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69A3551747131, According to the National Highway Traffic Safety Administration (NHTSA), speeding is a major contributory factor to severe injuries and deaths in school zones. In a study performed at the University of Puerto Rico at Mayaguez, data showed that in 68% of school zones in the western region of Puerto Rico, the average speed of users was higher than the posted speed limit and that in 89% of the school zones, the 85th percentile of the speed was higher than the posted speed limit. A research project was conducted to assess the (Manual on Uniform Traffic Control Devices (MUTCD)) guidelines on where to install signs that alert the driver to slow down and reduce their speed. Although several countermeasures decreased speeding, proper compliance with the speed limit was not obtained. To address this issue, the University of Central Florida developed and tested four countermeasures to reduce speeding in school zones.

69A3551747131, Temporary traffic control in work zones, often referred to as TTC, lead to several challenges in terms of safety for both crew workers and drivers in both rural and urban settings. The changes imposed by the work zone in the road operating conditions or alignment geometry, including the presence of temporary signs, channelizing devices, and barriers, lane merging, shifting or closing tapers, width changes, and equipment, personnel, and materials on the roadway, increase the driver workload and the risk of crashes. This report presents the results of a driving simulator research project that investigated the potential safety implications associated with severe injuries and fatalities related to the use of GPS while driving on a two-lane rural highway with work zones consisting of one-lane closures due to operations and maintenance activities. Specifically, the effects of distractions caused by the audible messages of an active GPS while approaching or entering the advanced warning area of the TTC and the drivers’ compliance with work zone regulations on the workspace were investigated.

69A3551747131, This report is a product of a cooperative research program between the AAA Foundation, for Traffic Safety and the SAFER-SIM University Transportation Center., The effectiveness of the human–machine interface (HMI) in a driving automation system during takeover situations is based, in part, on its design. Past research has indicated that modality, specificity, and timing of the HMI have an impact on driver behavior. The objective of this study was to examine the effectiveness of two HMIs, which vary by modality, specificity, and timing, on drivers’ takeover time, performance, eye glance behavior, and subjective evaluation. Drivers’ behavior was examined in a driving simulator study with different levels of automation, varying traffic conditions, and while completing a non-driving related task. Results indicated that HMI type had a statistically significant effect on velocity and off-road eye glances such that those who were exposed to an HMI that gave multimodal warnings with greater specificity exhibited better performance. There were no effects of HMI on acceleration, lane position, other eye glance metrics (e.g., on road glance duration), trust, or usability. Future work should disentangle HMI design further to determine exactly which aspects of design yield differences in safety critical behavior.

69A3551747131, Given the importance of mental models towards safe interaction with Advanced Driver Assistance Systems (ADAS) and the various human factors challenges regarding ADAS such as miscalibrated trust and the effect on workload, it is important to understand how different types of driving experiences and exposures affect drivers’ mental models about ADAS. The objective of this study was to examine how the frequency and quality of exposure (exposure defined as driving through events or situations that have some bearing on the functions of the Adaptive Cruise Control (ACC)) affect drivers’ mental models about ACC, their trust, workload, and their use of the systems as measured by their behaviors around disengaging ACC.

Advanced Driver Assistance Systems (ADAS) support drivers with some driving tasks. However, drivers may lack appropriate knowledge about ADAS (referred to as their mental model), which can translate to drivers misusing or mistrusting the technologies, especially in situations beyond the capability of the system (i.e., edge cases). Past research suggests that mental models may be improved through exposure to ADAS-related driving situations, especially those related to the system capabilities as well as limitations.