Abstract
Supervisory control of multiple autonomous vehicles raises many issues concerning the balance of system autonomy with human interaction for optimal operator situation awareness and system performance. An unmanned vehicle simulation designed to manipulate the application of automation was used to evaluate participants’ performance on image analysis tasks under two automation conditions: static (level of automation remained constant throughout trials) and adaptive (level of automation adapted as a function of performance on five types of tasks). The results showed that performance-based adaptation of the image task autonomy level improved performance on this task, as well as other tasks. Additionally, participants preferred the adaptive automation condition and felt that it reduced their cognitive workload and aided performance. Research issues are identified to further evaluate performance-based adaptation for supervisory control.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
