A Real-World Testbed for V2X in Autonomous Vehicles: From Simulation to Actual Road Testing

This paper proposes an edge computing-enhanced testbed for Vehicle-to-Everything (V2X) communication, using a scaled autonomous vehicle model. Addressing the limitations of traditional vehicle testing and simulations, our approach utilizes an F1Tenth scale vehicle to replicate real-world traffic scenarios, with a focus on non-line-of-sight and pedestrian-occluded challenges. The testbed incorporates a Road Side Unit (RSU) for edge-based data processing, allowing rapid data acquisition and transmission. Our controlled experiments assess the vehicle's response to V2X communication in complex environments specifically in NLOS scenarios such as occluded pedestrians, highlighting the potential of edge computing to augment autonomous vehicle perception and decision-making. Performance evaluation shows that the vehicle can effectively handle emergencies, achieving an end-to-end response time of less than 146 ms on an edge device. Compared to other popular autonomy frameworks, which rely on higher computational units unsuitable for deployment on edge devices. This study advances V2X technology in edge computing contexts for autonomous vehicles and paves the way for future research in robust, safe, and reliable autonomous systems.