Quantifying Drivers' Use of In-Vehicle Systems: Implications for Long-term Behavior

Abstract

The modern day vehicles encompass many in-vehicle Intelligent Transportation Systems (ITS), which can provide continuous feedback to drivers but do require an understanding of the driver, system, and vehicle interactions. A conceptual model of adaptive behavior was developed and used as a framework for the analysis of operators' responses to one such ITS: Adaptive Cruise Control (ACC). Data from both observed (i.e., real world setting) and controlled (i.e., driving simulator) studies was used to examine drivers' adaptation to ACC (novice and experienced users) based on their experience gained and the effectiveness of the system algorithm. The overall goal was to use a systems approach to identify how adaptation to and functionality of the system might affect driving performance and overall safety. The findings of this dissertation revealed several underlying environmental (roadway type) and driver factors (selected settings, speed, and age) that influence drivers' responses. The drivers were further segmented based on their use of the ACC: risky, moderately risky, and conservative. System effectiveness was evaluated by quantifying attention placed on controllers (ACC and drivers) to hazardous situations from internal and external factors. The combined outcomes provided insights on driver differences and system effectiveness that should be considered by designers, engineers, and policy makers.