  Misunderstanding of driver correction behaviors (DCB) is the primary reason
for false warnings of lane-departure-prediction systems. We propose a
learning-based approach to predicting unintended lane-departure behaviors (LDB)
and the chance for drivers to bring the vehicle back to the lane. First, in
this approach, a personalized driver model for lane-departure and lane-keeping
behavior is established by combining the Gaussian mixture model and the hidden
Markov model. Second, based on this model, we develop an online model-based
prediction algorithm to predict the forthcoming vehicle trajectory and judge
whether the driver will demonstrate an LDB or a DCB. We also develop a warning
strategy based on the model-based prediction algorithm that allows the
lane-departure warning system to be acceptable for drivers according to the
predicted trajectory. In addition, the naturalistic driving data of 10 drivers
is collected through the University of Michigan Safety Pilot Model Deployment
program to train the personalized driver model and validate this approach. We
compare the proposed method with a basic time-to-lane-crossing (TLC) method and
a TLC-directional sequence of piecewise lateral slopes (TLC-DSPLS) method. The
results show that the proposed approach can reduce the false-warning rate to
3.07\%.
