# CoCAtt: A Cognitive-Conditioned Driver Attention Dataset

arXiv: https://arxiv.org/abs/2111.10014 · HTML: https://mlsanity.com/paper/2111.10014/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 47 references that are in the map.
- First posted on arXiv 19 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [CondConv: Conditionally Parameterized Convolutions for Efficient Inference](https://mlsanity.com/paper/1904.04971/) (2019)
- [Predicting the Driver's Focus of Attention: the DR(eye)VE Project](https://mlsanity.com/paper/1705.03854/) (2017)
- [Predicting Driver Attention in Critical Situations](https://mlsanity.com/paper/1711.06406/) (2017)
- [DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark](https://mlsanity.com/paper/1904.12634/) (2019)
- [Looking at the right stuff: Guided semantic-gaze for autonomous driving](https://mlsanity.com/paper/1911.10455/) (2019)
- [MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/1912.07773/) (2019)
- [Goal-oriented Object Importance Estimation in On-road Driving Videos](https://mlsanity.com/paper/1905.02848/) (2019)
- [The Multimodal Driver Monitoring Database: A Naturalistic Corpus to Study Driver Attention](https://mlsanity.com/paper/2101.04639/) (2020)
- [Human-Vehicle Cooperation on Prediction-Level: Enhancing Automated Driving with Human Foresight](https://mlsanity.com/paper/2104.03019/) (2021)

## Built on by (most cited first, 8 of 8)

- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [SalM2: An Extremely Lightweight Saliency Mamba Model for Real-Time Cognitive Awareness of Driver Attention](https://mlsanity.com/paper/2502.16214/) (2025)
- [Neuro-Cognitive Reward Modeling for Human-Centered Autonomous Vehicle Control](https://mlsanity.com/paper/2603.25968/) (2026)
- [A Survey on Datasets for Decision-making of Autonomous Vehicle](https://mlsanity.com/paper/2306.16784/) (2023)
- [Gated Driver Attention Predictor](https://mlsanity.com/paper/2308.02530/) (2023)
- [AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systems](https://mlsanity.com/paper/2504.05950/) (2025)
- [Causal-Entity Reflected Egocentric Traffic Accident Video Synthesis](https://mlsanity.com/paper/2506.23263/) (2025)
- [A Simulator Dataset to Support the Study of Impaired Driving](https://mlsanity.com/paper/2507.02867/) (2025)

## Abstract (arXiv)

The task of driver attention prediction has drawn considerable interest among researchers in robotics and the autonomous vehicle industry. Driver attention prediction can play an instrumental role in mitigating and preventing high-risk events, like collisions and casualties. However, existing driver attention prediction models neglect the distraction state and intention of the driver, which can significantly influence how they observe their surroundings. To address these issues, we present a new driver attention dataset, CoCAtt (Cognitive-Conditioned Attention). Unlike previous driver attention datasets, CoCAtt includes per-frame annotations that describe the distraction state and intention of the driver. In addition, the attention data in our dataset is captured in both manual and autopilot modes using eye-tracking devices of different resolutions. Our results demonstrate that incorporating the above two driver states into attention modeling can improve the performance of driver attention prediction. To the best of our knowledge, this work is the first to provide autopilot attention data. Furthermore, CoCAtt is currently the largest and the most diverse driver attention dataset in terms of autonomy levels, eye tracker resolutions, and driving scenarios.