datasets_by_year.md

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Home  | Behavioral  | Applications  | Datasets  

By application  | By year  

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Click on each entry below to see additional information.

2024

CL-Drive | paper | link

Description: Dataset for studying driver cognitive load recorded in simulation

Data: scene video, EEG, ECG, EDA, eye-tracking

Annotations: cognitive load labels

@article{2024_T-ITS_Angkan,
    author = "Angkan, Prithila and Behinaein, Behnam and Mahmud, Zunayed and Bhatti, Anubhav and Rodenburg, Dirk and Hungler, Paul and Etemad, Ali",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Multimodal Brain--Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines",
    year = "2024"
}

Used in papers:

Angkan et al., Multimodal Brain–Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines, Trans. ITS, 2024 | paper | code

Dataset(s): CL-Drive

@article{2024_T-ITS_Angkan,
    author = "Angkan, Prithila and Behinaein, Behnam and Mahmud, Zunayed and Bhatti, Anubhav and Rodenburg, Dirk and Hungler, Paul and Etemad, Ali",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Multimodal Brain--Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines",
    year = "2024"
}

HOIST | paper | link

Full name: Object Importance Estimation Using Counterfactual Reasoning

Description: Simulated driving scenarios with object importance annotations

Data: scene video (BEV)

Annotations: bounding boxes, object importance labels

@article{2024_RAL_Gupta,
    author = "Gupta, Pranay and Biswas, Abhijat and Admoni, Henny and Held, David",
    journal = "IEEE Robotics and Automation Letters",
    publisher = "IEEE",
    title = "Object Importance Estimation using Counterfactual Reasoning for Intelligent Driving",
    year = "2024"
}

Used in papers:

Gupta et al., Object Importance Estimation Using Counterfactual Reasoning for Intelligent Driving, R-AL, 2024 | paper | code

Dataset(s): HOIST

@article{2024_R-AL_Gupta,
    author = "Gupta, Pranay and Biswas, Abhijat and Admoni, Henny and Held, David",
    journal = "IEEE Robotics and Automation Letters",
    publisher = "IEEE",
    title = "Object Importance Estimation using Counterfactual Reasoning for Intelligent Driving",
    year = "2024"
}

IVGaze | paper | link

Full name: In-Vehicle Gaze Dataset

Description: 44K images of 25 subjects looking at different areas inside the vehicle

Data: driver video, eye-tracking

Annotations: gaze area labels

@inproceedings{2024_CVPR_Cheng,
    author = "Cheng, Yihua and Zhu, Yaning and Wang, Zongji and Hao, Hongquan and Liu, Yongwei and Cheng, Shiqing and Wang, Xi and Chang, Hyung Jin",
    booktitle = "Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition",
    pages = "1556--1565",
    title = "What Do You See in Vehicle? Comprehensive Vision Solution for In-Vehicle Gaze Estimation",
    year = "2024"
}

Used in papers:

Cheng et al., What Do You See in Vehicle? Comprehensive Vision Solution for In-Vehicle Gaze Estimation, CVPR, 2024 | paper | code

Dataset(s): IVGaze

@inproceedings{2024_CVPR_Cheng,
    author = "Cheng, Yihua and Zhu, Yaning and Wang, Zongji and Hao, Hongquan and Liu, Yongwei and Cheng, Shiqing and Wang, Xi and Chang, Hyung Jin",
    booktitle = "Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition",
    pages = "1556--1565",
    title = "What Do You See in Vehicle? Comprehensive Vision Solution for In-Vehicle Gaze Estimation",
    year = "2024"
}

SCOUT | paper | link

Full name: Task and Context-Modulated Attention

Description: Extended annotations for four public datasets for studying drivers’ attention: DR(eye)VE, BDD-A, MAAD, LBW

Data: eye-tracking

Annotations: action labels, context labels, map information

@inproceedings{2024_IV_Kotseruba_1,
    author = "Kotseruba, Iuliia and Tsotsos, John K",
    booktitle = "Intelligent Vehicles Symposium (IV)",
    title = "Data Limitations for Modeling Top-Down Effects on Drivers' Attention",
    year = "2024"
}

Used in papers:

Kotseruba et al., SCOUT+: Towards Practical Task-Driven Drivers’ Gaze Prediction, IV, 2024 | paper | code

Dataset(s): DR(eye)VE, BDD-A, SCOUT

@inproceedings{2024_IV_Kotseruba_2,
    author = "Kotseruba, Iuliia and Tsotsos, John K",
    booktitle = "Intelligent Vehicles Symposium (IV)",
    title = "{SCOUT+: Towards Practical Task-Driven Drivers' Gaze Prediction}",
    year = "2024"
}

Kotseruba et al., Data Limitations for Modeling Top-Down Effects on Drivers’ Attention, IV, 2024 | paper | code

Dataset(s): DR(eye)VE, BDD-A, MAAD, LBW, SCOUT

@inproceedings{2024_IV_Kotseruba_1,
    author = "Kotseruba, Iuliia and Tsotsos, John K",
    booktitle = "Intelligent Vehicles Symposium (IV)",
    title = "Data Limitations for Modeling Top-Down Effects on Drivers' Attention",
    year = "2024"
}

Kotseruba et al., Understanding and Modeling the Effects of Task and Context on Drivers’ Gaze Allocation, IV, 2024 | paper | code

Dataset(s): DR(eye)VE, SCOUT

@inproceedings{2024_IV_Kotseruba,
    author = "Kotseruba, Iuliia and Tsotsos, John K",
    booktitle = "2024 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "1337--1344",
    title = "Understanding and modeling the effects of task and context on drivers’ gaze allocation",
    year = "2024"
}

2023

100-Driver | paper | link

Description: Videos of drivers performing secondary tasks

Data: driver video

Annotations: action labels

@article{2023_T-ITS_Wang,
    author = "Wang, Jing and Li, Wenjing and Li, Fang and Zhang, Jun and Wu, Zhongcheng and Zhong, Zhun and Sebe, Nicu",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "100-Driver: A Large-Scale, Diverse Dataset for Distracted Driver Classification",
    year = "2023"
}

Used in papers:

Li et al., A Lightweight and Efficient Distracted Driver Detection Model Fusing Convolutional Neural Network and Vision Transformer, Trans. ITS, 2024 | paper | code

Dataset(s): SFDDD, 100-Driver

@article{2024_T-ITS_Li_1,
    author = "Li, Zhao and Zhao, Xia and Wu, Fuwei and Chen, Dan and Wang, Chang",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "A Lightweight and Efficient Distracted Driver Detection Model Fusing Convolutional Neural Network and Vision Transformer",
    year = "2024"
}

AI CITY NDAR | paper | link

Full name: AI CITY Naturalistic Driving Action Recognition

Description: 594 video clips (90 hours) of 99 drivers performing 16 secondary tasks during driving

Data: driver video

@inproceedings{2023_CVPRW_Naphade,
    author = "Naphade, Milind and Wang, Shuo and Anastasiu, David C and Tang, Zheng and Chang, Ming-Ching and Yao, Yue and Zheng, Liang and Rahman, Mohammed Shaiqur and Arya, Meenakshi S and Sharma, Anuj and others",
    booktitle = "Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition",
    pages = "5538--5548",
    title = "The 7th ai city challenge",
    year = "2023"
}

Used in papers:

Chai et al., Rethinking the Evaluation of Driver Behavior Analysis Approaches, Trans. ITS, 2024 | paper

Dataset(s): SFDDD, AI CITY NDAR

@article{2024_T-ITS_Chai,
    author = "Chai, Weiheng and Wang, Jiyang and Chen, Jiajing and Velipasalar, Senem and Sharma, Anuj",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Rethinking the Evaluation of Driver Behavior Analysis Approaches",
    year = "2024"
}

Zhou et al., Multi View Action Recognition for Distracted Driver Behavior Localization, CVPRW, 2023 | paper | code

Dataset(s): AI CITY NDAR

@inproceedings{2023_CVPRW_Zhou,
    author = "Zhou, Wei and Qian, Yinlong and Jie, Zequn and Ma, Lin",
    booktitle = "Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition",
    pages = "5375--5380",
    title = "Multi view action recognition for distracted driver behavior localization",
    year = "2023"
}

AIDE | paper | link

Full name: Assistive Driving Perception Dataset

Description: Naturalistic dataset with multi-camera views of drivers performing normal driving and secondary tasks

Data: driver video, scene video

Annotations: distraction state, action labels

@inproceedings{2023_ICCV_Yang,
    author = "Yang, Dingkang and Huang, Shuai and Xu, Zhi and Li, Zhenpeng and Wang, Shunli and Li, Mingcheng and Wang, Yuzheng and Liu, Yang and Yang, Kun and Chen, Zhaoyu and others",
    booktitle = "Proceedings of the IEEE/CVF International Conference on Computer Vision",
    pages = "20459--20470",
    title = "AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking Dataset for Assistive Driving Perception",
    year = "2023"
}

DRAMA | paper | link

Full name: Driving Risk Assessment Mechanism with A captioning module

Description: Driving scenarios recorded in Tokyo, Japan with video and object-level importance labels and captions

Data: scene video

Annotations: bounding boxes, captions

@inproceedings{2023_WACV_Malla,
    author = "Malla, Srikanth and Choi, Chiho and Dwivedi, Isht and Choi, Joon Hee and Li, Jiachen",
    booktitle = "Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision",
    pages = "1043--1052",
    title = "DRAMA: Joint Risk Localization and Captioning in Driving",
    year = "2023"
}

DrFixD-night | paper | link

Full name: Driver Fixation Dataset in night

Description: 15 videos of night-time driving with eye-tracking data from 30 participants

Data: scene video, eye-tracking

@article{2023_T-ITS_Deng,
    author = "Deng, Tao and Jiang, Lianfang and Shi, Yi and Wu, Jiang and Wu, Zhangbi and Yan, Shun and Zhang, Xianshi and Yan, Hongmei",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Driving Visual Saliency Prediction of Dynamic Night Scenes via a Spatio-Temporal Dual-Encoder Network",
    year = "2023"
}

