Skip to main content
SpringerLink
Log in

Autonomous Vehicles and Pedestrians: A Case Study of Human Computer Interaction

  • Conference paper
  • First Online:
HCI in Mobility, Transport, and Automotive Systems (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12791))

Included in the following conference series:

Abstract

Understanding public attitudes, sentiments, and perceptions is a significant first step to the widespread acceptance of autonomous vehicles (AVs). In recent years, many studies been conducted in recent years examining the general perception of AVs. The implementation of AVs has potential challenges involving pedestrians and bicyclists that need special attention. The current study analyzes survey data obtained from BikePGH, a non-profit organization in Pittsburgh, Pennsylvania . This study applied multiple correspondence analysis (MCA) to explore response patterns among the participants. The findings of the survey reveal that certain groups of people are much more receptive to AV technology than others who are vehemently opposed to the introduction of AVs to the roadways. The findings indicate that participants who have real world experience with human-AV interactions have more positive expectations and a higher level of interest than participants with no previous experience. The authors expect that these findings will contribute greatly to the development of safety policies related AV and pedestrian interactions.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Website of Bike PGH. Accessed July 2019, https://www.bikepgh.org/

  2. Das, S., Dutta, A., Lindheimer, T., Jalayer, M., Elgart, Z.: YouTube as a source of information in understanding autonomous vehicle consumers: natural language processing study. Transp. Res. Rec. 2673(8), 242–253 (2019)

    Article  Google Scholar 

  3. Ali, F., Dissanayake, D., Bell, M., Farrow, M.: Investigating car users’ attitudes to climate change using multiple correspondence analysis. J. Transp. Geogr. 72, 237–247 (2018)

    Article  Google Scholar 

  4. Baireddy, R., Zhou, H., Jalayer, M.: Multiple correspondence analysis of pedestrian crashes in rural Illinois. Transp. Res. Rec. 2672(38), 116–127 (2018)

    Article  Google Scholar 

  5. Bansal, P., Kockelman, K.M.: Forecasting Americans long-term adoption of connected and autonomous vehicle technologies. Transp. Res. Part A Policy Pract. 95, 49–63 (2017)

    Article  Google Scholar 

  6. Bansal, P., Kockelman, K.: Are we ready to embrace connected and self-driving vehicles? a case study of Texans. Transportation 45(2), 641–675 (2018)

    Article  Google Scholar 

  7. Bansal, P., Kockelman, K.M., Singh, A.: Assessing public opinions of and interest in new vehicle technologies An Austin perspective. Transp. Res. Part C Emerg. Technol. 67, 1–14 (2016)

    Article  Google Scholar 

  8. Canis, B.: Issues in Autonomous Vehicle Deployment. Technical Report (2018)

    Google Scholar 

  9. Choi, J.K., Ji, Y.G.: Investigating the importance of trust on adopting an autonomous vehicle. Int. J. Human-Comput. Interact. 31(10), 692–702 (2015)

    Article  Google Scholar 

  10. Das, S., Jha, K., Fitzpatrick, K., Brewer, M., Shimu, T.H.: Pattern identification from older bicyclist fatal crashes. Transp. Res. Rec. 267, 638–649 (2019)

    Google Scholar 

  11. Das, S., Minjares-Kyle, L., Lingtao, W., Henk, R.H.: Understanding crash potential associated with teen driving: survey analysis using multivariate graphical method. J. Safety Res. 70, 213–222 (2019)

    Article  Google Scholar 

  12. Das, S., Sun, X.: Factor association with multiple corre-spondence analysis in vehicle–pedestrian crashes. Transp. Res. Rec. 2519(1), 95–103 (2015)

    Article  Google Scholar 

  13. Das, S., Sun, X.: Association knowledge for fatal run-off- road crashes by Multiple Correspondence Analysis. IATSS Res. 39(2), 146–155 (2016)

    Article  Google Scholar 

  14. Daziano, R.A., Sarrias, M., Leard, B.: Are consumers willing to pay to let cars drive for them? analyzing response to autonomous vehicles. Transp. Res. Part C Emerg. Technol. 78, 150–164 (2017)

    Article  Google Scholar 

  15. Degraeve, B., Granie, M.A., Pravossoudovitch, K.: Men and women drivers: a study of social representations through prototypical and correspondence analysis (2014)

    Google Scholar 

  16. Diana, M., Pronello, C.: Traveler segmentation strategy with nominal variables through correspondence analysis. Transp. Policy 17(3), 183–190 (2010)

    Article  Google Scholar 

  17. Fagnant, D.J., Kockelman, K.M.: Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas. Transportation 45(1), 143–158 (2018)

    Article  Google Scholar 

  18. Fontaine, H.: A typological analysis of pedestrian accidents. In: 7th workshop of ICTCT, pp. 26–27 (1995)

    Google Scholar 

  19. Fort, E., Gadegbeku, B., Gat, E., Pelissier, C., Hours, M., Charbotel, B.: Working conditions and risk exposure of employees whose occupations require driving on public roads – factorial analysis and classification. Accid. Anal. Prev. 131, 254–267 (2019)

