CONTACT US: Contact info
|
Citation |
Beggiato M, Krems JF. Transp. Res. F Traffic Psychol. Behav. 2013; 18: 47-57. |
|
Copyright |
(Copyright © 2013, Elsevier Publishing) |
|
DOI |
|
PMID |
unavailable |
|
Abstract |
Adaptive cruise control (ACC) automates vehicle speed and distance control. Due to sensor limitations, not every situation can be handled by the system and, therefore, driver intervention is required. Trust, acceptance and mental model of system functionality are considered key variables for appropriate system use. This study systematically investigates the effect of divergent initial mental models of ACC (i.e., varying according to correctness) on trust, acceptance and mental model evolvement. A longitudinal driving simulator study was conducted, using a two-way (3 × 3) repeated measures mixed design with a matched sample of 51 subjects. Three experimental groups received (1) a correct ACC description, (2) an incomplete and idealised account omitting potential problems, and (3) an incorrect description including non-occurring problems. All subjects drove a 56-km track of highway with an identical ACC system, three times, and within a period of 6 weeks. After using the system, participants' mental model of ACC converged towards the profile of the correct group. Non-experienced problems tended to disappear from the mental model network when they were not activated by experience. Trust and acceptance grew steadily for the correct condition. The same trend was observed for the group with non-occurring problems, starting from a lower initial level. Omitted problems in the incomplete group led to a constant decrease in trust and acceptance without recovery. This indicates that automation failures do not negatively affect trust and acceptance if they are known beforehand. A strategy reliant upon trial-and-error alone is considered insufficient for developing an appropriate trust, acceptance and mental model. Implications on information and learning strategies are discussed. |