This paper addresses the challenge of offloading resource-intensive image segmentation tasks and allocating computing resources within the Internet of Vehicles (IoV) using edge-based AI. To overcome the limitations of onboard computing in smart vehicles, this study develops an efficient edge computing resource allocation system. The core of this system is an improved model-free soft actor–critic (iSAC) algorithm, which is enhanced by incorporating prioritized experience replay (PER). This PER-iSAC algorithm is designed to accelerate the learning process, maintain stability, and improve the efficiency and accuracy of computation offloading. Furthermore, an integrated computing and networking scheduling framework is employed to minimize overall task completion time. Simulation experiments were conducted to compare the PER-iSAC algorithm against baseline algorithms (Standard SAC and PPO). The results demonstrate that the proposed PER-iSAC significantly reduces task allocation error rates and optimizes task completion times. This research offers a practical engineering solution for enhancing the computational capabilities of IoV systems, thereby contributing to the development of more responsive and reliable autonomous driving applications.

This paper investigates the offloading of image segmentation tasks and the allocation of corresponding computing resources for the Internet of Vehicles (IoV) supported by edge intelligence. With the convergence of 5G technology and artificial intelligence, the demand for high-precision sensors and navigation in smart connected vehicles is growing. Image segmentation technology, a crucial component of autonomous driving systems, requires substantial computing power and communication bandwidth. Faced with the shortage of onboard computing power and rising costs, edge computing offers a solution by offloading computing tasks to roadside servers close to the data source, i.e., connected cars, thereby alleviating network bandwidth and power consumption pressures and reducing system latency. This paper constructs an efficient edge computing resource allocation system based on an improved model-free Soft Actor-Critic (iSAC) algorithm with maximum entropy, and enhances the offloading efficiency by employing an integrated computing and networking scheduling framework to minimize task completion time. By incorporating Prioritized Experience Replay (PER), the iSAC algorithm accelerates the learning process while maintaining stability and improving the efficiency and accuracy of computation offloading. Simulation experiments compare the performance of iSAC with baseline algorithms, demonstrating its advantages in reducing error rates and optimizing task completion time. Future research will investigate task diversity and priority requirements in IoV.