Adaptive Task Allocation Preferences in Different Workload Scenarios in Driving Automation Systems

Abstract

Adaptive task allocation is used in many human-machine systems and has been proven to improve operators’ performance with automated systems. However, there has been limited knowledge surrounding the benefits of adaptive task allocation in automated vehicles. In this study, participants were presented with photos and videos depicting driving scenarios of low or high workloads at two levels of automation (SAE Levels 2 and 3). The participants reported which tasks they felt comfortable allocating to themselves or to the driving automation system (DAS) in each driving scenario, as well as whether they would conduct the task allocation manually or have the DAS automatically allocate the tasks. Our results showed that participants preferred conducting manual task allocation and preferred the system to complete more tasks when the 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.

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