Frederik Wiehr
ABSTRACT
In highly automated driving (HAD), it is still an open question how machines can safely hand over control to humans, and if an advance notice with additional explanations can be beneficial in critical situations. Conceptually, use of formal methods from AI – description logic (DL) and automated planning – in order to more reliably predict when a handover is necessary, and to increase the advance notice for handovers by planning ahead at runtime, can provide a technological support for explanations using natural language generation. However, in this work we address only the user’s perspective with two contributions: First, we evaluate our concept in a driving simulator study (N=23) and find that an advance notice and spoken explanations were preferred over classical handover methods. Second, we propose a framework and an example test scenario specific to handovers that is based on the results of our study.
Supplemental Material
- 2019. Windridge City. https://naturemanufacture.com/windridge-city/Google Scholar
- 2020. 2048 (video game). https://2048game.comGoogle Scholar
- Marilyn Jager Adams, Yvette J. Tenney, and Richard W. Pew. 1995. Situation Awareness and the Cognitive Management of Complex Systems. Hum Factors 37, 1 (1995), 85–104.Google ScholarCross Ref
- Jackie Ayoub, Feng Zhou, Shan Bao, and X. Jessie Yang. 2019. From Manual Driving to Automated Driving. In Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications - AutomotiveUI '19. ACM Press.Google ScholarDigital Library
- Franz Baader, Ian Horrocks, Carsten Lutz, and Uli Sattler. 2017. An Introduction to Description Logic. Cambridge University Press.Google Scholar
- Shadan Sadeghian Borojeni, Lewis Chuang, Wilko Heuten, and Susanne Boll. 2016. Assisting Drivers with Ambient Take-over Requests in Highly Automated Driving. In Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (Ann Arbor, MI, USA) (Automotive’UI 16). Association for Computing Machinery, New York, NY, USA, 237–244.Google ScholarDigital Library
- Shadan Sadeghian Borojeni, Lars Weber, Wilko Heuten, and Susanne Boll. 2018. From Reading to Driving: Priming Mobile Users for Take-over Situations in Highly Automated Driving. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 14, 12 pages.Google ScholarDigital Library
- John Brooke 1996. SUS - A Quick and Dirty Usability Scale. Usability Evaluation in Industry 189, 194 (1996), 4–7.Google Scholar
- Philip R. Cohen and David R. McGee. 2004. Tangible Multimodal Interfaces for Safety-critical Applications. Commun. ACM 47, 1 (2004), 41–46.Google ScholarDigital Library
- Daniel Damböck and Klaus Bengler. 2012. Übernahmezeiten beim hochautomatisierten Fahren. In 5. Tagung Fahrerassistenz.Google Scholar
- D. Damböck, M. Farid, L. Tönert, and K. Bengler. 2012. Übernahmezeiten beim hochautomatisierten Fahren. In Autofahren. 5. Tagung Fahrerassistenz. München, Germany.Google Scholar
- Vera Demberg and Asad Sayeed. 2016. The Frequency of Rapid Pupil Dilations As a Measure of Linguistic Processing Difficulty. PLOS One 11, 1 (2016), e0146194.Google ScholarCross Ref
- Vera Demberg, Asad Sayeed, Angela Mahr, and Christian Müller. 2013. Measuring Linguistically-induced Cognitive Load during Driving Using the ConTRe Task. In ACM International Conference on Automotive User Interfaces and Interactive Vehicular Applications. 176–183.Google ScholarDigital Library
- Murat Dikmen and Catherine M. Burns. 2016. Autonomous Driving in the Real World: Experiences with Tesla Autopilot and Summon. In Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (Ann Arbor, MI, USA) (Automotive’UI 16). Association for Computing Machinery, New York, NY, USA, 225–228.Google ScholarDigital Library
- Thomas A. Dingus, Sheila G. Klauer, Vicki Lewis Neale, Andy Petersen, Suzanne E. Lee, Jeremy Sudweeks, Miguel A. Perez, Jonathan Hankey, David Ramsey, Santosh Gupta, 2006. The 100-car Naturalistic Driving Study, Phase II - Results of the 100-car Field Experiment. Technical Report.Google Scholar
