Bayesian Optimal UAV Trajectory Planning for Minimising the Uncertainty of Traffic Density Estimations

EasyChair Preprint 15011

Abstract

Traffic monitoring is one of the major tools used for transportation operations and planning. With the emergence of Unmanned Aerial Vehicles (UAVs), new capabilities for enhancing traffic management have emerged. Despite their potential, UAV applications in traffic management have primarily focused on sporadic surveillance of road networks and historical traffic data extraction. Path planning stands out as a critical challenge for UAVs, aiming to optimise routes from initial to target points for specific tasks. In this study, we concentrate on traffic monitoring, and more specifically on the efficient traffic density estimation. Towards this, we propose an online Bayesian optimal UAV trajectory construction methodology. The proposed method strategically selects the next sampling points to obtain traffic density measurements, while minimising the total uncertainty of the traffic density across all time-space points within the studied time-horizon. The proposed approach integrates the Gaussian Process (GP) model into a Bayesian framework to accurately estimate traffic density in multi-lane highways, considering both temporal and spatial correlations, even when data points are sparse. Employing a decision-theoretic approach, we develop a Bayesian optimal UAV trajectory construction scheme to mitigate traffic density uncertainty. Lastly, we conduct a simulation study to evaluate the proposed Bayesian optimal UAV trajectory construction methodology, showing a significant reduction of up to 70\% in the uncertainty of traffic density estimations, compared to a simplistic cyclical UAV trajectory.

Keyphrases: Gaussian process model, UAV trajectory planning, traffic state estimation, uncertainty propagation, uncertainty quantification

@booklet{EasyChair:15011,
  author    = {Yiolanda Englezou and Stelios Timotheou and Christos Panayiotou},
  title     = {Bayesian Optimal UAV Trajectory Planning for Minimising the Uncertainty of Traffic Density Estimations},
  howpublished = {EasyChair Preprint 15011},
  year      = {EasyChair, 2024}}