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Articles

A Hidden Markov Framework to Capture Human–Machine Interaction in Automated Vehicles

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References

  • Abe, G., & Richardson, J. (2006). Alarm timing, trust and driver expectation for forward collision warning systems. Applied Ergonomics, 37(5), 577–586. doi:10.1016/j.apergo.2005.11.001
  • Baker, C. L., Jara-Ettinger, J., Saxe, R., & Tenenbaum, J. B. (2017). Rational quantitative attribution of beliefs, desires and percepts in human mentalizing. Nature Human Behaviour, 1, 0064. doi:10.1038/s41562-017-0064
  • Bengler, K., Dietmayer, K., Farber, B., Maurer, M., Stiller, C., & Winner, H. (2014). Three decades of driver assistance systems: Review and future perspectives. IEEE Intelligent Transportation Systems Magazine, 6(4), 6–22. doi:10.1109/MITS.2014.2336271
  • Bliss, J. P., & Acton, S. A. (2003, nov). Alarm mistrust in automobiles: How collision alarm reliability affects driving. Applied Ergonomics, 34(6), 499–509. doi:10.1016/j.apergo.2003.07.003
  • Bonnefon, J.-F., Shariff, A., & Rahwan, I. (2016, June). The social dilemma of autonomous vehicles. Science, 352(6293), 1573–1576. doi:10.1126/science.aaf2654
  • Brumby, D. P., Janssen, C. P., Kujala, T., & Salvucci, D. D. (2018). Computational models of user multitasking. In A. Oulasvirta, P. Kristensson, X. Bi, & A. Howes (Eds.), Computational interaction design (pp. 341–362). Oxford, United Kingdom: Oxford University Press.
  • Card, S. K., Moran, T., & Newell, A. (1983). The psychology of human-computer interaction. Hillsdale, NJ: Lawrence Erlbaum Associates.
  • De Winter, J. C., Happee, R., Martens, M. H., & Stanton, N. A. (2014). Effects of adap- tive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence. Transportation Research Part F: Traffic Psychology and Behaviour, 27, 196–217. doi:10.1016/j.trf.2014.06.016
  • Endsley, M. R. (2017). Autonomous driving systems: A preliminary naturalistic study of the Tesla Model S. Journal of Cognitive Engineering and Decision Making, 11(3), 225–238. doi:10.1177/1555343417695197
  • Federal Automated Vehicles Policy. (2016). Accelerating the next revolution in roadway safety (Tech. Rep.). National Highway Traffic Safety Administration, Department of Transportation, Washington, DC.
  • Flemisch, F., Heesen, M., Hesse, T., Kelsch, J., Schieben, A., & Beller, J. (2012). Towards a dynamic balance between humans and automation: Authority, ability, responsibility and control in shared and cooperative control situations. Cognition, Technology & Work, 14(1), 3–18. doi:10.1007/s10111-011-0191-6
  • Gasser, T., & Westhoff, D. (2012). BASt-study: Definitions of automation and legal issues in Germany (Tech. Rep.). Federal Highway Research Institute (BASt). Retrieved from http://onlinepubs.trb.org/onlinepubs/conferences/2012/Automation/presentations/Gasser.pdf
  • Gold, C., Körber, M., Lechner, D., & Bengler, K. (2016). Taking over control from highly automated vehicles in complex traffic situations. Human Factors, 58(4), 642–652. doi:10.1177/0018720816634226
  • Habib, K. (2017). Automatic vehicle control systems (Tech. Rep. No. PE 16-007). National Highway Traffic Safety Administration, Department of Transportation, Washington, DC.
  • Howes, A., Chen, X., Acharya, A., & Lewis, R. L. (2018). Interaction as an emergent property of a partially observable markov decision process. In A. Oulasvirta, P. Kristensson, X. Bi, & A. Howes (Eds.), Computational interaction design (pp.287–310). Oxford, United Kingdom: Oxford University Press.
