Browsing by Author "Manser, Michael P."

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    Driver Training Research and Guidelines for Automated Vehicle Technology
    Manser, Michael P.; Noble, Alexandria M.; Machiani, Sahar Ghanipoor; Shortz, Ashley; Klauer, Charlie; Higgins, Laura L.; Ahmadi, Alidad (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-07)
    The advent of advanced driver-assistance systems presents the opportunity to significantly improve transportation safety. Complex sensor-based systems within vehicles can take responsibility for tasks typically performed by drivers, thus reducing driver-related error as a source of crashes. While there may be a reduction in driver errors, these systems fundamentally change the driving task from manual control to supervisory control. A significant challenge, given this fundamental change in the driving task, is that there are no established methods to train drivers on the use of these systems, which may be counterproductive to safety improvements. The aim of the project was to develop training protocol guidelines that could be used by advanced driver-assistance system trainers to optimize driving safety. The guidelines were developed based on the results of three activities that included the development of a taxonomy of the knowledge and skills necessary to operate advanced driver-assistance systems, a driving simulator study that examined the effectiveness of traditional training protocols, and a test track study that examined the efficacy of a vehicle-based training protocol. Results of both studies suggest that differing training protocols are most beneficial in terms of driver cognitive load and visual scanning as opposed to short-term changes in performance.
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    Preventing Crashes in Mixed Traffic with Automated and Human-Driven Vehicles
    Talebpour, Alireza; Lord, Dominique; Manser, Michael P.; Machiani, Sahar Ghanipoor (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-11)
    Reducing crash counts on saturated road networks is one of the most significant benefits of autonomous vehicle (AV) technology. To date, many researchers have studied how AVs maneuver in different traffic situations, but less attention has been paid to car-following scenarios between AVs and human drivers. Braking and accelerating decision mismatches in this car-following scenario can lead to rear-end near-crashes and therefore warrant further study. This project aims to investigate the behavior of human drivers following an AV leader vehicle in a car-following situation and compare the results with a scenario in which the leader is a vehicle with human-modeled braking behavior. In this study, speed trajectory data was collected from 48 participants using a driving simulator.The results indicated a significant difference between the overall deceleration rates and braking speeds of the participants and the designated AV lead vehicle; however, no such difference was found between the participants and the human-modeled lead vehicle.