Effects of Cognitive Load on Takeover Requests in Conditionally Automated Driving
With increases in vehicle automation, drivers can engage in non-driving related tasks while trusting automation to maintain driving control. When the vehicle issues a takeover request (TOR), drivers must disengage attention from their non-driving task to direct attention toward the task of driving. Attentional disengagement takes time, making takeover requests limited by drivers’ attentional control. In the current project, we investigate how the cognitive complexity, or cognitive load, of the non-driving task impacts the time to disengage attention and respond to a TOR. Specifically, is the cost in disengaging attention from a non-driving task and switching to regaining vehicle control affected by the difficulty, or cognitive load, of the non-driving related task? Participants will perform a simulated automated drive while performance a secondary non-driving task under either a high- or low-cognitive load. We will measure the impact of the cognitive load on driving parameters (e.g., lane position) and the time and quality of the driver’s takeover.