Validation of Stochastic Optimal Control Models for Goal-Directed Human Movements on the Example of Human Driving Behavior

TL;DR

This study validates stochastic optimal control models, especially the linear-quadratic sensorimotor model, for accurately representing goal-directed human movements like driving, using a novel inverse control algorithm to identify model parameters from data.

Contribution

Introduces a new inverse stochastic optimal control algorithm to identify model parameters from human movement data, demonstrating the superiority of the LQS model in modeling driving behavior.

Findings

LQS model outperforms other models with statistical significance.

Identified LQS model better captures average human steering behavior.

Signal-dependent noise processes improve movement modeling accuracy.

Abstract

Stochastic Optimal Control models represent the state-of-the-art in modeling goal-directed human movements. The linear-quadratic sensorimotor (LQS) model based on signal-dependent noise processes in state and output equation is the current main representative. With our newly introduced Inverse Stochastic Optimal Control algorithm building upon two bi-level optimizations, we can identify its unknown model parameters, namely cost function matrices and scaling parameters of the noise processes, for the first time. In this paper, we use this algorithm to identify the parameters of a deterministic linear-quadratic, a linear-quadratic Gaussian and a LQS model from human measurement data to compare the models' capability in describing goal-directed human movements. Human steering behavior in a simplified driving task shown to posses similar features as point-ot-point human hand reaching movements serves as our example movement. The results show that the identified LQS model outperforms the others with statistical significance. Particularly, the average human steering behavior is modeled significantly better by the LQS model. This validates the positive impact of signal-dependent noise processes on modeling human average behavior.