ABSTRACT

Unmanned aerial vehicles (UAVs) provide a novel means of extracting road and traffic information from video data. In particular, by analyzing objects in a video frame, UAVs can detect traffic characteristics and road incidents. Leveraging the mobility and detection capabilities of UAVs, we investigate a navigation algorithm that seeks to maximize information on the road/traffic state under non-recurrent congestion. We propose an active exploration framework that (1) assimilates UAV observations with speed-density sensor data, (2) quantifies uncertainty on the road/traffic state, and (3) adaptively navigates the UAV to minimize this uncertainty. The navigation algorithm uses the A-optimal information measure (mean uncertainty), and it depends on covariance matrices generated by a dual state ensemble Kalman filter (EnKF). Our results indicate that targeted UAV observations aid in the detection of incidents under congested conditions where speed-density data are not informative.

摘要

无人机 (UAV) 提供了一种从视频数据中提取道路和交通信息的新方法。特别是，通过分析视频帧中的对象，无人机可以检测交通特征和道路事故。利用无人机的移动性和检测能力，我们研究了一种导航算法，该算法旨在最大化非经常性拥堵下的道路/交通状态信息。我们提出了一个主动探索框架，该框架 (1) 将无人机观测与速度密度传感器数据同化，(2) 量化道路/交通状态的不确定性，以及 (3) 自适应导航无人机以最小化这种不确定性。导航算法使用 A 最优信息度量（平均不确定性），它依赖于由双状态集成卡尔曼滤波器 (EnKF) 生成的协方差矩阵。