## 1 Introduction  
Under the synergistic evolution of 5G wireless communication and artificial intelligence (AI), transportation systems are undergoing a profound transformation toward distributed intelligence and cooperative autonomy. The integration of vehicle communication technologies, edge computing, and AI marks a paradigm shift in the architecture and capabilities of intelligent transportation systems (ITS) [1]. Unlike traditional ITS models, which are limited by siloed sensing and centralized cloud-based decision making, the new generation of connected vehicles operates within a dynamic, low-latency, and resource-optimized network environment enabled by edge computing [2, 3]. Edge computing decentralizes computational resources by moving core cloud functionalities closer to data sources, such as roadside units (RSUs) [4], which enables the rapid processing of data from local vehicles and infrastructure. The promise of edge computing lies not only in offloading computationally intensive tasks--such as image segmentation, sensor fusion, and path planning--from onboardunits to proximate edge nodes, but also in enabling real-time coordination across the vehicle-to-everything (V2X) spectrum. This coordination spans vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-network (V2N), and vehicle-to-pedestrian (V2P) communications, laying the foundation for collective behavior, system-level optimization, and safety-critical interactions [5; 6]. By aggregating high-frequency data streams from multiple sources, including cameras, lidars, GPS, and vehicle dynamics [7], edge servers construct a spatiotemporally rich digital twin of local traffic environments. This enables predictive analytics and collaborative decision making across agents, vastly improving situational awareness and operational efficiency [8]. However, this distributed intelligence paradigm introduces significant challenges. First, the surge in connected vehicles and their sensing capabilities results in explosive data generation--modern autonomous vehicles can produce terabytes of data every day [9]. Processing this volume in real time with stringent latency and energy constraints requires efficient task offloading, computing, and bandwidth allocation mechanisms [3]. Onboard computing power, though improving, cannot keep pace with the rising demand for high-precision applications such as semantic image segmentation, multi-object tracking, and real-time inference [10], leading to bottlenecks in performance and cost escalation. Edge computing, therefore, emerges as a critical architectural solution. By decentralizing computational resources and bringing them closer to data sources, it reduces transmission latency, alleviates cloud dependence, and supports the responsiveness required for safety and efficiency in vehicular environments. Yet the success of such a system hinges on intelligent resource scheduling and task management across heterogeneous edge nodes and dynamic network conditions. Through this design, we shift the edge server's role from a passive computational assistant to an active orchestrator of cooperative transport behavior. This holistic framework not only accelerates perception and decision-making tasks but also facilitates system-level coordination for platooning, intersection management, and hazard mitigation. The contributions of this research thus extend beyond algorithmic efficiency to propose a foundational infrastructure model for next-generation intelligent transportation systems. To address this issue, this paper analyzed mainstream task allocation and resource scheduling algorithms and constructed a more efficient edge computing resource allocation system based on the model-free deep reinforcement learning (DRL) algorithm with maximum entropy, i.e., soft actor-critic (SAC) [11]. The improved soft actor-critic (iSAC) algorithm proposed optimizes the allocation of computing resources based on the principles of time priority and resource priority within the CPN.