ABSTRACT
Current conversational and hierarchical structures between Personal Assistants (PAs) and drivers are clearly and explicitly defined, in that users are either hierarchically superordinate or on par with their PAs. Simultaneously, technological advances around intelligence, personalization and proactivity have gained momentum in the most recent past. The exponential development of intelligence-based technologies will soon enable PAs to take over large parts of the driving process. PAs will convince drivers to trust in their capabilities, by explaining themselves and their decision-making processes. They will consider drivers’ knowledge, experience, and needs to transform interactions into deeply personal experiences. Furthermore, proactive PAs will provide context-sensitive content in adequate situations. These developments may just tip the scale and challenge currently valid PA-driver-relations. We envision roles to undergo a reversal: where conversations are now driven by users and assisted by assistants, intelligent, personalized, and proactive PAs will take over the lead and, figuratively, the driver seat.
- Brett Aho and Roberta Duffield. 2020. Beyond surveillance capitalism: Privacy, regulation and big data in Europe and China. In Economy and Society, 49:2, (May 2020), 187–212. DOI: 10.1080/03085147.2019.1690275Google ScholarCross Ref
- Salvatore Andolina, Valeria Orso, Hendrik Schneider, Khalil Klouche, Tuukka Ruotsalo, Luciano Gamberini, and Giulio Jacucci. 2018. Investigating Proactive Search Support in Conversations. In Proceedings of the 2018 Designing Interactive Systems Conference (DIS '18), June 9-13, 2018, Hong Kong, China. ACM Inc., New York, NY, 1295–1307. DOI: 10.1145/3196709.3196734Google ScholarDigital Library
- Shahin Atakishiyeva, Mohammad Salameh, Hengshuai Yao, and Randy Goebel. 2023. Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions, 2023,. https://arxiv.org/pdf/2112.11561.pdfGoogle Scholar
- Nathan Benaich and Ian Hogarth. 2022. State of AI Report. (October 2022). Retrieved April 12, 2023 from https://www.stateof.ai/Google Scholar
- Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21), March 3-10, 2021, Virtual Event, Canada. ACM Inc., New York, NY, 610–623. DOI: 10.1145/3442188.3445922Google ScholarDigital Library
- Som Biswas. 2023. Prospective Role of Chat GPT in the Military: According to ChatGPT. (February 2023). Retrieved April 17, 2023 from https://www.qeios.com/read/8WYYODGoogle Scholar
- Stacy R. Branham and Antony R. Mukkath Roy. 2019. Reading Between the Guidelines: How Commercial Voice Assistant Guidelines Hinder Accessibility for Blind Users. In ASSETS '19: Proceedings of the 21st International ACM SIGACCESS Conference on Computers and Accessibility, October 28-30, 2019, Pittsburgh, PA. ACM Inc., New York, NY, 446–458. DOI: 10.1145/3308561.3353797Google ScholarDigital Library
- Michael Braun, Anja Mainz, Ronee Chadowitz, Bastian Pfleging, and Florian Alt. 2019. At your service: Designing voice assistant personalities to improve automotive user interfaces a real world driving study. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI'19), May 4-9, 2019, Glasgow, UK. ACM Inc., New York, NY, 1–11. DOI: 10.1145/3290605.3300270Google ScholarDigital Library
- Narae Cha, Auk Kim, Cheul Y. Park, Soowon Kang, Mingyu Park, Jae-Gil Lee, Sangsu Lee, and Uichin Lee. 2020. “Hello There! Is Now a Good Time to Talk?”: Opportune Moments for Proactive Interactions with Smart Speakers. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 4, Issue 3, 74 (September 2020), 1–28. https://doi.org/10.1145/3411810Google ScholarDigital Library
- Leigh Clark, Nadia Pantidi, Orla Cooney, Philip Doyle, Diego Garaialde, Justin Edwards, Brenan Spillane, Emer Gilmartin, Christine Murad, Cosmin Munteanu, Vincent Wade, and Benjamin R. Cowan. 2019. What Makes a Good Conversation?: Challenges in Designing Truly Conversational Agents. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19), May 4-9, 2019, Glasgow, UK. ACM Inc., New York, NY, 1–12. DOI: 10.1145/3290605.3300705Google ScholarDigital Library
- Arthur C. Clarke. 1968. 2001: A Space Odyssey, Hutchinson, London.Google Scholar
