Generalized two-point visual control model of human steering for accurate state estimation

TL;DR

This paper presents a generalized two-point visual control model of human steering that improves state estimation accuracy, enabling better interpretation of ambiguous lane markings and human driving behavior.

Contribution

The paper introduces a generalized model of human steering behavior that enhances accuracy and simplicity for online state estimation compared to existing models.

Findings

The generalized model reduces modeling error by 85%.

It enables accurate lateral state estimation with under 0.25 m error.

Without the model, state estimation accuracy significantly decreases.

Abstract

We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85\% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.25 m error on average across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle's lateral state.