Exploring the effectiveness of a digital voice assistant to maintain driver alertness in partially automated vehicles

Abstract

Objective

Vehicle automation shifts the driver's role from active operator to passive observer at the potential cost of degrading their alertness. This study investigated the role of an in-vehicle voice-based assistant (VA; conversing about traffic/road environment) to counter the disengaging and fatiguing effects of automation.

Method

Twenty-four participants undertook two drives– with and without VA in a partially automated vehicle. Participants were subsequently categorized into high and low participation groups (based on their proportion of vocal exchanges with VA). The effectiveness of VA was assessed based on driver alertness measured using Karolinska Sleepiness Scale (KSS), eye-based sleepiness indicators and glance behavior, NASA-TLX workload rating and time to gain motor readiness in response to take-over request and performance rating made by the drivers.

Results

Paired samples t-tests comparison of alertness measures across the two drives were conducted. Lower KSS rating, larger pupil diameter, higher glances (rear-mirror, roadside vehicles and signals in the drive with VA) and higher feedback ratings of VA indicated the efficiency of VA in improving driver alertness during automation. However, there was no significant difference in alertness or glance behavior between the driver groups (high and low-PR), although the time to resume steering control was significantly lower in the higher engagement group.

Conclusion

The study successfully demonstrated the advantages of using a voice assistant (VA) to counter these effects of passive fatigue, for example, by reducing the time to gain motor-readiness following a TOR. The findings show that despite the low engagement in spoken conversation, active listening also positively influenced driver alertness and awareness during the drive in an automated vehicle.

Keywords:

• Conversation participation
• voice user interfaces
• passive fatigue
• time to take-over
• visual behavior

Data availability

The datasets used in the current study are protected by relevant general data protection regulation(GDPR), University of Nottingham policies. Selected, anonymised datasets may be available from the authors upon reasonable request.

Additional information

Funding

This research was conducted through the financial support from Commonwealth Scholarship Commission (CSC) of UK under the split-site PhD research grant (INCN-2018-92). The contents of this manuscript are the sole responsibility of the authors of this paper and can in no way be taken to reflect the views of the CSC.