研究地下矿井中自动驾驶车队的协调策略，提升效率与安全。
Evaluation of Coordination Strategies for Underground Automated Vehicle Fleets in Mixed Traffic
- 用中心化协调器动态响应人类车辆行为，优化车队调度。
- 提出路径重叠密度（POD）指标，可预测车队效率与安全。
- 隧道网络特征影响效率，自适应策略保障安全，适合矿区应用。
本研究探讨了在由中心协调器管理的自动化车辆（AV）车队中，采用不同自适应协调模型对效率和安全性的影响。模拟场景复现了狭窄隧道、通信受限的地下矿井环境。为应对此类环境的独特挑战，本文提出一种新指标——路径重叠密度（Path Overlap Density, POD），用于预测车队的效率及潜在安全表现。研究还分析了地图特征对车队性能的影响。结果表明，车队协调策略与地下隧道网络特性均显著影响系统整体表现。尽管地图特征对优化效率至关重要，但自适应协调策略对于确保安全运行不可或缺。
原文摘要 · Abstract (English)
This study investigates the efficiency and safety outcomes of implementing different adaptive coordination models for automated vehicle (AV) fleets, managed by a centralized coordinator that dynamically responds to human-controlled vehicle behavior. The simulated scenarios replicate an underground mining environment characterized by narrow tunnels with limited connectivity. To address the unique challenges of such settings, we propose a novel metric - Path Overlap Density (POD) - to predict efficiency and potentially the safety performance of AV fleets. The study also explores the impact of map features on AV fleets performance. The results demonstrate that both AV fleet coordination strategies and underground tunnel network characteristics significantly influence overall system performance. While map features are critical for optimizing efficiency, adaptive coordination strategies are essential for ensuring safe operations.
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