Integrated sensing, communication, and computation (ISCC) is emerging as a unified design paradigm for future vehicular networks that require joint environment perception, safety-critical information exchange, and latency-sensitive task processing. In New Radio Vehicle-to-Everything (NR-V2X) Mode 2, autonomous resource selection is performed through sensing-based semi-persistent scheduling (SB-SPS), which is effective for distributed communication resource reservation but does not explicitly consider sensing-resource demand, task-induced computation workload, and the additional latency introduced by mobile edge computing (MEC) offloading. This paper develops multi-agent proximal policy optimization-based SB-SPS (MAPPO-SPS), an ISCC-aware cross-layer scheduler that jointly adapts SB-SPS reservation, radio-resource partitioning, and overflow-driven computation-offloading decisions at control epochs. The scheduling problem is formulated as a cooperative partially observable Markov game and solved using MAPPO with centralized training and decentralized execution (CTDE). Simulation results show that MAPPO-SPS achieves a balanced tradeoff among CRLB-based sensing accuracy, packet reception ratio (PRR), effective throughput, energy consumption, and end-to-end delay.

翻译：通感算一体化(ISCC)正成为未来车载网络的一种统一设计范式，该类网络要求实现联合的环境感知、安全紧要信息交换和延迟敏感型任务处理。在新无线电车联网(NR-V2X)模式2中，通过基于感知的半持久调度(SB-SPS)执行自主资源选择，该方法能有效实现分布式通信资源预留，但未显式考虑感知资源需求、任务引发的计算工作负载以及移动边缘计算(MEC)卸载引入的额外延迟。本文开发了基于多智能体近端策略优化的SB-SPS(MAPPO-SPS)，这是一种感知通算感知型跨层调度器，可在控制周期内联合调整SB-SPS预留、无线资源划分和溢流驱动型计算卸载决策。调度问题被建模为协作式部分可观测马尔可夫博弈，并采用具有集中训练与分散执行(CTDE)机制的MAPPO求解。仿真结果表明，MAPPO-SPS能在基于CRLB的感知精度、分组接收率(PRR)、有效吞吐量、能耗和端到端延迟之间实现均衡折中。