RoboSense：利用机器人出租车车队作为城市交通监测的行驶传感器

中文摘要 AI 辅助

城市交通监测在安全分析、拥堵管理和事件响应中发挥着关键作用。机器人出租车的日益部署为网络级交通监测创造了新的机遇。尽管机器人出租车主要设计用于服务乘客，但它们也可以被用作行驶传感器来收集交通数据。与传统的路侧基础设施传感器或探测车辆相比，机器人出租车车队形成了一个协作感知环境，能够共同收集空间和时间上连续的交通信息。本文提出了一种新颖的动态机器人出租车路径规划框架，明确将交通监测任务作为目标纳入其中。该框架引入了：（1）一种与机器人出租车感知能力对齐的基于单元格的网络表示；（2）一种单元格级监测指标，用于量化机器人出租车的时空覆盖；（3）一种混合整数线性规划（MILP）公式，该公式联合最小化时变出行时间并最大化交通监测性能。在SUMO中构建了一个5乘5的城市网格网络，以在三种机器人出租车市场渗透率（2%、5%和10%）以及一系列目标权重组合下评估该框架。结果表明，在目标函数中纳入时空网络覆盖可以有效提高交通监测性能。有趣的是，在两个目标之间采用适当的权重时，监测性能和机器人出租车平均速度可以同时得到改善。这表明更好的网络监测能够带来更准确的交通状态预测和改善的出行流动性。这种双赢局面可以激励机器人出租车运营商将其车辆贡献为用于交通监测的行驶传感器。

英文摘要

Urban traffic monitoring plays a critical role in safety analysis, congestion management, and incident response. The growing deployment of robotaxis creates a new opportunity for network-level traffic monitoring. Although robotaxis are primarily designed to serve passengers, they can also be leveraged as drive-by sensors to collect traffic data. Compared to conventional infrastructure sensors or probe vehicles, a fleet of robotaxis forms a cooperative perception environment, which can collectively gather spatially and temporally continuous traffic information. This paper proposes a novel dynamic robotaxi routing framework that explicitly incorporates traffic monitoring tasks as an objective. The framework introduces: (1) a cell-based network representation that aligns with sensing capabilities of robotaxis; (2) a cell-level monitoring metric to quantify spatiotemporal robotaxi coverage; and (3) a mixed-integer linear programming (MILP) formulation that jointly minimizes time-dependent travel time and maximizes traffic monitoring performance. A 5 by 5 urban grid network is built in SUMO to evaluate the framework under three robotaxi market penetration rates (2%, 5%, and 10%) with a range of objective weight combinations. Results show that incorporating spatiotemporal network coverage in the objective function can effectively improve the traffic monitoring performance. Interestingly, with appropriate weights between the two objectives, monitoring performance and robotaxi average speed can be improved simultaneously. This suggests better network monitoring leads to more accurate traffic state prediction and improved mobility. This win-win situation could incentivize robotaxi operators to contribute their vehicles as drive-by sensors for traffic monitoring.

发表机构

• Lyles School of Civil and Construction Engineering, Purdue University（普渡大学莱尔土木与建筑工程学院）

机构由 AI 辅助整理，请以论文原文为准。