Skip to main content
SpringerLink
Log in

Leaders or Team-Mates: Exploring the Role-Based Relationship Between Multiple Intelligent Agents in Driving Scenarios

Research on the Role-Based Relationship Between Multiple Intelligent Agents in Driving Scenarios

  • Conference paper
  • First Online:
HCI in Mobility, Transport, and Automotive Systems (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14048))

Included in the following conference series:

  • 736 Accesses

Abstract

Intelligent agents (IAs) are increasingly used in vehicles and associated services (e.g. navigation, entertainment) to enhance user experience, as IAs were applied to the car and turned the vehicle into a service platform under the rapid development of the intellectualized and connected vehicle. However, various IAs may be employed by other services and devices. In the case of in-vehicle cross-device interaction, when users interact simultaneously with multiple services or devices, the actions and decisions of one IA may conflict with those of others. This paper presents a role-based relationship framework to resolve potential conflicts between different IAs in the driving scenarios. The article discusses four types of IA relationships: Partnership, Representative, Subordinate, and Co-embodiment. To examine people's perceptions and attitudes towards different types of relationships, we apply an evaluation system and conduct user studies (N = 30). In two scenarios (Navigation Plan & Music Switching), Participants are required to engage in conversations with IAs based on various types of relationships. Data analysis and user interviews show that Partnership is gaining popularity in leisure and entertainment settings. Moreover, Representative is more effective in efficiency-oriented use cases. In addition, the research on driver's attention behavior suggests that Representatives can convince the driver to focus on the road more efficiently in navigation scenarios than in music settings. After evaluating the different role-based relationships of IAs, design recommendations for user interactions with multiple IAs in driving scenarios are offered.

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 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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. Andrist, S., Tan, X.Z., Gleicher, M., Mutlu, B.: Conversational gaze aversion for humanlike robots. In: Proceedings of the 2014 ACM/IEEE International Conference on Human-robot Interaction, pp. 25–32. Association for Computing Machinery (2014). https://doi.org/10.1145/2559636.2559666

  2. Bellamy, R.K.E., Andrist, S., Bickmore, T., Churchill, E.F., Erickson, T.: Human-agent collaboration: can an agent be a partner? In: Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 1289–1294. Association for Computing Machinery (2017). https://doi.org/10.1145/3027063.3051138

  3. Braun, M., Broy, N., Pfleging, B., Alt, F.: A design space for conversational in-vehicle information systems. In: Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services, pp. 1–8. Association for Computing Machinery (2017). https://doi.org/10.1145/3098279.3122122

  4. Braun, M., Mainz, A., Chadowitz, R., Pfleging, B., Alt, F.: At Your Service: Designing Voice Assistant Personalities to Improve Automotive User Interfaces (2019). https://doi.org/10.1145/3290605.3300270

  5. Carpinella, C., Wyman, A., Perez, M., Stroessner, S.: The Robotic Social Attributes Scale (RoSAS): development and validation, 254–262 (2017). https://doi.org/10.1145/2909824.3020208

  6. Chen, X., et al.: Chat with smart conversational agents: how to evaluate chat experience in smart home. In: Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI 2019), New York, NY, USA, pp. 1–6. Association for Computing Machinery (2019). https://doi.org/10.1145/3338286.3344408

  7. Clark, L., et al.: The State of Speech in HCI: Trends, Themes and Challenges. Interact. Comput. 31(4), 349–371 (2019). https://doi.org/10.1093/iwc/iwz016

  8. Darling, N., Hamilton, S., Toyokawa, T., Matsuda, S.: Naturally occurring mentoring in Japan and the United States: social roles and correlates. Am. J. Community Psychol. 30(2), 245–270 (2002). https://doi.org/10.1023/A:1014684928461

  9. Dong, J., Lawson, E., Olsen, J., Jeon, M.: Female voice agents in fully autonomous vehicles are not only more likeable and comfortable, but also more competent. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 64(1), 1033–1037 (2020). https://doi.org/10.1177/1071181320641248

