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Paper WE-LA-T10.2

DiPirro, Rachel (University of New Mexico), Devonport, Rosalyn (University of New Mexico), Calderone, Daniel J. (University of New Mexico), Yang, Chishang (Mario) (Cornell University), Ju, Wendy (Cornell Tech), Oishi, Meeko (University of New Mexico)

Characterizing Human Feedback-Based Control in Naturalistic Driving Interactions Via Gaussian Process Regression with Linear Feedback

Scheduled for presentation during the Regular Session "S10c-Cooperative and Connected Autonomous Systems" (WE-LA-T10), Wednesday, November 19, 2025, 16:20−16:40, Cooleangata 4

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 19, 2025

Keywords Cooperative Driving Systems and Vehicle Coordination in Multi-vehicle Scenarios, Driver Behavior Monitoring and Feedback Systems for Semi-autonomous Vehicles, Data Analytics and Real-time Decision Making for Autonomous Traffic Management

Abstract

Understanding driver interactions is critical to designing autonomous vehicles to interoperate safely with human-driven cars. We consider the impact of these interactions on the policies drivers employ when navigating unsigned intersections in a driving simulator. The simulator allows the collection of naturalistic decision-making and behavior data in a controlled environment. Using these data, we model the human driver responses as state-based feedback controllers learned via Gaussian Process regression methods. We compute the feedback gain of the controller using a weighted combination of linear and nonlinear priors. We then analyze how the individual gains are reflected in driver behavior. We also assess differences in these controllers across populations of drivers. Our work in data-driven analyses of how drivers determine their policies can facilitate future work in the design of socially responsive autonomy for vehicles.

 

 

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