Human factors assessment for drone operations: towards a virtual drone co-pilot

Open Access
Conference Proceedings
Authors: Daniela DorofteiGeert De CubberHans De Smet

Abstract: As the number of drone operations increases, so does the risk of incidents with these novel, yet sometimes dangerous unmanned systems. Research has shown that over 70% of drone incidents are caused by human error, so in order to reduce the risk of incidents, the human factors related to the operation of the drone should be studied. However, this is not a trivial exercise, because on the one hand, a realistic operational environment is required (in order to study the human behaviour in realistic conditions), while on the other hand a standardised environment is required, such that repeatable experiments can be set up in order to ensure statistical relevance. In order to remedy this, within the scope of the ALPHONSE project, a realistic simulation environment was developed that is specifically geared towards the evaluation of human factors for military drone operations. Within the ALPHONSE simulator, military (and other) drone pilots can perform missions in realistic operational conditions. At the same time, they are subjected to a range of factors that can influence operator performance. These constitute both person-induced factors like pressure to achieve the set goals in time or people talking to the pilot and environment-induced stress factors like changing weather conditions. During the flight operation, the ALPHONSE simulator continuously monitors over 65 flight parameters. After the flight, an overall performance score is calculated, based upon the achievement of the mission objectives. Throughout the ALPHONSE trials, a wide range of pilots has flown in the simulator, ranging from beginner to expert pilots. Using all the data recorded during these flights, three actions are performed:-An Artificial Intelligence (AI) - based classifier was trained to automatically recognize in real time ‘good’ and ‘bad’ flight behaviour. This allows for the development of a virtual co-pilot that can warn the pilot at any given moment when the pilot is starting to exhibit behaviour that is recognized by the classifier to correspond mostly to the behaviour of inexperienced pilots and not to the behaviour of good pilots.-An identification and ranking of the human factors and their impact on the flight performance, by linking the induced stress factors to the performance scores-An update of the training procedures to take into consideration the human factors that impact flight performance, such that newly trained pilots are better aware of these influences.The objective of this paper is to present the complete ALPHONSE simulator system for the evaluation of human factors for drone operations and present the results of the experiments with real military flight operators. The focus of the paper will be on the elaboration of the design choices for the development of the AI - based classifier for real-time flight performance evaluation.The proposed development is highly significant, as it presents a concrete and cost-effective methodology for developing a virtual co-pilot for drone pilots that can render drone operations safer. Indeed, while the initial training of the AI model requires considerable computing resources, the implementation of the classifier can be readily integrated in commodity flight controllers to provide real-time alerts when pilots are manifesting undesired flight behaviours.The paper will present results of tests with drone pilots from Belgian Defence and civilian Belgian Defence researchers that have flown within the ALPHONSE simulator. These pilots have first acted as data subjects to provide flight data to train the model and have later been used to validate the model. The validation shows that the virtual co-pilot achieves a very high accuracy and can in over 80% of the cases correctly identify ‘bad’ flight profiles in real-time.We propose the submission of this paper to the track “Human Factors in Robots, Drones and Unmanned Systems”

Keywords: human factors, drones, simulation, training

DOI: 10.54941/ahfe1003747

Cite this paper: