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

Summary

Vehicle coordination at unsignalized intersections relies on accurate real-time vehicle state acquisition and reliable command-and-control (C&C) signal delivery.

Abstract Summary

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studies typically treat sensing, communication, and control separately, which may lead to redundant transmissions, outdated state information, and unreliable vehicle coordination.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.

Key Points

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studies typically treat sensing, communication, and control separately, which may lead to redundant transmissions, outdated state information, and unreliable vehicle coordination.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.

System Model and Problem Formulation

In this section, we first introduce the distributed ISAC-enabled vehicle coordination task at an unsignalized intersection. We then present the sensing and C&C signal transmission and reception models, followed by the kinematic vehicle model. Finally, we formulate an optimization problem that aims to improve intersection traffic throughput while minimizing the total number of transmitted sensing and C&C signals.

As illustrated in Fig. 1 , we consider a distributed ISAC-enabled vehicle coordination task at an unsignalized intersection.

System Model and Problem Formulation — Goal-Oriented Semantic Communication for Distributed ISAC-Enabled Vehicle Coordination — Fig. 1: Illustration for the distributed ISAC-enabled automated vehicle coordination at an unsignalized intersection.

Proposed Framework

In this section, we present our proposed unified GSC framework to tackle $\mathcal{P}_{2.1}$, $\mathcal{P}_{2.2}$, and $\mathcal{P}_{3}$. As shown in Fig.

2 , the framework consists of three tightly coupled components: extended Kalman filter (EKF)-based vehicle state prediction, masked hybrid proximal policy optimization (MHPPO)-based value of information (VoI)-driven decision-making, and uncertainty-aware transmission design (UTD).

Proposed Framework — Goal-Oriented Semantic Communication for Distributed ISAC-Enabled Vehicle Coordination — Fig. 2: The proposed GSC framework.

Simulation and Analysis

In this section, extensive simulations are conducted to evaluate the performance of our proposed GSC framework. The key simulation parameters are summarized as follows. The time slot interval is set to $\Delta t=5$ ms, and the total number of time slots is $\mathrm{I}=12000$.

We leverage Simulation of Urban MObility (SUMO) platform [ 33 ] to construct the intersection scenario, generate vehicle traffic, and control vehicle motion.

Sources and Demonstrations

Figures are reproduced from Liu et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.