Used in papers:

Deng et al., Driving Visual Saliency Prediction of Dynamic Night Scenes via a Spatio-Temporal Dual-Encoder Network, Trans. ITS, 2023 | paper | code

Dataset(s): DrFixD-night, DR(eye)VE

@article{2023_T-ITS_Deng,
    author = "Deng, Tao and Jiang, Lianfang and Shi, Yi and Wu, Jiang and Wu, Zhangbi and Yan, Shun and Zhang, Xianshi and Yan, Hongmei",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Driving Visual Saliency Prediction of Dynamic Night Scenes via a Spatio-Temporal Dual-Encoder Network",
    year = "2023"
}

SAM-DD | paper | link

Full name: Singapore AutoMan@NTU Distracted Driving Dataset

Description: Videos of drivers performing secondary tasks

Data: driver video, depth

Annotations: distraction state

@article{2023_T-ITS_Yang,
    author = "Yang, Haohan and Liu, Haochen and Hu, Zhongxu and Nguyen, Anh-Tu and Guerra, Thierry-Marie and Lv, Chen",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Quantitative Identification of Driver Distraction: A Weakly Supervised Contrastive Learning Approach",
    year = "2023"
}

Used in papers:

Yang et al., Quantitative Identification of Driver Distraction: A Weakly Supervised Contrastive Learning Approach, Trans. ITS, 2024 | paper | code

Dataset(s): 3MDAD, AUCD2, SAM-DD

@article{2024_T-ITS_Yang,
    author = "Yang, Haohan and Liu, Haochen and Hu, Zhongxu and Nguyen, Anh-Tu and Guerra, Thierry-Marie and Lv, Chen",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Quantitative identification of driver distraction: A weakly supervised contrastive learning approach",
    year = "2023"
}

Hasan et al., Vision-Language Models Can Identify Distracted Driver Behavior From Naturalistic Videos, Trans. ITS, 2024 | paper | code

Dataset(s): DMD, SAM-DD, SFDDD, SynDD1

@article{2024_T-ITS_Hasan,
    author = "Hasan, Md Zahid and Chen, Jiajing and Wang, Jiyang and Rahman, Mohammed Shaiqur and Joshi, Ameya and Velipasalar, Senem and Hegde, Chinmay and Sharma, Anuj and Sarkar, Soumik",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Vision-language models can identify distracted driver behavior from naturalistic videos",
    year = "2024"
}

SynDD1 | paper | link

Full name: Synthetic Distracted Driving Dataset

Description: Synthetic dataset for machine learning models to detect and analyze drivers' various distracted behavior and different gaze zones.

Data: driver video

Annotations: gaze area labels, action labels, appearance labels

@article{2023_DiB_Rahman,
    author = "Rahman, Mohammed Shaiqur and Venkatachalapathy, Archana and Sharma, Anuj and Wang, Jiyang and Gursoy, Senem Velipasalar and Anastasiu, David and Wang, Shuo",
    journal = "Data in brief",
    pages = "108793",
    publisher = "Elsevier",
    title = "Synthetic distracted driving (syndd1) dataset for analyzing distracted behaviors and various gaze zones of a driver",
    volume = "46",
    year = "2023"
}

Used in papers:

Hasan et al., Vision-Language Models Can Identify Distracted Driver Behavior From Naturalistic Videos, Trans. ITS, 2024 | paper | code

Dataset(s): DMD, SAM-DD, SFDDD, SynDD1

@article{2024_T-ITS_Hasan,
    author = "Hasan, Md Zahid and Chen, Jiajing and Wang, Jiyang and Rahman, Mohammed Shaiqur and Joshi, Ameya and Velipasalar, Senem and Hegde, Chinmay and Sharma, Anuj and Sarkar, Soumik",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Vision-language models can identify distracted driver behavior from naturalistic videos",
    year = "2024"
}

Doshi et al., Federated Learning-based Driver Activity Recognition for Edge Devices, CVPRW, 2022 | paper

Dataset(s): SynDD1

@inproceedings{2022_CVPRW_Doshi,
    author = "Doshi, Keval and Yilmaz, Yasin",
    booktitle = "Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition",
    pages = "3338--3346",
    title = "Federated learning-based driver activity recognition for edge devices",
    year = "2022"
}

Ding et al., A Coarse-to-Fine Boundary Localization method for Naturalistic Driving Action Recognition, CVPRW, 2022 | paper

Dataset(s): SynDD1

@article{2022_CVPRW_Ding,
    author = "Du, Guanglong and Zhang, Linlin and Su, Kang and Wang, Xueqian and Teng, Shaohua and Liu, Peter X",
    journal = "Ieee Transactions on Intelligent Transportation Systems",
    number = "11",
    pages = "21810--21820",
    publisher = "IEEE",
    title = "A multimodal fusion fatigue driving detection method based on heart rate and PERCLOS",
    volume = "23",
    year = "2022"
}

Alyahya et al., Temporal Driver Action Localization using Action Classification Methods, CVPRW, 2022 | paper

Dataset(s): SynDD1

@inproceedings{2022_CVPRW_Alyahya,
    author = "Alyahya, Munirah and Alghannam, Shahad and Alhussan, Taghreed",
    booktitle = "Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition",
    pages = "3319--3326",
    title = "Temporal Driver Action Localization using Action Classification Methods",
    year = "2022"
}

2022

CoCAtt | paper | link

Full name: A Cognitive-Conditioned Driver Attention Dataset

Description: Videos of drivers and driver scenes in automated and manual driving conditions with per-frame gaze and distraction annotations

Data: driver video, scene video, eye-tracking

Annotations: distraction state, car telemetry, intention labels

@inproceedings{2022_ITSC_Shen,
    author = "Shen, Yuan and Wijayaratne, Niviru and Sriram, Pranav and Hasan, Aamir and Du, Peter and Driggs-Campbell, Katherine",
    booktitle = "2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)",
    organization = "IEEE",
    pages = "32--39",
    title = "CoCAtt: A Cognitive-Conditioned Driver Attention Dataset",
    year = "2022"
}

Fatigueview | paper | link

Description: Multi-camera video dataset for vision-based drowsiness detection.

Data: driver video

Annotations: facial landmarks, face/hand bounding boxes, head pose, eye status, pose, drowsiness labels

@article{2022_T-ITS_Yang,
    author = "Yang, Cong and Yang, Zhenyu and Li, Weiyu and See, John",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "FatigueView: A Multi-Camera Video Dataset for Vision-Based Drowsiness Detection",
    year = "2022"
}

LBW | paper | link

Full name: Look Both Ways

Description: Synchronized videos from scene and driver-facing cameras of drivers performing various maneuvers in traffic

Data: driver video, scene video, eye-tracking

@inproceedings{2022_ECCV_Kasahara,
    author = "Kasahara, Isaac and Stent, Simon and Park, Hyun Soo",
    booktitle = "Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XIII",
    organization = "Springer",
    pages = "126--142",
    title = "Look Both Ways: Self-supervising Driver Gaze Estimation and Road Scene Saliency",
    year = "2022"
}

Used in papers:

Kotseruba et al., Data Limitations for Modeling Top-Down Effects on Drivers’ Attention, IV, 2024 | paper | code

Dataset(s): DR(eye)VE, BDD-A, MAAD, LBW, SCOUT

@inproceedings{2024_IV_Kotseruba_1,
    author = "Kotseruba, Iuliia and Tsotsos, John K",
    booktitle = "Intelligent Vehicles Symposium (IV)",
    title = "Data Limitations for Modeling Top-Down Effects on Drivers' Attention",
    year = "2024"
}

Kasahara et al., Look Both Ways: Self-Supervising Driver Gaze Estimation and Road Scene Saliency, ECCV, 2022 | paper | code

Dataset(s): LBW

@inproceedings{2022_ECCV_Kasahara,
    author = "Kasahara, Isaac and Stent, Simon and Park, Hyun Soo",
    booktitle = "Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XIII",
    organization = "Springer",
    pages = "126--142",
    title = "Look Both Ways: Self-supervising Driver Gaze Estimation and Road Scene Saliency",
    year = "2022"
}

2021

55 Rides | paper | link

Description: Naturalistic dataset recorded by four drivers and annotated by three raters to determine distraction states

Data: driver video, eye-tracking

Annotations: distraction state, head pose

@inproceedings{2021_ETRA_Kubler,
    author = {K{\"u}bler, Thomas C and Fuhl, Wolfgang and Wagner, Elena and Kasneci, Enkelejda},
    booktitle = "ACM Symposium on Eye Tracking Research and Applications",
    pages = "1--8",
    title = "55 Rides: attention annotated head and gaze data during naturalistic driving",
    year = "2021"
}

DAD | paper | link

Full name: Driver Anomaly Detection

Description: Videos of normal and anomalous behaviors (manual/visual distractions) of drivers.

Data: driver video

Annotations: action labels

@inproceedings{2021_WACV_Kopuklu,
    author = "Kopuklu, Okan and Zheng, Jiapeng and Xu, Hang and Rigoll, Gerhard",
    booktitle = "Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision",
    pages = "91--100",
    title = "Driver anomaly detection: A dataset and contrastive learning approach",
    year = "2021"
}

Used in papers:

Chen et al., A Driving Risk Assessment Framework Considering Driver’s Fatigue State and Distraction Behavior, Trans. ITS, 2024 | paper

Dataset(s): SFDDD, DAD, private

@article{2024_T-ITS_Chen,
    author = "Chen, Jiansong and Zhang, Qixiang and Chen, Jinxin and Wang, Jinxiang and Fang, Zhenwu and Liu, Yahui and Yin, Guodong",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "A Driving Risk Assessment Framework Considering Driver’s Fatigue State and Distraction Behavior",
    year = "2024"
}

Kopuklu et al., Driver Anomaly Detection: A Dataset and Contrastive Learning Approach, WACV, 2021 | paper

Dataset(s): DAD

@inproceedings{2021_WACV_Kopuklu,
    author = "Kopuklu, Okan and Zheng, Jiapeng and Xu, Hang and Rigoll, Gerhard",
    booktitle = "Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision",
    pages = "91--100",
    title = "Driver anomaly detection: A dataset and contrastive learning approach",
    year = "2021"
}

MAAD | paper | link

Full name: Attended Awareness in Driving

Description: A subset of videos from DR(eye)VE annotated with gaze collected in lab conditions.

