## 1 Introduction  
The sixth-generation (6G) wireless system will evolve into an intelligent system that integrates multiple functions, including sensing, communication, and computing, dedicated to providing users with more efficient, low-latency, and highly reliable network services [1]. As one of the key technologies for 6G wireless systems, integrated sensing and communication (ISAC) enables the efficient integration of sensing and communication functions through the sharing of hardware platforms and spectrum resources [2, 3]. It not only saves network resources but also improves the performance of sensing and communication in complex environments, with widespread applications in Internet of Vehicles (IoV)scenarios [4; 5]. Autonomous vehicles utilize ISAC technology to sense environmental information, such as vehicles and traffic signs, and rapidly transmit these data to other vehicles, thereby reducing the risk of traffic accidents [6]. Similarly, roadside units (RSUs) can sense road conditions and share real-time data with nearby vehicles to support decision-making. Therefore, ISAC technology enhances sensing accuracy and optimizes wireless communication quality by leveraging integrated and coordinated gains, further strengthening the collaborative capabilities of the IoV [7]. Furthermore, as more sensors (e.g., cameras, LiDAR, and millimeter-wave radar) are integrated into vehicles for environmental perception, the volume of data generated per second is substantial and must be processed in real time to ensure safe driving [8]. However, the limited onboard computing resources are insufficient to meet the high computational demands. Recent research [9; 10] has combined Mobile Edge Computing (MEC) with ISAC, proposing Integrated Sensing, Communication, and Computation (ISCC) technology to address the processing requirements of vehicular tasks. Computation can provide intelligent and efficient processing capabilities for autonomous vehicles, enhancing sensing and communication performance. Specifically, MEC reduces task transmission delay and minimizes task computation delay by syncing computing capabilities to wireless infrastructure close to the vehicles [11]. Although the application of ISCC-enabled IoV holds great potential, processing the massive sensing data generated by onboard sensors presents significant challenges to the communication and computing capabilities of the network [12]. For example, in the three-dimensional reconstruction process for autonomous driving, tasks typically include image acquisition, camera calibration, pose estimation, depth estimation, depth map fusion, and rendering [13]. If the depth map fusion task needs to be processed by the MEC, the original high-resolution video frames should be uploaded to the RSU. Taking an 8K resolution video with 12 bits per pixel and a frame rate of 30 fps as an example, the data volume can reach up to 11.9 Gb, which imposes extremely high demands on communication bandwidth and delay [14]. In addition, the uneven geographical distribution of vehicles may cause load imbalance on MEC across different RSUs. Specifically, when a large number of tasks are offloaded to an RSU in high-density areas, the MEC will become overloaded and prevent the provision of high-quality services to vehicles within the coverage area. Collaborative sensing has emerged as a promising solution to address the limitations of communication resources in IoV. In fact, RSUs equipped with sensors can acquire redundant environmental data from vehicles within their coverage area [15]. When vehicles offload tasks to the RSU, only the non-redundant sensing data need to be offloaded to reduce the communication bandwidth overhead. In addition, due to the different perception perspectives of RSUs and vehicles, RSUs need to preprocess redundant data to eliminate perception discrepancies. However, performing preprocessing at the RSU further increases the computational burden on the MEC.