Dynamic Task Allocation and Understanding of Situation Awareness Under Different Levels of Autonomy in Closed-Hatch Military Vehicles Using Virtual Reality
The current state of the art in autonomous vehicles is partial autonomy, and to optimize the use of these vehicles, it is essential to understand the interactions between the vehicles and their operators. Partially autonomous vehicles require operators to perform parts of the driving task and to be alert and ready to take over full control of the vehicle at any point. Due to the requirements on the human operator, it is necessary to know how the operators' abilities are impacted by the amount of autonomy present in the system. There are known effects of autonomous systems on performance, cognitive load, and situation awareness, but little is known about how these effects change in relation to distinct, increasing autonomy levels. It is also necessary to consider these abilities with the addition of secondary tasks due to the appeal of using autonomous systems for multitasking. The goal of this research is to use a web-based virtual reality study to model operator situation awareness, cognitive load, driving performance, and secondary task performance as a function of distinct, increasing levels of partial vehicle autonomy with both a constant and a steadily increasing rate of secondary tasks.
Cossitt, Jessie E., "Dynamic Task Allocation and Understanding of Situation Awareness Under Different Levels of Autonomy in Closed-Hatch Military Vehicles Using Virtual Reality" (2022). Link Foundation Modeling, Simulation and Training Fellowship Reports. 1.
Year Link Fellowship Received: 2020-2022