Human-driven vehicles (HVs) will be interacting with automated vehicles (AVs) at AV market penetrations between 0% and 100%. However, little is known about how HVs interact with AVs. This study addresses knowledge gaps related to how HVs will interact with AVs on highways. The research was conducted in Oregon State University's Passenger Car Driving Simulator. Additionally, a Shimmer3 GSR+ sensor was used to measure participants' galvanic skin response (GSR). Two independent variables (i.e. leading vehicle speed and autonomy) were selected and resulted in a 2x2 factorial design. Participants were also exposed to two hard-braking scenarios: one with a leading HV and one with a leading AV. A post-drive survey included questions about the participant's level of comfort following HVs and AVs. The driving simulator experiment was successfully completed by 36 participants. Results from the linear mixed model show that driver level of stress was 70% higher in hard-brake scenarios involving HVs versus AVs. Of the 78 hard-braking scenarios tested in this study, 10 crashes were observed (4 with an HV, 6 with an AV). Half of the participants involved in a crash with an HV perceived the leading vehicle to be at fault, while all the participants who crashed with an AV blamed themselves for the error. Additionally, drivers over the age of 34.5 were found to give AVs 2% larger headways than HVs, while younger drivers gave AVs 18% smaller headways than HVs. Zero participants above the age of 34.5 years self-reported being ‘unconcerned’ when following an AV in the post-drive survey, while 38% of participants under the age of 34.5 did. This study supports the need for a better understanding of how human drivers will interact with AVs to calibrate human driver models when AV market penetrations are between 0% and 100%.


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    Title :

    Evaluation of the human interaction with automated vehicles on highways


    Contributors:


    Publication date :

    2025




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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