Christian Lehsing
ABSTRACT
Autonomous vehicles will encounter all the traffic situations that current drivers are confronted with. These vehicles are expected to handle the situations at least as good as human drivers or even better. This "better" can be split up in various ways and address different facets of traffic: safety, efficiency, and cooperativity to name a few. The driving simulator study at hand investigated the effect of different braking trajectories of a fully autonomous vehicle (SAE Level 5) approaching a zebra crossing. Participants had to rate two aspects: (1) the perceived cooperativity and (2) the perceived criticality of the programmed braking trajectories, in addition to a replay of their own, manual approach, in a dynamic driving simulator. The results show significant differences between the approaches in terms of how critical and cooperative they were perceived. Remarkably, the participants' individual driving style was, on average, not the safest or most cooperative one. Participants favourized an approach with an early brake onset with gradually increasing and subsequent decreasing brake intensity (bell-shaped curve) until full stop in front of the pedestrian crossing.
- Evans, L. (1996). The dominant role of driver behavior in traffic safety. American Journal of Public Health, 86(6), 784--786.Google Scholar
Cross Ref
- Shinar D. (1978). Psychology on the Road. The Human Factor in Traffic Safety. USA: John Wiley & Sons.Google Scholar
- Deehy, P. T. (1968, October). Sociology and road safety, The Engineering Institute of Canada, Committee on Road Safety Research. In Proceedings of a Seminar, Royal Military College, Kingston, Ontario.Google Scholar
- Juhlin, O. (1999). Traffic behaviour as social interaction-implications for the design of artificial drivers. In Proceedings of the 6th World Congress on Intelligent Transportation Systems (ITS), Toronto, Canada.Google Scholar
- Lehsing, C., Kracke, A., & Bengler, K. (2015, September). Urban perception-a cross-correlation approach to quantify the social interaction in a multiple simulator setting. In 2015 IEEE 18th international conference on intelligent transportation systems (pp. 1014--1021). IEEE.Google Scholar
- Lehsing, C., Fleischer, M., & Bengler, K. (2016, November). On the track of social interaction-A nonlinear quantification approach in traffic conflict research. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) (pp. 2046--2051). IEEE.Google ScholarDigital Library
- ORAD (2018). Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems J3016_201806, SAE International: Warrendale, PA, https://doi.org/10.4271/J3016_201806Google Scholar
- Wolf, I. (2016). The interaction between humans and autonomous agents. In Autonomous driving (pp. 103--124). Springer, Berlin, Heidelberg.Google ScholarCross Ref
- Song, Y. E., Lehsing, C., Fuest, T., & Bengler, K. (2018, January). External HMIs and their effect on the interaction between pedestrians and automated vehicles. In International Conference on Intelligent Human Systems Integration (pp. 13--18). Springer, Cham.Google ScholarCross Ref
- Walker, G., Stanton, N., & Salmon, P. (2016). Trust in vehicle technology. International journal of vehicle design, 70(2), 157--182.Google ScholarCross Ref
- Schaefer, K. E., Chen, J. Y., Szalma, J. L., & Hancock, P. A. (2016). A meta-analysis of factors influencing the development of trust in automation: Implications for understanding autonomy in future systems. Human factors, 58(3), 377--400.Google ScholarCross Ref
- Koo, J., Kwac, J., Ju, W., Steinert, M., Leifer, L., & Nass, C. (2015). Why did my car just do that? Explaining semi-autonomous driving actions to improve driver understanding, trust, and performance. International Journal on Interactive Design and Manufacturing (IJIDeM), 9(4), 269--275.Google ScholarCross Ref
- Wagner, M., & Koopman, P. (2015). A philosophy for developing trust in self-driving cars. In Road Vehicle Automation 2 (pp. 163--171). Springer, Cham.Google ScholarCross Ref
- Elbanhawi, M., Simic, M., & Jazar, R. (2015). In the passenger seat: investigating ride comfort measures in autonomous cars. IEEE Intelligent Transportation Systems Magazine, 7(3), 4--17.Google ScholarCross Ref
- Nees, M. A. (2016, September). Acceptance of self-driving cars: An examination of idealized versus realistic portrayals with a self-driving car acceptance scale. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 60, No. 1, pp. 1449--1453). Sage CA: Los Angeles, CA: SAGE Publications.Google Scholar