Data: eye-tracking, scene video

Annotations: task labels

@inproceedings{2021_ICCVW_Gopinath,
    author = "Gopinath, Deepak and Rosman, Guy and Stent, Simon and Terahata, Katsuya and Fletcher, Luke and Argall, Brenna and Leonard, John",
    booktitle = "Proceedings of the IEEE/CVF International Conference on Computer Vision",
    pages = "3426--3436",
    title = {MAAD: A Model and Dataset for" Attended Awareness" in Driving},
    year = "2021"
}

Used in papers:

Kotseruba et al., Data Limitations for Modeling Top-Down Effects on Drivers’ Attention, IV, 2024 | paper | code

Dataset(s): DR(eye)VE, BDD-A, MAAD, LBW, SCOUT

@inproceedings{2024_IV_Kotseruba_1,
    author = "Kotseruba, Iuliia and Tsotsos, John K",
    booktitle = "Intelligent Vehicles Symposium (IV)",
    title = "Data Limitations for Modeling Top-Down Effects on Drivers' Attention",
    year = "2024"
}

Gopinath et al., MAAD: A Model and Dataset for “Attended Awareness” in Driving, ICCVW, 2021 | paper | code

Dataset(s): MAAD

@inproceedings{2021_ICCVW_Gopinath,
    author = "Gopinath, Deepak and Rosman, Guy and Stent, Simon and Terahata, Katsuya and Fletcher, Luke and Argall, Brenna and Leonard, John",
    booktitle = "Proceedings of the IEEE/CVF International Conference on Computer Vision",
    pages = "3426--3436",
    title = {MAAD: A Model and Dataset for" Attended Awareness" in Driving},
    year = "2021"
}

2020

BDD-OIA | paper | link

Full name: BDD Object Induced Action

Description: Extension of the BDD100K dataset with labels for driver actions and explanations for why the action was taken

Data: scene video

Annotations: bounding boxes, action labels, explanations

@inproceedings{2020_CVPR_Xu,
    author = "Xu, Yiran and Yang, Xiaoyin and Gong, Lihang and Lin, Hsuan-Chu and Wu, Tz-Ying and Li, Yunsheng and Vasconcelos, Nuno",
    booktitle = "Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition",
    pages = "9523--9532",
    title = "Explainable object-induced action decision for autonomous vehicles",
    year = "2020"
}

Used in papers:

Araluce et al., Leveraging Driver Attention for an End-to-End Explainable Decision-Making From Frontal Images, Trans. ITS, 2024 | paper

Dataset(s): DADA-2000, BDD-A, BDD-OIA

@article{2024_T-ITS_Araluce,
    author = "Araluce, Javier and Bergasa, Luis M and Oca{\\textasciitilde n}a, Manuel and Llamazares, {\'A}ngel and L{\'o}pez-Guill{\'e}n, Elena",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Leveraging Driver Attention for an End-to-End Explainable Decision-Making From Frontal Images",
    year = "2024"
}

CCD | paper | link

Full name: Car Crash Dataset

Description: A dataset of YouTube videos with traffic accidents

Data: scene video

Annotations: bounding boxes, accident labels

@inproceedings{2020_MM_Bao,
    author = "Bao, Wentao and Yu, Qi and Kong, Yu",
    booktitle = "Proceedings of the 28th ACM International Conference on Multimedia",
    pages = "2682--2690",
    title = "Uncertainty-based traffic accident anticipation with spatio-temporal relational learning",
    year = "2020"
}

Used in papers:

Li et al., Cognitive Traffic Accident Anticipation, ITS Mag., 2024 | paper

Dataset(s): DADA-2000, CCD

@article{2024_ITSM_Li,
    author = "Li, Lei-Lei and Fang, Jianwu and Xue, Jianru",
    journal = "IEEE Intelligent Transportation Systems Magazine",
    publisher = "IEEE",
    title = "Cognitive Traffic Accident Anticipation",
    year = "2024"
}

DGAZE | paper | link

Description: A dataset mapping drivers’ gaze to different areas in a static traffic scene in lab conditions

Data: driver video, scene video

Annotations: bounding boxes

@inproceedings{2020_IROS_Dua,
    author = "Dua, Isha and John, Thrupthi Ann and Gupta, Riya and Jawahar, CV",
    booktitle = "IROS",
    title = "DGAZE: Driver Gaze Mapping on Road",
    year = "2020"
}

Used in papers:

Dua et al., DGAZE: Driver Gaze Mapping on Road, IROS, 2020 | paper

Dataset(s): DGAZE

@inproceedings{2020_IROS_Dua,
    author = "Dua, Isha and John, Thrupthi Ann and Gupta, Riya and Jawahar, CV",
    booktitle = "IROS",
    title = "DGAZE: Driver Gaze Mapping on Road",
    year = "2020"
}

DGW | paper | link

Full name: Driver Gaze in the Wild

Description: Videos of drivers fixating on different areas in the vehicle without constraining their head and eye movements

Data: driver video

Annotations: gaze area labels

@inproceedings{2021_ICCVW_Ghosh,
    author = "Ghosh, Shreya and Dhall, Abhinav and Sharma, Garima and Gupta, Sarthak and Sebe, Nicu",
    booktitle = "ICCVW",
    title = "Speak2label: Using domain knowledge for creating a large scale driver gaze zone estimation dataset",
    year = "2021"
}

Used in papers:

Stappen et al., X-AWARE: ConteXt-AWARE Human-Environment Attention Fusion for Driver Gaze Prediction in the Wild, ICMI, 2020 | paper | code

Dataset(s): DGW

@inproceedings{2020_ICMI_Stappen,
    author = {Stappen, Lukas and Rizos, Georgios and Schuller, Bj{\"o}rn},
    booktitle = "ICMI",
    title = "X-AWARE: ConteXt-AWARE Human-Environment Attention Fusion for Driver Gaze Prediction in the Wild",
    year = "2020"
}

DMD | paper | link

Full name: Driving Monitoring Dataset

Description: A diverse multi-modal dataset of drivers performing various secondary tasks, observing different regions inside the car, and showing signs of drowsiness recorded on-road and in simulation environment

Data: driver video, scene video, vehicle data

Annotations: bounding boxes, action labels

@inproceedings{2020_ECCVW_Ortega,
    author = "Ortega, Juan Diego and Kose, Neslihan and Ca{\\textasciitilde n}as, Paola and Chao, Min-An and Unnervik, Alexander and Nieto, Marcos and Otaegui, Oihana and Salgado, Luis",
    booktitle = "ECCV",
    title = "Dmd: A large-scale multi-modal driver monitoring dataset for attention and alertness analysis",
    year = "2020"
}

Used in papers:

Hasan et al., Vision-Language Models Can Identify Distracted Driver Behavior From Naturalistic Videos, Trans. ITS, 2024 | paper | code

Dataset(s): DMD, SAM-DD, SFDDD, SynDD1

@article{2024_T-ITS_Hasan,
    author = "Hasan, Md Zahid and Chen, Jiajing and Wang, Jiyang and Rahman, Mohammed Shaiqur and Joshi, Ameya and Velipasalar, Senem and Hegde, Chinmay and Sharma, Anuj and Sarkar, Soumik",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Vision-language models can identify distracted driver behavior from naturalistic videos",
    year = "2024"
}

Palo et al., Holistic Driver Monitoring: A Multi-Task Approach for In-Cabin Driver Attention Evaluation through Multi-Camera Data, IV, 2024 | paper

Dataset(s): DMD

@inproceedings{2024_IV_Palo,
    author = "Palo, Patitapaban and Nayak, Satyajit and Modhugu, Durga Nagendra Raghava Kumar and Gupta, Kwanit and Uttarkabat, Satarupa",
    booktitle = "2024 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "1361--1366",
    title = "Holistic Driver Monitoring: A Multi-Task Approach for In-Cabin Driver Attention Evaluation through Multi-Camera Data",
    year = "2024"
}

LISA v2 | paper | link

Full name: Laboratory for Intelligent and Safe Automobiles

Description: Videos of drivers with and without eyeglasses recorded under different lighting conditions

Data: driver video

@inproceedings{2020_IV_Rangesh,
    author = "Rangesh, Akshay and Zhang, Bowen and Trivedi, Mohan M",
    booktitle = "IV",
    title = "Driver gaze estimation in the real world: Overcoming the eyeglass challenge",
    year = "2020"
}

NeuroIV | paper | link

Full name: Neuromorphic Vision Meets Intelligent Vehicle

Description: Videos of drivers performing secondary tasks, making hand gestures and observing different regions inside the vehicle recorded with DAVIS and depth sensor

Data: driver video

@article{2020_T-ITS_Chen,
    author = {Chen, Guang and Wang, Fa and Li, Weijun and Hong, Lin and Conradt, J{\"o}rg and Chen, Jieneng and Zhang, Zhenyan and Lu, Yiwen and Knoll, Alois},
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "2",
    pages = "1171--1183",
    publisher = "IEEE",
    title = "NeuroIV: Neuromorphic vision meets intelligent vehicle towards safe driving with a new database and baseline evaluations",
    volume = "23",
    year = "2020"
}

TrafficSaliency | paper | link

Description: 16 videos of driving scenes with gaze data of 28 subjects recorded in the lab with eye-tracker

