Citation

Rahman MM, Strawderman L, Lesch MF, Horrey WJ, Babski-Reeves K, Garrison TM. Accid. Anal. Prev. 2018; 121: 134-147.

Affiliation

Human-Autonomy Interaction Laboratory, Sonalysts, Inc., 215 Parkway North, Waterford, CT 06385, USA.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.aap.2018.08.028

PMID

30245477

Abstract

Driver support systems are intended to enhance driver performance and improve transportation safety. Even though these systems afford safety advantages, they challenge the traditional role of drivers in operating vehicles. Driver acceptance, therefore, is essential for the adoption of new in-vehicle technologies into the transportation system. In this study, a model of driver acceptance of driver support systems was developed. A conceptual driver acceptance model, including several components, was proposed based on a review of current literature. An empirical study was subsequently carried out using an online survey approach. The study collected data on participants' perceptions of two driver support systems (a fatigue monitoring system and an adaptive cruise control system combined with a lane-keeping system) in terms of attitude, perceived usefulness, and other components of driver acceptance.

RESULTS identified five components of driver acceptance (attitude, perceived usefulness, endorsement, compatibility, and affordability). The results also confirmed several mediating effects. The developed model was able to explain 85% of the variability in driver acceptance. The model provides an improved understanding how driver acceptance is formed, including which factors affect driver acceptance and how they affect it. The model can also help automakers and researchers to assess the design and estimate the potential use of a driver support system. The model could also be highly beneficial in developing a questionnaire to assess driver acceptance.

Copyright © 2018 Elsevier Ltd. All rights reserved.

Language: en

Keywords

Advanced driver assistance systems; Driver acceptability; Intelligent transport technology; Vehicle automation