# Behavioral Research and Practical Models of Drivers' Attention arXiv: https://arxiv.org/abs/2104.05677 ยท HTML: https://mlsanity.com/paper/2104.05677/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 477 references that are in the map. - First posted on arXiv 12 Apr 2021. ## Builds on (most cited first, 28 of 28) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016) - [Textual Explanations for Self-Driving Vehicles](https://mlsanity.com/paper/1807.11546/) (2018) - [Salient Object Detection in the Deep Learning Era: An In-Depth Survey](https://mlsanity.com/paper/1904.09146/) (2019) - [End-to-end Learning of Driving Models from Large-scale Video Datasets](https://mlsanity.com/paper/1612.01079/) (2016) - [A Survey of End-to-End Driving: Architectures and Training Methods](https://mlsanity.com/paper/2003.06404/) (2020) - [Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention](https://mlsanity.com/paper/1703.10631/) (2017) - [Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning](https://mlsanity.com/paper/1811.02307/) (2018) - [Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice](https://mlsanity.com/paper/1805.11773/) (2018) - [Grounding Human-to-Vehicle Advice for Self-driving Vehicles](https://mlsanity.com/paper/1911.06978/) (2019) - [Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car](https://mlsanity.com/paper/1704.07911/) (2017) - [Predicting Driver Attention in Critical Situations](https://mlsanity.com/paper/1711.06406/) (2017) - [Learning Spatiotemporal Features with 3D Convolutional Networks](https://mlsanity.com/paper/1412.0767/) (2014) - [Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture](https://mlsanity.com/paper/1509.05016/) (2015) - [Deep Object-Centric Policies for Autonomous Driving](https://mlsanity.com/paper/1811.05432/) (2018) - [Learning a Driving Simulator](https://mlsanity.com/paper/1608.01230/) (2016) - [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) - [Explaining Autonomous Driving by Learning End-to-End Visual Attention](https://mlsanity.com/paper/2006.03347/) (2020) - [Driver Gaze Zone Estimation using Convolutional Neural Networks: A General Framework and Ablative Analysis](https://mlsanity.com/paper/1802.02690/) (2018) - [Digging Deeper into Egocentric Gaze Prediction](https://mlsanity.com/paper/1904.06090/) (2019) - [Owl and Lizard: Patterns of Head Pose and Eye Pose in Driver Gaze Classification](https://mlsanity.com/paper/1508.04028/) (2015) - [Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework](https://mlsanity.com/paper/1910.09722/) (2019) - [What Can Be Predicted from Six Seconds of Driver Glances?](https://mlsanity.com/paper/1611.08754/) (2016) - [Real-Time Workload Classification during Driving using HyperNetworks](https://mlsanity.com/paper/1810.03145/) (2018) - [Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models](https://mlsanity.com/paper/1504.02789/) (2015) - [Learning Where to Attend Like a Human Driver](https://mlsanity.com/paper/1611.08215/) (2016) ## Built on by (most cited first, 9 of 9) - [Autonomous Vehicles that Alert Humans to Take-Over Controls: Modeling with Real-World Data](https://mlsanity.com/paper/2104.11489/) (2021) - [On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset](https://mlsanity.com/paper/2112.00942/) (2021) - [Gaze-based intention estimation: principles, methodologies, and applications in HRI](https://mlsanity.com/paper/2302.04530/) (2023) - [SCOUT+: Towards Practical Task-Driven Drivers' Gaze Prediction](https://mlsanity.com/paper/2404.08756/) (2024) - [Understanding and Modeling the Effects of Task and Context on Drivers' Gaze Allocation](https://mlsanity.com/paper/2310.09275/) (2023) - [Data Limitations for Modeling Top-Down Effects on Drivers' Attention](https://mlsanity.com/paper/2404.08749/) (2024) - [AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systems](https://mlsanity.com/paper/2504.05950/) (2025) - [ANNA: A Deep Learning Based Dataset in Heterogeneous Traffic for Autonomous Vehicles](https://mlsanity.com/paper/2401.11358/) (2024) - [Eyes on the Road: A Naturalistic Comparison of MTW Rider Gaze in Urban Indian Traffic](https://mlsanity.com/paper/2609.33811/) (2026) ## Abstract (arXiv) Driving is a routine activity for many, but it is far from simple. Drivers deal with multiple concurrent tasks, such as keeping the vehicle in the lane, observing and anticipating the actions of other road users, reacting to hazards, and dealing with distractions inside and outside the vehicle. Failure to notice and respond to the surrounding objects and events can cause accidents. The ongoing improvements of the road infrastructure and vehicle mechanical design have made driving safer overall. Nevertheless, the problem of driver inattention has remained one of the primary causes of accidents. Therefore, understanding where the drivers look and why they do so can help eliminate sources of distractions and identify unsafe attention patterns. Research on driver attention has implications for many practical applications such as policy-making, improving driver education, enhancing road infrastructure and in-vehicle infotainment systems, as well as designing systems for driver monitoring, driver assistance, and automated driving. This report covers the literature on changes in drivers' visual attention distribution due to factors, internal and external to the driver. Aspects of attention during driving have been explored across multiple disciplines, including psychology, human factors, human-computer interaction, intelligent transportation, and computer vision, each offering different perspectives, goals, and explanations for the observed phenomena. We link cross-disciplinary theoretical and behavioral research on driver's attention to practical solutions. Furthermore, limitations and directions for future research are discussed. This report is based on over 175 behavioral studies, nearly 100 practical papers, 20 datasets, and over 70 surveys published since 2010. A curated list of papers used for this report is available at urlhttps://github.com/ykotseruba/attentionanddriving.