Performance and Relative Risk Dynamics during Driving Simulation Tasks under Distinct Automation Conditions

Lucero Rodriguez Rodriguez, Carlos Bustamante Orellana, Gregory M. Gremillion, Lixiao Huang, Mustafa Demir, Nancy Cooke, Jason S. Metcalfe, Polemnia G. Amazeen, Yun Kang

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

Abstract

Risk has been a key factor influencing trust in Human-Automation interactions, though there is no unified tool to study its dynamics. We provide a framework for defining and assessing relative risk of automation usage through performance dynamics and apply this framework to a dataset from a previous study. Our approach allows us to explore how operators’ ability and different automation conditions impact the performance and relative risk dynamics. Our results on performance dynamics show that, on average, operators perform better(1) using automation that is more reliable and (2) using partial automation (more workload) than full automation(less workload). Our analysis of relative risk dynamics indicates that automation with higher reliability has higher relative risk dynamics. This suggests that operators are willing to take more risk for automation with higher reliability. Additionally, when the reliability of automation is lower, operators adapt their behaviour to result in lower risk.

Original languageEnglish (US)
Pages (from-to)1230-1234
Number of pages5
JournalProceedings of the Human Factors and Ergonomics Society
Volume66
Issue number1
DOIs
StatePublished - 2022
Event66th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2022 - Atlanta, United States
Duration: Oct 10 2022Oct 14 2022

ASJC Scopus subject areas

  • Human Factors and Ergonomics

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