- Mica R. Endsley. 1995. Measurement of Situation Awareness in Dynamic Systems. Hum Factors 37, 1 (1995), 65–84.Google ScholarCross Ref
- Mica R. Endsley. 1996. Automation and Situation Awareness. Automation and Human Performance: Theory and Applications (1996), 163–181.Google Scholar
- Nikolaos Engonopoulos, Asad B. Sayeed, and Vera Demberg. 2013. Language and Cognitive Load in a Dual Task Environment. In Annual Meeting of the Cognitive Science Society (CogSci). 2249–2254.Google Scholar
- A. Eriksson and N. A. Stanton. 2017. Take-over Time in Highly Automated Vehicles: Non-critical Transitions to and from Manual Control. In Human Factors.Google Scholar
- David Garcia, Christine Kreutzer, Karla Badillo-Urquiola, and Mustapha Mouloua. 2015. Measuring Trust of Autonomous Vehicles: A Development and Validation Study. In Communications in Computer and Information Science. Springer International Publishing, 610–615.Google Scholar
- Malik Ghallab, Dana S. Nau, and Paolo Traverso. 2004. Automated Planning - Theory and Practice. Elsevier.Google Scholar
- Jon Arne Glomsrud, André Ødegårdstuen, Asun Lera St Clair, and Øyvind Smogeli. 2019. Trustworthy Versus Explainable AI in Autonomous Vessels. In Proceedings of the International Seminar on Safety and Security of Autonomous Vessels (ISSAV) and European STAMP Workshop and Conference (ESWC). 37–47.Google Scholar
- Christian Gold, Daniel Damböck, Lutz Lorenz, and Klaus Bengler. 2013. “Take Over!” How Long Does It Take to Get the Driver Back Into the Loop?Proceedings of the Human Factors and Ergonomics Society Annual Meeting 57, 1 (Sept. 2013), 1938–1942.Google ScholarCross Ref
- Renate Haeuslschmid, Max von Buelow, Bastian Pfleging, and Andreas Butz. 2017. Supporting Trust in Autonomous Driving. In International Conference on Intelligent User Interfaces (IUI). 319–329.Google Scholar
- Sandra G. Hart and Lowell E. Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. In Advances in Psychology. Vol. 52. Elsevier, 139–183.Google Scholar
- Dominik Heckmann and Antonio Krüger. 2003. A User Modeling Markup Language (UserML) for Ubiquitous Computing. In International Conference on User Modeling (UM). 393–397.Google ScholarCross Ref
- Christian P. Janssen, Shamsi T. Iqbal, Andrew L. Kun, and Stella F. Donker. 2019. Interrupted by My Car? Implications of Interruption and Interleaving Research for Automated Vehicles. Int. J. Hum. Comput. Stud. 130 (2019), 221–233.Google ScholarDigital Library
- Vaiva Kalnikaite, Yvonne Rogers, Jon Bird, Nicolas Villar, Khaled Bachour, Stephen Payne, Peter M. Todd, Johannes Schöning, Antonio Krüger, and Stefan Kreitmayer. 2011. How to Nudge in Situ: Designing Lambent Devices to Deliver Salient Information in Supermarkets. In International Conference on Ubiquitous Computing (UbiComp). 11–20.Google ScholarDigital Library
- Steven J. Kass, Kerstan S. Cole, and Claudia J. Stanny. 2007. Effects of Distraction and Experience on Situation Awareness and Simulated Driving. Transportation Research Part F: Traffic Psychology and Behavior 10, 4(2007), 321–329.Google ScholarCross Ref
- Dagmar Kern, Angela Mahr, Sandro Castronovo, Albrecht Schmidt, and Christian Müller. 2010. Making Use of Drivers' Glances onto the Screen for Explicit Gaze-based Interaction. In Proceedings of the 2nd International Conference on Automotive User Interfaces and Interactive Vehicular Applications - AutomotiveUI '10. ACM Press.Google ScholarDigital Library
- Sheila G. Klauer, Feng Guo, Jeremy Sudweeks, and Thomas A. Dingus. 2010. An Analysis of Driver Inattention Using a Case-crossover Approach on 100-car Data. Technical Report.Google Scholar
- Alfred Kobsa and Wolfgang Wahlster. 1989. User Models in Dialog Systems. Springer.Google ScholarDigital Library
- Jeamin Koo, Jungsuk Kwac, Wendy Ju, Martin Steinert, Larry Leifer, and Clifford Nass. 2014. Why Did My Car Just Do That? Explaining Semi-autonomous Driving Actions to Improve Driver Understanding, Trust, and Performance. International Journal on Interactive Design and Manufacturing (IJIDeM) 9, 4 (April 2014), 269–275.Google Scholar
