Affiliation:
1. North Carolina State University
Abstract
This paper aims to examine the effect of age and various characteristics of non-driving related activities during highly automated driving on subsequent performance in notified takeovers among younger and older drivers. The paper presents new analyses of data collected in our earlier study (Clark & Feng, 2016). Non-driving-related activities that participants voluntarily chose to engage in during automated driving were categorized according to their cognitive dimensions in information processing. Using hierarchical multiple regressions, we analyzed the effect of driver age, total duration and number of engagement in non-driving-related activities, the duration and cognitive dimensions of the last activity prior to takeover on average speed during takeover and the response time to a takeover notification. We found that older drivers speed was negatively predicted by age while their response time to a notification was not predicted by any factor. In contrast, younger drivers showed a trend of positive relationship between age and average speed and the characteristics of the last task engagement explained a significant portion of the variance of response time to a notification.
Subject
General Medicine,General Chemistry
Cited by
14 articles.
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