Estimation of Driver’s Gaze Region From Head Position and Orientation Using Probabilistic Confidence Regions

Visual attention is one of the most important aspects related to driver distraction. Estimating 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 head pose. 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 driver’s visual attention. The area of the estimated region is small when the model has high confidence, 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 estimations with localized salient regions. For example, the 95% confidence region is defined by an area covering 3.77% region of a sphere surrounding the driver.