Skip to main content
SpringerLink
Log in
Book cover

International Conference on Human-Computer Interaction

HCII 2021: Social Computing and Social Media: Experience Design and Social Network Analysis pp 190–201Cite as

Human-Machine Interaction for Autonomous Vehicles: A Review

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12774))

Abstract

The rate of advancement in autonomous systems has been increasing and humans rely on such systems for every aspect of daily life. This is especially true in the area of autonomous vehicles, where new techniques and discoveries have been uncovered and Society of Automotive Engineers (SAE) Level 5 self-driving might be a reality in a few years. Despite the significant body of work on self driving technology, many people are still sceptical about the idea of riding in a fully autonomous vehicle (AV). There is a need to build trust between humans and vehicles for successful adoption of AVs. In this paper we complement existing surveys by describing 3 active research areas that are key for enhancing trust in autonomous vehicles, namely 1) Trust in Autonomous Vehicles, 2) Human Machine Interfaces, and 3) Driver Activity Detection. We discuss and highlight the key ideas and techniques in recent research works of each field, and discuss potential future directions.

This is a preview of subscription content, 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

Learn about institutional subscriptions

References

  1. Administration, N.H.T.S.: Federal motor vehicle safety standards; minimum sound requirements for hybrid and electric vehicles (2016)

    Google Scholar 

  2. Akash, K., Hu, W.L., Reid, T., Jain, N.: Dynamic modeling of trust in human-machine interactions. In: 2017 American Control Conference (ACC), pp. 1542–1548. IEEE (2017)

    Google Scholar 

  3. Alvarez, W.M., de Miguel, M.Á., García, F., Olaverri-Monreal, C.: Response of vulnerable road users to visual information from autonomous vehicles in shared spaces. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp. 3714–3719. IEEE (2019)

    Google Scholar 

  4. Asha, A.Z., Anzum, F., Finn, P., Sharlin, E., Costa Sousa, M.: Designing external automotive displays: VR prototypes and analysis. In: 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 74–82 (2020)

    Google Scholar 

  5. Awad, E., et al.: The moral machine experiment. Nature 563(7729), 59–64 (2018)

    Article  Google Scholar 

  6. Bazilinskyy, P., Dodou, D., De Winter, J.: Survey on eHMI concepts: the effect of text, color, and perspective. Transp. Res. Part F: Traffic Psychol. Behav. 67, 175–194 (2019)

    Article  Google Scholar 

  7. Beiker, S.A.: Legal aspects of autonomous driving. Santa Clara L. Rev. 52, 1145 (2012)

    Google Scholar 

  8. Braunagel, C., Kasneci, E., Stolzmann, W., Rosenstiel, W.: Driver-activity recognition in the context of conditionally autonomous driving. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pp. 1652–1657. IEEE (2015)

    Google Scholar 

  9. Colley, M., Mytilineos, S.C., Walch, M., Gugenheimer, J., Rukzio, E.: Evaluating highly automated trucks as signaling lights. In: 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 111–121 (2020)

    Google Scholar 

  10. Committee, S.O.R.A.V.S., et al.: Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. SAE Stan. J. 3016, 1–16 (2014)

    Google Scholar 

  11. Crawford, K., Calo, R.: There is a blind spot in AI research. Nat. News 538(7625), 311 (2016)

    Article  Google Scholar 

  12. Dang, L.M., Min, K., Wang, H., Piran, M.J., Lee, C.H., Moon, H.: Sensor-based and vision-based human activity recognition: a comprehensive survey. Pattern Recogn. 108, 107561 (2020)

    Article  Google Scholar 

  13. Dey, D., et al.: Taming the eHMI jungle: a classification taxonomy to guide, compare, and assess the design principles of automated vehicles’ external human-machine interfaces. Transp. Res. Interdiscip. Perspect. 7, 100174 (2020)

    Google Scholar 

  14. Eisma, Y.B., van Bergen, S., Ter Brake, S., Hensen, M., Tempelaar, W.J., De Winter, J.C.: External human-machine interfaces: the effect of display location on crossing intentions and eye movements. Information 11(1), 13 (2020)

    Article  Google Scholar 

  15. Erdélyi, O.J., Goldsmith, J.: Regulating artificial intelligence: proposal for a global solution. In: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pp. 95–101 (2018)

    Google Scholar 

  16. Fridman, L., Mehler, B., Xia, L., Yang, Y., Facusse, L.Y., Reimer, B.: To walk or not to walk: Crowdsourced assessment of external vehicle-to-pedestrian displays. arXiv preprint arXiv:1707.02698 (2017)

  17. Hannah Topliss, B., Harvey, C., Burnett, G.: How long can a driver look? exploring time thresholds to evaluate head-up display imagery. In: 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 9–18 (2020)

    Google Scholar 

  18. Hengstler, M., Enkel, E., Duelli, S.: Applied artificial intelligence and trust-the case of autonomous vehicles and medical assistance devices. Technol. Forecast. Soc. Change 105, 105–120 (2016)

    Article  Google Scholar 

  19. Hoc, J.M.: From human-machine interaction to human-machine cooperation. Ergonomics 43(7), 833–843 (2000)

    Article  Google Scholar 

  20. Hoc, J.M.: Towards a cognitive approach to human-machine cooperation in dynamic situations. Int. J. Hum. Comput. Stud. 54(4), 509–540 (2001)

