Unmanned aerial vehicle path planning for traffic estimation and detection of non-recurrent congestion

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.

KEYWORDS:

• UAV navigation
• a-optimal control
• traffic state estimation
• non-recurrent congestion
• ensemble Kalman filter

Acknowledgments

The authors gratefully acknowledge the support of the Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE) and the National Science Foundation under Grant No. 1636154.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Science Foundation [1636154].