Communicating Qualitative Uncertainty in Multi-Sensor Data Fusion

This project will develop algorithms, knowledge representation and human-computer interface design to better communicate qualitative uncertainty to the end users, and better support decision making based on existing observations. 

The Problem

Predictive models used in conjunction with multi-sensor systems have associated uncertainties in prediction.

Our Solution

The project will investigate qualitative aspects of uncertainty in multi-sensor data fusion and how to maximise utility to human operators, and higher-level decision-making algorithms

Collaborators

This project is lead by Neil Francis and is supported by Postdoctoral Fellow Tian Cong.