Natural language interfaces for vehicle navigation systems

Antrobus, Vicki Roselyn (2019) Natural language interfaces for vehicle navigation systems. PhD thesis, University of Nottingham.

[img] PDF (Thesis - as examined) - Repository staff only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (14MB)


Around three quarters of drivers in England regularly use a GPS system to guide them on journeys yet prefer to use a smartphone or personal device for navigation and wayfinding, rather than a factory-fitted system. For car manufacturers, therefore, it is important to consider the potential for novel Human-Machine Interfaces (HMIs) that are more integral to the vehicle. For the research community, there is also a fundamental desire to understand the balance required between HMIs for navigation systems as ‘uncertainty minimisers’ (the prevailing approach emphasising the desire for low workload/distraction) and a longer-term perspective in which engagement with the drivers’ surroundings is encouraged.

This PhD, co-funded by the University of Nottingham and Jaguar Land Rover, explored the way in which drivers and passengers interact when navigating as a means of informing future HMIs. The work proposes that Natural Language Interfaces (NLIs) can be created which mimic this social relationship, allowing personalised, context-sensitive, environmental information to be presented.

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 conversational natural language NAV-NLI). The NAV-NLI was created by curating linguistic intercourse extracted from the earlier conditions, and delivering this using a Wizard-of-Oz technique. 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 (TDT, NASA-TLX) indicated no differences between conditions, although objective workload dropped significantly, in the Informed and Collaborative passenger conditions, on their second final drive. Moreover, satnav users and collaborative passenger and NAV drivers reported lower subjective workload during this 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.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Burnett, Gary
Large, David R
Lawson, Glyn
Keywords: vehicle navigation, Natural language interfaces
Subjects: T Technology > TL Motor vehicles. Aeronautics. Astronautics
Faculties/Schools: UK Campuses > Faculty of Engineering
Item ID: 56558
Depositing User: Antrobus, Vicki
Date Deposited: 09 Aug 2019 13:10
Last Modified: 07 May 2020 10:46

Actions (Archive Staff Only)

Edit View Edit View