    Article  Google Scholar 

  20. Gold, C., Korber, M., Hohenberger, C., Lechner, D., Bengler, K.: Trust in automation–before and after the experience of take-over scenarios in a highly automated vehicle. Procedia Manuf. 3, 3025–3032 (2015)

    Article  Google Scholar 

  21. Greenacre, M., Blasius, J.: Multiple Correspondence Analysis and Related Methods. Chapman and Hall/CRC, Boca Raton (2006)

    Book  Google Scholar 

  22. Heide, A., Henning, K.: The “Cognitive Car” a roadmap for research issues in the automotive sector. IFAC Proc. Vol. 39(4), 44–50 (2006)

    Article  Google Scholar 

  23. Hoffman, D.L., De Leeuw, J.: Interpreting multiple correspondence analysis as a multidimensional scaling method. Mark. Lett. 3(3), 259–272 (1992)

    Article  Google Scholar 

  24. Hohenberger, C., Sporrle, M., Welpe, I.M.: Not fearless, but self-enhanced: the effects of anxiety on the willingness to use autonomous cars depend on individual levels of self-enhancement. Technol. Forecast. Soc. Chang. 116, 40–52 (2017)

    Article  Google Scholar 

  25. Hou, G., Chen, S., Chen, F.: Framework of simulation- based vehicle safety performance assessment of highway system under hazardous driving conditions. Transp. Res. Part C Emerg. Technol. 105, 23–36 (2019)

    Article  Google Scholar 

  26. Howard, D., Dai, D.: Public perceptions of self-driving cars: the case of Berkeley, California. In: Transportation Research Board 93rd Annual Meeting, vol. 14, pp. 1–16 (2014)

    Google Scholar 

  27. Hulse, L.M., Xie, H., Galea, E.R.: Perceptions of autonomous vehicles relationships with road users, risk, gender and age. Saf. Sci. 102, 1–13 (2018)

    Article  Google Scholar 

  28. Keeney, T.: Mobility-as-a-service: Why self-driving cars could change everything. Ark Invest 1, 3 (2017)

    Google Scholar 

  29. Krueger, R., Rashidi, T.H., Rose, J.M.: Preferences for shared autonomous vehicles. Transp. Res. Part C Emerg. Technol. 69, 343–355 (2016)

    Article  Google Scholar 

  30. Kyriakidis, M., Happee, R., de Winter, J.C.F.: Public opinion on automated driving: results of an international questionnaire among 5000 respondents. Transport. Res. F Traffic Psychol. Behav. 32, 127–140 (2015)

    Article  Google Scholar 

  31. Lee, C., Ward, C., Raue, M., D’Ambrosio, L., Coughlin, J.: Age differences in acceptance of self-driving cars: a survey of perceptions and attitudes. In: Zhou, J., Salvendy, G. (eds.) ITAP 2017. LNCS, vol. 10297, pp. 3–13. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58530-7_1

    Chapter  Google Scholar 

  32. Menon, N.: Autonomous vehicles an empirical assessment of consumers’ perceptions, intended adoption, and impacts on household vehicle ownership (2017)

    Google Scholar 

  33. National Highway Traffic Safety Administration (NHTSA). Traffic Safety Facts: 2017. Bicyclists and Other Cyclists (2019)

    Google Scholar 

  34. National Highway Traffic Safety Administration (NHTSA). Traffic Safety Facts: 2017. Pedestrians (2019)

    Google Scholar 

  35. U.S. Government Accountability Office. Automated Vehicle: Comprehensive Plan Could Help DOT Address Challenges (2017)

    Google Scholar 

  36. National Research Council (US). Transportation Research Board. Task Force on Development of the Highway Safety Manual and Transportation Officials. Joint Task Force on the Highway Safety Manual. Highway safety manual, vol. 1. AASHTO (2010)

    Google Scholar 

  37. Penmetsa, P., Adanu, E.K., Wood, D., Wang, T., Jones, S.L.: Perceptions and expectations of autonomous vehicles–a snapshot of vulnerable road user opinion. Technol. Forecast. Soc. Chang. 143, 9–13 (2019)

    Article  Google Scholar 

  38. Schoettle, B., Sivak, M.: A survey of public opinion about autonomous and self-driving vehicles in the US, the UK, and Australia. Technical Report. University of Michigan, Ann Arbor, Transportation Research Institute (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Texas A&M University, 3135 TAMU, College Station, TX, 77843, USA

    Subasish Das

  2. Oregon State University, Corvallis, OR, 97331, USA

    Hamsa Zubaidi

Authors
  1. Subasish Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamsa Zubaidi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Subasish Das .

Editor information

Editors and Affiliations

  1. Technische Universität Ilmenau, Ilmenau, Germany

    Heidi Krömker

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Das, S., Zubaidi, H. (2021). Autonomous Vehicles and Pedestrians: A Case Study of Human Computer Interaction. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2021. Lecture Notes in Computer Science(), vol 12791. Springer, Cham. https://doi.org/10.1007/978-3-030-78358-7_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-78358-7_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78357-0

  • Online ISBN: 978-3-030-78358-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation