Computation of cause and effect relationship for acceptance of autonomous mobile robots in industries

References

• Agarwal, D., Bharti, P. S. : A case study on AGV’s Alternatives Selection Problem. International Journal of Information Technology, Springer, pp. 1-13 (2018).
   Google Scholar
• Bahmanziari, T., Pearson, J.M., Crosby, L : Is trust important in technology adoption? A policy capturing approach. Journal of Computation and Information System 43(4), pp. 46–54 (2003).
   Web of Science ®Google Scholar
• Bansal, P., Kockelman, K.M., Singh, A. : Assessing public opinions of and interest in new vehicle technologies: an Austin perspective. Transport Research Part C: Emerging Technologies 67, pp. 1–14 (2016). doi: 10.1016/j.trc.2016.01.019
   Web of Science ®Google Scholar
• Benenson, R., Petti, S., Fraichard, T., Parent, M. : Towards urban driverless vehicles. Intermational Journal of Vehicle Autonomous System 6(2), pp. 4–23 (2008). doi: 10.1504/IJVAS.2008.016486
   Google Scholar
• Carnegie Mellon University’s Navlab The Robotics Institute, 2016, 28 December: Retrieved from http://www.ri.cmu.edu/research_lab_group_detail.html?lab_id=28
   Google Scholar
• Driverless Tractor, 2017, 22 January: Retrieved from https://en.wikipedia.org/wiki/Driverless_tractorr
   Google Scholar
• Fagnant, D.J., Kockelman, K. : Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Res. Part. A: Policy Practice 77, pp. 167–181 (2015).
   Web of Science ®Google Scholar
• Gao, S., Chen, L.S., Chen, P.L. : A fuzzy DEMATEL method for analyzing key factors of the product promotion. Journal of Discrete Mathematical Sciences and Cryptography, 21(6), pp. 1225-1228 (2018). doi: 10.1080/09720529.2018.1525904
   Web of Science ®Google Scholar
• Glancy, D.J. : Privacy in autonomous vehicles. Santa Clara L. Rev, 52, pp. 1171 (2012).
   Google Scholar
• Graefe, V. : Vision for Intelligent Road Vehicles, In Proceedings of the IEEE Symposium of Intelligent Vehicles, pp. 135-140 (1993 ).
   Google Scholar
• Grunwald, A. : Societal Risk Constellations for Autonomous Driving. Analysis, Historical Context and Assessment. In: Autonomous Driving. Springer, pp. 641-663 (2016).
   Google Scholar
• Kaur, K., Rampersad, G. : Trust in driverless cars: Investigating key factors influencing the adoption of driverless cars. Journal of Engineering and Technology Management 48(2), pp. 87-96 (2018). doi: 10.1016/j.jengtecman.2018.04.006
   Google Scholar
• Kim, D.J., Ferrin, D.L., Rao, H.R. : A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision support systems 44, pp. 544-564 (2008). doi: 10.1016/j.dss.2007.07.001
   Web of Science ®Google Scholar
• Kyriakidis, M., Happee, R., de Winter, J.C. : Public opinion on automated driving: results of an international questionnaire among 5000 respondents. Transportation research part F: traffic psychology and behaviour 32, pp. 127-140 (2015). doi: 10.1016/j.trf.2015.04.014
   Web of Science ®Google Scholar
• LLaneras, R.E., Salinger, J., Green, C.A. : Human factors issues associated with limited ability autonomous driving systems: Drivers’ allocation of visual attention to the forward roadway. In: Proceedings of the 7th International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, pp 92-98 (2013).
   Google Scholar
• Marinik, A., Bishop, R., Fitchett, V.L., Morgan, J.F., Trimble, T.E., Blanco, M. : Human factors evaluation of level 2 and level 3 automated driving concepts -Concepts of operation. (Report No. DOT HS 812 044). Washington, DC: National Highway Traffic Safety Administration (2014).
   Google Scholar
• NHTSA : US department of transportation releases policy on automated vehicle development Policy, pp. 13-14 (2013).
   Google Scholar
• Paden, B., Čáp, M., Yong, S.Z., Yershov, D., Frazzoli, E. : A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Transactions of Intelligent Vehicles 1(1), pp. 33–55 (2016) doi: 10.1109/TIV.2016.2578706
   Google Scholar
• Payre, W., Cestac, J., Delhomme, P. : Intention to use a fully automated car: Attitudes and a priori acceptability. Transportation research Part F: traffic psychology and behaviour 27(B), pp. 252-263 (2014). doi: 10.1016/j.trf.2014.04.009
   Google Scholar
• Ring, T. : Connected cars–the next target for hackers. Network Security, 2015(11), pp. 11–16 (2015). doi: 10.1016/S1353-4858(15)30100-8
   Google Scholar
• Souza, J.R., Pessin, G., Eboli, G.B., Mendes, C.C.T., Osório, F.S., Wolf, D.F. : Vision and GPS-based Autonomous Vehicle Navigation Using Templates and Artificial Neural Networks. SAC’12, Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 280-285 (2012 ).
   Google Scholar
• Strand, N., Nilsson, J., Karlsson, I. C. M., Nilsson, L. : Semi-automated versus highly automated driving in critical situations caused by automation failures. Transportation Research Part F 27, pp. 218-228 (2014). doi: 10.1016/j.trf.2014.04.005
   Google Scholar
• Tesla Electric Car Model on Autopilot mode, 17 January: Retrieved from https://www.tesla.com/models (2017 ).
   Google Scholar
• Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D. : User acceptance of information technology: toward a unified view. MIS Quarterly, pp. 425–478 (2003). doi: 10.2307/30036540
   Web of Science ®Google Scholar
• Villagra, J., Pérez, D.H. : A Comparison of Control Techniques for Robust Docking Maneuvers of an AGV. IEEE Transactions on Control Systems Technology 20(4), pp. 1116-1123 (2012). doi: 10.1109/TCST.2011.2159794
   Web of Science ®Google Scholar
• Wu, K., Zhao, Y., Zhu, Q., Tan, X., Zheng, H. : A meta-analysis of the impact of trust on technology acceptance model: investigation of moderating influence of subject and context type. International Journal of Information Management 31(6), pp. 572–581 (2011). doi: 10.1016/j.ijinfomgt.2011.03.004
   Web of Science ®Google Scholar