Collaborative perception (CP) is a critical technology in applications like autonomous driving and smart cities. It involves the sharing and fusion of information among sensors to overcome the limitations of individual perception, such as blind spots and range limitations. However, CP faces two primary challenges. First, due to the dynamic nature of the environment, the timeliness of the transmitted information is critical to perception performance. Second, with limited computational power at the sensors and constrained wireless bandwidth, the communication volume must be carefully designed to ensure feature representations are both effective and sufficient. This work studies the dynamic scheduling problem in a multi-region CP scenario, and presents a Timeliness-Aware Multi-region Prioritized (TAMP) scheduling algorithm to trade-off perception accuracy and communication resource usage. Timeliness reflects the utility of information that decays as time elapses, which is manifested by the perception performance in CP tasks. We propose an empirical penalty function that maps the joint impact of Age of Information (AoI) and communication volume to perception performance. Aiming to minimize this timeliness-oriented penalty in the long-term, and recognizing that scheduling decisions have a cumulative effect on subsequent system states, we propose the TAMP scheduling algorithm. TAMP is a Lyapunov-based optimization policy that decomposes the long-term average objective into a per-slot prioritization problem, balancing the scheduling worth against resource cost. We validate our algorithm in both intersection and corridor scenarios with the real-world Roadside Cooperative perception (RCooper) dataset. Extensive simulations demonstrate that TAMP outperforms the best-performing baseline, achieving an Average Precision (AP) improvement of up to 27% across various configurations.

翻译：协同感知（CP）是自动驾驶和智慧城市等应用中的关键技术。它通过传感器间的信息共享与融合，克服单点感知的局限性（如盲区和范围限制）。然而，协同感知面临两大主要挑战。首先，由于环境的动态性，传输信息的时效性对感知性能至关重要。其次，在传感器计算能力有限且无线带宽受限的条件下，必须精心设计通信量，以确保特征表示既有效又充分。本文研究了多区域协同感知场景中的动态调度问题，并提出了一种面向时效性的多区域优先级（TAMP）调度算法，以权衡感知精度与通信资源使用。时效性反映了信息效用随时间推移而衰减的特性，在协同感知任务中体现为感知性能。我们提出了一个经验惩罚函数，将信息年龄（AoI）与通信量的联合影响映射到感知性能。为了最小化这种面向时效性的长期惩罚，并认识到调度决策对后续系统状态具有累积效应，我们提出了TAMP调度算法。TAMP是一种基于Lyapunov的优化策略，它将长期平均目标分解为每时隙的优先级排序问题，平衡调度价值与资源成本。我们在交叉路口和走廊场景中使用真实世界路侧协同感知（RCooper）数据集验证了算法。大量仿真实验表明，TAMP优于性能最佳的基线方法，在各种配置下平均精度（AP）提升最高可达27%。