Data: eye-tracking, scene video

@article{2020_T-ITS_Deng,
    author = "Deng, Tao and Yan, Hongmei and Qin, Long and Ngo, Thuyen and Manjunath, BS",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "5",
    pages = "2146--2154",
    publisher = "IEEE",
    title = "{How do drivers allocate their potential attention? Driving fixation prediction via convolutional neural networks}",
    volume = "21",
    year = "2019"
}

Used in papers:

Li et al., Adaptive Short-Temporal Induced Aware Fusion Network for Predicting Attention Regions Like a Driver, Trans. ITS, 2022 | paper | code

Dataset(s): BDD-A, DADA-2000, TrafficSaliency

@article{2022_T-ITS_Li,
    author = "Li, Qiang and Liu, Chunsheng and Chang, Faliang and Li, Shuang and Liu, Hui and Liu, Zehao",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "10",
    pages = "18695--18706",
    publisher = "IEEE",
    title = "Adaptive short-temporal induced aware fusion network for predicting attention regions like a driver",
    volume = "23",
    year = "2022"
}

Gan et al., Multisource Adaption for Driver Attention Prediction in Arbitrary Driving Scenes, Trans. ITS, 2022 | paper

Dataset(s): BDD-A, DADA-2000, DR(eye)VE, TrafficSaliency

@article{2022_T-ITS_Gan,
    author = "Gan, Shun and Pei, Xizhe and Ge, Yulong and Wang, Qingfan and Shang, Shi and Li, Shengbo Eben and Nie, Bingbing",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "11",
    pages = "20912--20925",
    publisher = "IEEE",
    title = "Multisource Adaption for Driver Attention Prediction in Arbitrary Driving Scenes",
    volume = "23",
    year = "2022"
}

Fang et al., DADA: Driver Attention Prediction in Driving Accident Scenarios, Trans. ITS, 2021 | paper | code

Dataset(s): TrafficSaliency, DR(eye)VE, DADA-2000

@article{2022_T-ITS_Fang,
    author = "Fang, Jianwu and Yan, Dingxin and Qiao, Jiahuan and Xue, Jianru and Yu, Hongkai",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "6",
    pages = "4959--4971",
    publisher = "IEEE",
    title = "DADA: Driver attention prediction in driving accident scenarios",
    volume = "23",
    year = "2021"
}

Deng et al., How Do Drivers Allocate Their Potential Attention? Driving Fixation Prediction via Convolutional Neural Networks, Trans. ITS, 2020 | paper | code

Dataset(s): TrafficSaliency

@article{2020_T-ITS_Deng,
    author = "Deng, Tao and Yan, Hongmei and Qin, Long and Ngo, Thuyen and Manjunath, BS",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "5",
    pages = "2146--2154",
    publisher = "IEEE",
    title = "{How do drivers allocate their potential attention? Driving fixation prediction via convolutional neural networks}",
    volume = "21",
    year = "2019"
}

Huang et al., Driver Distraction Detection Based on the True Driver’s Focus of Attention, Trans. ITS, 2021 | paper

Dataset(s): DADA-2000, TrafficSaliency, BDD-A, DR(eye)VE, private

@article{2021_T-ITS_Huang,
    author = "Huang, Jianling and Long, Yan and Zhao, Xiaohua",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Driver Glance Behavior Modeling Based on Semi-Supervised Clustering and Piecewise Aggregate Representation",
    year = "2021"
}

2019

3MDAD | paper | link

Full name: Multimodal Multiview and Multispectral Driver Action Dataset

Description: Videos of drivers performing secondary tasks

Data: driver video

Annotations: action labels, bounding boxes

@inproceedings{2019_CAIP_Jegham,
    author = "Jegham, Imen and Ben Khalifa, Anouar and Alouani, Ihsen and Mahjoub, Mohamed Ali",
    booktitle = "Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Salerno, Italy, September 3--5, 2019, Proceedings, Part I 18",
    organization = "Springer",
    pages = "518--529",
    title = "Mdad: A multimodal and multiview in-vehicle driver action dataset",
    year = "2019"
}

Used in papers:

Yang et al., Quantitative Identification of Driver Distraction: A Weakly Supervised Contrastive Learning Approach, Trans. ITS, 2024 | paper | code

Dataset(s): 3MDAD, AUCD2, SAM-DD

@article{2024_T-ITS_Yang,
    author = "Yang, Haohan and Liu, Haochen and Hu, Zhongxu and Nguyen, Anh-Tu and Guerra, Thierry-Marie and Lv, Chen",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Quantitative identification of driver distraction: A weakly supervised contrastive learning approach",
    year = "2023"
}

Kuang et al., MIFI: MultI-Camera Feature Integration for Robust 3D Distracted Driver Activity Recognition, Trans. ITS, 2023 | paper

Dataset(s): 3MDAD

@article{2023_T-ITS_Kuang,
    author = "Kuang, Jian and Li, Wenjing and Li, Fang and Zhang, Jun and Wu, Zhongcheng",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "MIFI: MultI-Camera Feature Integration for Robust 3D Distracted Driver Activity Recognition",
    year = "2023"
}

DADA-2000 | paper | link

Full name: Driver Attention in Driving Accident Scenarios

Description: 2000 videos of accident videos collected from video hosting websites with eye-tracking data from 20 subjects collected in the lab.

Data: eye-tracking, scene video

Annotations: bounding boxes, accident category labels

@inproceedings{2019_ITSC_Fang,
    author = "Fang, Jianwu and Yan, Dingxin and Qiao, Jiahuan and Xue, Jianru and Wang, He and Li, Sen",
    booktitle = "ITSC",
    title = "{DADA-2000: Can Driving Accident be Predicted by Driver Attentionƒ Analyzed by A Benchmark}",
    year = "2019"
}

Used in papers:

Araluce et al., Leveraging Driver Attention for an End-to-End Explainable Decision-Making From Frontal Images, Trans. ITS, 2024 | paper

Dataset(s): DADA-2000, BDD-A, BDD-OIA

@article{2024_T-ITS_Araluce,
    author = "Araluce, Javier and Bergasa, Luis M and Oca{\\textasciitilde n}a, Manuel and Llamazares, {\'A}ngel and L{\'o}pez-Guill{\'e}n, Elena",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Leveraging Driver Attention for an End-to-End Explainable Decision-Making From Frontal Images",
    year = "2024"
}

Li et al., Cognitive Traffic Accident Anticipation, ITS Mag., 2024 | paper

Dataset(s): DADA-2000, CCD

@article{2024_ITSM_Li,
    author = "Li, Lei-Lei and Fang, Jianwu and Xue, Jianru",
    journal = "IEEE Intelligent Transportation Systems Magazine",
    publisher = "IEEE",
    title = "Cognitive Traffic Accident Anticipation",
    year = "2024"
}

Zhao et al., Gated Driver Attention Predictor, ITSC, 2023 | paper | code

Dataset(s): DADA-2000, BDD-A

@inproceedings{2023_ITSC_Zhao,
    author = "Zhao, Tianci and Bai, Xue and Fang, Jianwu and Xue, Jianru",
    booktitle = "2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)",
    organization = "IEEE",
    pages = "270--276",
    title = "Gated Driver Attention Predictor",
    year = "2023"
}

Zhu et al., Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding, ICCV, 2023 | paper | code

Dataset(s): DR(eye)VE, BDD-A, DADA-2000

@inproceedings{2023_ICCV_Zhu,
    author = "Zhu, Pengfei and Qi, Mengshi and Li, Xia and Li, Weijian and Ma, Huadong",
    booktitle = "Proceedings of the IEEE/CVF International Conference on Computer Vision",
    pages = "8558--8568",
    title = "Unsupervised self-driving attention prediction via uncertainty mining and knowledge embedding",
    year = "2023"
}

Chen et al., FBLNet: FeedBack Loop Network for Driver Attention Prediction, ICCV, 2023 | paper

Dataset(s): BDD-A, DADA-2000

@inproceedings{2023_ICCV_Chen,
    author = "Chen, Yilong and Nan, Zhixiong and Xiang, Tao",
    booktitle = "Proceedings of the IEEE/CVF International Conference on Computer Vision",
    pages = "13371--13380",
    title = "FBLNet: FeedBack Loop Network for Driver Attention Prediction",
    year = "2023"
}

Li et al., Adaptive Short-Temporal Induced Aware Fusion Network for Predicting Attention Regions Like a Driver, Trans. ITS, 2022 | paper | code

Dataset(s): BDD-A, DADA-2000, TrafficSaliency

@article{2022_T-ITS_Li,
    author = "Li, Qiang and Liu, Chunsheng and Chang, Faliang and Li, Shuang and Liu, Hui and Liu, Zehao",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "10",
    pages = "18695--18706",
    publisher = "IEEE",
    title = "Adaptive short-temporal induced aware fusion network for predicting attention regions like a driver",
    volume = "23",
    year = "2022"
}

Gan et al., Multisource Adaption for Driver Attention Prediction in Arbitrary Driving Scenes, Trans. ITS, 2022 | paper

Dataset(s): BDD-A, DADA-2000, DR(eye)VE, TrafficSaliency

@article{2022_T-ITS_Gan,
    author = "Gan, Shun and Pei, Xizhe and Ge, Yulong and Wang, Qingfan and Shang, Shi and Li, Shengbo Eben and Nie, Bingbing",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "11",
    pages = "20912--20925",
    publisher = "IEEE",
    title = "Multisource Adaption for Driver Attention Prediction in Arbitrary Driving Scenes",
    volume = "23",
    year = "2022"
}

Fang et al., DADA: Driver Attention Prediction in Driving Accident Scenarios, Trans. ITS, 2021 | paper | code

Dataset(s): TrafficSaliency, DR(eye)VE, DADA-2000

@article{2022_T-ITS_Fang,
    author = "Fang, Jianwu and Yan, Dingxin and Qiao, Jiahuan and Xue, Jianru and Yu, Hongkai",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "6",
    pages = "4959--4971",
    publisher = "IEEE",
    title = "DADA: Driver attention prediction in driving accident scenarios",
    volume = "23",
    year = "2021"
}

