Goal-Oriented Semantic Communication for Distributed ISAC-Enabled Vehicle Coordination

2026-07-16

AI Summary

This work addresses the limitations of conventional vehicle coordination at unsignalized intersections, where decoupled perception, communication, and control lead to redundant information exchange, state latency, and unreliable coordination. To overcome these issues, the paper introduces the first goal-oriented semantic communication framework that tightly integrates distributed sensing with command-and-control, triggering transmissions only at semantically critical moments. The approach leverages an extended Kalman filter for state prediction and fusion, combines an information-value-driven decision mechanism with uncertainty-aware transmission design, and employs masked mixture proximal policy optimization to jointly determine transmission timing and content while optimizing robust beamforming and power allocation. Experimental results demonstrate that the proposed method achieves 100% collision-free coordination, significantly reduces signaling overhead, and outperforms existing predictive ISAC schemes and multiple ablation baselines.

Abstract

Vehicle coordination at unsignalized intersections relies on accurate real-time vehicle state acquisition and reliable command-and-control (C&C) signal delivery. However, existing studies typically treat sensing, communication, and control separately, which may lead to redundant transmissions, outdated state information, and unreliable vehicle coordination. In this paper, we investigate a new scenario of distributed integrated sensing and communication (ISAC)-enabled vehicle coordination at intersections, where multiple roadside units (RSUs) collaboratively transmit sensing signals for vehicle state acquisition and C&C signals for vehicle movement control under the management of a central base station (BS). To improve signaling efficiency, we propose a unified goal-oriented semantic communication (GSC) framework, which transmits sensing and C&C signals only when they are semantically important for improving intersection traffic throughput. Specifically, an extended Kalman filter (EKF) is adopted to predict vehicle states and fuse distributed sensing measurements. A masked hybrid proximal policy optimization (MHPPO) framework is then developed to jointly determine sensing transmission decisions, C&C transmission decisions, and C&C signal contents based on a value-of-information (VoI) reward. Furthermore, we propose an uncertainty-aware transmission design (UTD), including robust beamforming and VoI-based time-division power allocation, to improve sensing and communication reliability under vehicle state uncertainty and inter-RSU interference. Simulation results show that our proposed framework achieves 100% collision-free vehicle coordination with significantly reduced signaling overhead compared with predictive ISAC baselines adapted from state-of-the-art related studies and several ablation baselines.

Problem

• vehicle coordination
• integrated sensing and communication
• semantic communication
• unsignalized intersections
• signaling efficiency

Innovation

• Goal-Oriented Semantic Communication
• Integrated Sensing and Communication (ISAC)
• Value of Information (VoI)
• Uncertainty-Aware Transmission
• Masked Hybrid PPO