Citation

Carney C, Gaspar JG, Horrey WJ. Transp. Res. F Traffic Psychol. Behav. 2022; 91: 329-345.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.trf.2022.09.017

PMID

unavailable

Abstract

Drivers often learn about the advanced driver assistance systems (ADAS) on their vehicles over time and through trial and error. While this experience can aid drivers' understanding about the systems, it may not necessarily lead to sufficient and accurate mental models, especially concerning less frequent "edge case" situations. This study recruited 39 new owners of vehicles equipped with ADAS technology to which the owners were naïve. The initial mental model of these owners was evaluated using a mental model assessment. To understand changes in mental models over time the assessment was repeated six times over the course of approximately 6 months. Weekly mileage, technology usage, and information regarding their exposure to edge case scenarios was also collected. At the end of the 6 months, participants completed a simulator drive using adaptive cruise control (ACC) that included several edge cases. Over the course of the first 6 months of vehicle ownership, drivers' scores on the mental model assessment improved. These improvements were largely due to increased understanding of the technology's limitations as opposed to improvements in knowledge about system function. With respect to driving performance in the simulator session, the mental model scores were not predictive of responses to the edge cases. However, a comparison of those mental model scores against weak and strong mental model benchmark scores gathered in a previous study revealed that mental models improve over 6 months (for some drivers), but not to the level of understanding of a group that received a short but extensive introduction to ACC. This suggests that there is room for improvement in how drivers gain understanding about driver support features and further underscores the need of training and education for proper use and interactions.

Language: en

Keywords

Advanced driver assistance systems; Driving simulation; Exposure; Mental models; Naturalistic data