- Moritz Körber. 2018. Theoretical Considerations and Development of a Questionnaire to Measure Trust in Automation. In Congress of the International Ergonomics Association. Springer, 13–30.Google ScholarCross Ref
- Antonio Krüger, Ilhan Aslan, and Hubert Zimmer. 2004. The Effects of Mobile Pedestrian Navigation Systems on the Concurrent Acquisition of Route and Survey Knowledge. In International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI). 446–450.Google Scholar
- M. Kuehn, Tobias Vogelpohl, and M. Vollrath. 2017. Takeover Times in Highly Automated Driving (level 3).Google Scholar
- J. D. Lee and K. A. See. 2004. Trust in Automation: Designing for Appropriate Reliance. Human Factors: The Journal of the Human Factors and Ergonomics Society 46, 1 (Jan. 2004), 50–80.Google ScholarCross Ref
- Robert E. Llaneras, Jeremy Salinger, and Charles A. Green. 2013. Human Factors Issues Associated with Limited Ability Autonomous Driving Systems: Drivers’ Allocation of Visual Attention to the Forward Roadway. In Proceedings of the 7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design: Driving Assessment 2013. University of Iowa.Google ScholarCross Ref
- Paul Lukowicz, Holger Junker, and Gerhard Tröster. 2004. Automatic Calibration of Body Worn Acceleration Sensors. In International Conference on Pervasive Computing. 176–181.Google Scholar
- Sandra P. Marshall. 2002. The Index of Cognitive Activity: Measuring Cognitive Workload. In IEEE Conference on Human Factors and Power Plants. 7–5–7–9.Google Scholar
- Natasha Merat, A. Hamish Jamson, Frank C. H. Lai, Michael Daly, and Oliver M. J. Carsten. 2014. Transition to Manual: Driver Behaviour When Resuming Control from a Highly Automated Vehicle. Transportation Research Part F: Traffic Psychology and Behaviour 27 (2014), 274–282.Google ScholarCross Ref
- Abhijai Miglani, Cyriel Diels, and Jacques Terken. 2016. Compatibility Between Trust and Non-driving Related Tasks in UI Design for Highly and Fully Automated Driving. In Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (Ann Arbor, MI, USA) (AutomotiveUI ’16 Adjunct). Association for Computing Machinery, New York, NY, USA, 75–80.Google Scholar
- Tina Mioch, Liselotte Kroon, and Mark A. Neerincx. 2017. Driver Readiness Model for Regulating the Transfer from Automation to Human Control. In Proceedings of the 22nd International Conference on Intelligent User Interfaces(Limassol, Cyprus) (IUI ’17). Association for Computing Machinery, New York, NY, USA, 205–213.Google ScholarDigital Library
- B. Mok, M. Johns, K. J. Lee, D. Miller, D. Sirkin, P. Ive, and W. Ju. 2015. Emergency, Automation Off: Unstructured Transition Timing for Distracted Drivers of Automated Vehicles. In 2015 IEEE 18th International Conference on Intelligent Transportation Systems. 2458–2464.Google Scholar
- Jörg Müller, Juliane Exeler, Markus Buzeck, and Antonio Krüger. 2009. ReflectiveSigns: Digital Signs That Adapt to Audience Attention. In IEEE International Conference on Pervasive Computing and Communications (PerCom). 17–24.Google ScholarDigital Library
- A. Pauzié and G. Pachiaudi. 1997. Traffic and Transport Psychology: Theory and Application. Chapter 18. Subjective Evaluation of the Mental Workload in the Driving Context. Traffic and Transport Psychology: Theory and Application (1997).Google Scholar
- Ehud Reiter and Robert Dale. 1997. Building Applied Natural Language Generation Systems. Nat Lang Eng 3, 1 (1997), 57–87.Google ScholarDigital Library
- Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011. Recommender Systems Handbook. Springer.Google Scholar
- Silvia Richter and Matthias Westphal. 2010. The LAMA Planner: Guiding Cost-based Anytime Planning with Landmarks. J. Artif. Intell. Res. 39 (2010), 127–177.Google ScholarCross Ref
- SAE International. 2016. Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-road Motor Vehicles. SAE International, (J3016)(2016).Google Scholar
