mlsanityData to 14 Oct 2026

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

arXiv 2110.08610 ↗First posted 16 Oct 2021cs.HC cs.CV cs.LGcited by 9

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Abstract (arXiv)

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/.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 19 of its 68 references that are in the map. First posted on arXiv 16 Oct 2021.

Builds on

19 of 68 references.

References · 19

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7 more
  1. 2016A Deep Multi-Level Network for Saliency Predictioncited by 54
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 3; 2026: 2.

9 papers.

  1. 2023Understanding and Modeling the Effects of Task and Context on Drivers' Gaze Allocationcited by 4
  2. 2023Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Predictioncited by 3
  3. 2024Data Limitations for Modeling Top-Down Effects on Drivers' Attentioncited by 3
  4. 2024Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Datacited by 3
  5. 2026Neuro-Cognitive Reward Modeling for Human-Centered Autonomous Vehicle Controlcited by 3
  6. 2025AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systemscited by 2
  7. 2025Causal-Entity Reflected Egocentric Traffic Accident Video Synthesiscited by 2
  8. 2025Estimating cognitive biases with attention-aware inverse planningcited by 2
  9. 2026From Scene to Object: Text-Guided Dual-Gaze Predictioncited by 1