Beacon: 停電時の自然走行データセットによる交通再現と制御のベンチマーク
Beacon: A Naturalistic Driving Dataset During Blackouts for Benchmarking Traffic Reconstruction and Control
停電時の交差点における車両の動きを記録した初の公開データセットを構築し、ロボット車両導入による遅延削減効果と環境影響を分析した。
著者: Supriya Sarker, Iftekharul Islam, Bibek Poudel, Weizi Li
分類: cs.RO
原文アブストラクト
Extreme weather and infrastructure vulnerabilities pose significant challenges to urban mobility, particularly at intersections where signals become inoperative. To address this growing concern, we introduce Beacon, a naturalistic driving dataset capturing traffic dynamics during blackouts at two major intersections in Memphis, TN, USA. The dataset provides detailed traffic movements, including timesteps, origin, and destination lanes for each vehicle over four hours of peak periods. We analyze traffic demand, vehicle trajectories, and density across different scenarios, demonstrating high-fidelity reconstruction under unsignalized, signalized, and mixed traffic conditions. We find that integrating robot vehicles (RVs) into traffic flow can substantially reduce intersection delays, with wait time improvements of up to 82.6%. However, this enhanced traffic efficiency comes with varying environmental impacts, as decreased vehicle idling may lead to higher overall CO2 emissions. To the best of our knowledge, Beacon is the first publicly available traffic dataset for naturalistic driving behaviors during blackouts at intersections.
