Abstract
The long awaited arrival of automated driving technology has the automotive industry perched on the precipice of radical change when it comes to the design of vehicle interiors and user experience. Recently, much thinking and many vehicle concepts have been devoted to demonstrating how vehicle interiors might change when vehicles reach full automation, where a human driver is neither required nor in some cases, even allowed to control the vehicle. However, looking more near term across all global market segments, we will likely see an increasing number of vehicles with widely varying automation capabilities emerging simultaneously. Any system short of full automation will still require driver control in some set of situations, and some fully automated vehicles will still allow driver control when desired. While it is unlikely that the basic seating arrangement, steering wheel, and pedals will be radically altered in this emerging segment of partial to highly automated vehicles, it is quite clear that the overall user experience during automated driving will need to evolve. Drivers will not be content to hold the steering wheel and stare at the road waiting for what may be a very infrequent request to take-over driving. The chapter presents the research conducted to develop the Valeo Mobius® Intuitive Driving solution for providing an embedded digital experience, even in lower levels of automation, and all while still promoting both shorter transition response times and better transition quality when emergency situations call for a transition from automated to manual control.
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
Blanco, M., Atwood, J., Vasquez, H. M., Trimble, T. E., Fitchett, V. L., & Radlbeck, J. (2015). Human factors evaluation of level 2 and level 3 automated driving concepts (Report No. DOT HS 812 182). Washington, DC: National Highway Traffic Safety Administration.
Blincoe, L. J., Miller, T. R., Zaloshnja, E., & Lawrence, B. A. (2014). The economic and societal impact of motor vehicle crashes, 2010 (Report No. DOT HS 812 013). Washington, DC: National Highway Traffic Safety Administration.
Brooke, J. (1986). System Usability Scale. Reading, England: Digital Equipment Corporation.
California DMV. (2015, December 16). Proposed Express Terms, Title 13, Division 1, Chapter 1, Article 3.7—Autonomous Vehicles. Sacramento, CA: California Department of Motor Vehicles. https://www.dmv.ca.gov/portal/wcm/connect/ed6f78fe-fe38-4100-b5c21656f555e841/AVExpressTerms.pdf?MOD=AJPERES. Accessed 27 June 2016.
Diederichs, F., Pöhler, G., & Spath, D. (2014). Driving maneuver prediction based on driver behavior observation. In Proceedings of the 5th International Conference on Applied Human Factors and Ergonomics, Kraków, Poland, July 19–23.
Diederichs, F., Bischoff, S., Widlroither, H., Reilhac, P., Hottelart, K., & Moizard, J. (2015). New HMI concept for an intuitive automated driving experience and enhanced transitions. In Proceedings from the 7th Conference on Automotive User Interfaces and Interactive Vehicular Interactions, Nottingham, UK, Sept. 1–3.
Diederichs, F., Schüttke, T., & Spath, D. (2015). Driver intention algorithm for pedestrian protection and automated emergency braking systems. In Proceedings of IEEE 18th International Conference on Intelligent Transportation Systems—(ITSC 2015), Canary Islands, Spain, Sept. 15–18.
Diels, C., & Bos J. (2015). Self-driving carsickness. In Special Issue of Applied Ergonomics—Transport in the 21st Century: The Application of Human Factors to Future User Needs (In Press).
Diels, C., Bos J., Reilhac, P., & Hottelart, K. (2016). Motion sickness in automated vehicles: the elephant in the room. In G. Meyer & S. Beiker (Ed.), Road Vehicle Automation 2. Lecture Notes in Mobility. Springer International Publishing Switzerland.
Feron, S., & Nicolas, L. (2012). Eyes close to the road: the perfect restitution? Ergonomic and technical perspectives on two solutions: HUD and top mounted instrument cluster. In IQPC 3rd International Conference on Automotive Cockpit HMI, Bonn, Germany, Sept. 24–26.
Flemisch F. O., Adams C. A., Conway S. R., Goodrich K. H., Palmer M. T., & Schutte P. C. (2003). The h-metaphor as a guideline for vehicle automation and interaction, NASA/TM-2003-212672.
Ganzhorn, M., Diederichs, F., & Höfer, M. (2013). Eine bewerterbasierte Ablenkungsskala (BABS) zur objektiven Beurteilung von Unaufmerksamkeit. In: Der Fahrer im 21. Jahrhundert. Düsseldorf: VDI-Verlag. pp 133–146.
Geddes, N. B. (1940). Magic Motorways. New York, NY: Random House.
Gold, C., Damböck, D., Lorenz, L., & Bengler, K. (2013). “Take over!” How long does it take to get the driver back into the loop? In Proceedings of the Human Factors and Ergonomics Society 57th Annual Meeting, San Diego, CA, Sept. 30–Oct. 4.
Llaneras, R. E., Salinger, J., & Green, C. A. (2013). Human factors issues associated with limited ability autonomous driving systems: drivers’ allocation of visual attention to the forward roadway. In Proceedings of the Seventh International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, Bolton Landing, NY, June 17–20, pp. 92–98.
