Unmanned aerial vehicle path planning for traffic estimation and detection of non-recurrent congestion
Cesar N. Yahia, Shannon E. Scott, Stephen D. Boyles, Christian Claudel
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.