## 7 Limitations and Conclusions
### Conclusions  
This paper primarily investigates the offloading and allocation of image segmentation tasks for connected cars in the IoV supported by CPNs. The experiments take into account the task transmission delays under varying link bandwidths and transmission distances in 5G and optical networks, as well as the computational delays and queuing delays of servers. Additionally, the data size and model size of image segmentation tasks on smart vehicles, along with their computing power requirements, are considered. This paper designs a PER-iSAC algorithm to implement server selection, optimizing task completion time while maintaining a low decision-making error rate. The simulation experiments confirm that PER-iSAC is effective and more efficient than the PPO and Standard SAC algorithms. Concluding the analysis, this paper arrived at a fundamental insight: the true value of edge servers within the IoV context extends far beyond mere computational acceleration through task offloading. Their pivotal role lies in enabling the infrastructure for cooperative vehicle behavior, actualized via V2X (Vehicle-to-Everything) communications. These servers act as local coordination and computation hubs, providing the ultra-low latency essential for exchanging safety-critical information between vehicles (V2V), infrastructure (V2I), pedestrians (V2P), and the network (V2N). This facilitates a paradigm shift from a model where each vehicle relies solely on its onboard sensors and processing power to one of collective intelligence. Data aggregation at the edge servers fuses information (video streams, LiDAR/radar data, GPS coordinates, vehicle status) from numerous road users and infrastructure elements. Based on this enriched, comprehensive view of the traffic environment (akin to a \"digital twin\" of the local road segment), movement coordination becomes feasible: synchronizing speeds for platooning, optimizing intersection passage, warning about beyond-line-of-sight hazards, and enabling collaborative maneuver planning. Consequently, an integrated transport network (or grid) is formed, where decisions are made not just at the individual vehicle level, but also at a system level to optimize overall performance. This leads to significant enhancements in safety (reducing collision probability through extended awareness) and traffic efficiency (mitigating congestion, optimizing routes, reducing fuel/energy consumption). Within this complex ecosystem, video information processing, whether performed locally or at the edge, serves as a crucial but not solitary component--it is one data stream feeding the larger process of cooperative transport system management orchestrated by the edge infrastructure. Conceptualization, B.Y., W.Z. and H.Y. ; methodology, W.Z. ; software, W.Z. and A.R. ; validation, W.Z., B.Y. and W.L. ; formal analysis, W.Z. ; investigation, B.Y. and W.Z. ; resources, B.Y. ; data curation, W.Z., A.R. and W.L. ; writing--original draft preparation, W.Z. and B.Y. ; writing--review and editing, W.Z. and B.Y. ; funding acquisition, H.Y., B.Y. and W.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJ202301165), the Innovation and Development Joint Fund of Natural Science Foundation of Chongqing (CSTB2024 NSQCQ-LMX0010), the Scientific Research Foundation of Chongqing University of Technology (Grant No. 0121230236), and the Higher Education Research Project of Chongqing University of Technology (Grant No. 2024 YB09). The APC was funded by the Innovation and Development Joint Fund of Natural Science Foundation of Chongqing (CSTB2024 NSCQ-LMX0010). Data Availability Statement: Data are contained within the article; further inquiries can be directed to the corresponding author. Conflicts of Interest:Author [PERSON] was employed by the company China Satellite Network Exploration Co., Ltd.