Araluce et al., ARAGAN: A dRiver Attention estimation model based on conditional Generative Adversarial Network, IV, 2022 | paper | code

Dataset(s): BDD-A, DADA-2000

@inproceedings{2022_IV_Araluce,
    author = "Araluce, Javier and Bergasa, Luis M and Oca{\\textasciitilde n}a, Manuel and Barea, Rafael and L{\'o}pez-Guill{\'e}n, Elena and Revenga, Pedro",
    booktitle = "2022 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "1066--1072",
    title = "ARAGAN: A dRiver Attention estimation model based on conditional Generative Adversarial Network",
    year = "2022"
}

Huang et al., Driver Distraction Detection Based on the True Driver’s Focus of Attention, Trans. ITS, 2021 | paper

Dataset(s): DADA-2000, TrafficSaliency, BDD-A, DR(eye)VE, private

@article{2021_T-ITS_Huang,
    author = "Huang, Jianling and Long, Yan and Zhao, Xiaohua",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Driver Glance Behavior Modeling Based on Semi-Supervised Clustering and Piecewise Aggregate Representation",
    year = "2021"
}

Drive&Act | paper | link

Description: Videos of drivers performing various driving- and non-driving-related tasks

Data: driver video

Annotations: semantic maps, action labels

@inproceedings{2019_ICCV_Martin,
    author = "Martin, Manuel and Roitberg, Alina and Haurilet, Monica and Horne, Matthias and Rei{\ss}, Simon and Voit, Michael and Stiefelhagen, Rainer",
    booktitle = "ICCV",
    title = "Drive\\&act: A multi-modal dataset for fine-grained driver behavior recognition in autonomous vehicles",
    year = "2019"
}

Used in papers:

Pardo-Decimavilla et al., Do You Act Like You Talk? Exploring Pose-based Driver Action Classification with Speech Recognition Networks, IV, 2024 | paper | code

Dataset(s): Drive&Act

@inproceedings{2024_IV_Pardo-Decimavilla,
    author = "Pardo-Decimavilla, Pablo and Bergasa, Luis M and Montiel-Mar{\'\i}n, Santiago and Antunes, Miguel and Llamazares, {\'A}ngel",
    booktitle = "2024 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "1395--1400",
    title = "Do You Act Like You Talk? Exploring Pose-based Driver Action Classification with Speech Recognition Networks",
    year = "2024"
}

Tanama et al., Quantized Distillation: Optimizing Driver Activity Recognition Models for Resource-Constrained Environments, IROS, 2023 | paper | code

Dataset(s): Drive&Act

@inproceedings{2023_IROS_Tanama,
    author = "Tanama, Calvin and Peng, Kunyu and Marinov, Zdravko and Stiefelhagen, Rainer and Roitberg, Alina",
    booktitle = "2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",
    organization = "IEEE",
    pages = "5479--5486",
    title = "Quantized Distillation: Optimizing Driver Activity Recognition Models for Resource-Constrained Environments",
    year = "2023"
}

Peng et al., TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature Calibration, IROS, 2022 | paper | code

Dataset(s): Drive&Act

@inproceedings{2022_IROS_Peng,
    author = "Peng, Kunyu and Roitberg, Alina and Yang, Kailun and Zhang, Jiaming and Stiefelhagen, Rainer",
    booktitle = "2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",
    organization = "IEEE",
    pages = "278--285",
    title = "TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature Calibration",
    year = "2022"
}

Pizarro et al., DRVMon-VM: Distracted driver recognition using large pre-trained video transformers, IV, 2024 | paper

Dataset(s): Drive&Act

@inproceedings{2024_IV_Pizarro,
    author = "Pizarro, Ricardo and Bergasa, Luis M and Baumela, Luis and Buenaposada, Jos{\'e} M and Barea, Rafael",
    booktitle = "2024 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "1901--1906",
    title = "DRVMon-VM: Distracted driver recognition using large pre-trained video transformers",
    year = "2024"
}

Morales-Alvarez et al., On Transferability of Driver Observation Models from Simulated to Real Environments in Autonomous Cars, ITSC, 2023 | paper

Dataset(s): Drive&Act, private

@inproceedings{2023_ITSC_Morales-Alvarez,
    author = "Morales-Alvarez, Walter and Certad, Novel and Roitberg, Alina and Stiefelhagen, Rainer and Olaverri-Monreal, Cristina",
    booktitle = "2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)",
    organization = "IEEE",
    pages = "3129--3134",
    title = "On Transferability of Driver Observation Models from Simulated to Real Environments in Autonomous Cars",
    year = "2023"
}

EBDD | paper | link

Full name: EEE BUET Distracted Driving Dataset

Description: Videos of drivers performing secondary tasks

Data: driver video

Annotations: action labels, bounding boxes

@article{2019_TCSVT_Billah,
    author = "Billah, Tashrif and Rahman, SM Mahbubur and Ahmad, M Omair and Swamy, MNS",
    journal = "IEEE Transactions on Circuits and Systems for Video Technology",
    number = "4",
    pages = "1048--1062",
    publisher = "IEEE",
    title = "Recognizing distractions for assistive driving by tracking body parts",
    volume = "29",
    year = "2018"
}

H3D | paper | link

Full name: H3D Honda 3D Dataset

Description: A subset of videos from HDD dataset with 3D bounding boxes and object ids for tracking

Data: driver video, vehicle data

Annotations: bounding boxes

@inproceedings{2019_ICRA_Patil,
    author = "Patil, Abhishek and Malla, Srikanth and Gang, Haiming and Chen, Yi-Ting",
    booktitle = "2019 International Conference on Robotics and Automation (ICRA)",
    organization = "IEEE",
    pages = "9552--9557",
    title = "The h3d dataset for full-surround 3d multi-object detection and tracking in crowded urban scenes",
    year = "2019"
}

Used in papers:

Li et al., Important Object Identification with Semi-Supervised Learning for Autonomous Driving, ICRA, 2022 | paper

Dataset(s): H3D

@inproceedings{2022_ICRA_Li,
    author = "Li, Jiachen and Gang, Haiming and Ma, Hengbo and Tomizuka, Masayoshi and Choi, Chiho",
    booktitle = "2022 International Conference on Robotics and Automation (ICRA)",
    organization = "IEEE",
    pages = "2913--2919",
    title = "Important object identification with semi-supervised learning for autonomous driving",
    year = "2022"
}

HAD | paper | link

Full name: HAD HRI Advice Dataset

Description: A subset of videos from HDD naturalistic dataset annotated with textual advice containing 1) goals – where the vehicle should move and 2) attention – where the vehicle should look

Data: scene video, vehicle data

Annotations: goal and attention labels

@inproceedings{2019_CVPR_Kim,
    author = "Kim, Jinkyu and Misu, Teruhisa and Chen, Yi-Ting and Tawari, Ashish and Canny, John",
    booktitle = "CVPR",
    title = "Grounding human-to-vehicle advice for self-driving vehicles",
    year = "2019"
}

Used in papers:

Kim et al., Grounding Human-to-Vehicle Advice for Self-driving Vehicles, CVPR, 2019 | paper

Dataset(s): HAD

@inproceedings{2019_CVPR_Kim,
    author = "Kim, Jinkyu and Misu, Teruhisa and Chen, Yi-Ting and Tawari, Ashish and Canny, John",
    booktitle = "CVPR",
    title = "Grounding human-to-vehicle advice for self-driving vehicles",
    year = "2019"
}

PRORETA 4 | paper | link

Description: Videos of traffic scenes recorded in instrumented vehicle with driver’s gaze data for evaluating accuracy of detecting driver’s current object of fixation

Data: eye-tracking, driver video, scene video

@inproceedings{2019_IV_Schwehr,
    author = "Schwehr, Julian and Knaust, Moritz and Willert, Volker",
    booktitle = "IV",
    title = "How to evaluate object-of-fixation detection",
    year = "2019"
}

Used in papers:

Schwehr et al., How to Evaluate Object-of-Fixation Detection, IV, 2019 | paper

Dataset(s): PRORETA 4

@inproceedings{2019_IV_Schwehr,
    author = "Schwehr, Julian and Knaust, Moritz and Willert, Volker",
    booktitle = "IV",
    title = "How to evaluate object-of-fixation detection",
    year = "2019"
}

RLDD | paper | link

Full name: Real-Life Drowsiness Datase

Description: Crowdsourced videos of people in various states of drowsiness recorded in indoor environments

Data: driver video

Annotations: drowsiness labels

@inproceedings{2019_CVPRW_Ghoddoosian,
    author = "Ghoddoosian, Reza and Galib, Marnim and Athitsos, Vassilis",
    booktitle = "CVPRW",
    title = "A realistic dataset and baseline temporal model for early drowsiness detection",
    year = "2019"
}

Used in papers:

Du et al., A Multimodal Fusion Fatigue Driving Detection Method Based on Heart Rate and PERCLOS, Trans. ITS, 2022 | paper

Dataset(s): RLDD

@article{2022_T-ITS_Du,
    author = "Du, Guanglong and Zhang, Linlin and Su, Kang and Wang, Xueqian and Teng, Shaohua and Liu, Peter X",
    journal = "Ieee Transactions on Intelligent Transportation Systems",
    number = "11",
    pages = "21810--21820",
    publisher = "IEEE",
    title = "A multimodal fusion fatigue driving detection method based on heart rate and PERCLOS",
    volume = "23",
    year = "2022"
}

Du et al., A Multimodal Fusion Fatigue Driving Detection Method Based on Heart Rate and PERCLOS, Trans. ITS, 2021 | paper

Dataset(s): RLDD

@article{2021_T-ITS_Du,
    author = "Du, Guanglong and Zhang, Linlin and Su, Kang and Wang, Xueqian and Teng, Shaohua and Liu, Peter X",
    journal = "Ieee Transactions on Intelligent Transportation Systems",
    number = "11",
    pages = "21810--21820",
    publisher = "IEEE",
    title = "A multimodal fusion fatigue driving detection method based on heart rate and PERCLOS",
    volume = "23",
    year = "2022"
}

