  A smart vehicle should be able to understand human behavior and predict their
actions to avoid hazardous situations. Specific traits in human behavior can be
automatically predicted, which can help the vehicle make decisions, increasing
safety. One of the most important aspects pertaining to the driving task is the
driver's visual attention. Predicting the driver's visual attention can help a
vehicle understand the awareness state of the driver, providing important
contextual information. While estimating the exact gaze direction is difficult
in the car environment, a coarse estimation of the visual attention can be
obtained by tracking the position and orientation of the head. Since the
relation between head pose and gaze direction is not one-to-one, this paper
proposes a formulation based on probabilistic models to create salient regions
describing the visual attention of the driver. The area of the predicted region
is small when the model has high confidence on the prediction, which is
directly learned from the data. We use Gaussian process regression (GPR) to
implement the framework, comparing the performance with different regression
formulations such as linear regression and neural network based methods. We
evaluate these frameworks by studying the tradeoff between spatial resolution
and accuracy of the probability map using naturalistic recordings collected
with the UTDrive platform. We observe that the GPR method produces the best
result creating accurate predictions with localized salient regions. For
example, the 95% confidence region is defined by an area that covers 3.77%
region of a sphere surrounding the driver.
