Performance and Relative Risk Dynamics during Driving Simulation Tasks under Distinct Automation Conditions
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 behavior to result in lower risk.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 01, 2022
- Source ID
- 10.1177/1071181322661471
Entities
People
- Carlos Bustamante Orellana
- Gregory M. Gremillion
- Jason S. Metcalfe
- Lixiao Huang
- Lucero Rodriguez Rodriguez
- Mustafa Demir
- Nancy J Cooke
- Polemnia G. Amazeen
- Yun Kang
Organizations
- Arizona State University
- United States Army Research Laboratory