Abstract
As automated assistance systems become prevalent in driving operations, understanding their impact on workload and team performance is critical. This study examined the effectiveness of automated and AI assistance—specifically, object detection systems (ODS) and autonomous driving—in alleviating individual and team workload in a three-vehicle convoy operating in a networked simulation environment. The convoy consisted of six participants assigned distinct roles: Lead Car Driver (n=1), Desktop Drivers (n=2), Lead Car Lookout (n=1), and Desktop Lookouts (n=2). Each role had unique task demands contributing to baseline workload differences, while all members collaborated on a shared target search and identification goal. Lookouts searched for targets while completing a distractor task. Drivers followed the lead car, with the Lead Car Driver accessing an overhead map. The primary objective was to determine if assistance could enable a six-person team to effectively perform tasks typically requiring seven people. Workload measures were collected to assess the feasibility and benefits for substituting a human crew member. Participants (N=240) completed four randomized simulated driving scenarios: (1) Vehicle Automation—where all three vehicles were operated autonomously; (2) ODS Assistance—where the Lead Car Lookout received visual and auditory alerts via a tablet upon target detection; (3) Combined Assistance—featuring fully automated vehicles alongside ODS engagement; and (4) No Assistance—where participants manually controlled the vehicles without support. To assess perceived individual and team workload, participants completed the NASA Task Load Index (NASA-TLX) and the Team Workload Questionnaire (TWLQ) post-drives. Workload scores were analyzed using a mixed linear model to account for repeated drives and assess the effects of roles and assistance conditions. Results indicated that while automation significantly reduced driver workload, lookout workload remained unchanged and higher overall. These findings suggest future assistance designs must prioritize a balanced workload distribution to optimize performance across all team members. OPSEC#9425