SCOUT+: Towards Practical Task-Driven Drivers' Gaze Prediction
Iuliia Kotseruba John K. Tsotsos
Accurate prediction of drivers' gaze is an important component of
vision-based driver monitoring and assistive systems. Of particular interest
are safety-critical episodes, such as performing maneuvers or crossing
intersections. In such scenarios, drivers' gaze distribution changes
significantly and becomes difficult to predict, especially if the task and
context information is represented implicitly, as is common in many
state-of-the-art models. However, explicit modeling of top-down factors
affecting drivers' attention often requires additional information and
annotations that may not be readily available.
  In this paper, we address the challenge of effective modeling of task and
context with common sources of data for use in practical systems. To this end,
we introduce SCOUT+, a task- and context-aware model for drivers' gaze
prediction, which leverages route and map information inferred from commonly
available GPS data. We evaluate our model on two datasets, DR(eye)VE and BDD-A,
and demonstrate that using maps improves results compared to bottom-up models
and reaches performance comparable to the top-down model SCOUT which relies on
privileged ground truth information. Code is available at
https://github.com/ykotseruba/SCOUT.