Citation

Simmons SM, Caird JK, Steel P. Accid. Anal. Prev. 2017; 106: 31-43.

Affiliation

Department of Human Resources & Organizational Dynamics, University of Calgary, Calgary, Alberta, Canada. Electronic address: piers.steel@haskayne.ucalgary.ca.

Copyright

(Copyright © 2017, Elsevier Publishing)

DOI

10.1016/j.aap.2017.05.013

PMID

28554063

Abstract

Driver distraction is a growing and pervasive issue that requires multiple solutions. Voice-recognition (V-R) systems may decrease the visual-manual (V-M) demands of a wide range of in-vehicle system and smartphone interactions. However, the degree that V-R systems integrated into vehicles or available in mobile phone applications affect driver distraction is incompletely understood. A comprehensive meta-analysis of experimental studies was conducted to address this knowledge gap. To meet study inclusion criteria, drivers had to interact with a V-R system while driving and doing everyday V-R tasks such as dialing, initiating a call, texting, emailing, destination entry or music selection. Coded dependent variables included detection, reaction time, lateral position, speed and headway. Comparisons of V-R systems with baseline driving and/or a V-M condition were also coded. Of 817 identified citations, 43 studies involving 2000 drivers and 183 effect sizes (r) were analyzed in the meta-analysis. Compared to baseline, driving while interacting with a V-R system is associated with increases in reaction time and lane positioning, and decreases in detection. When V-M systems were compared to V-R systems, drivers had slightly better performance with the latter system on reaction time, lane positioning and headway. Although V-R systems have some driving performance advantages over V-M systems, they have a distraction cost relative to driving without any system at all. The pattern of results indicates that V-R systems impose moderate distraction costs on driving. In addition, drivers minimally engage in compensatory performance adjustments such as reducing speed and increasing headway while using V-R systems. Implications of the results for theory, design guidelines and future research are discussed.

Copyright © 2017 Elsevier Ltd. All rights reserved.

Language: en

Keywords

Driver distraction; Driving performance; Meta-analysis; Speech-to-text; Voice-recognition