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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)
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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 

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Footnotes

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

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