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

TL;DR

This paper models human driver responses in naturalistic driving interactions using Gaussian Process regression to understand decision-making and inform autonomous vehicle design.

Contribution

It introduces a data-driven approach to characterize driver feedback control policies in realistic driving scenarios using Gaussian Process regression.

Findings

Driver feedback gains vary across individuals and populations.

The model captures both linear and nonlinear aspects of driver responses.

Insights can inform the development of socially responsive autonomous vehicles.

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.