  • Inners, M., & Kun, A. L. (2017). Beyond liability: Legal issues of human-machine interaction for automated vehicles. Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 245–253). New York, NY: ACM.
  • Kun, A. L., Boll, S., & Schmidt, A. (2016). Shifting gears: User interfaces in the age of autonomous driving. IEEE Pervasive Computing, 15(1), 32–38. doi:10.1109/MPRV.2016.14
  • Kyriakidis, M., de Winter, J. C., Stanton, N., Bellet, T., van Arem, B., Brookhuis, K., … Happee, R. (2017). A human factors perspective on automated driving. Theoretical Issues in Ergonomics Science, 1–27. doi:10.1080/1463922X.2017.1293187
  • Luettel, T., Himmelsbach, M., & Wuensche, H.-J. (2012). Autonomous ground vehiclescon- cepts and a path to the future. Proceedings of the IEEE, 100(Special Centennial Issue), 1831–1839. doi:10.1109/JPROC.2012.2189803
  • McNicol, D. (2005). A primer of signal detection theory. New York, NY: Routledge.
  • Michon, J. A. (1985). A critical view of driver behavior models: What do we know, what should we do? In L. Evans & R. C. Schwing (Eds.), Human behavior and traffic safety (pp. 485–520). Boston, MA: Springer.
  • Mok, B., Johns, M., Gowda, N., Sibi, S., & Ju, W. (2016). Take the wheel: Effects of available modalities on driver intervention. Intelligent vehicles symposium, 2016 IEEE (pp. 1358–1365). Gothenburg, Sweden: IEEE.
  • Mok, B., Johns, M., Miller, D., & Ju, W. (2017). Tunneled in: Drivers with active secondary tasks need more time to transition from automation. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2840–2844). New York, NY: ACM.
  • National Highway Traffic Safety Administration. (2013). Preliminary statement of policy concerning automated vehicles (Tech. Rep.). Washington, DC: National Highway Traffic Safety Administration, Department of transportation.
  • Pearl, T. H. (2017). Hands on the wheel: A call for greater regulation of semi-autonomous cars. Indiana Law Journal, (March), 1–47.
  • Rabiner, L. R. (1989). A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257–286. doi:10.1109/5.18626
  • Riener, A., Boll, S., & Kun, A. L. (2016). Automotive user interfaces in the age of au- tomation (Dagstuhl Seminar 16262). Dagstuhl Reports, 6(6), 111–159. Retrieved from http://drops.dagstuhl.de/opus/volltexte/2016/6758
  • SAE International. (2014). J3016: Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. Warrendale, PA, USA: SAE International.
  • Stanton, N. A., & Marsden, P. (1996). From fly-by-wire to drive-by-wire: Safety implications of automation in vehicles. Safety Science, 24(1), 35–49. doi:10.1016/S0925-7535(96)00067-7
  • Teslavangeliste. (2015). Tesla model s adaptive cruise control explanation and demonstration 70d. Retrieved from https://www.youtube.com/watch?v=yZO5PjeLmnE
  • Thimbleby, H. (2007). Press on: Principles of interaction programming. Cambridge, MA: MIT Press.
  • van der Heiden, R. M., Iqbal, S. T., & Janssen, C. P. (2017). Priming drivers before handover in semi-autonomous cars. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 392–404). New York, NY: ACM.
  • Warm, J. S., Parasuraman, R., & Matthews, G. (2008). Vigilance requires hard mental work and is stressful. Human Factors, 50(3), 433–441. doi:10.1518/001872008X312152
  • Wickens, C. D., & Dixon, S. R. (2007). The benefits of imperfect diagnostic automation: A synthesis of the literature. Theoretical Issues in Ergonomics Science, 8(3), 201–212. doi:10.1080/14639220500370105
  • Xiong, H., Boyle, L. N., Moeckli, J., Dow, B. R., & Brown, T. L. (2012). Use patterns among early adopters of adaptive cruise control. Human Factors, 54(5), 722–733. doi:10.1177/0018720811434512