  10. Doyle, P.R., Edwards, J., Dumbleton, O., Clark, L., Cowan, B.R.: Mapping perceptions of humanness in intelligent personal assistant interaction. In: Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI 2019), New York, NY, USA, pp. 1–12. Association for Computing Machinery (2019). https://doi.org/10.1145/3338286.3340116

  11. Fraune, M.R., Šabanović, S.: Robot gossip: effects of mode of robot communication on human perceptions of robots. In: Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction (HRI 2014), New York, NY, USA, pp. 160–161. Association for Computing Machinery (2014). https://doi.org/10.1145/2559636.2559832

  12. Fraune, M.R., Šabanović, S., Smith, E.R., Nishiwaki, Y., Okada, M.: Threatening flocks and mindful snowflakes: how group entitativity affects perceptions of robots. In: 2017 12th ACM/IEEE International Conference on Human-Robot Interaction, HRI, pp. 205–213 (2017)

    Google Scholar 

  13. Fraune, M., Sabanovic, S.: Negative attitudes toward minimalistic robots with intragroup communication styles. In: Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, vol. 2014 (2014)

    Google Scholar 

  14. Guisewite, A.: Leveraging robot embodiment to facilitate trust and smoothness. The Robotics Institute Carnegie Mellon University. https://www.ri.cmu.edu/publications/leveraging-robot-embodiment-to-facilitate-trust-and-smoothness/

  15. Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. In: Hancock, P.A., Meshkati, N. (eds.) Advances in Psychology, vol. 52, pp. 139–183. North-Holland (1988)

    Google Scholar 

  16. Hayashi, K., et al.: Humanoid robots as a passive-social medium: a field experiment at a train station. In: Proceedings of the ACM/IEEE International Conference on Human-robot interaction, pp. 137–144. Association for Computing Machinery (2007). https://doi.org/10.1145/1228716.1228735

  17. Hock, P., Kraus, J., Walch, M., Lang, N., Baumann, M.: Elaborating feedback strategies for maintaining automation in highly automated driving. In: Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 105–112. ACM (2016). https://doi.org/10.1145/3003715.3005414

  18. Jonsson, I.-M., Zajicek, M., Harris, H., Nass, C.: In-car speech based information systems for older adults. In: Intelligent Transportation Society of America - 12th World Congress on Intelligent Transport Systems 2005, vol. 4, pp. 2113–2124 (2009)

    Google Scholar 

  19. Karatas, N., Yoshikawa, S., Okada, M.: NAMIDA: sociable driving agents with multiparty conversation. In: Proceedings of the Fourth International Conference on Human Agent Interaction, pp. 35–42 (ACM, 2016). https://doi.org/10.1145/2974804.2974811

  20. Karatas, N., et al.: Sociable driving agents to maintain driver’s attention in autonomous driving. In: 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 143–149, IEEE (2017). https://doi.org/10.1109/ROMAN.2017.8172293

  21. Kitamura, Y.: Web information integration using multiple character agents. In: Prendinger, H., Ishizuka, M. (eds.) Life-Like Characters, pp. 295–315. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-662-08373-4_13

  22. Kraus, J.M., et al.: Human after all: effects of mere presence and social interaction of a humanoid robot as a co-driver in automated driving. In: Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 129–134. ACM (2016). https://doi.org/10.1145/3004323.3004338

  23. Lee, S.C., et al.: To Go or Not To Go? That is the Question”: When In-Vehicle Agents Argue with Each Other. In: 13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 223–224. Association for Computing Machinery (2021). https://doi.org/10.1145/3473682.3481876

  24. Lee, S.C., Sanghavi, H., Ko, S., Jeon, M.: Autonomous driving with an agent: speech style and embodiment. In: Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings, pp. 209–214. ACM (2019). https://doi.org/10.1145/3349263.3351515

  25. Li, Y., Hollender, N., Held, T.: Task Sequence Effects in Usability Tests (2013)

    Google Scholar 

  26. Luria, M., et al.: Re-Embodiment and Co-Embodiment: Exploration of social presence for robots and conversational agents. In: Proceedings of the 2019 on Designing Interactive Systems Conference, pp. 633–644. ACM (2019). https://doi.org/10.1145/3322276.3322340

