  Several studies have shown the relevance of biosignals in driver stress
recognition. In this work, we examine something important that has been less
frequently explored: We develop methods to test if the visual driving scene can
be used to estimate a drivers' subjective stress levels. For this purpose, we
use the AffectiveROAD video recordings and their corresponding stress labels, a
continuous human-driver-provided stress metric. We use the common class
discretization for stress, dividing its continuous values into three classes:
low, medium, and high. We design and evaluate three computer vision modeling
approaches to classify the driver's stress levels: (1) object presence
features, where features are computed using automatic scene segmentation; (2)
end-to-end image classification; and (3) end-to-end video classification. All
three approaches show promising results, suggesting that it is possible to
approximate the drivers' subjective stress from the information found in the
visual scene. We observe that the video classification, which processes the
temporal information integrated with the visual information, obtains the
highest accuracy of $0.72$, compared to a random baseline accuracy of $0.33$
when tested on a set of nine drivers.
