Skip to main content
SpringerLink
Log in

Watch Out Car, He’s Drunk! How Passengers of Vehicles Perceive Risky Crossing Situations Based on Situational Parameters

  • Conference paper
  • First Online:
HCI in Mobility, Transport, and Automotive Systems (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13335))

Included in the following conference series:

  • 1486 Accesses

Abstract

Automated vehicles promise enhanced road safety for their passengers, other vehicles, and vulnerable road user (VRU). To do so, automated vehicles must be designed to reliably detect potentially critical situations [1]. Humans can detect such situations using context cues. Context cues allow humans drivers to anticipate unexpected crossings, e.g., of intoxicated night owls in a street full of bars and clubs on a Friday night and, consequently, to decelerate in advance to prevent critical incidents [2].

We used the “Incident Detector” to identify possible context cues that human drivers might use to assess the criticality of traffic situations in which a car encounters a VRU [3]. Investigated potential predictors include VRUs’ mode of transport, VRUs’ speed, VRUs’ age, VRUs’ predictability of behavior, and visibility obstruction of VRUs by parked cars.

In an online study, 133 participants watched videos of potentially risky crossing situations with VRUs from the driver’s point of view. In addition, the participants’ age, gender, status of driver’s license, sense of presence, and driving style were queried.

The results show that perceived risk correlates significantly with age, speed, and predictability of VRUs behavior, as well as with visibility obstruction and participants’ age. We will use the results to include detected influence factors on perceived subjective risk into virtual test scenarios. Automated vehicles will need to pass these virtual test scenarios to be deemed acceptable regarding objective and subjective risk. These test scenarios can support road safety and thus, greater acceptance of automated vehicles.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.openstreetmap.org/#map=5/51.330/10.453, last checked on 08 February 2022.

  2. 2.

    https://www.google.de/maps, last checked on 08 February 2022.

  3. 3.

    http://igroup.org/pq/ipq/index.php.

References

  1. Inoue, H., El-Haji, M., Freudenmann, T., Zhang, H., Raksincharoensak, P., Saito, Y.: Validation methodology to establish safe autonomous driving algorithms with a high driver acceptance using a virtual environment (2019)

    Google Scholar 

  2. Freudenmann, T., Bopp-Bertenbreiter, V., El-Haji, M., Martin, M.: Project RELAI: risk assessment for automated driving based on multiple data sources. In: ITS World Congress, Hamburg, 11.-15.10 Ertico ITS Europe (2021)

    Google Scholar 

  3. Saito, Y., Raksincharoensak, P., Inoue, H., El-Haji, M., Freudenmann, T.: Context-sensitive hazard anticipation based on driver behavior analysis and cause-and-effect chain study. AVEC (2018)

    Google Scholar 

  4. European Commission. Directorate-General for Mobility and Transport: Next steps towards ‘Vision Zero’: EU road safety policy framework 2021-2030. Publications Office (2020)

    Google Scholar 

  5. SafetyNet: Pedestrians & Cyclists (2009)

    Google Scholar 

  6. Statistisches Bundesamt (Destatis): Verkehr. Verkehrsunfälle (2019). https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Verkehrsunfaelle/Publikationen/Downloads-Verkehrsunfaelle/verkehrsunfaelle-jahr-2080700187004.pdf?__blob=publicationFile&v=2. Accessed 8 Feb 2022

  7. Lindström, A., et al.: Safety through automation? Ensuring that automated and connected driving contribute to a safer transportation system. FERSI Position Paper – January 19, 2018. Forum of European Road Safety Research Institutes (FERSI) (2018). https://fersi.org/wp-content/uploads/2019/02/180202-Safety-through-automation-final.pdf. Accessed 10 Oct 2021

  8. Deublein, M.: Automatisiertes Fahren. Mischverkehr, Bern (2020)

    Google Scholar 

  9. Schlag, B.: Risikoverhalten im Straßenverkehr. Wiss. Z. Tech. Univ. Dresden 55, 35–40 (2006)

    Google Scholar 

  10. Timm, J.: Theorie der gesundheitlichen Risiken: Zwei Welten im Streit (Theory of health risks: dispute of disciplines). Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz 52, 1122–1128 (2009). https://doi.org/10.1007/s00103-009-0968-4

  11. Pfister, H.-R., Jungermann, H., Fischer, K.: Die Psychologie der Entscheidung. Springer, Heidelberg (2017)

    Book  Google Scholar 

  12. Lee, D.N.: A theory of visual control of braking based on information about time-to-collision. Perception (1976). https://doi.org/10.1068/p050437

  13. Hayward, J.C.: Near-miss determination through use of a scale of danger. Highway Research Record (1972)

    Google Scholar 

  14. Hensch, A.-C., Neumann, I., Beggiato, M., Halama, J., Krems, J.F.: How should automated vehicles communicate? – effects of a light-based communication approach in a Wizard-of-Oz study. In: Stanton, N. (ed.) AHFE 2019. AISC, vol. 964, pp. 79–91. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-20503-4_8

