Exploring human error in an RPA target detection task

Abstract

Intelligence, Surveillance, and Reconnaissance (ISR) sensing platforms are becoming increasingly complex. Consequently, the fidelity of collected data is continuing to increase, along with the number of deployable sensors that retrieve these data, such as those found on Remotely Piloted Aircraft (RPAs). There are numerous, critical challenges when designing ISR systems because the technology and the human are tightly integrated, resulting in interdependent performance and behaviors. Predicting operator error can inform more effective means of managing erroneous decisions, but current methods of doing so are impractical because of the effort required to construct operator models. We explored human performance in a target detection task by conducting a human-in-the-loop experiment that examined the performance of operators who simultaneously monitored four simulated RPA video feeds and determined the presence of targets at points of interest (POIs). The results of this experiment confirm that performance varies significantly across certain flight conditions (e.g., combinations of altitude, speed, aspect angle). A statistical model was constructed from the human data to predict operator error in new situations. In future work, the model predictions will be integrated with an automated flight planner that will adjust RPA air tasking orders in real time and intelligently revisit POIs when human error is likely.