EMVP: An Edge-Assisted Multi-Task Visual Perception System for Multi-Vehicle Scenarios

Visual perception, as a core component of Intelligent Transportation Systems (ITS), plays a key role in enhancing safety and efficiency in urban mobility. While single-task visual perception methods have applications in areas like pedestrian detection and traffic sign recognition, the complexity of real-world scenarios necessitates a shift toward multi-task approaches. This paper introduces the Edge-assisted Multi-task Visual Perception (EMVP) system, which is specifically designed to address the computational intensity and dynamic concurrency challenges inherent to multi-task processing in edge environments. EMVP adopts a collaborative architecture that strategically partitions computational tasks between vehicles and Road-Side Units (RSUs). By integrating Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), the system achieves a lightweight yet efficient multi-task model for resource-constrained environments. To adapt to the dynamic and concurrent nature of multi-vehicle scenarios, EMVP incorporates a content-aware adaptive inference mechanism based on reinforcement learning, enabling dynamic task scheduling to improve Quality of Service (QoS). Experimental results demonstrate that, compared to the single-task baseline model, the multi-task model of EMVP reduces the computational cost by 86.59% on average while achieving a 4.32% improvement in accuracy. Additionally, in dynamic multi-access environments, EMVP’s adaptive scheduling mechanism, which leverages spatiotemporal content awareness, achieves an average QoS improvement of 7.36% over the sub-optimal method.