Abstract
This paper proposes a systematic framework for operability evaluation of remotely controlled ground combat systems (RGCS) in a simulated environment. The popular human-robot interaction metric used in unmanned vehicle systems is called fan-out (FO) and represents the maximum number of robots/vehicles that could be controlled by a single human operator. However, FO is inappropriate for systems with a lower level of automation where vehicles are remotely controlled by a human, such as RGCS. The theoretical background of the suggested framework is based on McRuer’s crossover model that was initially developed in the aviation domain for explaining pilot handling issues. In this study, an evaluation/analysis software prototype was developed, known as the RGCS operability evaluation tool in a simulated environment (ROPES). The ROPES was designed to be a simple tool for use by officers or researchers who only have intuitive understanding on the human adaptability. The ROPES includes two sub-modules; 1) an interactive interface for the configuration of the RGCS dynamic parameters and user interfaces and 2) a time-varying graphical display of system and human performance. Examples case studies demonstrate the advantage of the ROPES, and improvement points were identified for future development.
Similar content being viewed by others
Abbreviations
- FO:
-
fan-out
- IT:
-
interaction time
- LAM:
-
lethal aerial matrix
- NHTSA:
-
national highway traffic safety administration
- NT:
-
neglect time
- RGCS:
-
remotely controlled ground combat system
- ROPES:
-
RGCS operability evaluation tool in a simulated environment
- UV:
-
unmanned vehicle
- WT:
-
wait time
References
Crandall, J. W. and Cummings, M. L. (2007a). Developing performance metrics for the supervisory control of multiple robot. Proc. ACM/IEEE Int. Conf. Human-Robot Interaction, Virginia, USA, 33–40.
Crandall, J. W. and Cummings, M. L. (2007b). Identifying predictive metrics for supervisory control of multiple robots. IEEE Trans. Robotics 23, 5, 942–951.
Cummings, M. L. and Mitchell, P. J. (2008). Predicting controller capacity in supervisory control of multiple UAVs. IEEE Trans. Systems, Man, and Cybernetics - Part A: Systems and Humans 38, 2, 451–460.
Cummings, M. L., Nehme, C. E., Crandall, J. W. and Mitchell, P. (2007). Innovations in Intelligent Machines-1. Springer. Heidelberg, Germany.
Defense Agency for Technology and Quality (2013). Defense Agency for Technology and Quality Report.
Driels, M. R. (2004). Weaponeering: Conventional Weapon System Effectiveness (AIAA Education Series). 1st edn. American Institute of Aeronautics and Astronautics. Reston, Virginia, USA.
Egelund, N. (1982). Spectral analysis of heart rate variability as an indicator of driver fatigue. Ergonomics 25, 7, 663–672.
McRuer, D. T. and Jex, H. R. (1967). A review of quasilinear pilot models. IEEE Trans. Human Factors in Electronics 8, 3, 231–249.
Murphy, R. and Shields, J. (2012). The Role of Autonomy in DoD Systems. Defense Science Board Task Force Report.
NHTSA (2013). Preliminary Statement of Policy Concerning Automated Vehicles. National Highway Traffic Safety Administration Report. https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/automated_vehicles_policy.pdf
Nehme, C. E. (2009). Modeling Human Supervisory Control in Heterogeneous Unmanned Vehicle Systems. Ph. D. Dissertation. Massachusetts Institute of Technology. Cambridge, Massachusetts, USA.
Olsen, D. R. and Woods, S. B. (2004). Fan-out: Measuring human control of multiple robots. Proc. SIGCHI Conf., Vienna, Austria, 231–238.
Patel, M., Lal, S. K., Kavanagh, D. and Rossiter, P. (2011). Applying neural network analysis on heart rate variability data to assess driver fatigue. Expert Systems with Applications 38, 6, 7235–7242.
Qing, W., BingXi, S., Bin, X. and Junjie, Z. (2010). A PERCLOS-based driver fatigue recognition application for smart vehicle space. Proc. IEEE Int. Symp. Information Processing (ISIP), Qingdao, China, 437–441.
Schwalm, M., Keinath, A. and Zimmer, H. D. (2008). Pupillometry as a method for measuring mental workload within a simulated driving task. Human Factors for Assistance and Automation, 1–13.
Weir, D. H. and McRuer, D. T. (1972). A Computer Simulation of Headlamp Variables and Drivers Sight Distances: Operating Instructions. National Aeronautics and Space Administration Report. CR-2019.
Wickens, C. D. and Hollands, J. G. (2000). Engineering Psychology and Human Performance. 3rd edn. Prentice-Hall. Upper Saddle River, New Jersey, USA.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yang, J.H., Choi, S.Y. & Park, K. Development of an Operability Evaluation Framework for Remotely Controlled Ground Combat Vehicles in a Simulated Environment. Int.J Automot. Technol. 19, 915–922 (2018). https://doi.org/10.1007/s12239-018-0088-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12239-018-0088-y