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Although great progress has been made to improve the safety and performance of autonomous vehicles with the ultimate goal of meeting the public expectation of preventing most accidents, the current fleet of autonomous vehicles being tested continues to demonstrate that we still remain distant from that holy grail. One rationalization for some of these accidents has been that different maneuvers performed by such cars are not human-like (i.e. they do not display certain driving patterns to which human drivers are accustomed to). With that in mind, it would be hard to dispute the need for such vehicles to adapt to and somewhat imitate human driving in order to gradually integrate human-driven traffic in the future.

Despite public expectations that autonomous vehicles should be able to avoid most accidents, the existing fleet of autonomous test vehicles has demonstrated this is simply not the case. An explanation for some of these accidents has been that these vehicles do not drive like humans and therefore do not exhibit certain driving patterns expected by human drivers. With the high likelihood of a gradual integration of autonomous vehicles into our traffic system in the future, there will be a need for such vehicles to adapt to, and mimic, human driving. Although much work has been done to understand human behavior and performance in driving, it has been mostly geared towards defining human capabilities and limitations. Little work has been done on the interactions between human-driven and autonomous vehicles.

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.

The purpose of this paper is to present an updated methodology for validating the visibility of objects in low-illumination digital photographs. A procedure is presented for creating prints from digital images that accurately represent the observers' view at a low-illumination scene. In addition, procedures are presented that allow representative displays of digital images on CRT and projector devices. The results indicate that the selected luminance settings of experimenters closely matched the selections of test subjects viewing similar images in print form and on CRT and projector displays. Further, both between-subject and between-experimenter variability was small.