Natural language interfaces for vehicle navigation systems

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

Enhancing environmental engagement with natural language interfaces for in-vehicle navigation systems

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 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

Driven to discussion: engaging drivers in conversation with a digital assistant as a countermeasure to passive task-related fatigue

Using a Wizard-of-Oz approach, we explored the effectiveness of engaging drivers in conversation with a digital assistant as an operational strategy to combat the symptoms of passive task-related fatigue. Twenty participants undertook two 30-minute drives in a medium-fidelity driving simulator between 13:00 and 16:30, when circadian and homeostatic influences naturally reduce alertness. Participants were asked to follow a lead-car travelling at a constant speed of 68mph, in a sparsely-populated UK motorway scenario. During one of the counterbalanced drives, participants were engaged in conversation by a digital assistant (‘Vid’). Results show that interacting with Vid had a positive effect on driving performance and arousal, evidenced by better lane-keeping, earlier response to a potential hazard situation, larger pupil diameter, and an increased spread of attention to the road-scene (i.e. fewer fixations concentrated on the road-centre indicating a lower incidence of ‘cognitive tunnelling’). Drivers also reported higher levels of alertness and lower sleepiness following the Vid drive. Subjective workload ratings suggest that drivers exerted less effort to ‘stay awake’ when engaged with Vid. The findings support the development and application of in-vehicle natural language interfaces, and can be used to inform the design of novel countermeasures for driver fatigue

Driver-passenger collaboration as a basis for human-machine interface design for vehicle navigation systems

Human Factors concerns exist with vehicle navigation systems, particularly relating to the effects of current Human-Machine Interfaces (HMIs) on driver disengagement from the environment. A road study was conducted aiming to provide initial input for the development of intelligent HMIs for in-vehicle systems, using the traditional collaborative navigation relationship between the driver and passenger to inform future design. Sixteen drivers navigated a predefined route in the city of Coventry, UK with the assistance of an existing vehicle navigation system (SatNav), whereas a further 16 followed the navigational prompts of a passenger who had been trained along the same route. Results found that there were no significant differences in the number of navigational errors made on route for the two different methods. However, drivers utilising a collaborative navigation approach had significantly better landmark and route knowledge than their SatNav counterparts. Analysis of individual collaborative transcripts revealed the large individual differences in descriptor use by passengers and reference to environmental landmarks, illustrating the potential for the replacement of distance descriptors in vehicle navigation systems. Results are discussed in the context of future HMIs modelled on a collaborative navigation relationship

Natural language interfaces for vehicle navigation systems

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

A Longitudinal Simulator Study to Evaluate Driver Acceptance of the PROSPECT Proactive Safety System for Cyclists

ICSC2018 7th International Cycling Safety Conference, BARCELONE, ESPAGNE, 10-/10/2018 - 11/10/2018Accidents involving vulnerable road users (VRUs), such as cyclists, remain a major issue for road safety, accounting for almost 40% of road fatalities in Europe, and almost 50% worldwide [1]. Active safety systems have the potential to mitigate the risk to cyclists by warning drivers and providing braking and/or steering interventions if a cyclist is present within their vehicle's path, and/or a collision is likely based on the current speed and trajectory of each party. How-ever, current systems can be prone to issuing high numbers of false declarations, due the in-herent difficulty in predicting intent; this can annoy drivers, encouraging them to ignore the system, find creative workarounds or deactivate it completely [2]. The PROSPECT project [3] aims to significantly improve the effectiveness of current VRU safety systems (in particular, for cyclists) by expanding the scope of scenarios addressed, and improving overall system perfor-mance through enhanced VRU sensing and advanced system control strategies. Nevertheless, false activations remain inevitable due to the dynamic behaviour of both parties. The nature and frequency of false activations and drivers' understanding of the system's intentions are therefore important, and likely to play a significant role in shaping drivers' trust and ac-ceptance, and their ultimate uptake of the technology. Acceptance testing is often limited to surveys or short-term, single-exposures to 'the system'. Strictly speaking, such studies measure acceptability (the driver's intention to use the system) rather than acceptance (how the driver incorporates the system into their driving, based on their experience of actually using it); this is a particular concern given that high acceptability does not necessarily predict high acceptance. In contrast, in a longitudinal simulator study, fifty participants undertook a 20-minute urban journey, on each of five consecutive week-days (representing their daily commute). On day five, the vehicle identified an approaching cyclist (obscured to the driver), and predicted their intention to enter the roadway into which the car was driving. As a consequence, the system activated an emergency braking manoeuvre, shortly preceded by an audible alarm (emulating the functionality of the new PROSPECT system). In a balanced between-subjects design, the behaviour of the cyclist varied: they either stopped at the road-side or continued to cross the road, giving rise to a false-positive and true-positive alarm, respectively. Driver acceptance was subsequently assessed at the end of the week based on the testing protocol developed as part of the PROSPECT project. Acceptance ratings, driving performance, and the driver's inclination to use or purchase the system following false activation, will be reported. Findings will subse-quently be used to understand the likely acceptance and impact of the PROSPECT active safety system on driver behaviour and cyclist safety