    Chapter  Google Scholar 

  15. Rothenbücher, D., Li, J., Sirkin, D., Mok, B., Ju, W.: Ghost driver: a field study investigating the interaction between pedestrians and driverless vehicles. In: 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), New York, NY, USA, 26–31 August 2016, pp. 795–802. IEEE (2016). https://doi.org/10.1109/ROMAN.2016.7745210

  16. Kesharwani, A., Singh Bisht, S.: The impact of trust and perceived risk on internet banking adoption in India. Int. J. Bank Mark. (2012). https://doi.org/10.1108/02652321211236923

  17. Furian, G., Kaiser, S., Senitschnig, N., Soteropoulos, A.: Subjective safety and risk perception. ESRA2 Thematic report Nr. 7, Vienna, Austria Austrian Road Safety Board KFV (2020)

    Google Scholar 

  18. Batsch, F., Kanarachos, S., Cheah, M., Ponticelli, R., Blundell, M.: A taxonomy of validation strategies to ensure the safe operation of highly automated vehicles. J. Intell. Transport. Syst. 26, 14–33 (2022). https://doi.org/10.1080/15472450.2020.1738231

    Article  Google Scholar 

  19. Schmidt, J., Funk, W.: Stand der Wissenschaft: Kinder im Straßenverkehr. Bergisch Gladbach (2021)

    Google Scholar 

  20. Schieber, R.A., Thompson, N.J.: Developmental risk factors for childhood pedestrian injuries. Injury Prevent. 2, 228–236 (1996)

    Article  Google Scholar 

  21. Tabibi, Z., Pfeffer, K.: Finding a safe place to cross the road: the effect of distractors and the role of attention in children’s identification of safe and dangerous road-crossing sites. Infant Child Dev. 16, 193–206 (2007)

    Article  Google Scholar 

  22. Habibovic, A., Davidsson, J.: Requirements of a system to reduce car-to-vulnerable road user crashes in urban intersections. Accid. Anal. Prevent. 43, 1570–1580 (2011). https://doi.org/10.1016/j.aap.2011.03.019

    Article  Google Scholar 

  23. Schüller, H., et al. (eds.): Systematische Untersuchung sicherheitsrelevanten Fußgängerverhaltens. Berichte der Bundesanstalt für Straßenwesen: Mensch und Sicherheit, Heft 299. Fachverlag NW in Carl Schünemann Verlag GmbH, Bremen (2020)

    Google Scholar 

  24. Walter, E., Achermann Stürmer, Y., Scaramuzza, G., Cavegn, M., Niemann, S.: Fussverkehr, Bern (2013)

    Google Scholar 

  25. Pizzamiglio, S., Naeem, U., Réhman, S.U., Saeed Sharif, M., Abdalla, H., Turner, D.L.: A mutlimodal approach to measure the distraction levels of pedestrians using mobile sensing. Proc. Comput. Sci. 113, 89–96 (2017). https://doi.org/10.1016/j.procs.2017.08.297

    Article  Google Scholar 

  26. Rasouli, A., Tsotsos, J.K.: Autonomous vehicles that interact with pedestrians: a survey of theory and practice. IEEE Trans. Intell. Transp. Syst. 21, 900–918 (2019)

    Article  Google Scholar 

  27. Tian, R., et al.: Pilot study on pedestrian step frequency in naturalistic driving environment. In: 2013 IEEE Intelligent Vehicles Symposium (IV), Gold Coast City, Australia, 23–26 June 2013, pp. 1215–1220. IEEE (2013). https://doi.org/10.1109/IVS.2013.6629632

  28. Willis, A., Gjersoe, N., Havard, C., Kerridge, J., Kukla, R.: Human movement behaviour in urban spaces: implications for the design and modelling of effective pedestrian environments. Environ. Plann. B Plann. Des. 31, 805–828 (2004). https://doi.org/10.1068/b3060

    Article  Google Scholar 

  29. Ishaque, M.M., Noland, R.B.: Behavioural issues in pedestrian speed choice and street crossing behaviour: a review. Transp. Rev. 28, 61–85 (2008). https://doi.org/10.1080/01441640701365239

    Article  Google Scholar 

  30. Dipietro, C.M., King, L.E.: Pedestrian gap-acceptance (1970)

    Google Scholar 

  31. Wright, K.B.: Researching internet-based populations: advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services. J. Comput.-Mediat. Commun. (2005). https://doi.org/10.1111/j.1083-6101.2005.tb00259.x

  32. WIVW GmbH: SILAB

    Google Scholar 

  33. Limesurvey GmbH. / LimeSurvey: An Open Source survey tool/ LimeSurvey GmbH. Hamburg, Germany (2021)

    Google Scholar 

  34. Neukum, A., Krüger, H.: Fahrerreaktionen bei Lenksystemstörungen: Untersuchungsmethodik und Bewertungskriterien. VDI Bericht, pp. 297–318 (2003)