    Article  Google Scholar 

  21. Kim, Y., Kim, I.: Security issues in vehicular networks. In: The International Conference on Information Networking 2013 (ICOIN), pp. 468–472. IEEE (2013)

    Google Scholar 

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

    Article  Google Scholar 

  23. Lee, J.M., Park, S.W., et al.: Drivers’ user-interface information prioritization in manual and autonomous vehicles. Int. J. Automot. Technol. 21(6), 1355–1367 (2020)

    Article  Google Scholar 

  24. Martin, M., Voit, M., Stiefelhagen, R.: Dynamic interaction graphs for driver activity recognition. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pp. 1–7. IEEE (2020)

    Google Scholar 

  25. Millot, P.: Toward human-machine cooperation. In: Filipe, J., Cetto, J.A., Ferrier, J.L. (eds.) Informatics in Control, Automation and Robotics. LNEE, vol. 24, pp. 3–20. Springer, Berlin (2009). https://doi.org/10.1007/978-3-540-85640-5_1

  26. Moore, D., Currano, R., Sirkin, D.: Sound decisions: how synthetic motor sounds improve autonomous vehicle-pedestrian interactions. In: 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 94–103 (2020)

    Google Scholar 

  27. Olaverri-Monreal, C.: Promoting trust in self-driving vehicles. Nat. Electron. 3(6), 292–294 (2020)

    Article  Google Scholar 

  28. Pan, C., Cao, H., Zhang, W., Song, X., Li, M.: Driver activity recognition using spatial-temporal graph convolutional LSTM networks with attention mechanism. IET Intell. Transport Syst. 15(2), 297–307 (2020)

    Article  Google Scholar 

  29. Parker, J., Danks, D.: How technological advances can reveal rights. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, p. 201 (2019)

    Google Scholar 

  30. Roitberg, A., Ma, C., Haurilet, M., Stiefelhagen, R.: Open set driver activity recognition. In: 2020 IEEE Intelligent Vehicles Symposium (IV), pp. 1048–1053. IEEE (2020)

    Google Scholar 

  31. Rouchitsas, A., Alm, H.: External human-machine interfaces for autonomous vehicle-to-pedestrian communication: a review of empirical work. Front. Psychol. 10, 2757 (2019)

    Article  Google Scholar 

  32. Alpers, S., et al.: Capturing passenger experience in a ride-sharing autonomous vehicle: the role of digital assistants in user interface design. In: 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 83–93 (2020)

    Google Scholar 

  33. Shahrdar, S., Menezes, L., Nojoumian, M.: A survey on trust in autonomous systems. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) SAI 2018. AISC, vol. 857, pp. 368–386. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01177-2_27

    Chapter  Google Scholar 

  34. Shahrdar, S., Park, C., Nojoumian, M.: Human trust measurement using an immersive virtual reality autonomous vehicle simulator. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pp. 515–520 (2019)

    Google Scholar 

  35. Uggirala, A., Gramopadhye, A.K., Melloy, B.J., Toler, J.E.: Measurement of trust in complex and dynamic systems using a quantitative approach. Int. J. Ind. Ergon. 34(3), 175–186 (2004)

    Article  Google Scholar 

  36. Wagner, M., Koopman, P.: A philosophy for developing trust in self-driving cars. In: Meyer, G., Beiker, S. (eds.) Road Vehicle Automation 2. LNM, pp. 163–171. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19078-5_14

    Chapter  Google Scholar 

  37. Wilder, B., Horvitz, E., Kamar, E.: Learning to complement humans. arXiv preprint arXiv:2005.00582 (2020)

  38. Xing, Y., Lv, C., Wang, H., Cao, D., Velenis, E., Wang, F.Y.: Driver activity recognition for intelligent vehicles: a deep learning approach. IEEE Trans. Veh. Technol. 68(6), 5379–5390 (2019)

    Article  Google Scholar 

  39. Yu, H., Shen, Z., Miao, C., Leung, C., Lesser, V.R., Yang, Q.: Building ethics into artificial intelligence. arXiv preprint arXiv:1812.02953 (2018)

  40. Zell, E., Krizan, Z.: Do people have insight into their abilities? a metasynthesis. Perspect. Psychol. Sci. 9(2), 111–125 (2014)

    Article  Google Scholar 

Download references

Acknowledgement

This research is supported, in part, by Nanyang Technological University, Nanyang Assistant Professorship (NAP); Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI) (Alibaba-NTU-AIR2019B1), Nanyang Technological University, Singapore; the RIE 2020 Advanced Manufacturing and Engineering (AME) Programmatic Fund (No. A20G8b0102), Singapore; and the Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Jiehuang Zhang, Ying Shu & Han Yu

  2. Alibaba-NTU Singapore Joint Research Institute, Singapore, Singapore

    Jiehuang Zhang & Ying Shu

Authors
  1. Jiehuang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ying Shu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Han Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jiehuang Zhang , Ying Shu or Han Yu .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Towson University, Towson, MD, USA

    Gabriele Meiselwitz

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

Zhang, J., Shu, Y., Yu, H. (2021). Human-Machine Interaction for Autonomous Vehicles: A Review. In: Meiselwitz, G. (eds) Social Computing and Social Media: Experience Design and Social Network Analysis . HCII 2021. Lecture Notes in Computer Science(), vol 12774. Springer, Cham. https://doi.org/10.1007/978-3-030-77626-8_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-77626-8_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77625-1

  • Online ISBN: 978-3-030-77626-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation