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Yielding Light Signal Evaluation for Self-driving Vehicle and Pedestrian Interaction

  • Stefanie M. FaasEmail author
  • Martin Baumann
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1026)

Abstract

An external Human-Machine-Interface (eHMI) signaling the vehicle’s intended movements facilitates pedestrians’ encounters with self-driving vehicles (SDV). However, there is no standard for automated driving system (ADS) lamps today. This study compares the efficacy of a steady, a flashing and a sweeping light signal to communicate an SDV’s intention to yield. The eHMI designs were evaluated at an unsignalized intersection with participants crossing in front of a yielding Wizard-of-Oz SDV. We analyzed crossing behavior and conducted questionnaires and structured interviews with N = 30 participants to identify eHMI design recommendations. Our research provides evidence that a steady and a flashing signal facilitate user experience, learnability and likeability more than a sweeping light. With a flashing signal, pedestrians tend to cross sooner compared to a sweeping signal, and thus improving traffic flow. Design adjustments to the present signals are proposed. This paper provides guidance in the development of a standardized yielding light signal.

Keywords

Automated vehicles External Human-Machine-Interface Automated driving system lamps Pedestrian safety Interface design 

References

  1. 1.
    SAE International: Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (J3016) (2018)Google Scholar
  2. 2.
    Šucha, M., Dostal, D., Risser, R.: Pedestrian-driver communication and decision strategies at marked crossings. Accid. Anal. Prev. 102, 41–50 (2017)CrossRefGoogle Scholar
  3. 3.
    Mahadevan, K., Somanath, S., Sharlin, E.: Communicating awareness and intent in autonomous vehicle-pedestrian interaction. In: 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–12 (2018)Google Scholar
  4. 4.
    Matthews, M., Chowdhary, G.V., Kieson, E.: Intent communication between autonomous vehicles and pedestrians (2017)Google Scholar
  5. 5.
    Lagström, T., Lundgren, V.M.: Automated vehicle’s interaction with pedestrians (2015)Google Scholar
  6. 6.
    Rothenbücher, D., Li, J., Sirkin, D., Mok, B., Ju, W.: Ghost driver: a field study investigating the interaction between pedestrians and driverless vehicles. In: 25th IEEE International Symposium on Robot and Human Interactive Communication, pp. 795–802 (2016)Google Scholar
  7. 7.
    Stadler, S., Cornet, H., Theoto, T.N., Frenkler, F.: A tool, not a toy: using virtual reality to evaluate the communication between autonomous vehicles and pedestrians. In: tom Dieck, M.C., Jung, T. (eds.) Augmented Reality and Virtual Reality, pp. 203–216. Springer, Cham (2019)CrossRefGoogle Scholar
  8. 8.
    Merat, N., Louw, T., Madigan, R., Wilbrink, M., Schieben, A.: What externally presented information do VRUs require when interacting with fully automated road transport systems in shared space? Accid. Anal. Prev. 118, 244–252 (2018)CrossRefGoogle Scholar
  9. 9.
    Petzoldt, T., Schleinitz, K., Banse, R.: The potential safety effects of a frontal brake light for motor vehicles. Intell. Transp. Syst. 12, 449–453 (2018)CrossRefGoogle Scholar
  10. 10.
  11. 11.
    ISO: Road Vehicles: Ergonomic aspects of external visual communication from automated vehicles to other road users (ISO/TR 23049:2018) (2018)Google Scholar
  12. 12.
    SAE International: Automated Driving System (ADS) Marker Lamp (J3134_201905) (2019)Google Scholar
  13. 13.
    Dietrich, A., Willrodt, J.-H., Wagner, K., Bengler, K.: Projection-based external human machine interfaces: Enabling interaction between automated vehicles and pedestrians. In: 17th European VR, Driving Simulation and Virtual Reality Conference, pp. 43–50 (2018)Google Scholar
  14. 14.
    Werner, A.: New colors for autonomous driving: an evaluation of chromaticities for the external lighting equipment of autonomous vehicles. Colour Turn 1, 1–15 (2018)Google Scholar
  15. 15.
  16. 16.
  17. 17.
    Bartneck, C., Kulic, D., Croft, E., Zoghbi, S.: Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. Int. J. Soc. Robot. 1, 71–81 (2009)CrossRefGoogle Scholar
  18. 18.
    Mayring, P.: Qualitative content analysis: theoretical foundation, basic procedures and software solution (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Daimler AGBöblingenGermany
  2. 2.Department of Human FactorsUlm UniversityUlmGermany

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