Melcher, V., Rauh, S., Diederichs, F., Widlroither, H., & Bauer, W. (2015). Take-over requests for automated driving. In 6th International Conference on Applied Human Factors and Ergonomics, Las Vegas, NV, July 27–30.
Mercedes-Benz. (2015). The Mercedes-Benz F 015 Luxury in Motion Concept Car. https://www.mercedes-benz.com/en/mercedes-benz/innovation/research-vehicle-f-015-luxury-in-motion/. Accessed 27 June 2016.
National Center for Statistics and Analysis. (2015). Early estimate of motor vehicle traffic fatalities for the first quarter of 2015 (Crash Stats Brief Statistical Summary Report No. DOT HS 812 190). Washington, DC: National Highway Traffic Safety Administration.
NHTSA. (2008). National Motor Vehicle Crash Causation Survey: Report to Congress (Technical Report DOT-HS-811-059). Washington, DC: U.S. Department of Transportation, the National Highway Traffic Safety Administration (NHTSA).
Naughton, N. (2015, July 29). Average age of the U.S. fleet hits record 11.5 years, IHS says. Automotive News. http://www.autonews.com/article/20150729/RETAIL/150729861/.average-age-of-u.s.-fleet-hits-record-11.5-years-ihs-says. Accessed 27 June 2016.
Radlmayr, J., Gold, C., Lorenz, L., Farid, M., & Bengler, K. (2014). How traffic situations and non-driving related tasks affect the take-over quality in highly automated driving. In Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting, Chicago, IL, Oct. 27–31.
Reilhac, P., Millet, N., & Hottelart, K. (2015). Thinking intuitive driving automation. tractatus technologico-humanicus. In: G. Meyer & S. Beiker (Eds.), Road Vehicle Automation 2. Lecture Notes in Mobility. Springer International Publishing Switzerland.
Reilhac, P., Moizard, J., Kaiser, F., & Hottelart, K. (2016). Cockpit concept for conditional automated driving. In: ATZ Magazine, No 3, 2016, p. 42–46 (In press).
SAE. (2014). SAE J3016: Taxonomy and Definitions for Terms Related to On-Road Automated Motor Vehicles. Warrendale, PA: Society of Automotive Engineers.
Schoettle, B., & Sivak, M. (2014a). A Survey of Public Opinion About Autonomous and Self-Driving Vehicles in the U.S., the U.K., and Australia (UMTRI-2014-21). Ann Arbor, MI.: University of Michigan Transportation Research Institute.
Schoettle, B., & Sivak, M. (2014b). Public Opinion About Self-Driving Vehicles in China, India, Japan, the U.S., the U.K., and Australia (UMTRI-2104-30). Ann Arbor, MI.: University of Michigan Transportation Research Institute.
Schoettle, B., & Sivak, M. (2015). Motorists’ Preferences for Different Levels of Vehicle Automation (UMTRI-2015-22). Ann Arbor, MI.: University of Michigan Transportation Research Institute.
Schoettle, B., & Sivak, M. (2016). Motorists’ Preferences for Different Levels of Vehicle Automation: 2016 (UMTRI-2016-8). Ann Arbor, MI.: University of Michigan Transportation Research Institute.
Stinson, L. (2014). IDEO Imagines the Wild Future of Self-Driving Cars. Wired. http://www.wired.com/2014/11/ideo-imagines-self-driving-cars/. Accessed 27 June 2016.
Stokes, S. (2015). Consumers expect to use mobile devices, read and eat in self-driving cars. http://engineering.cmu.edu/media/press/2015/01_22_autonomous_vehicle_survey.html. Accessed 5 July 2016.
Sulzmann, F., Melcher, V., Diederichs, F., & Sayar, R. (2013). Modular dashboard for flexible in car HMI testing. In N. A. Stanton (Ed.), Advances in human aspects of road and rail transportation (Advances in human factors and ergonomics series) (pp. 97–106). Boca Raton, FL: CRC.
Tesla Motors. (2016, June 30). A Tragic Loss [Tesla Motors Blog]. https://www.teslamotors.com/blog/tragic-loss. Accessed 30 June 2015.
Treat, J. R., Tumbas, N. S., McDonald, S. T., Shinar, D., Hume, R. D., Mayer, R. E., Stanisfer, R. L. & Castellan, N. J. (1977). Tri-Level Study of the Causes of Traffic Accidents (Technical Report DOT-HS-034-3-535-77). Washington, D.C.: U.S. Department of Transportation, National Highway Traffic Safety Administration (NHTSA).
Volvo. (2016). Concept 26. http://www.volvocars.com/intl/About/Our-Innovation-Brands/IntelliSafe/IntelliSafe-Autopilot/C26. Accessed 27 June 2016.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Reilhac, P., Hottelart, K., Diederichs, F., Nowakowski, C. (2017). User Experience with Increasing Levels of Vehicle Automation: Overview of the Challenges and Opportunities as Vehicles Progress from Partial to High Automation. In: Meixner, G., Müller, C. (eds) Automotive User Interfaces. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-49448-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-49448-7_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49447-0
Online ISBN: 978-3-319-49448-7
eBook Packages: Computer ScienceComputer Science (R0)