  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). In
the EnKF procedure, since observations are a nonlinear function of the incident
state variables, we use diagnostic variables that represent model predicted
measurements. We also present a state update procedure that maintains a
monotonic relationship between incident parameters and measurements. We compare
the traffic/incident state estimates resulting from the UAV
navigation-estimation procedure against corresponding estimates that do not use
targeted UAV observations. Our results indicate that UAVs aid in detection of
incidents under congested conditions where speed-density data are not
informative.
