Modeling Driver Behavior in Speed Advisory Systems: Koopman-based Approach with Online Update

TL;DR

This paper introduces a Koopman-based model with online updates for accurately predicting driver responses to speed advisories, enhancing real-time driver assistance systems through adaptive, linearized dynamics modeling.

Contribution

It presents a novel Koopman operator approach combined with online RLS updates for real-time, adaptive driver behavior modeling in speed advisory systems.

Findings

Koopman-based model accurately predicts driver responses.

Online updates improve model adaptability over time.

Validated with driving simulator data.

Abstract

Accurate driver behavior modeling is essential for improving the interaction and cooperation of the human driver with the driver assistance system. This paper presents a novel approach for modeling the response of human drivers to visual cues provided by a speed advisory system using a Koopman-based method with online updates. The proposed method utilizes the Koopman operator to transform the nonlinear dynamics of driver-speed advisory system interactions into a linear framework, allowing for efficient real-time prediction. An online update mechanism based on Recursive Least Squares (RLS) is integrated into the Koopman-based model to ensure continuous adaptation to changes in driver behavior over time. The model is validated using data collected from a human-in-the-loop driving simulator, capturing diverse driver-specific trajectories. The results demonstrate that the offline learned Koopman-based model can closely predict driver behavior and its accuracy is further enhanced through an online update mechanism with the RLS method.