It is estimated that as many as 2.7 million crashes occur each year at intersections or are intersection related; resulting in over 8500 fatalities each year. These statistics have prompted government and corporate sponsored research into collision countermeasure systems that can enhance safety at intersections. Researchers are investigating technologies to provide an infrastructure-based or infrastructure-cooperative Intersection Decision Support (IDS) systems. Such systems would use pre-specified algorithms to identify drivers that have a high likelihood of violating the traffic signal and thus increase the risk of a collision. The system would subsequently warn the violating driver to stop though an in-vehicle or infrastructure-mounted interface. An IDS algorithm must be designed to provide adequate time for the driver to perceive, react, and stop the vehicle, while simultaneously avoiding a high false alarm rate. Prior to developing these algorithms, scientists must understand how drivers respond to traffic signals. Little research has focused on the influence of driver state on red-light running behavior or methods for distinguishing red light violators from non-violators. The objective of the present study was to define trends associated with intersection crossings under different driver states and to explore the point detection method of predicting red light running upstream of the intersection. This was accomplished through a test-track mixed-factor experiment with 28 participants. Each participant experienced a baseline (complete a full stop at the red light), distracted (misses signal phase change due to inattention), and willful (driver knowingly makes a late crossing in an attempt to 'beat the light') driver state conditions. To provide the opportunity for red-light running behavior from participants, the amber change interval began at five different distances from the intersection. These distances were located near and within the dilemma zone, a region in which drivers have a difficult time deciding whether to go or to stop. Data collected from in-vehicle sensors was statistically analyzed to determine significant effects between driver states, and to investigate point detection algorithms. / Master of Science

Intersection crashes account for over one-third of all crashes in the U.S., and 39% of these result in injury or death. As part of a larger effort to develop and evaluate in-vehicle countermeasures to reduce the number of intersection-related crashes, haptic warnings and a combined haptic/auditory warning were explored and compared to combined visual/auditory warnings. The first phase of this study determined which haptic brake pulse warning candidate most often resulted in the driver successfully stopping for an intersection. Five brake pulse warnings were tested (varied with respect to jerk, duration, and the number of pulses). Participants receiving the haptic warnings were 38 times more likely to stop at the intersection than those receiving no warning and 7.6 times more likely to stop than those receiving a combined visual/auditory tone warning. The 600ms-3 pulses condition was advanced to the second phase because it provided the longest warning and had a more favorable subjective rating; it was then combined with an auditory verbal warning (urgent "STOP"). This phase determined whether the added verbal warning resulted in differences from the haptic warning alone. Although the warning was activated 7.62 m (25 ft) closer to the intersection in the second phase than in the first phase, there were no significant differences for the reaction times and distance to stop bar. Participants receiving the haptic plus auditory verbal warning were also 1.5 times more likely to stop than those who received the haptic warning alone. Overall, this study shows that haptic warnings show promise for warning drivers of impending intersection violations. Guidelines for haptic intersection warnings were developed, including a recommendation that haptic warnings be combined with auditory verbal warnings for increased warning effectiveness. / Master of Science