utilized in closed-loop control. This study addresses the topic of detection of malicious attacks in a decentralized traffic control system for land vehicles. The proposed method employs trajectory prediction based on a hierarchical Model Predictive Control scheme, as well as, vehicle-to-vehicle communication in order to generate redundancy of collision risk information. The efficacy of the method is demonstrated in Eclipse Simulation of Urban Mobility (SUMO) considering the scenario of junction traffic management.

In the rapidly advancing domain of autonomous vehicle technology, the management of intersection traffic emerges as a demanding challenge. This paper describes the development and implementation of a novel testbed designed for the investigation of an autonomous intersection management system (AIMS). The research primarily focuses on the complex coordination required among autonomous vehicles for efficient operation at urban intersections. At the heart of this system lies the Intersection Coordination Unit (ICU), which centralizes traffic management. Robotic vehicles, equipped with On-Board Units (OBU) and advanced sensors, communicate wirelessly with the ICU. This communication facilitates real-time updates on vehicle positions and movements, enhancing environmental perception and aiding in collision prevention. The paper includes a validation section, demonstrating the testbed’s capabilities and the quality of data it generates.