Understanding and Modeling the Effects of Task and Context on Drivers' Gaze Allocation
arxiv(2023)
摘要
To further advance driver monitoring and assistance systems, it is important
to understand how drivers allocate their attention, in other words, where do
they tend to look and why. Traditionally, factors affecting human visual
attention have been divided into bottom-up (involuntary attraction to salient
regions) and top-down (driven by the demands of the task being performed).
Although both play a role in directing drivers' gaze, most of the existing
models for drivers' gaze prediction apply techniques developed for bottom-up
saliency and do not consider influences of the drivers' actions explicitly.
Likewise, common driving attention benchmarks lack relevant annotations for
drivers' actions and the context in which they are performed. Therefore, to
enable analysis and modeling of these factors for drivers' gaze prediction, we
propose the following: 1) we correct the data processing pipeline used in
DR(eye)VE to reduce noise in the recorded gaze data; 2) we then add per-frame
labels for driving task and context; 3) we benchmark a number of baseline and
SOTA models for saliency and driver gaze prediction and use new annotations to
analyze how their performance changes in scenarios involving different tasks;
and, lastly, 4) we develop a novel model that modulates drivers' gaze
prediction with explicit action and context information. While reducing noise
in the DR(eye)VE gaze data improves results of all models, we show that using
task information in our proposed model boosts performance even further compared
to bottom-up models on the cleaned up data, both overall (by 24
NSS) and on scenarios that involve performing safety-critical maneuvers and
crossing intersections (by up to 10–30
are available at https://github.com/ykotseruba/SCOUT.
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