    Google Scholar 

  35. Marina Martinez, C., Heucke, M., Wang, F.-Y., Gao, B., Cao, D.: Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey. IEEE Trans. Intell. Transport. Syst. 19, 666–676 (2018). https://doi.org/10.1109/TITS.2017.2706978

    Article  Google Scholar 

  36. Schulz, A., Fröming, R.: Analyse des Fahrerverhaltens zur Darstellung adaptiver Eingriffs-strategien von Assistenzsystemen. ATZ Automobiltech Z 110, 1124–1131 (2008). https://doi.org/10.1007/BF03222040

    Article  Google Scholar 

  37. Johnson, D.A., Trivedi, M.M.: Driving style recognition using a smartphone as a sensor platform. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington, DC, USA, 5–7 October 2011, pp. 1609–1615. IEEE (2011). https://doi.org/10.1109/ITSC.2011.6083078

  38. Schubert, T., Friedmann, F., Regenbrecht, H.: The experience of presence: factor analytic insights. Presence Teleoper. Virtual Environ. 10, 266–281 (2001). https://doi.org/10.1162/105474601300343603

  39. RStudio Team: RStudio: Integrated Development for R. RStudio. PBC, Boston (2021)

    Google Scholar 

  40. R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna (2021)

    Google Scholar 

  41. Cohen, J.: Statistical Power Analysis for the Behavioral Sciences, 2nd edn. (1988)

    Google Scholar 

  42. Zeileis, A., Hothorn, T.: Diagnostic checking in regression relationships. R News 2, 7–10 (2002)

    Google Scholar 

  43. Zeileis, A., Köll, S., Graham, N.: Various versatile variances: an object-oriented implementation of clustered covariances in R. J. Stat. Soft. 95, 1–36 (2020). https://doi.org/10.18637/jss.v095.i01

  44. Hayes, A.F., Cai, L.: Using heteroskedasticity-consistent standard error estimators in OLS regression: an introduction and software implementation. Behav. Res. Methods 39, 709–722 (2007). https://doi.org/10.3758/bf03192961

    Article  Google Scholar 

  45. Hlavac, M.: stargazer. Well-Formatted Regression and Summary Statistics Tables (2018)

    Google Scholar 

  46. European Commission: Pedestrians and Cyclists. European Commission, Directorate General for Transport (2018)

    Google Scholar 

  47. Nasar, J., Hecht, P., Wener, R.: Mobile telephones, distracted attention, and pedestrian safety. Accid. Anal. Prevent. 40, 69–75 (2008). https://doi.org/10.1016/j.aap.2007.04.005

    Article  Google Scholar 

  48. Schwebel, D.C., Stavrinos, D., Byington, K.W., Davis, T., O’Neal, E.E., de Jong, D.: Distraction and pedestrian safety: how talking on the phone, texting, and listening to music impact crossing the street. Accid. Anal. Prevent. 45, 266–271 (2012). https://doi.org/10.1016/j.aap.2011.07.011

    Article  Google Scholar 

  49. Matthews, M.L., Moran, A.R.: Age differences in male drivers’ perception of accident risk: the role of perceived driving ability. Accid. Anal. Prevent. 18, 299–313 (1986). https://doi.org/10.1016/0001-4575(86)90044-8

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Stuttgart, Nobelstr. 12, 70569, Stuttgart, Germany

    Valeria Bopp-Bertenbreiter & Sabina Bähr

  2. Fraunhofer IAO, Nobelstr. 12, 70569, Stuttgart, Germany

    Simon Albrecht

  3. EDI GmbH – Engineering Data Intelligence, Wöschbacherstr. 73, 76327, Karlsruhe, Germany

    Thomas Freudenmann & Mohanad El-Haji

  4. Fraunhofer IOSB, Fraunhoferstr. 1, 76131, Karlsruhe, Germany

    Manuel Martin

  5. IPG Automotive GmbH, Bannwaldallee 60, 76185, Karlsruhe, Germany

    Natalya Anh & Stephan Rauber

Authors
  1. Valeria Bopp-Bertenbreiter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sabina Bähr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simon Albrecht
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thomas Freudenmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohanad El-Haji
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Manuel Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Natalya Anh
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Stephan Rauber
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Valeria Bopp-Bertenbreiter .

Editor information

Editors and Affiliations

  1. Technische Universität Ilmenau, Ilmenau, Germany

    Heidi Krömker

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bopp-Bertenbreiter, V. et al. (2022). Watch Out Car, He’s Drunk! How Passengers of Vehicles Perceive Risky Crossing Situations Based on Situational Parameters. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2022. Lecture Notes in Computer Science, vol 13335. Springer, Cham. https://doi.org/10.1007/978-3-031-04987-3_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-04987-3_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04986-6

  • Online ISBN: 978-3-031-04987-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation