検出より予測が有効：ドローンによる交通渋滞制御
Prediction is Better than Detection: Traffic Congestion Control using Drones
ドローン群が交通渋滞を検出・予測し、その情報で信号制御を適応化する閉ループ系をシミュレーションし、予測に基づく制御が検出に基づく制御より渋滞時間を約2倍削減できることを示した。
著者: Samira Hayat, Christian Raffelsberger
分類: cs.RO, cs.MA
原文アブストラクト
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.