Graduate Thesis Or Dissertation


Integrating Driving Simulator Experiment Data with Multi-Agent Connected Automated Vehicle Simulation (Ma-Cavs) Platform to Quantify Improved Capacity Public Deposited

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  • Autonomous vehicles (AVs) at varying market penetrations will change traffic flow and highway performance. At AV market penetrations between 0% and 100%, human driven vehicles (HVs) will be interacting with AVs. However, little is known about how HVs interact with AVs. This study attempts to quantify HV headways when following an AV using driving simulator data and integrates that data into a multi-agent simulation to quantify new highway travel time and flow predictions at varying AV market penetrations. This study also collected biometric feedback data to quantify driver level of stress when presented with a hard-breaking AV and HV. The driving simulator experiment was successfully completed by 36 participants. The results of this study show that driver level of stress is 70% higher in hard break scenarios involving HVs versus AVs. Additionally, drivers over the age of 34.5 were found to give AVs 2% more headway than HVs, while younger drivers gave AVs 18% less headway than HVs. Thirty-six scenarios were tested in the multi-agent simulation using results from the driving simulator. Using the driving simulator results, average travel times were found to increase at most by 2.3%.
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  • Pending Publication
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  • 2020-06-08 to 2021-01-08



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