Unmanned aerial vehicle path planning for traffic estimation and detection of non-recurrent congestion
| Autor: | Stephen D. Boyles, Shannon E. Scott, Christian Claudel, Cesar N. Yahia |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Estimation Computer science Real-time computing Frame (networking) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ComputerApplications_COMPUTERSINOTHERSYSTEMS Transportation Systems and Control (eess.SY) Statistics - Applications Electrical Engineering and Systems Science - Systems and Control ComputingMethodologies_ARTIFICIALINTELLIGENCE Drone FOS: Electrical engineering electronic engineering information engineering Applications (stat.AP) ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS Ensemble Kalman filter Motion planning |
| Zdroj: | Transportation Letters. 14:849-862 |
| ISSN: | 1942-7875 1942-7867 |
| DOI: | 10.1080/19427867.2021.1951524 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|