发表机构
Lakeside Labs GmbH（湖畔实验室有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过多智能体仿真证明，在无人机交通拥堵控制中，基于预测调整信号比基于检测更有效，可使拥堵减少量翻倍，且预测准确性成为性能提升的关键约束。
AI 中文摘要
在部署移动机器人团队进行持续监测时，一个核心问题是任务性能如何随车队规模扩展，以及当感知驱动下游行动而非仅仅观察时，这种扩展是否仍然成立。我们针对一组在模拟道路网络中执行交通拥堵检测与预测的无人机团队研究了这一问题，其报告在闭环中驱动自适应交通信号控制器。我们构建了一个多智能体仿真，其中车辆遵循Nagel-Schreckenberg元胞自动机动力学，无人机通过轮询策略巡逻交叉口，并扫描车队规模、交通水平和网络规模以评估检测率、检测延迟和预测率。我们展示了当车队规模接近被监测交叉口数量时性能如何达到平台期，并为持续监测部署提供了一条通用的车队配置规则。更重要的是，基于预测的拥堵而非检测到的拥堵来调整信号，使拥堵持续时间减少量大约翻倍，表明在感知到行动管道中，机载预测的价值可能超过增加更多机器人的价值。预测准确性，而非感知覆盖，现在是进一步改进的约束条件，指向机载推理而非车队规模作为未来工作更有前景的方向。
英文摘要
A central question in deploying teams of mobile robots for persistent monitoring is how task performance scales with fleet size, and whether this scaling holds once sensing drives downstream action rather than mere observation. We study this question for a team of drones performing traffic-jam detection and prediction in a simulated road network, whose reports drive an adaptive traffic-signal controller in closed loop. We build a multi-agent simulation, with vehicles following Nagel-Schreckenberg cellular-automaton dynamics and drones patrolling junctions via a round-robin policy, and sweep fleet size, traffic level, and network size to evaluate detection rate, detection delay, and prediction rate. We show how performance plateaus for fleet size approximating the number of junctions being monitored, and offer a general fleet-provisioning rule for persistent-monitoring deployments. More significantly, adapting the signal on a predicted jam, rather than a detected one, roughly doubles the resulting reduction in jam duration, showing that the value of onboard prediction in a sensing-to-action pipeline can exceed the value of adding more robots. Prediction accuracy, not sensing coverage, is now the binding constraint on further improvement, pointing to onboard inference, not fleet size, as the more promising direction for future work.
Comments7 pages, 9 figures
