mlsanityData to 14 Oct 2026

Predicting the Driver's Focus of Attention: the DR(eye)VE Project

arXiv 1705.03854 ↗First posted 10 May 2017cs.CVcited by 79

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Abstract (arXiv)

In this work we aim to predict the driver's focus of attention. The goal is to estimate what a person would pay attention to while driving, and which part of the scene around the vehicle is more critical for the task. To this end we propose a new computer vision model based on a multi-branch deep architecture that integrates three sources of information: raw video, motion and scene semantics. We also introduce DR(eye)VE, the largest dataset of driving scenes for which eye-tracking annotations are available. This dataset features more than 500,000 registered frames, matching ego-centric views (from glasses worn by drivers) and car-centric views (from roof-mounted camera), further enriched by other sensors measurements. Results highlight that several attention patterns are shared across drivers and can be reproduced to some extent. The indication of which elements in the scene are likely to capture the driver's attention may benefit several applications in the context of human-vehicle interaction and driver attention analysis.

Key facts. Cited by 79 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 May 2017.

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Citing papers per year: 2019: 2; 2020: 1; 2021: 10; 2022: 11; 2023: 13; 2024: 13; 2025: 16; 2026: 13.

79 papers.

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  1. 2025EgoLife: Towards Egocentric Life Assistantcited by 91
  2. 2021Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmarkcited by 82
  3. 2024A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlookcited by 66
  4. 2024Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Reviewcited by 53
  5. 2019MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learningcited by 29
  6. 2023Towards Knowledge-driven Autonomous Drivingcited by 29
  7. 2023Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embeddingcited by 19
  8. 2024Learning from Observer Gaze:Zero-Shot Attention Prediction Oriented by Human-Object Interaction Recognitioncited by 17
  9. 2022Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmarkcited by 16
  10. 2021Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Taskscited by 13
  11. 2022Important Object Identification with Semi-Supervised Learning for Autonomous Drivingcited by 13
  12. 2022FBLNet: FeedBack Loop Network for Driver Attention Predictioncited by 12
38 more
  1. 2023A Review of Driver Gaze Estimation and Application in Gaze Behavior Understandingcited by 12
  2. 2019Gaze Training by Modulated Dropout Improves Imitation Learningcited by 10
  3. 2025Where, What, Why: Towards Explainable Driver Attention Predictioncited by 10
  4. 2021MAAD: A Model and Dataset for "Attended Awareness" in Drivingcited by 9
  5. 2024Trends, Applications, and Challenges in Human Attention Modellingcited by 9
  6. 2021CoCAtt: A Cognitive-Conditioned Driver Attention Datasetcited by 8
  7. 2022Visual Attention Prediction Improves Performance of Autonomous Drone Racing Agentscited by 7
  8. 2022Feature-Aligned Video Raindrop Removal with Temporal Constraintscited by 7
  9. 2023Video Waterdrop Removal via Spatio-Temporal Fusion in Driving Scenescited by 7
  10. 2024M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Drivingcited by 7
  11. 2020Selective Eye-gaze Augmentation To Enhance Imitation Learning In Atari Gamescited by 6
  12. 2025PDB: Not All Drivers Are the Same -- A Personalized Dataset for Understanding Driving Behaviorcited by 6
  13. 2024SCOUT+: Towards Practical Task-Driven Drivers' Gaze Predictioncited by 5
  14. 2023Learning Road Scene-level Representations via Semantic Region Predictioncited by 4
  15. 2023Understanding and Modeling the Effects of Task and Context on Drivers' Gaze Allocationcited by 4
  16. 2023What Do Deep Saliency Models Learn about Visual Attention?cited by 4
  17. 2021Artificial Intelligence Methods in In-Cabin Use Cases: A Surveycited by 3
  18. 2021A Gated Fusion Network for Dynamic Saliency Predictioncited by 3
  19. 2021Predicting Driver Self-Reported Stress by Analyzing the Road Scenecited by 3
  20. 2021Explainable, automated urban interventions to improve pedestrian and vehicle safetycited by 3
  21. 2023Gaze-Informed Vision Transformers: Predicting Driving Decisions Under Uncertaintycited by 3
  22. 2023Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Predictioncited by 3
  23. 2024A Transformer-Based Model for the Prediction of Human Gaze Behavior on Videoscited by 3
  24. 2024Data Limitations for Modeling Top-Down Effects on Drivers' Attentioncited by 3
  25. 2024EvTTC: An Event Camera Dataset for Time-to-Collision Estimationcited by 3
  26. 20253D Prior is All You Need: Cross-Task Few-shot 2D Gaze Estimationcited by 3
  27. 2021Matching Representations of Explainable Artificial Intelligence and Eye Gaze for Human-Machine Interactioncited by 2
  28. 2021Reason induced visual attention for explainable autonomous drivingcited by 2
  29. 2023Gated Driver Attention Predictorcited by 2
  30. 2025Salient Object Detection in Traffic Scene through the TSOD10K Datasetcited by 2
  31. 2025AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systemscited by 2
  32. 2025Causal-Entity Reflected Egocentric Traffic Accident Video Synthesiscited by 2
  33. 2025Towards Physics-informed Spatial Intelligence with Human Priors: An Autonomous Driving Pilot Studycited by 2
  34. 2025FSDAM: Few-Shot Driving Attention Modeling via Vision-Language Couplingcited by 2
  35. 2022Attention in Reasoning: Dataset, Analysis, and Modelingcited by 1
  36. 2022Where and What: Driver Attention-based Object Detectioncited by 1
  37. 2022Decoding Attention from Gaze: A Benchmark Dataset and End-to-End Modelscited by 1
  38. 2023Confidence-aware 3D Gaze Estimation and Evaluation Metriccited by 1

50 most cited of 79 All 79 (JSON)