Performance improvement in cellular (C-V2X) by using edge computing

Abstract

In the domain of vehicular communication, ensuring rapid and reliable exchange of information among vehicles, infrastructure, and pedestrians is paramount for enhancing safety and situational awareness. The ability to accurately assess the surrounding environment and predict potential adverse situations is vital for taking timely and appropriate actions. In this context, factors such as bit error rate, latency and throughput play a crucial role in establishing a robust communication framework. Vehicular communication relies on two distinct communication frameworks: Cellular V2X (C-V2X) and wireless access in vehicular environments (WAVE). It is essential to clarify that C-V2X and WAVE are primarily designed for direct communication between vehicles (V2V) and road-side infrastructure (V2I), representing a 'horizontal' communication paradigm. In contrast, the utilization of edge or cloud computing is strictly confined to accessing network infrastructure (V2N) exclusively over the Uu interface. It is crucial to underscore that the application of edge/cloud computing is not extended to V2V or V2I scenarios, ensuring clarity and preventing any potential confusion among readers. Addressing the challenge of low latency in C-V2X applications is of utmost importance, given that even a minor delay in communication can result in severe consequences. In response to this challenge, our research introduces an innovative approach designed to substantially mitigate communication latency in the context of autonomous vehicles. The core of our work revolves around the integration of cutting-edge edge computing techniques, which play a pivotal role in reducing latency. Edge computing involves relocating computational processes closer to the data source, thereby diminishing reliance on remote cloud servers. This integration of edge computing offers a multitude of advantages for C-V2X applications. We rigorously evaluate the latency reduction achieved through edge computing for autonomous vehicles using the OMNeT++ simulator. Our results demonstrate a significant advancement in the field of vehicular communication, presenting an improved C-V2X algorithm that holds promise for enhancing the safety and performance of autonomous vehicles. We are pleased to introduce this refined C-V2X algorithm for autonomous vehicles, representing a noteworthy advancement in the field of vehicular communication. While acknowledging the foundation laid by previous work, our novel contributions in leveraging edge computing techniques uniquely position our algorithm as a significant stride forward in addressing communication latency challenges for autonomous vehicles.