Ghoddoosian et al., A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection, CVPRW, 2019 | paper

Dataset(s): RLDD

@inproceedings{2019_CVPRW_Ghoddoosian,
    author = "Ghoddoosian, Reza and Galib, Marnim and Athitsos, Vassilis",
    booktitle = "CVPRW",
    title = "A realistic dataset and baseline temporal model for early drowsiness detection",
    year = "2019"
}

2018

BDD-A | paper | link

Full name: Berkeley Deep Drive-A (Attention) Dataset

Description: A set of short video clips extracted from the Berkeley Deep Drive (BDD) dataset with additional eye-tracking data collected in the lab from 45 subjects

Data: eye-tracking, scene video, vehicle data

@inproceedings{2018_ACCV_Xia,
    author = "Xia, Ye and Zhang, Danqing and Kim, Jinkyu and Nakayama, Ken and Zipser, Karl and Whitney, David",
    booktitle = "ACCV",
    title = "Predicting driver attention in critical situations",
    year = "2018"
}

Used in papers:

Araluce et al., Leveraging Driver Attention for an End-to-End Explainable Decision-Making From Frontal Images, Trans. ITS, 2024 | paper

Dataset(s): DADA-2000, BDD-A, BDD-OIA

@article{2024_T-ITS_Araluce,
    author = "Araluce, Javier and Bergasa, Luis M and Oca{\\textasciitilde n}a, Manuel and Llamazares, {\'A}ngel and L{\'o}pez-Guill{\'e}n, Elena",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Leveraging Driver Attention for an End-to-End Explainable Decision-Making From Frontal Images",
    year = "2024"
}

Hu et al., Context-Aware Driver Attention Estimation Using Multi-Hierarchy Saliency Fusion With Gaze Tracking, Trans. ITS, 2024 | paper

Dataset(s): DR(eye)VE, BDD-A, private

@article{2024_T-ITS_Hu,
    author = "Hu, Zhongxu and Cai, Yuxin and Li, Qinghua and Su, Kui and Lv, Chen",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Context-Aware Driver Attention Estimation Using Multi-Hierarchy Saliency Fusion With Gaze Tracking",
    year = "2024"
}

Kotseruba et al., SCOUT+: Towards Practical Task-Driven Drivers’ Gaze Prediction, IV, 2024 | paper | code

Dataset(s): DR(eye)VE, BDD-A, SCOUT

@inproceedings{2024_IV_Kotseruba_2,
    author = "Kotseruba, Iuliia and Tsotsos, John K",
    booktitle = "Intelligent Vehicles Symposium (IV)",
    title = "{SCOUT+: Towards Practical Task-Driven Drivers' Gaze Prediction}",
    year = "2024"
}

Kotseruba et al., Data Limitations for Modeling Top-Down Effects on Drivers’ Attention, IV, 2024 | paper | code

Dataset(s): DR(eye)VE, BDD-A, MAAD, LBW, SCOUT

@inproceedings{2024_IV_Kotseruba_1,
    author = "Kotseruba, Iuliia and Tsotsos, John K",
    booktitle = "Intelligent Vehicles Symposium (IV)",
    title = "Data Limitations for Modeling Top-Down Effects on Drivers' Attention",
    year = "2024"
}

Adhikari et al., Comparative Study of Attention among Drivers with Varying Driving Experience, IV, 2024 | paper

Dataset(s): BDD-A, private

@inproceedings{2024_IV_Adhikari,
    author = "Adhikari, Bikram and Duri{\'c}, Zoran and Wijesekera, Duminda and Yu, Bo",
    booktitle = "2024 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "1353--1360",
    title = "Comparative Study of Attention among Drivers with Varying Driving Experience",
    year = "2024"
}

Zhao et al., Gated Driver Attention Predictor, ITSC, 2023 | paper | code

Dataset(s): DADA-2000, BDD-A

@inproceedings{2023_ITSC_Zhao,
    author = "Zhao, Tianci and Bai, Xue and Fang, Jianwu and Xue, Jianru",
    booktitle = "2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)",
    organization = "IEEE",
    pages = "270--276",
    title = "Gated Driver Attention Predictor",
    year = "2023"
}

Zhu et al., Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding, ICCV, 2023 | paper | code

Dataset(s): DR(eye)VE, BDD-A, DADA-2000

@inproceedings{2023_ICCV_Zhu,
    author = "Zhu, Pengfei and Qi, Mengshi and Li, Xia and Li, Weijian and Ma, Huadong",
    booktitle = "Proceedings of the IEEE/CVF International Conference on Computer Vision",
    pages = "8558--8568",
    title = "Unsupervised self-driving attention prediction via uncertainty mining and knowledge embedding",
    year = "2023"
}

Chen et al., FBLNet: FeedBack Loop Network for Driver Attention Prediction, ICCV, 2023 | paper

Dataset(s): BDD-A, DADA-2000

@inproceedings{2023_ICCV_Chen,
    author = "Chen, Yilong and Nan, Zhixiong and Xiang, Tao",
    booktitle = "Proceedings of the IEEE/CVF International Conference on Computer Vision",
    pages = "13371--13380",
    title = "FBLNet: FeedBack Loop Network for Driver Attention Prediction",
    year = "2023"
}

Li et al., Adaptive Short-Temporal Induced Aware Fusion Network for Predicting Attention Regions Like a Driver, Trans. ITS, 2022 | paper | code

Dataset(s): BDD-A, DADA-2000, TrafficSaliency

@article{2022_T-ITS_Li,
    author = "Li, Qiang and Liu, Chunsheng and Chang, Faliang and Li, Shuang and Liu, Hui and Liu, Zehao",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "10",
    pages = "18695--18706",
    publisher = "IEEE",
    title = "Adaptive short-temporal induced aware fusion network for predicting attention regions like a driver",
    volume = "23",
    year = "2022"
}

Gan et al., Multisource Adaption for Driver Attention Prediction in Arbitrary Driving Scenes, Trans. ITS, 2022 | paper

Dataset(s): BDD-A, DADA-2000, DR(eye)VE, TrafficSaliency

@article{2022_T-ITS_Gan,
    author = "Gan, Shun and Pei, Xizhe and Ge, Yulong and Wang, Qingfan and Shang, Shi and Li, Shengbo Eben and Nie, Bingbing",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "11",
    pages = "20912--20925",
    publisher = "IEEE",
    title = "Multisource Adaption for Driver Attention Prediction in Arbitrary Driving Scenes",
    volume = "23",
    year = "2022"
}

Araluce et al., ARAGAN: A dRiver Attention estimation model based on conditional Generative Adversarial Network, IV, 2022 | paper | code

Dataset(s): BDD-A, DADA-2000

@inproceedings{2022_IV_Araluce,
    author = "Araluce, Javier and Bergasa, Luis M and Oca{\\textasciitilde n}a, Manuel and Barea, Rafael and L{\'o}pez-Guill{\'e}n, Elena and Revenga, Pedro",
    booktitle = "2022 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "1066--1072",
    title = "ARAGAN: A dRiver Attention estimation model based on conditional Generative Adversarial Network",
    year = "2022"
}

Pal et al., “Looking at the right stuff” - Guided semantic-gaze for autonomous driving, CVPR, 2020 | paper | code

Dataset(s): DR(eye)VE, BDD-A, JAAD

@inproceedings{2020_CVPR_Pal,
    author = "Pal, Anwesan and Mondal, Sayan and Christensen, Henrik I",
    booktitle = "CVPR",
    title = {{" Looking at the Right Stuff"-Guided Semantic-Gaze for Autonomous Driving}},
    year = "2020"
}

Xia et al., Predicting Driver Attention in Critical Situations, ACCV, 2018 | paper | code

Dataset(s): BDD-A

@inproceedings{2018_ACCV_Xia,
    author = "Xia, Ye and Zhang, Danqing and Kim, Jinkyu and Nakayama, Ken and Zipser, Karl and Whitney, David",
    booktitle = "ACCV",
    title = "Predicting driver attention in critical situations",
    year = "2018"
}

Huang et al., Driver Distraction Detection Based on the True Driver’s Focus of Attention, Trans. ITS, 2021 | paper

Dataset(s): DADA-2000, TrafficSaliency, BDD-A, DR(eye)VE, private

@article{2021_T-ITS_Huang,
    author = "Huang, Jianling and Long, Yan and Zhao, Xiaohua",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Driver Glance Behavior Modeling Based on Semi-Supervised Clustering and Piecewise Aggregate Representation",
    year = "2021"
}

Xia et al., Periphery-Fovea Multi-Resolution Driving Model Guided by Human Attention, WACV, 2020 | paper | code

Dataset(s): BDD-X, BDD-A, DR(eye)VE

@inproceedings{2020_WACV_Xia,
    author = "Xia, Ye and Kim, Jinkyu and Canny, John and Zipser, Karl and Canas-Bajo, Teresa and Whitney, David",
    booktitle = "WACV",
    title = "Periphery-fovea multi-resolution driving model guided by human attention",
    year = "2020"
}

BDD-X | paper | link

Full name: Berkeley Deep Drive-X (eXplanation) Dataset

Description: A subset of videos from BDD dataset annotated with textual descriptions of actions performed by the vehicle and explanations justifying those actions

Data: scene video, vehicle data

Annotations: action explanations

@inproceedings{2018_ECCV_Kim,
    author = "Kim, Jinkyu and Rohrbach, Anna and Darrell, Trevor and Canny, John and Akata, Zeynep",
    booktitle = "ECCV",
    title = "Textual explanations for self-driving vehicles",
    year = "2018"
}

Used in papers:

Xia et al., Periphery-Fovea Multi-Resolution Driving Model Guided by Human Attention, WACV, 2020 | paper | code

