Author Information

Ethan JonesFollow

individual

What campus are you from?

Daytona Beach

Authors' Class Standing

Ethan Jones, Senior

Lead Presenter's Name

Ethan Jones

Faculty Mentor Name

Subhradeep Roy

Abstract

In this ongoing work, we introduce a Citizen Science project that leverages a simple, web-based driving application to study human driving behavior in realistic yet accessible settings. In this interactive platform, participants from anywhere can remotely control a virtual vehicle with minimal instructions, such as maintaining a speed limit and choosing to drive in a polite or aggressive manner, while following a lead car that consistently adheres to traffic rules. This setup allows for large-scale, diverse data collection on how individuals interpret and express driving styles under constrained instructions. Using data-driven modeling techniques, including Dynamic Mode Decomposition (DMD), we analyze the collected trajectories to extract and characterize underlying patterns that distinguish aggressive from polite driving behavior. We will present our ongoing progress on the application development, data acquisition framework, and preliminary DMD-based insights, demonstrating how distributed participation can advance behavioral modeling in traffic research.

Did this research project receive funding support from the Office of Undergraduate Research.

No

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A Citizen Science Approach to Understanding Human Driving Behavior through Interactive Vehicle Control

In this ongoing work, we introduce a Citizen Science project that leverages a simple, web-based driving application to study human driving behavior in realistic yet accessible settings. In this interactive platform, participants from anywhere can remotely control a virtual vehicle with minimal instructions, such as maintaining a speed limit and choosing to drive in a polite or aggressive manner, while following a lead car that consistently adheres to traffic rules. This setup allows for large-scale, diverse data collection on how individuals interpret and express driving styles under constrained instructions. Using data-driven modeling techniques, including Dynamic Mode Decomposition (DMD), we analyze the collected trajectories to extract and characterize underlying patterns that distinguish aggressive from polite driving behavior. We will present our ongoing progress on the application development, data acquisition framework, and preliminary DMD-based insights, demonstrating how distributed participation can advance behavioral modeling in traffic research.

 

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