MAAD: A Model and Dataset for "Attended Awareness" in Driving

TL;DR

This paper introduces a computational model and a new dataset to estimate and analyze a driver's attended awareness in driving scenes, aiming to improve safety systems by understanding where drivers focus and are aware.

Contribution

The paper presents a novel model for estimating attended awareness from video and gaze data, along with a new high-quality dataset capturing driving scene attention.

Findings

Model reasonably estimates attended awareness in controlled settings.

Model improves saliency, gaze calibration, and denoising tasks.

Dataset and model are publicly available for further research.

Abstract

We propose a computational model to estimate a person's attended awareness of their environment. We define attended awareness to be those parts of a potentially dynamic scene which a person has attended to in recent history and which they are still likely to be physically aware of. Our model takes as input scene information in the form of a video and noisy gaze estimates, and outputs visual saliency, a refined gaze estimate, and an estimate of the person's attended awareness. In order to test our model, we capture a new dataset with a high-precision gaze tracker including 24.5 hours of gaze sequences from 23 subjects attending to videos of driving scenes. The dataset also contains third-party annotations of the subjects' attended awareness based on observations of their scan path. Our results show that our model is able to reasonably estimate attended awareness in a controlled setting, and in the future could potentially be extended to real egocentric driving data to help enable more effective ahead-of-time warnings in safety systems and thereby augment driver performance. We also demonstrate our model's effectiveness on the tasks of saliency, gaze calibration, and denoising, using both our dataset and an existing saliency dataset. We make our model and dataset available at https://github.com/ToyotaResearchInstitute/att-aware/.