- Häuslschmid, R., von Buelow, M., Pfleging, B., & Butz, A. (2017, March). Supportingtrust in autonomous driving. In Proceedings of the 22nd international conference on intelligent user interfaces (pp. 319--329). ACM.Google ScholarDigital Library
- Fraedrich E., Lenz B. (2016) Societal and Individual Acceptance of Autonomous Driving. In: Maurer M., Gerdes J., Lenz B., Winner H. (eds) Autonomous Driving. Springer, Berlin, HeidelbergGoogle ScholarCross Ref
- Kuderer, M., Gulati, S., & Burgard, W. (2015, May). Learning driving styles for autonomous vehicles from demonstration. In 2015 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2641--2646). IEEE.Google ScholarCross Ref
- Lefèvre, S., Carvalho, A., & Borrelli, F. (2016). A learning-based framework for velocity control in autonomous driving. IEEE Transactions on Automation Science and Engineering, 13(1), 32--42.Google ScholarCross Ref
- Scherer, S., Dettmann, A., Hartwich, F., Pech, T., Bullinger, A. C., & Wanielik, G.: How the driver wants to be driven -Modelling driving styles in highly automated driving. 7. Tagung Fahrerassistenz, München, 2015.Google Scholar
- Wu, Z., Liu, Y., & Pan, G. (2009). A smart car control model for brake comfort based on car following. IEEE transactions on intelligent transportation systems, 10(1), 42--46.Google Scholar
- Basu, C., Yang, Q., Hungerman, D., Sinahal, M., & Draqan, A. D. (2017, March). Do you want your autonomous car to drive like you? In 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI (pp. 417--425). IEEE.Google ScholarDigital Library
- Katz, A., Zaidel, D., & Elgrishi, A. (1975). An Experimental Study of Driver and Pedestrian Interaction During the Crossing Conflict. Human Factors: The Journal of the Human Factors and Ergonomics Society, vol. 17, no. 5, pp. 514.527.Google ScholarCross Ref
- Schneemann, F., & Gohl, I. (2016). Analyzing driver-pedestrian interaction at crosswalks: A contribution to autonomous driving in urban environments. In 2016 IEEE intelligent vehicles symposium (IV) (pp. 38--43). IEEE.Google Scholar
- Varhelyi, A. (1998). Drivers' Speed Behaviour at a Zebra Crossing: a Case Study, Accident Analysis & Prevention, 30(6), pp. 731.743.Google ScholarCross Ref
- Schroeder, B.J. (2008) A Behavior-Based Methodology for Evaluating Pedestrian-Vehicle Interaction at Crosswalks, Dissertation, North Carolina State University, 2008.Google Scholar
- Liebl, J., Lederer, M., Rohde-Brandenburger, K., Biermann, J.-W., Roth, M., & Schafer, H. (2014). Energiemanagement im Kraftfahrzeug. Wiesbaden: Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-04451-0Google ScholarCross Ref
- Markschläger, P., Wahl, H.-G., Weberbauer, F., & Lederer, M. (2012). Assistenzsystem für Mehr Kraftstoffeffizienz. ATZ - Automobiltechnische Zeitschrift, 114(11), 850--855. https://doi.org/10.1007/s35148-012-0496-7Google ScholarCross Ref
- Kauffmann, N., Winkler, F., & Vollrath, M. (2018). What Makes an Automated Vehicle a Good Driver? (S. 1--9). ACM Press. https://doi.org/10.1145/3173574.3173742Google ScholarDigital Library
- Neukum, A., Lubbeke, T., Kruger, H.-P., Mayser, C., & Steinle, J. (Hrsg.). (2008). 5. Workshop Fahrerassistenzsysteme: FAS 2008 - ACC-Stop & Go: Fahrerverhalten an funktionalen Systemgrenzen. Karlsruhe: Fmrt.Google Scholar
- Phillips, N. D. (2017). Yarrr! The pirate's guide to R. APS Observer, 30(3).Google Scholar
- Maurer, M., Gerdes, J. C., Lenz, B., & Winner, H. (2016). Autonomous driving. Berlin, Germany: Springer Berlin Heidelberg, 10, 978--3.Google Scholar
- Ersoy, M., & Gies, S. (Hrsg.). (2017). Fahrwerkhandbuch. Wiesbaden: Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-15468-4Google ScholarCross Ref
- Schroeder, B. J. (2008). A Behavior-Based Methodology for Evaluating Pedestrian-Vehicle Interaction at Crosswalks (Dissertation). North Carolina State University, Raleigh, NC, USA.Google Scholar
- Van der Horst, A.R.A., Hogema, J. (1993). Time-to-collision and collision avoidance systems. Proceedings of the 6th ICTCT Workshop. pp. 1--12.Google Scholar
- Griesche, S., Nicolay, E., Assmann, D., Dotzauer, M., & Käthner, D. (2016). Should my car drive as I do? What kind of driving style do drivers prefer for the design of automated driving functions? In Braunschweiger Symposium (Vol. 10, No. 11, pp. 185--204). Conference Name:ACM Woodstock conferenceGoogle Scholar
Index Terms
- Don't Drive Me My Way: Subjective Perception of Autonomous Braking Trajectories for Pedestrian Crossings
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