- Philip R. Doyle, Justin Edwards, Odile Dumbleton, Leigh Clark, and Benjamin R. Cowan. 2019. 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 '19), October 1-4, 2019, Taipei, Taiwan. ACM Inc., New York, NY, 1–12. DOI: 10.1145/3338286.3340116Google ScholarDigital Library
- Lex Fridman. 2018. Human-centered autonomous vehicle systems: Principles of effective shared autonomy. arXiv:1810.01835. Retrieved from https://arxiv.org/pdf/1810.01835.pdfGoogle Scholar
- Lex Fridman. 2017. MIT 6.S094: Introduction to Deep Learning and Self-Driving Cars. (January 2017). Retrieved April 12, 2023 from https://www.youtube.com/watch?v=1L0TKZQcUtAGoogle Scholar
- Lex Fridman, Daniel E. Brown, Michael Glazer, William Angell, Spencer Dodd, Benedikt Jenik, Jack Terwilliger, Aleksands Patsekin, Julia Kindelsberger, Li Ding, Sean Seaman, Alea Mehler, Andrew Sipperley, Anthony Pettinato, Seppelt, Bobbie, Angell, Linda, Bruce Mehler, and Bryan Reimer. 2019. MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction with Automation. arXiv: 1711.06976. Retrieved from: https://arxiv.org/pdf/1711.06976.pdfGoogle Scholar
- Balint Gyevnar. 2022. Cars that Explain: Building Trust in Autonomous Vehicles through Explanations and Conversations . Retrieved April 12, 2023 from https://gbalint.me/assets/IEEE_ITS_Essay.pdfGoogle Scholar
- Shamsi T. Iqbal and Brian T. Bailey. 2005. Investigating the Effectiveness of Mental Workload as a Predictor of Opportune Moments for Interruption. In Extended Abstracts on Human Factors in Computing Systems (CHI EA '05), April 2-7, 2005, Portland OR, USA. ACM Inc., New York, NY, 1489–1492. DOI: 10.1145/1056808.1056948Google ScholarDigital Library
- Spike Jonze. 2013. Her, Los Angeles: Annapurna Pictures.Google Scholar
- Matthias Kraus, Nicolas Wagner, Zoraida Callejas, and Wolfgang Minker. 2021. The Role of Trust in Proactive Conversational Assistants. In IEEE Access, vol. 9 (August 2021), 112821–112836. DOI: 10.1109/ACCESS.2021.3103893Google ScholarCross Ref
- Esko Lehtonen, Fanny Malin, Tyron Louw, Yee M. Lee, Teemu Itkonen, and Satu Innamaa. 2022. Why would people want to travel more with automated cars? In Transportation Research Part F: Traffic Psychology and Behaviour, Volume 89, 2022, 143–154. DOI. 10.1016/j.trf.2022.06.014Google Scholar
- Hannah Limerick, David Coyle, and James W. Moore. 2015. Empirical Evidence for a Diminished Sense of Agency in Speech Interfaces. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI'15), April 18-23, 2015, Seoul, South Korea. ACM Inc., New York, NY, 3967–3970. DOI: 10.1145/2702123.2702379Google ScholarDigital Library
- Shreyas A. Madhav and Tyagi Amit K. 2022. Explainable Artificial Intelligence (XAI): Connecting Artificial Decision-Making and Human Trust in Autonomous Vehicles. In Proceedings of Third International Conference on Computing, Communications, and Cyber-Security. Lecture Notes in Networks and Systems, vol 421., 2022, Springer, Singapore. DOI: 10.1007/978-981-19-1142-2_10Google ScholarCross Ref
- OpenAI. 2023. ChatGPT . Retrieved April 17, 2023 from https://chat.openai.com/Google Scholar
- Veljko Pejovic and Mirco Musolesi. 2014. InterruptMe: Designing Intelligent Prompting Mechanisms for Pervasive Applications. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '14), September 13-17, 2014, Seattle Washington, USA. ACM Inc., New York, NY, 897–908. DOI: 10.1145/2632048.2632062Google ScholarDigital Library
- Leon Reicherts, Nima Zargham, Michael Bonfert, Yvonne Rogers, and Rainer Malaka. 2021. May I Interrupt? Diverging Opinions on Proactive Smart Speakers. In 3rd Conference on Conversational User Interfaces (CUI '21), July 27-29, 2021, Bilbao, Spain. ACM Inc., New York, NY, 1-10. DOI: 10.1145/3469595.3469629Google ScholarDigital Library
- Maria Schmidt, Wolfgang Minker, and Steffen Werner. 2020. How Users React to Proactive Voice Assistant Behavior While Driving. In Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), May 11-16, 2020, Marseille, France. ELRA Paris, France, 485–490. https://aclanthology.org/2020.lrec-1.61.pdfGoogle Scholar
