Unmanned Aerial Vehicle Path Planning for Traffic Estimation and Detection of Non-Recurrent Congestion

TL;DR

This paper introduces a UAV-based active exploration framework that enhances traffic incident detection during non-recurrent congestion by adaptively navigating to minimize uncertainty using a dual ensemble Kalman filter.

Contribution

The paper presents a novel UAV navigation algorithm that integrates traffic sensor data and uses an ensemble Kalman filter to improve incident detection during traffic congestion.

Findings

UAVs improve detection of traffic incidents under congested conditions.

The proposed method reduces uncertainty in traffic state estimation.

Active UAV navigation outperforms non-targeted observation strategies.

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). 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.