- Holger Schmidt, Gottfried Zimmermann, and Albrecht Schmidt. 2019. Using Gaze-based Interactions in Automated Vehicles for Increased Road Safety. In Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings (Utrecht, Netherlands) (AutomotiveUI ’19). Association for Computing Machinery, New York, NY, USA, 321–326.Google ScholarDigital Library
- Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor. 2018. AirSim: High-fidelity Visual and Physical Simulation for Autonomous Vehicles. In Field and Service Robotics. Springer, 621–635.Google Scholar
- Yuan Shen, Shanduojiao Jiang, Yanlin Chen, Eileen Yang, Xilun Jin, Yuliang Fan, and Katie Driggs Campbell. 2020. To Explain or Not to Explain: A Study on the Necessity of Explanations for Autonomous Vehicles. arxiv:2006.11684 [cs.AI]Google Scholar
- Annika Silvervarg, Sofia Lindvall, Jonatan Andersson, Ida Esberg, Christian Jernberg, Filip Frumerie, and Arne Jönsson. 2016. Perceived Usability and Cognitive Demand of Secondary Tasks in Spoken Versus Visual-Manual Automotive Interaction. In Interspeech 2016. ISCA.Google Scholar
- Oliviero Stock, Massimo Zancanaro, Paolo Busetta, Charles Callaway, Antonio Krüger, Michael Kruppa, Tsvi Kuflik, Elena Not, and Cesare Rocchi. 2007. Adaptive, Intelligent Presentation of Information for the Museum Visitor in PEACH. User Modeling and User-Adapted Interaction 17, 3 (2007), 257–304.Google ScholarDigital Library
- Sandra Trösterer, Alexander Meschtscherjakov, Alexander G. Mirnig, Artur Lupp, Magdalena Gärtner, Fintan McGee, Rod McCall, Manfred Tscheligi, and Thomas Engel. 2017. What We Can Learn from Pilots for Handovers and (De)skilling in Semi-Autonomous Driving: An Interview Study. In Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications(Oldenburg, Germany) (AutomotiveUI ’17). Association for Computing Machinery, New York, NY, USA, 173–182.Google ScholarDigital Library
- Remo M.A. van der Heiden, Shamsi T. Iqbal, and Christian P. Janssen. 2017. Priming Drivers before Handover in Semi-autonomous Cars. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). ACM, New York, NY, USA, 392–404.Google ScholarDigital Library
- Remo M. A. van der Heiden, Shamsi T. Iqbal, and Christian P. Janssen. 2017. Priming Drivers before Handover in Semi-autonomous Cars. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 392–404.Google ScholarDigital Library
- J. P. Verma. 2015. Repeated Measures Design for Empirical Researchers. John Wiley & Sons.Google Scholar
- Marcel Walch, Kristin Lange, Martin Baumann, and Michael Weber. 2015. Autonomous Driving: Investigating the Feasibility of Car-driver Handover Assistance. In ACM International Conference on Automotive User Interfaces and Interactive Vehicular Applications. 11–18.Google Scholar
- Marcel Walch, David Lehr, Mark Colley, and Michael Weber. 2019. Don’t You See Them? Towards Gaze-based Interaction Adaptation for Driver-vehicle Cooperation. In Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings (Utrecht, Netherlands) (AutomotiveUI ’19). Association for Computing Machinery, New York, NY, USA, 232–237.Google Scholar
- Rainer Wasinger, Antonio Krüger, and Oliver Jacobs. 2005. Integrating Intra and Extra Gestures into a Mobile and Multimodal Shopping Assistant. In International Conference on Pervasive Computing. 297–314.Google ScholarDigital Library
- E. J. Williams. 1949. Experimental Designs Balanced for the Estimation of Residual Effects of Treatments. Aust. J. Chem. 2, 2 (1949), 149–168.Google ScholarCross Ref
- Philipp Wintersberger, Clemens Schartmüller, and Andreas Riener. 2019. Attentive User Interfaces to Improve Multitasking and Take-over Performance in Automated Driving: The Auto-net of Things. International Journal of Mobile Human Computer Interaction (IJMHCI) 11, 3(2019), 40–58.Google ScholarCross Ref
- Bo Zhang, Joost de Winter, Silvia Varotto, Riender Happee, and Marieke Martens. 2019. Determinants of Take-over Time from Automated Driving: A Meta-analysis of 129 Studies. Transportation Research Part F: Traffic Psychology and Behaviour 64 (2019), 285–307.Google ScholarCross Ref
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