Dataset(s): BDD-X, BDD-A, DR(eye)VE

@inproceedings{2020_WACV_Xia,
    author = "Xia, Ye and Kim, Jinkyu and Canny, John and Zipser, Karl and Canas-Bajo, Teresa and Whitney, David",
    booktitle = "WACV",
    title = "Periphery-fovea multi-resolution driving model guided by human attention",
    year = "2020"
}

Kim et al., Advisable Learning for Self-driving Vehicles by Internalizing Observation-to-Action Rules, CVPR, 2020 | paper | code

Dataset(s): BDD-X, CARLA

@inproceedings{2020_CVPR_Kim,
    author = "Kim, Jinkyu and Moon, Suhong and Rohrbach, Anna and Darrell, Trevor and Canny, John",
    booktitle = "CVPR",
    title = "Advisable learning for self-driving vehicles by internalizing observation-to-action rules",
    year = "2020"
}

Kim et al., Textual Explanations for Self-Driving Vehicles, ECCV, 2018 | paper | code

Dataset(s): BDD-X

@inproceedings{2018_ECCV_Kim,
    author = "Kim, Jinkyu and Rohrbach, Anna and Darrell, Trevor and Canny, John and Akata, Zeynep",
    booktitle = "ECCV",
    title = "Textual explanations for self-driving vehicles",
    year = "2018"
}

DR(eye)VE | paper | link

Description: Driving videos recorded on-road with corresponding gaze data of the driver

Data: eye-tracking, scene video, vehicle data

Annotations: weather and road type labels

@article{2018_PAMI_Palazzi,
    author = "Palazzi, Andrea and Abati, Davide and Solera, Francesco and Cucchiara, Rita and others",
    journal = "IEEE TPAMI",
    number = "7",
    pages = "1720--1733",
    title = "{Predicting the Driver's Focus of Attention: the DR (eye) VE Project}",
    volume = "41",
    year = "2018"
}

Used in papers:

Hu et al., Context-Aware Driver Attention Estimation Using Multi-Hierarchy Saliency Fusion With Gaze Tracking, Trans. ITS, 2024 | paper

Dataset(s): DR(eye)VE, BDD-A, private

@article{2024_T-ITS_Hu,
    author = "Hu, Zhongxu and Cai, Yuxin and Li, Qinghua and Su, Kui and Lv, Chen",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Context-Aware Driver Attention Estimation Using Multi-Hierarchy Saliency Fusion With Gaze Tracking",
    year = "2024"
}

Kotseruba et al., SCOUT+: Towards Practical Task-Driven Drivers’ Gaze Prediction, IV, 2024 | paper | code

Dataset(s): DR(eye)VE, BDD-A, SCOUT

@inproceedings{2024_IV_Kotseruba_2,
    author = "Kotseruba, Iuliia and Tsotsos, John K",
    booktitle = "Intelligent Vehicles Symposium (IV)",
    title = "{SCOUT+: Towards Practical Task-Driven Drivers' Gaze Prediction}",
    year = "2024"
}

Kotseruba et al., Data Limitations for Modeling Top-Down Effects on Drivers’ Attention, IV, 2024 | paper | code

Dataset(s): DR(eye)VE, BDD-A, MAAD, LBW, SCOUT

@inproceedings{2024_IV_Kotseruba_1,
    author = "Kotseruba, Iuliia and Tsotsos, John K",
    booktitle = "Intelligent Vehicles Symposium (IV)",
    title = "Data Limitations for Modeling Top-Down Effects on Drivers' Attention",
    year = "2024"
}

Kotseruba et al., Understanding and Modeling the Effects of Task and Context on Drivers’ Gaze Allocation, IV, 2024 | paper | code

Dataset(s): DR(eye)VE, SCOUT

@inproceedings{2024_IV_Kotseruba,
    author = "Kotseruba, Iuliia and Tsotsos, John K",
    booktitle = "2024 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "1337--1344",
    title = "Understanding and modeling the effects of task and context on drivers’ gaze allocation",
    year = "2024"
}

Deng et al., Driving Visual Saliency Prediction of Dynamic Night Scenes via a Spatio-Temporal Dual-Encoder Network, Trans. ITS, 2023 | paper | code

Dataset(s): DrFixD-night, DR(eye)VE

@article{2023_T-ITS_Deng,
    author = "Deng, Tao and Jiang, Lianfang and Shi, Yi and Wu, Jiang and Wu, Zhangbi and Yan, Shun and Zhang, Xianshi and Yan, Hongmei",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Driving Visual Saliency Prediction of Dynamic Night Scenes via a Spatio-Temporal Dual-Encoder Network",
    year = "2023"
}

Zhu et al., Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding, ICCV, 2023 | paper | code

Dataset(s): DR(eye)VE, BDD-A, DADA-2000

@inproceedings{2023_ICCV_Zhu,
    author = "Zhu, Pengfei and Qi, Mengshi and Li, Xia and Li, Weijian and Ma, Huadong",
    booktitle = "Proceedings of the IEEE/CVF International Conference on Computer Vision",
    pages = "8558--8568",
    title = "Unsupervised self-driving attention prediction via uncertainty mining and knowledge embedding",
    year = "2023"
}

Gan et al., Multisource Adaption for Driver Attention Prediction in Arbitrary Driving Scenes, Trans. ITS, 2022 | paper

Dataset(s): BDD-A, DADA-2000, DR(eye)VE, TrafficSaliency

@article{2022_T-ITS_Gan,
    author = "Gan, Shun and Pei, Xizhe and Ge, Yulong and Wang, Qingfan and Shang, Shi and Li, Shengbo Eben and Nie, Bingbing",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "11",
    pages = "20912--20925",
    publisher = "IEEE",
    title = "Multisource Adaption for Driver Attention Prediction in Arbitrary Driving Scenes",
    volume = "23",
    year = "2022"
}

Fang et al., DADA: Driver Attention Prediction in Driving Accident Scenarios, Trans. ITS, 2021 | paper | code

Dataset(s): TrafficSaliency, DR(eye)VE, DADA-2000

@article{2022_T-ITS_Fang,
    author = "Fang, Jianwu and Yan, Dingxin and Qiao, Jiahuan and Xue, Jianru and Yu, Hongkai",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "6",
    pages = "4959--4971",
    publisher = "IEEE",
    title = "DADA: Driver attention prediction in driving accident scenarios",
    volume = "23",
    year = "2021"
}

Pal et al., “Looking at the right stuff” - Guided semantic-gaze for autonomous driving, CVPR, 2020 | paper | code

Dataset(s): DR(eye)VE, BDD-A, JAAD

@inproceedings{2020_CVPR_Pal,
    author = "Pal, Anwesan and Mondal, Sayan and Christensen, Henrik I",
    booktitle = "CVPR",
    title = {{" Looking at the Right Stuff"-Guided Semantic-Gaze for Autonomous Driving}},
    year = "2020"
}

Ning et al., An Efficient Model for Driving Focus of Attention Prediction using Deep Learning, ITSC, 2019 | paper

Dataset(s): DR(eye)VE

@inproceedings{2019_ITSC_Ning,
    author = "Ning, Minghao and Lu, Chao and Gong, Jianwei",
    booktitle = "ITCS",
    title = "{An Efficient Model for Driving Focus of Attention Prediction using Deep Learning}",
    year = "2019"
}

Palazzi et al., Predicting the Driver’s Focus of Attention: the DR(eye)VE Project, PAMI, 2018 | paper | code

Dataset(s): DR(eye)VE

@article{2018_PAMI_Palazzi,
    author = "Palazzi, Andrea and Abati, Davide and Solera, Francesco and Cucchiara, Rita and others",
    journal = "IEEE TPAMI",
    number = "7",
    pages = "1720--1733",
    title = "{Predicting the Driver's Focus of Attention: the DR (eye) VE Project}",
    volume = "41",
    year = "2018"
}

Tawari et al., A Computational Framework for Driver’s Visual Attention Using A Fully Convolutional Architecture, IV, 2017 | paper

Dataset(s): DR(eye)VE

@inproceedings{2017_IV_Tawari,
    author = "Tawari, Ashish and Kang, Byeongkeun",
    booktitle = "IV",
    title = "A computational framework for driver's visual attention using a fully convolutional architecture",
    year = "2017"
}

Palazzi et al., Learning Where to Attend Like a Human Driver, IV, 2017 | paper | code

Dataset(s): DR(eye)VE

@inproceedings{2017_IV_Palazzi,
    author = "Palazzi, Andrea and Solera, Francesco and Calderara, Simone and Alletto, Stefano and Cucchiara, Rita",
    booktitle = "IV",
    title = "Learning where to attend like a human driver",
    year = "2017"
}

Huang et al., Driver Distraction Detection Based on the True Driver’s Focus of Attention, Trans. ITS, 2021 | paper

Dataset(s): DADA-2000, TrafficSaliency, BDD-A, DR(eye)VE, private

@article{2021_T-ITS_Huang,
    author = "Huang, Jianling and Long, Yan and Zhao, Xiaohua",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Driver Glance Behavior Modeling Based on Semi-Supervised Clustering and Piecewise Aggregate Representation",
    year = "2021"
}

Xia et al., Periphery-Fovea Multi-Resolution Driving Model Guided by Human Attention, WACV, 2020 | paper | code

Dataset(s): BDD-X, BDD-A, DR(eye)VE

@inproceedings{2020_WACV_Xia,
    author = "Xia, Ye and Kim, Jinkyu and Canny, John and Zipser, Karl and Canas-Bajo, Teresa and Whitney, David",
    booktitle = "WACV",
    title = "Periphery-fovea multi-resolution driving model guided by human attention",
    year = "2020"
}

HDD | paper | link

Full name: HDD HRI Driving Dataset

Description: A large naturalistic driving dataset with driving footage, vehicle telemetry and annotations for vehicle actions and their justifications

