Human-Autonomy Teaming: Effects of Autonomy Level and UAV Integration on Operator Decision-Making

Abstract

As autonomous systems become increasingly integrated into safety-critical operations, understanding how human operators collaborate with heterogeneous robot teams is essential for effective system design. This thesis investigates how the level of automation (LOA) of an unmanned ground vehicle (UGV) and the integration of unmanned aerial vehicle (UAV) information influence operator state, performance, and decision-making in a human-UGV-UAV team. A controlled experiment was designed, varying UGV automation level — Assisted-Autonomous (AA-LOA), where the operator receives take-over requests (TORs), and Autonomous (A-LOA), where the system operates without operator input — and UAV information type — Conflicting or Non-Conflicting with the UGV sensor data. Thirty-three participants completed video-based simulations, representing logistics transport scenarios in which the UAV flew ahead of the UGV acting as a forward-looking sensor. Quantitative (e.g., operator response time, take-over probability, and eye-tracking gaze) and qualitative (e.g., mental workload and observations) data was collected. Results show that AA-LOA significantly increases perceived responsibility compared to A-LOA, without a corresponding increase in mental workload. Operators integrated the UAV-view throughout the mission, and their subjective estimates of UAV reliance closely matched that of the objective eye-tracking data. Conflicting UAV information increased the likelihood of manual intervention but did not significantly extend decision time. Despite a 50% conflict rate, operators consistently valued the UAV-view for its contextual overview and sense of safety. Demographic factors showed meaningful effects: gamers reported lower mental workload and trended toward faster decisions, while high driving frequency and strong sense of direction were the strongest predictors of longer decision times and greater confidence in own judgment during conflict scenarios. Based on these findings, 15 design guidelines were developed addressing LOA selection, transparency, UAV integration, and temporal awareness — including UAV recap functionality and a time-delta indicator to help operators contextualize temporally misaligned information. A dual-screen operator interface concept design implementing these guidelines is presented. To conclude, the findings in this thesis suggests that assisted-autonomy combined with time-transparent UAV integration provides operators with the best conditions for effective situational awareness and decision-making in heterogeneous human-autonomy systems.