Englezou, Yiolanda (University of Cyprus, KIOS CoE), Timotheou, Stelios (University of Cyprus), Panayiotou, Christos (University of Cyprus)

A Probabilistic Optimal UAV Trajectory Planning Approach to Minimise the Uncertainty of Traffic Density Estimations

Scheduled for presentation during the Regular Session "Unmanned aerial vehicles" (FrBT11), Friday, September 27, 2024, 14:50−15:10, Salon 19

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 8, 2024

Abstract

Traffic monitoring has been one of the major tools used for transportation operations and planning. The emergence of Unmanned Aerial Vehicles (UAVs) provides new capabilities for traffic management purposes. One of the most challenging tasks of UAVs is path planning, that aims to determine the most efficient route of a UAV from an initial point to a target point, with respect to a specific task to be optimised. In this work we focus on the efficient traffic density estimation. Towards this, we propose an online probabilistic UAV trajectory construction methodology, that aims to select the next moves from which the UAV should obtain measurements of the traffic density, while aiming to minimise the total uncertainty of the traffic density across all time-space points in the time- horizon under study. We incorporate the Gaussian Process (GP) model to accurately estimate traffic density of specific road segments in a multi-lane highway even when data points are sparse within the specified time-space region under study. We employ a decision-theoretic methodology to develop Bayesian optimal UAV trajectories aimed at minimizing traffic density uncertainty. Finally, a simulation study is performed to evaluate the proposed methodology that shows that it reduces the uncertainty of the traffic density up to 70% compared to a simple cyclical UAV trajectory.