  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.
