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The use of electric scooters (e-scooters), which are more generally categorized as motorized scooters, has undergone explosive growth owing to “scooter share” programs in which an e-scooter is rented for a limited period of time. The near-spontaneous ubiquity of e-scooters has prompted government and scooter share companies to address issues partly motivated by concerns related to the inclusion of a large population of e-scooters into vehicular traffic. These issues are influenced by the decisions and behaviors of the scooter operators, who, despite being licensed to drive passenger vehicles, potentially have limited experience operating an e-scooter in the presence of traffic. E-scooters are in a relative unique position where they are small enough to negotiate pedestrian traffic, yet fast enough to travel on roadways.

Vision plays a key role in the safe and proper operation of vehicles. To safely navigate, drivers constantly scan their environments, which includes attending to the outside environment as well as the inside of the driver compartment. For example, a driver may monitor various instruments and road signage to ensure that they are traveling at an appropriate speed. Although there has been work done on naturalistic driver gaze behavior, little is known about what information drivers glean while driving. Here, we present a methodology that has been used to build a database that seeks to provide a framework to supply answers to various ongoing questions regarding gaze and driver behavior. We discuss the simultaneous recording of eye-tracking, head rotation kinematics, and vehicle dynamics during naturalistic driving in order to examine driver behavior with a particular focus on how this correlates with gaze behavior.

Previous research [1] described a procedure for creating prints from digital photographs that accurately represent critical features of visual scenes at low levels of illumination. In this procedure, observers adjust the brightness of a digital photographs captured using standard photography until it best matches the visible characteristics of the actual scene. However, standard digital photography cannot capture the full dynamic range of a scene's luminous intensities in many low-illumination settings. High dynamic range (HDR) photography has the potential to more accurately represent a viewer's perception under low illumination. Such a capability can be critical to representing nighttime roadway scenes, where HDR photography can enable the creation of more accurate photographic representations of bright visual stimuli (e.g., vehicle headlamps, street lighting) while also maintaining the integrity of the photograph's darker portions.