Adaptive Task Allocation
Abstract
Adaptive task allocation is used in many human-machine systems and has been proven to improve operator’s 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 will be presented with media depicting driving scenarios with either low or high workload. The participants will report which tasks they feel comfortable allocating to themselves or the automated system in each driving scenario, as well as when they would conduct the manual task allocation. It is hypothesized that participants will assign a greater number of primary driving tasks, such as steering, to themselves when presented with depictions of low workload driving environments. When a high workload environment is depicted, participants are expected to allocate primary driving tasks to the vehicle. It is also expected that participants will conduct manual task allocation during periods where workload is low.