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Browsing by Author "Basantis, Alexis"

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    Assessing Alternate Approaches for Conveying Automated Vehicle ‘Intentions’
    Basantis, Alexis; Miller, Marty; Doerzaph, Zachary R.; Neurauter, Luke (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-05)
    One of the biggest highly automated vehicle (HAV) market barriers may be a lack of user trust in the automated driving system itself. Research has shown that this lack of faith in the system primarily stems from a lack of system transparency while the vehicle is in motion—users are not informed how the car will react in an upcoming scenario—and not having an effective way to control the vehicle in the event of a system failure. This problem is particularly prevalent in public transit or ridesharing applications, where HAVs are expected to first appear and where the user has less training on and control over the vehicle. To improve user trust and perceptions of comfort and safety, this study evaluated human-machine interface (HMI) systems, focused on visual and auditory displays, to better relay the perceived driving environment and the automated vehicle “intentions” to the user. These HMI systems were then implemented into a HAV developed at the Virginia Tech Transportation Institute and tested with volunteer participants on the Smart Roads.
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    Standardized Performance Evaluation of Vehicles with Automated Capabilities
    Basantis, Alexis; Harwood, Leslie C.; Doerzaph, Zachary R.; Neurauter, Luke (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-12)
    Advanced driver-assistance systems (ADAS) are becoming widely available in the new vehicle landscape, increasing of both vehicle occupants’ and other road users’ safety. In some vehicles, longitudinal and lateral positioning under certain conditions can be maintained, designating them as having either SAE level 1 (L1) or level 2 (L2) automated features. By developing a standardized set of tests to be applied to current L1 and L2 vehicles, while keeping the future advancement of automation in mind, these vehicles’ system performance, feature limitations, and performance consistency can be systematically evaluated. This project sought to develop an easily implementable, standardized set of testing procedures that could be quickly and inexpensively performed on automated vehicles to characterize their feature capabilities and limitations. Such information is useful to private or public organizations interested in a standardized approach to classifying vehicle capabilities, whether for informing the expectation of operators, or for cataloging and learning from the variety of implementation alternatives. Although not the primary purpose, this project may also help inform efforts to develop certification or other standardized vehicle performance efforts. The results of this project showed that specific roadway factors affected automated feature performance and that there was significant performance variability across test vehicles.