- Maria Schmidt, Wolfgang Minker, and Steffen Werner. 2020. User Acceptance of Proactive Voice Assistant Behavior. In Konferenz Elektronische Sprachsignalverarbeitung (ESSV), March 4-6, 2020, Magdeburg, Germany. Studientexte zur Sprachkommunikation, Tübingen, Germany, 18–25. https://www.essv.de/pdf/2020_18_25.pdfGoogle Scholar
- Maria Schmidt, Daniela Stier, Steffen Werner, and Wolfgang Minker. 2019. Exploration and assessment of proactive use cases for an in-car voice assistant. In Konferenz Elektronische Sprachsignalverarbeitung (ESSV), March 3-8, 2019, Dresden, Germany. Studientexte zur Sprachkommunikation, Tübingen, Germany, 148–155. https://www.essv.de/paper.php?id=76Google Scholar
- Rob Semmens, Nikolas Martelaro, Pushyami Kaveti, Simon. Stent, and Wendy Ju. 2019. Is Now A Good Time?: An Empirical Study of Vehicle-Driver Communication Timing. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19), May 4-9, 2019, Glasgow, UK. ACM Inc., New York, NY, 12 pages. DOI: 10.1145/3290605.3300867Google ScholarDigital Library
- William Seymour, Mark Coté, and Jose Such. 2022. Can you meaningfully consent in eight seconds? Identifying Ethical Issues with Verbal Consent for Voice Assistants. In 4th Conference on Conversational User Interfaces (CUI 2022), July 26-28, 2022, Glasgow, UK. ACM Inc., New York, NY, 4 pages. DOI: 10.1145/3543829.3544521Google ScholarDigital Library
- Keng Siau and Weiyu Wang. 2018. Building Trust in Artificial Intelligence, Machine Learning, and Robotics. In Cutter Business Technology Journal, Vol. 31, No. 2, 2018, 47–53Google Scholar
- Stanford Institute for Human-Centered Artificial Intelligence. 2023. Artificial Intelligence Index Report 2023 . Retrieved April 17, 2023 from https://aiindex.stanford.edu/report/Google Scholar
- Xu Sun, Jingpeng Li, Pinyan Tang, Siyuan Zhou, Xiangjun Peng, Hao N. Li, and Qingfeng Wang. 2020. Exploring Personalised Autonomous Vehicles to Influence User Trust. In Cogn Comput, 12 (September 2020), 1170–1186. DOI: 10.1007/s12559-020-09757-xGoogle ScholarCross Ref
- Romal Thoppilan, Daniel de Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu S. Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Vincent Zhao, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Pranesh Srinivasan, Laichee Man, Kathleen Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed Chi, and Quok Le. 2022. LaMDA: Language Models for Dialog Applications. arXiv: 2201.08239. Retrieved from https://arxiv.org/pdf/2201.08239.pdfGoogle Scholar
- Sarah T. Völkel, Daniel Buschek, Malin Eiband, Benjamin R. Cowan, and Heinrich Hussmann. 2021. Eliciting and Analysing Users’ Envisioned Dialogues with Perfect Voice Assistants. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI'21), May 8-13, 2021, Yokohama, Japan. ACM Inc., New York, NY, 1–15. DOI: 10.1145/3411764.3445536Google ScholarDigital Library
- Benjamin Weiss, Jürgen Trouvain, Melissa Barkat-Defradas, and John J. Ohala (Eds.). 2020. Voice Attractiveness. Prosody, Phonology and Phonetics, Springer, Singapore.Google Scholar
- Priscilla N. Wong, Duncan P. Brumby, Harsha V. Ramesh, and Kota Kobayashi. 2019. Voices in Self-Driving Cars Should be Assertive to More Quickly Grab a Distracted Driver's Attention. In Automotive User Interfaces and Interactive Vehicular Applications (Automotive'UI 19), September 21-25, 2019, Utrecht, The Netherlands. ACM Inc., New York, NY, 11 pages. DOI: 10.1145/3342197.3344535Google ScholarDigital Library
- Nima Zargham, Leon Reicherts, Michael Bonfert, Sarah T. Völkel, Johannes Schöning, Rainer Malaka, and Yvonne Rogers. 2022. Understanding Circumstances for Desirable Proactive Behaviour of Voice Assistants: The Proactivity Dilemma. In Proceedings of the 4th Conference on Conversational User Interfaces (CUI '22), July 26-28, 2022, Glasgow, UK. ACM Inc., New York, NY, 1–14. DOI: 10.1145/3543829.3543834Google ScholarDigital Library
- Ou Zheng, Mohamed Abdel-Aty, Dongdong Wang, Zijin Wang, and Shengxuan Ding. 2023. ChatGPT Is on the Horizon: Could a Large Language Model Be All We Need for Intelligent Transportation? arXiv: 2303.05382. Retrieved from: https://arxiv.org/abs/2303.05382Google Scholar
Index Terms
- Will the Assistant Become the Driver, and the Driver Become the Assistant?
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