Hostname: page-component-7479d7b7d-m9pkr Total loading time: 0 Render date: 2024-07-10T05:02:27.119Z Has data issue: false hasContentIssue false
  • English
  • Français

Enhancing Environmental Engagement with Natural Language Interfaces for In-Vehicle Navigation Systems

Published online by Cambridge University Press:  15 February 2019

Vicki Antrobus ,
David Large ,
Gary Burnett  and
Chrisminder Hare
Vicki Antrobus*
Affiliation:
(University of Nottingham, UK)
David Large
Affiliation:
(University of Nottingham, UK)
Gary Burnett
Affiliation:
(University of Nottingham, UK)
Chrisminder Hare
Affiliation:
(Jaguar Land Rover)
*
(E-mail: Vicki.Antrobus@nottingham.ac.uk)
Get access Rights & Permissions [Opens in a new window]

Abstract

Four on-road studies were conducted in the Clifton area of Nottingham, UK, aiming to explore the relationships between driver workload and environmental engagement associated with ‘active’ and ‘passive’ navigation systems. In a between-subjects design, a total of 61 experienced drivers completed two experimental drives comprising the same three routes (with overlapping sections), staged one week apart. Drivers were provided with the navigational support of a commercially-available navigation device (‘satnav’), an informed passenger (a stranger with expert route knowledge), a collaborative passenger (an individual with whom they had a close, personal relationship) or a novel interface employing a conversational natural language ‘NAV-NLI’ (Navigation Natural Language Interface). The NAV-NLI was created by curating linguistic intercourse extracted from the earlier conditions and delivering this using a ‘Wizard-of-Oz’ technique. This term describes a research experiment in which subjects interact with a computer system that they believe to be autonomous, but which is actually being operated or partially operated by an unseen human being. The different navigational methods were notable for their varying interactivity and the preponderance of environmental landmark information within route directions. Participants experienced the same guidance on each of the two drives to explore changes in reported and observed behaviour. Results show that participants who were more active in the navigation task (collaborative passenger or NAV-NLI) demonstrated enhanced environmental engagement (landmark recognition, route-learning and survey knowledge) allowing them to reconstruct the route more accurately post-drive, compared to drivers using more passive forms of navigational support (SatNav or informed passenger). Workload measures (the Tactile Detection Task (TDT) and the National Aeronautical and Space Administration Task Load Index (NASA-TLX)) indicated no differences between conditions, although SatNav users and collaborative passenger drivers reported lower workload during their second drive. The research demonstrates clear benefits and potential for a navigation system employing two-way conversational language to deliver instructions. This could help support a long-term perspective in the development of spatial knowledge, enabling drivers to become less reliant on the technology and begin to re-establish associations between viewing an environmental feature and the related navigational manoeuvre.

Keywords

Natural LanguageNavigationWorkloadLandmarksCognitive Map
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 3 , May 2019 , pp. 513 - 527
Copyright
Copyright © The Royal Institute of Navigation 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This peer reviewed paper was presented at the RIN's International Navigation Conference at Bristol, UK, November 2018