  27. Ma, X., Forlizzi, J., Dow, S.: Guidelines for Depicting Emotions in Storyboard Scenarios. undefined (2012)

    Google Scholar 

  28. Maciej, J., Vollrath, M.: Comparison of manual vs. speech-based interaction with in-vehicle information systems. Acc. Anal. Prevent. 41, 924–930 (2009)

    Google Scholar 

  29. Mao, X., Yu, E.: Organizational and social concepts in agent oriented software engineering. In: Odell, J., Giorgini, P., Müller, J.P. (eds.) AOSE 2004. LNCS, vol. 3382, pp. 1–15. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30578-1_1

    Chapter  Google Scholar 

  30. Ohmoto, Y., Karasaki, J., Nishida, T.: Inducing and maintaining the intentional stance by showing interactions between multiple agents. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents, pp. 203–210. ACM (2018). https://doi.org/10.1145/3267851.3267886

  31. Pauzié, A., Manzano, J., Dapzol, N.: Driver’s Behavior and Workload Assessment for New In-Vehicle Technologies Design. 9

    Google Scholar 

  32. Peissner, M., Doebler, V.: Can voice interaction help reducing the level of distraction and prevent accidents? Meta-Study on Driver Distraction and Voice Interaction (2011). https://www.semanticscholar.org/paper/Can-voice-interaction-help-reducing-the-level-of-on-Peissner-Doebler/5454baad8cb1d0c2702f1a3b020018a4b7d1956b

  33. Prajod, P., Al Owayyed, M., Rietveld, T., van der Steeg, J.-J., Broekens, J.: The effect of virtual agent warmth on human-agent negotiation. In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems 71–76. International Foundation for Autonomous Agents and Multiagent Systems (2019)

    Google Scholar 

  34. Romani, L.: Japanese views on superior-subordinate relationship in Swedish-Japanese collaboration. In: Proceedings of the 3rd International Conference on Intercultural Collaboration, pp. 231–234. Association for Computing Machinery (2010). https://doi.org/10.1145/1841853.1841895

  35. Santos, F.P.: Social norms of cooperation in multiagent systems. In: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems 1859–1860 (International Foundation for Autonomous Agents and Multiagent Systems (2017)

    Google Scholar 

  36. Sauro, J., Lewis, J.R.: Quantifying the User Experience: Practical Statistics for User Research. Morgan Kaufmann (2016)

    Google Scholar 

  37. Shneiderman, B., Maes, P.: Direct manipulation vs. interface agents. Interactions 4, 42–61 (1997)

    Article  Google Scholar 

  38. Suzuki, S.V., Yamada, S.: Persuasion through overheard communication by life-like agents. In: Proceedings. IEEE/WIC/ACM International Conference on Intelligent Agent Technology, 2004. (IAT 2004), pp. 225–231 (2004). https://doi.org/10.1109/IAT.2004.1342948

  39. Tan, X. Z., Luria, M., Steinfeld, A., Forlizzi, J.: Charting sequential person transfers between devices, agents, and robots. In: Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, pp. 43–52. ACM (2021). https://doi.org/10.1145/3434073.3444654

  40. Tan, X.Z., Reig, S., Carter, E.J., Steinfeld, A.: From one to another: how robot-robot interaction affects users’ perceptions following a transition between robots. In: 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 114–122. IEEE (2019). https://doi.org/10.1109/HRI.2019.8673304

  41. Tejwani, R., Moreno, F., Jeong, S., Won Park, H., Breazeal, C.: Migratable AI: effect of identity and information migration on users’ perception of conversational AI agents. In: 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 877–884. IEEE (2020). https://doi.org/10.1109/RO-MAN47096.2020.9223436

  42. Walster, E., Festinger, L.: The effectiveness of ‘over-heard’ persuasive communications. J. Abnorm. Soc. Psychol. 65, 395–402 (1962)

    Article  Google Scholar 

  43. Wang, M., Hock, P., Lee, S.C., Baumann, M., Jeon, M.: Genie vs. Jarvis: characteristics and design considerations of in-vehicle intelligent agents. In: 13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 197–199. ACM (2021). https://doi.org/10.1145/3473682.3479720

