Date of Degree
PhD (Doctor of Philosophy)
John D. Lee
In driving, multiple variables in automated systems such as adaptive cruise control (ACC) and active steering, and in the environment dynamically change and interact. This complexity makes it difficult for operators to track the activities and responses of automation. The inability of operators to monitor and understand automation's behavior contributes to inappropriate reliance, i.e. when an operator uses automation that performs poorly or fails to use automation that is superior to manual control. The decision to use or not use automation is one of the most important an operator can make, particularly in time-critical or emergency situations, therefore it is essential that an operator is calibrated in their automation use. An operator's decision to rely on automation depends on trust. System feedback provided to the operator is one means to calibrate trust in automation in that the type of feedback may differentially affect trust. The goal of this research is to help operators manage imperfect automation in real-time and to promote calibrated trust and reliance. A continuous information display that provides information on system behavior relative to its operating context is one means to promote such calibration. Three specific aims are pursued to test the central hypothesis of this dissertation that continuous feedback on the state and behavior of the automation informs operators of the evolving relationship between system performance and operating limits, therefore promoting accurate mental models and calibrated trust. The first aim applies a quantitative model to define the effect of understanding on driver-ACC interaction failures and to predict driver response to feedback. The second aim presents a systematic approach to define the feedback needed to support appropriate reliance in a demanding multi-task domain such as driving. The third aim assesses the costs and benefits of presenting drivers with continuous visual and auditory feedback. Together these aims indicate that continuous feedback on automation's behavior is a viable means to promote calibrated trust and reliance. The contribution of this dissertation is in providing purpose, process, and performance information to operators through a continuous, concurrent information display that indicates how the given situation interacts with the characteristics of the automation to affect its capability.
Copyright 2009 Bobbie Danielle Seppelt