References

REFERENCES

Antrobus, V., Burnett, G. and Krehl, C. (2016). Driver Passenger Collaboration as a basis for human-machine interface design for vehicle navigation systems. Ergonomics (just accepted), 1–26.Google Scholar
Appleyard, D. (1970). Styles and methods of structuring a city. Environment and Behaviour, 2, 100–118.Google Scholar
Archdeacon, T.J. (1994). Correlation and Regression Analysis: A Historians Guide. University of Wisconsin Press.Google Scholar
Bach, K. M., Jæger, M., Skov, M. B. and Thomasse. (2009). Interacting with in-vehicle systems: understanding, measuring, and evaluating attention. In Proceedings of the 23rd British HCI Group Annual Conference on People and Computers: Celebrating People and Technology (453–462). British Computer Society.Google Scholar
Barrow, K. (1991). Human Factors issues surrounding the implementation of in-vehicle navigation and Information systems. SAE Tech. Paper Series No. 910870. Warrendale, PA: Society of Automotive Engineers.Google Scholar
Burnett, G. E. (2000). 'Turn right at the traffic lights' The requirement for landmarks in vehicle navigation systems. The journal of Navigation, 53(3), 499–510.Google Scholar
Burnett, G. E. and Lee, K. (2005). The effect of vehicle navigation systems on the formation of cognitive maps. Final draft of paper for G. Underwood (Ed) Traffic and Transport Psychology: Theory and Application. Elsevier.Google Scholar
Burns, P. (1997). Navigation and the older driver. Unpublished Ph.D. thesis. Loughborough University, UK.Google Scholar
Buschmeier, H. and Kopp, S. (2011). Towards conversational agents that attend to and adapt to communicative user feedback. In International Workshop on Intelligent Virtual Agents (169–182). Berlin, Heidelberg: Springer.Google Scholar
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46.Google Scholar
Denis, M. (1997). The description of routes: A cognitive approach to the production of spatial discourse. Cahiers de Psychologie Cognitive/Current Psychology of Cognition, 16, 409–458.Google Scholar
Denis, M. (2017). Space and spatial cognition: A multidisciplinary perspective. London and New York: Routledge.Google Scholar
Diels, C. (2011). Tactile Detection Task as a real time cognitive workload measure. In Anderson, M., Contemporary Ergonomics and Human Factors (183–190). CRC Press.Google Scholar
Engström, J. (2010). The Tactile Detection Task as a method for assessing drivers' cognitive load. In Rupp, G. L., Performance Methods for Assessing Driver Distraction: The Quest for Improved Road Safety (90–103). Warrendale, PA: SAE Internationl.Google Scholar
Forbes, N. and Burnett, G. (2007). Investigating the contexts in which in-vehicle navigation system users have received and followed inaccurate route guidance instructions. Proof Copy, 111291.Google Scholar
Forlizzi, J., Barley, W. C. and Seder, T. (2010). Where should I turn? Moving from Individual to collaborative navigation sttategies to inform the interaction design of furture navigation systems. SIGCHI Conference on Human Factors in Computing Systems (1261–1270). ACM.Google Scholar
Galea, L. A. and Kimura, D. (1993). Sex differences in route learning. Personality and Individual Differences, 14(1), 53–65.Google Scholar
Gower, J. C. (1975). Generalized procrustes analysis. Psychometrika, 40(1), 33–51.Google Scholar
Golledge, R. G., Ruggles, A. J. and Pellegrino, J. W. (1993). Integrating route knowledge in an unfamiliar neighbourhood: Along and across route experiments. Journal of Environmental Psychology, 13(4), 293–307.Google Scholar
Hart, S. G. and Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Advances in Psychology, 52, 139–183. North-Holland.Google Scholar
Head, D. and Isom, M. (2010). Age effects on wayfinding and route learning skills. Behavioural Brain Research, 209(1), 49–58.Google Scholar
Jensen, B. S., Skov, M. B. and Thiruravichandran, N. (2010). Studying driver attention and behaviour for three configurations of GPS navigation in real traffic driving. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1271–1280.Google Scholar
Kelley, J. F. (1984). An iterative design methodology for user-friendly natural language office information applications. Transactions on Information Systems (TOIS), 2(1), 26–41. ACM.Google Scholar
King, G. (1986). Driver performance in Highway Navigation Tasks. Transportation Research Board, 1093, 1–10.Google Scholar
Kujala, T., Grahn, H., Mäkelä, J. and Lasch, A. (2016). On the Visual Distraction Effects of Audio-Visual Route Guidance. In Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 169–176.Google Scholar
Kruskal Wallis H test: Definition, Examples and Assumptions (2016). Retrieved from https://www.statisticshowto.datasciencecentral.com/kruskal-wallis/Google Scholar
Large, D. R., Clark, L., Quandt, A., Burnett, G. and Skrupchuk, L. (2017). Steering the conversation: a linguistic exploration of natural language interactions with a digital assistant during simulated driving. Applied ergonomics, 63, 53–61.Google Scholar
Lavie, T., Oron-Gilad, T. and Meyer, J. (2011). Aesthetics and usability of in-vehicle navigation displays. International Journal of Human-Computer Studies, 69(1–2), 80–99.Google Scholar
Leshed, G., Velden, T., Rieger, O., Kot, B. and Sengers, P. (2010). In-car GPS navigation: engagement with and disengagement from environment. SIGCHI Conference on Human Factors in Computing Systems, 1675–1684. ACM.Google Scholar
Liu, Y. C. (2001). Comparative study of the effects of auditory, visual and multimodality displays on drivers' performance in advanced traveller information systems. Ergonomics, 44(4), 425–442.Google Scholar
Lorimer, H. and Lund, K. (2003). Performing Facts: Finding a way over Scotland's mountains. The Sociological Review, 51 (s2), 130–144.Google Scholar
Martens, M. H. and Van Winsum, W. (2000). Measuring distraction: the peripheral detection task. TNO Human Factors. Soesterberg, Netherlands.Google Scholar
Nwakacha, A., Crabtree, A. and Burnett, G. (2013). Evaluating Distraction and Disengagement of Attention from the Road. International Conference on Virtual, Augmented and Mixed Reality. Heidelberg: Springer, Berlin, 261–270.Google Scholar
Perterer, N., Meschtscherjakov, A. and Tscheligi, M. (2015). Co-Navigator: an advanced navigation system for front-seat passengers. In Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 187–194. ACM.Google Scholar
Perterer, N., Sundström, P., Meschtscherjakov, A., Wilfinger, D. and Tscheligi, M. (2013). Come drive with me: an ethnographic study of driver-passenger pairs to inform future in-car assistance. In Proceedings of the 2013 conference on Computer supported cooperative work, 1539–1548. ACM.Google Scholar
Rovine, M. J. and Weisman, G. D. (1989). Sketch-map variables as predictors of way-finding performance. Journal of Environmental Psychology, 9(3), 217–232.Google Scholar
Streeter, L. A. and Vitello. (1986). A profile of drivers' map-reading abilities. Human Factors, 28(2), 223–239.Google Scholar
T Statistic: Definition, Types and Comparison to Z score. (2013). Retrieved from https://www.statisticshowto.datasciencecentral.com/t-statistic/Google Scholar
Webber, E. (2013). Strategy differences in the use of mobile devices for navigation. Unpublished doctoral thesis, University of Nottingham, England.Google Scholar