## 2 Related Work  
In recent years, numerous studies on resource scheduling have emerged across various fields. These studies have provided some reference methods and inspiration for this research. In the field of vehicular edge computing (VEC), several recent works have applied DRL to enhance task offloading and resource allocation. [PERSON] et al. [13] proposed a task offloading decision-making algorithm using a deep deterministic policy gradient (DDPG)-based approach in a three-tier \"cloud-edge-vehicle\" VEC architecture. Their TODM_DDPG algorithm optimized task scheduling and offloading proportions, reducing total system cost--including delay and energy consumption--by 13% compared to DQN and AC. However, the work focused on general computational tasks, not those specifically addressing image segmentation, limiting its applicability in high-precision Internet of Vehicles (IoV) scenarios. [PERSON] et al. [14] introduced a DRL-based scheme for task offloading and load balancing using the twin delayed deep deterministic policy gradient (TD3) algorithm. By integrating the technique for order preference by similarity to the ideal solution (TOP-SIS), their method achieved better server selection and reduced average system cost by 7.2% compared to TD3-TR and 61.1% compared to ARO. Nonetheless, their model assumes tasks are only offloaded within the current RSU coverage, failing to address challenges arising from high-mobility vehicles transitioning across RSUs. To further address latency-sensitive offloading and heterogeneous resource demands, [PERSON] et al. [15] introduced a resource allocation mechanism with strict latency guarantees for computing power networks (CPNs). Their approach considered heterogeneity in computation and communication resources while ensuring bounded delay, offering insights into latency-aware scheduling strategies applicable to vehicular and edge computing scenarios. In the domain of image-related edge computing, [PERSON] et al. [16] proposed a DRL-assisted task offloading and resource allocation algorithm for object detection in autonomous vehicles. The DRPL algorithm combined DRL with piecewise linearization to maximize a time utility function, achieving 12.8% to 17.4% improvements. Although closely related, this work focused on object detection rather than image segmentation and did not explore multi-vehicle coordination or network congestion issues. [PERSON] et al. [17] proposed a hierarchical flow learning framework for low-light image enhancement. Their model improved visibility and structural detail preservation, which is critical for upstream visual tasks such as image segmentation in low-visibility vehicular environments. This technique enhances the quality of pre-processed input data, which can directly affect the accuracy of downstream segmentation tasks. Furthermore, [PERSON] et al. [18] conducted a study on medical image edge computing, in which a DRL-based strategy was developed to offload dependent tasks modeled as directed acyclic graphs (DAGs), balancing delay and energy consumption. While not in the IoV domain, this work offers valuable insights into DRL applications for image-related tasks with dependency structures, which is relevant for collaborative computing. Addressing integrated sensing and communications, [PERSON] et al. [19] introduced the Vehicle-Assisted Fusion Perception Offloading (VAFPO) scheme using a state-normalized DDPG algorithm (SNDAO). By leveraging ISAC's perception features, the system dynamically chose whether to offload data to auxiliary nodes to minimize delay and energy consumption. However, the work emphasized general sensing data and not high-precision visual tasks like image segmentation. Security concerns also play a crucial role in distributed edge environments. [PERSON] et al. [20] proposed a fine-grained intrusion protection system that enables inter-edge trust transfer using contextual behavior modeling and federated decision making.