  44. Wang, M., et al.: In-vehicle intelligent agents in fully autonomous driving: the effects of speech style and embodiment together and separately. In: 13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 247–254. ACM (2021). https://doi.org/10.1145/3409118.3475142

  45. Wen, R., Han, Z., Williams, T.: Teacher, teammate, subordinate, friend: generating norm violation responses grounded in role-based relational norms 10 (2022)

    Google Scholar 

  46. Wen, R., Kim, B., Phillips, E., Zhu, Q., Williams, T.: Comparing strategies for robot communication of role-grounded moral norms. In: Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, pp. 323–327. Association for Computing Machinery (2021). https://doi.org/10.1145/3434074.3447185

  47. Williams, K., Flores, J.A., Peters, J.: Affective robot influence on driver adherence to safety, cognitive load reduction and sociability. In: Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 1–8. ACM (2014). https://doi.org/10.1145/2667317.2667342

  48. Williams, T., Zhu, Q., Wen, R., de Visser, E.J.: The confucian matador: three defenses against the mechanical bull. In: Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 25–33. Association for Computing Machinery (2020). https://doi.org/10.1145/3371382.3380740

  49. Wong, P.N.Y., Brumby, D., Babu, H., Kobayashi, K.: ‘Watch Out!’: Semi-Autonomous Vehicles Using Assertive Voices to Grab Distracted Drivers’ Attention. 6 (2019). https://doi.org/10.1145/3290607.3312838

  50. Zhu, Q., Williams, T., Wen, R. Confucian Robot Ethics. 12

    Google Scholar 

  51. Caste and ecology in the social insects. Acta Biotheor 28, 234–235 (1979)

    Google Scholar 

  52. Kraus - 2016 - Human After All Effects of Mere Presence and Soci.pdf

    Google Scholar 

  53. Design of Coal Mine Safety Monitoring System Based on Multi-Agent. In: 2021 2nd International Conference on Artificial Intelligence and Information Systems. ACM Other conferences https://dl.acm.org/doi/abs/10.1145/3469213.3470710

  54. The ontological properties of social roles in multi-agent systems: definitional dependence, powers and roles playing roles. SpringerLink. https://doi.org/10.1007/s10506-007-9030-8

  55. Multi-agent role allocation: issues, approaches, and multiple perspectives: Autonomous Agents and Multi-Agent Systems: Vol 22, No 2. https://dl.acm.org/doi/10.1007/s10458-010-9127-4

  56. Teams in animal societies. Behavioral Ecology. Oxford Academic. https://academic.oup.com/beheco/article/12/5/534/311666

  57. Karatas 等。 - 2016 - NAMIDA Sociable Driving Agents with Multiparty Co.pdf

    Google Scholar 

  58. Pay attention!. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. https://dl.acm.org/doi/10.1145/2207676.2207679

  59. Designing Emotional Expressions of Conversational States for Voice Assistants. In: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems. https://dl.acm.org/doi/10.1145/3170427.3188560

Download references

Acknowledgments

This research is supported by the National Key Research and Development Program of China (No. 2021YFF0900600).

Author information

Authors and Affiliations

  1. Hunan University, Changsha, Hunan, China

    Shuo Li, Xiang Yuan, Xinyuan Zhao & Shirao Yang

Authors
  1. Shuo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiang Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinyuan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shirao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiang Yuan .

Editor information

Editors and Affiliations

  1. Technische Universität Ilmenau, Ilmenau, Germany

    Heidi Krömker

Rights and permissions

Reprints and permissions

Copyright information

© 2023 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

Li, S., Yuan, X., Zhao, X., Yang, S. (2023). Leaders or Team-Mates: Exploring the Role-Based Relationship Between Multiple Intelligent Agents in Driving Scenarios. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2023. Lecture Notes in Computer Science, vol 14048. Springer, Cham. https://doi.org/10.1007/978-3-031-35678-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-35678-0_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35677-3

  • Online ISBN: 978-3-031-35678-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation