Abstract
In the real-world, manned and unmanned vehicles may be used for a number of applications. Visual technologies like indirect visual display (IVD) and virtual reality (VR) have been used to train operators in both manned and unmanned environments. The main objective of this research was to evaluate the effectiveness of manned and unmanned interfaces in IVD and VR display designs. Using an underwater search-and-shoot scenario, we developed two variations in display designs (IVD and VR) and two variations in type of interface-based training (manned and unmanned). A total of 60 subjects participated in the experiment, where 30 subjects were randomly assigned to simulations in IVD and the rest in VR. In both the simulations, 15 randomly selected participants executed the manned interface first and the remaining 15 executed the unmanned interface first. Results revealed that the subjects performed better in VR compared to IVD, and also performed better when they executed the unmanned interface first. We highlight the implications of our results for training personnel in scenarios involving manned and unmanned operations in IVD and VR interfaces.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kurzweil, R.: The law of accelerating returns. In: Alan Turing: Life and Legacy of a Great Thinker, pp. 381–416. Springer, Berlin (2004)
Cooke, N.J.: Human factors of remotely operated vehicles. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 50, no. 1, pp. 166–169. Sage Publications, Los Angeles, October 2006
Williams, K.W.: A summary of unmanned aircraft accident/incident data: human factors implications (No. DOT/FAA/AM-04/24). Federal Aviation Administration, Civil Aeromedical Institute, Oklahoma City, OK (2004)
Christ, D., Wernli, L.: The ROV Manual: A User Guide for Observation Class Remotely Operated Vehicles. Elsevier, Boston (2007)
Donovan, S.L., Triggs, T.: Investigating the effects of display design on unmanned underwater vehicle pilot performance (No. DSTO-TR-1931). Defense Science and Technology Organization, Maritime Platforms Division, Victoria, Australia (2006)
Donovan, S., Wharington, J., Gaylor, K., Henley, P.: Enhancing situation awareness for UUV operators. ADFA, Canberra (2004)
McCarley, J.S., Wickens, C.D.: Human factors implications of UAVs in the national airspace (2005)
Ho, G., Pavlovic, N.J., Arrabito, R., Abdalla, R.: Human factors issues when operating underwater remotely operated vehicles and autonomous underwater vehicles (No. DRDC TORONTO-TM-2011-100). Defence Research and Development, Toronto, Canada (2011)
Gonzalez, C., Dutt, V.: Instance-based learning: integrating sampling and repeated decisions from experience. Psychol. Rev. 118(4), 523 (2011)
ter Haar, R.: Virtual reality in the military: present and future. In: 3rd Twente Student Conference IT (2005)
Freina, L., Canessa, A.: Immersive vs desktop virtual reality in game based learning. In: European Conference on Games Based Learning, p. 195. Academic Conferences International Limited, October 2015
Creighton, R.H.: Unity 3D Game Development by Example: A Seat-of-Your-Pants Manual For Building Fun, Groovy Little Games Quickly. Packt Publishing Ltd., Birmingham (2010)
Roosendaal, T., Selleri, S. (eds.): The Official Blender 2.3 Guide: Free 3D Creation Suite for Modeling, Animation, and Rendering, vol. 3. No Starch Press, San Francisco (2004)
Ragunath, P.K., Velmourougan, S., Davachelvan, P., Kayalvizhi, S., Ravimohan, R.: Evolving a new model (SDLC Model-2010) for software development life cycle (SDLC). Int. J. Comput. Sci. Netw. Secur. 10(1), 112–119 (2010)
Behan, M., Wilson, M.: State anxiety and visual attention: the role of the quiet eye period in aiming to a far target. J. Sports Sci. 26(2), 207–215 (2008)
Kim, Y.Y., Oh, M.A.: U.S. Patent Application No. 29/476,471 (2015)
Adhikarla, V.K., Wozniak, P., Barsi, A., Singhal, D., Kovács, P.T., Balogh, T.: Freehand interaction with large-scale 3D map data. In: 2014 3DTV-Conference: The True Vision-Capture, Transmission and Display of 3D Video (3DTV-CON), pp. 1–4. IEEE, July 2014
Wang, Y., Hasegawa, K., Terasaki, K.: U.S. Patent Application No. 13/013,072 (2011)
Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. In: Advances in Psychology, vol. 52, pp. 139–183. North-Holland, Amsterdam (1988)
Metcalfe, J.S., Cosenzo, K.A., Johnson, T., Brumm, B., Manteuffel, C., Evans, A.W., Tierney, T.: Human dimension challenges to the maintenance of local area awareness using a 360 indirect-vision system. In: 2010 NDIA Ground Vehicle Systems Engineering and Technology Symposium: Modeling and Simulation, Testing and Validation Mini-Symposium (2010)
Acknowledgments
This research was supported by a grant from Defence Research and Development Organization (DRDO) titled “Development of a human performance modeling framework for visual cognitive enhancement in IVD, VR and AR paradigms” (IITM/DRDO-CARS/VD/110) to Varun Dutt.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Rao, A.K., Pramod, B.S., Chandra, S., Dutt, V. (2019). Influence of Indirect Vision and Virtual Reality Training Under Varying Manned/Unmanned Interfaces in a Complex Search-and-Shoot Simulation. In: Cassenti, D. (eds) Advances in Human Factors in Simulation and Modeling. AHFE 2018. Advances in Intelligent Systems and Computing, vol 780. Springer, Cham. https://doi.org/10.1007/978-3-319-94223-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-94223-0_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94222-3
Online ISBN: 978-3-319-94223-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)