Vehicle-Based Machine Vision Approaches in Intelligent Connected System

The application of machine vision techniques in Vehicle-to-Everything (V2X) scenarios within Intelligent Connected Systems (ICS) has gained increasing importance with advancements in 6G communication technology. However, the stringent latency and bandwidth requirements of most machine vision applications pose significant challenges to the existing infrastructure. Hence, there is a dearth of prior research examining whether the latency of real applications in ICS aligns with the needs of machine vision scenarios, let alone any performance evaluations conducted in this regard. In this paper, we conduct a comprehensive literature review and proposed a novel machine vision architecture that can analyze traffic data in real-time in the V2X scenario within ICS. Furthermore, based on the end-to-end latency assessment of the system, we outline a plan to optimize the latency as per the requirements of the machine vision application. Our findings show that with appropriate algorithms and architecture, the ICS system can meet the stringent needs of machine vision applications. Our research can provide valuable insights as a guideline on ICS with high latency requirements and therefore pave the way for future explorations in this field.