The results inform the design of algorithms to monitor driver state that support real-time distraction mitigation systems.

Although visual performance was measured in terms of visual response time in many psychophysical studies, such an approach has not been used in evaluating the effect of luminance on traffic sign legibility performance. Traffic sign and retroreflective sign sheeting performance at night have been historically identified with the threshold (farthest) distance for legibility and in many cases from stationary vehicles with no restrictions on viewing time. Because traffic signs are not always read at threshold distances or threshold luminances and because the time available to read traffic signs is usually limited in the real world, a proper assessment of sign legibility performance requires determining information acquisition times above threshold conditions. This study investigated the effect of (legend) luminance and letter size on the information acquisition time and transfer accuracy from simulated traffic signs. Luminances ranged from 3.2 cd/m2 to 80 cd/m2 on positive-contrast textual traffic sign stimuli with contrast ratios of 6:1 and 10:1, positioned at 33 ft/in. and 40 ft/in. legibility indices, and viewed under conditions simulating a nighttime driving environment. The findings suggest that increasing the sign luminance significantly reduces the time to acquire information. Similarly, increasing the sign size (or reducing the legibility index) also reduces the information acquisition time. These findings suggest that larger and brighter signs are more efficient in transferring their message to the driver by reducing information acquisition time, or alternatively, by increasing the transfer accuracy. In return, reduced sign viewing durations and increased reading accuracy are likely to improve roadway safety.

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Driver distraction contributes to approximately 43% of motor-vehicle crashes and 27% of near-crashes. Rapidly developing in-vehicle technology and electronic devices place additional demands on drivers, which might lead to distraction and diminished capacity to perform driving tasks. This situation threatens safe driving. Technology that can detect and mitigate distraction by alerting drivers could play a central role in maintaining safety. Correctly identifying driver distraction in real time is a critical challenge in developing distraction mitigation systems, and this function has not been well developed. Moreover, the greatest benefit may be from real-time distraction detection in advance of dangerous breakdowns in driver performance. Based on driver performance, two types of distraction-visual and cognitive-are identified. These types of distraction have very different effects on visual behavior and driving performance; therefore, they require different algorithms for detection. Distraction detection algorithms typically rely on either eye measures or driver performance measures because the effect of distraction on the coordination of measures has not been established. Combining both eye glance and vehicle data could enhance the ability of algorithms to detect and differentiate visual and cognitive distraction. The goal of this research is to examine whether poor coordination between visual behavior and vehicle control can identify diminished attention to driving in advance of breakdowns in lane keeping. The primary hypothesis of this dissertation is that detection of changes in eye-steering relationship caused by distraction could provide a prospective indication of vehicle state changes. Three specific aims are pursued to test this hypothesis. The first aim examines the effect of distracting activity on eye and steering movements to assess the degree to which the correlation parameters are indicative of distraction. The second aim applies a control-theoretic system identification approach to the eye movement and steering data to distinguish between distracted and non-distracted conditions. The third aim examines whether changes of eye-steering coordination