Home Institution, City, State
Virginia Tech, Blacksburg, VA
Major
Industrial and Systems Engineering
Publication Date
Summer 2021
Abstract
Adaptive task allocation is used in many human-machine systems and has been proven to improve operators’ monitoring and/or performance with automated systems. However, there is little knowledge surrounding the benefits of adaptive task allocation in automated vehicles. In this study, participants were presented with media depicting driving scenarios of both low and high workload at two levels of automation. The participants reported which tasks they felt comfortable allocating to themselves or to the automated system in each driving scenario, as well as whether they would conduct the task allocation manually or have the automated system automatically allocate the tasks. The results showed that participants preferred conducting manual task allocation and preferred the system to complete mostly secondary tasks when perceived workload was high. There was no significant difference between the high and low workload scenarios in terms of whether participants chose to allocate tasks.
Keywords
Adaptive Task Allocation, Automated Vehicles, Self-driving, Cars, Driving tasks
Disciplines
Ergonomics | Human Factors Psychology | Industrial and Organizational Psychology | Industrial Engineering | Industrial Technology | Navigation, Guidance, Control, and Dynamics | Other Operations Research, Systems Engineering and Industrial Engineering | Psychology
Recommended Citation
Taylor, Skye; Hu, Bin; and Chen, Jing, "Adaptive Task Allocation in Automated Vehicles" (2021). Psychology: Interdisciplinary Research in Behavioral Sciences of Transportation Issues [REU poster]. 13.
https://digitalcommons.odu.edu/reu2021_psychology/13
Included in
Ergonomics Commons, Human Factors Psychology Commons, Industrial and Organizational Psychology Commons, Industrial Engineering Commons, Industrial Technology Commons, Navigation, Guidance, Control, and Dynamics Commons, Other Operations Research, Systems Engineering and Industrial Engineering Commons
Comments
Special thanks to Dr. Bin Hu, Dr. Jing Chen, and Katie Garcia for assisting me with this project.