Data: scene video, vehicle data

Annotations: bounding boxes, action labels

@inproceedings{2018_CVPR_Ramanishka,
    author = "Ramanishka, Vasili and Chen, Yi-Ting and Misu, Teruhisa and Saenko, Kate",
    booktitle = "CVPR",
    title = "Toward driving scene understanding: A dataset for learning driver behavior and causal reasoning",
    year = "2018"
}

2017

AUCD2 | paper | link

Full name: American University in Cairo (AUC) Distracted Driver’s Dataset

Description: Videos of drivers performing secondary tasks

Data: driver video

Annotations: action labels

@inproceedings{2017_NeurIPS_Abouelnaga,
    author = "Abouelnaga, Yehya and Eraqi, Hesham M. and Moustafa, Mohamed N.",
    booktitle = "NeurIPS Workshop on Machine Learning for Intelligent Transportation Systems",
    title = "eal-time Distracted Driver Posture Classification",
    year = "2017"
}

Used in papers:

Li et al., Domain Adaptive Driver Distraction Detection Based on Partial Feature Alignment and Confusion-Minimized Classification, Trans. ITS, 2024 | paper

Dataset(s): SFDDD, AUCD2

@article{2024_T-ITS_Li_2,
    author = "Li, Guofa and Wang, Guanglei and Guo, Zizheng and Liu, Qing and Luo, Xiyuan and Yuan, Bangwei and Li, Mingrui and Yang, Lu",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Domain Adaptive Driver Distraction Detection Based on Partial Feature Alignment and Confusion-Minimized Classification",
    year = "2024"
}

Yang et al., Quantitative Identification of Driver Distraction: A Weakly Supervised Contrastive Learning Approach, Trans. ITS, 2024 | paper | code

Dataset(s): 3MDAD, AUCD2, SAM-DD

@article{2024_T-ITS_Yang,
    author = "Yang, Haohan and Liu, Haochen and Hu, Zhongxu and Nguyen, Anh-Tu and Guerra, Thierry-Marie and Lv, Chen",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Quantitative identification of driver distraction: A weakly supervised contrastive learning approach",
    year = "2023"
}

Li et al., Domain Adaptive Driver Distraction Detection Based on Partial Feature Alignment and Confusion-Minimized Classification, Trans. ITS, 2024 | paper

Dataset(s): SFDDD, AUCD2

@article{2024_T-ITS_Li,
    author = "Li, Guofa and Wang, Guanglei and Guo, Zizheng and Liu, Qing and Luo, Xiyuan and Yuan, Bangwei and Li, Mingrui and Yang, Lu",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Domain Adaptive Driver Distraction Detection Based on Partial Feature Alignment and Confusion-Minimized Classification",
    year = "2024"
}

Ma et al., ViT-DD: Multi-Task Vision Transformer for Semi-Supervised Driver Distraction Detection, IV, 2024 | paper | code

Dataset(s): SFDDD, AUCD2

@inproceedings{2024_IV_Ma,
    author = "Ma, Yunsheng and Wang, Ziran",
    booktitle = "2024 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "417--423",
    title = "Vit-dd: Multi-task vision transformer for semi-supervised driver distraction detection",
    year = "2024"
}

Mittal et al., CAT-CapsNet: A Convolutional and Attention Based Capsule Network to Detect the Driver’s Distraction, Trans. ITS, 2023 | paper

Dataset(s): AUCD2, Statefarm

@article{2023_T-ITS_Mittal,
    author = "Mittal, Himanshu and Verma, Bindu",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "CAT-CapsNet: A Convolutional and Attention Based Capsule Network to Detect the Driver’s Distraction",
    year = "2023"
}

C42CN | paper | link

Description: A multi-modal dataset acquired in a controlled experiment on a driving simulator under 4 conditions: no distraction, cognitive, emotional and sensorimotor distraction.

Data: eye-tracking, scene video, physiological signal

@article{2017_NatSciData_Taamneh,
    author = "Taamneh, Salah and Tsiamyrtzis, Panagiotis and Dcosta, Malcolm and Buddharaju, Pradeep and Khatri, Ashik and Manser, Michael and Ferris, Thomas and Wunderlich, Robert and Pavlidis, Ioannis",
    journal = "Scientific Data",
    pages = "170110",
    title = "A multimodal dataset for various forms of distracted driving",
    volume = "4",
    year = "2017"
}

Used in papers:

Chen et al., Fine-Grained Detection of Driver Distraction Based on Neural Architecture Search, Trans. ITS, 2021 | paper

Dataset(s): C42CN

@article{2021_T-ITS_Chen,
    author = "Chen, Jie and Jiang, YaNan and Huang, ZhiXiang and Guo, XiaoHui and Wu, BoCai and Sun, Long and Wu, Tao",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    title = "Fine-Grained Detection of Driver Distraction Based on Neural Architecture Search",
    year = "2021"
}

DDD | paper | link

Full name: Driver Drowsiness Detection Dataset

Description: Videos of human subjects simulating different levels of drowsiness while driving in a simulator

Data: driver video

Annotations: drowsiness labels

@inproceedings{2017_ACCV_Weng,
    author = "Weng, Ching-Hua and Lai, Ying-Hsiu and Lai, Shang-Hong",
    booktitle = "ACCV",
    title = "Driver drowsiness detection via a hierarchical temporal deep belief network",
    year = "2016"
}

Used in papers:

Chiou et al., Driver Monitoring Using Sparse Representation With Part-Based Temporal Face Descriptors, Trans. ITS, 2019 | paper

Dataset(s): YawDD, DDD

@article{2019_T-ITS_Chiou,
    author = "Chiou, Chien-Yu and Wang, Wei-Cheng and Lu, Shueh-Chou and Huang, Chun-Rong and Chung, Pau-Choo and Lai, Yun-Yang",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "1",
    pages = "346--361",
    publisher = "IEEE",
    title = "Driver monitoring using sparse representation with part-based temporal face descriptors",
    volume = "21",
    year = "2019"
}

Wu et al., Driver Drowsiness Detection Based on Joint Human Face and Facial Landmark Localization With Cheap Operations, Trans. ITS, 2024 | paper

Dataset(s): DDD

@article{2024_T-ITS_Wu,
    author = "Wu, Qingtian and Li, Nannan and Zhang, Liming and Yu, Fei Richard",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Driver Drowsiness Detection Based on Joint Human Face and Facial Landmark Localization With Cheap Operations",
    year = "2024"
}

Yang et al., Video-Based Driver Drowsiness Detection With Optimised Utilization of Key Facial Features, Trans. ITS, 2024 | paper

Dataset(s): YawDD, DDD, private

@article{2023_T-ITS_Yang,
    author = "Yang, Haohan and Liu, Haochen and Hu, Zhongxu and Nguyen, Anh-Tu and Guerra, Thierry-Marie and Lv, Chen",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Quantitative Identification of Driver Distraction: A Weakly Supervised Contrastive Learning Approach",
    year = "2023"
}

Tufekci et al., Detecting Driver Drowsiness as an Anomaly Using LSTM Autoencoders, ECCVW, 2022 | paper

Dataset(s): DDD

@inproceedings{2022_ECCVW_Tufekci,
    author = {T{\"u}fekci, G{\"u}lin and Kayaba{\c{s}}{\i}, Alper and Akag{\"u}nd{\"u}z, Erdem and Ulusoy, {\.I}lkay},
    booktitle = "Computer Vision--ECCV 2022 Workshops: Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part VI",
    organization = "Springer",
    pages = "549--559",
    title = "Detecting Driver Drowsiness as an Anomaly Using LSTM Autoencoders",
    year = "2023"
}

Ahmed et al., Intelligent Driver Drowsiness Detection for Traffic Safety Based on Multi CNN Deep Model and Facial Subsampling, Trans. ITS, 2021 | paper

Dataset(s): DDD

@article{2021_T-ITS_Ahmed,
    author = "Ahmed, Muneeb and Masood, Sarfaraz and Ahmad, Musheer and Abd El-Latif, Ahmed A",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "10",
    pages = "19743--19752",
    publisher = "IEEE",
    title = "Intelligent driver drowsiness detection for traffic safety based on multi CNN deep model and facial subsampling",
    volume = "23",
    year = "2021"
}

Huang et al., RF-DCM: Multi-Granularity Deep Convolutional Model Based on Feature Recalibration and Fusion for Driver Fatigue Detection, Trans. ITS, 2020 | paper

Dataset(s): DDD

@article{2020_T-ITS_Huang,
    author = "Huang, Rui and Wang, Yan and Li, Zijian and Lei, Zeyu and Xu, Yufan",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    title = "RF-DCM: Multi-Granularity Deep Convolutional Model Based on Feature Recalibration and Fusion for Driver Fatigue Detection",
    year = "2020"
}

Vijay et al., Real-Time Driver Drowsiness Detection using Facial Action Units, ICPR, 2020 | paper

Dataset(s): DDD

@inproceedings{2020_ICPR_Vijay,
    author = "Vijay, Malaika and Vinayak, Nandagopal Netrakanti and Nunna, Maanvi and Natarajan, Subramanyam",
    booktitle = "ICPR",
    title = "Real-Time Driver Drowsiness Detection using Facial Action Units",
    year = "2021"
}

Yu et al., Driver Drowsiness Detection Using Condition-Adaptive Representation Learning Framework, Trans. ITS, 2018 | paper

Dataset(s): DDD

@article{2018_T-ITS_Yu,
    author = "Yu, Jongmin and Park, Sangwoo and Lee, Sangwook and Jeon, Moongu",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "11",
    pages = "4206--4218",
    title = "Driver drowsiness detection using condition-adaptive representation learning framework",
    volume = "20",
    year = "2018"
}

Yu et al., Representation Learning, Scene Understanding, and Feature Fusion for Drowsiness Detection, ACCVW, 2017 | paper

Dataset(s): DDD

@inproceedings{2017_ACCVW_Yu,
    author = "Yu, Jongmin and Park, Sangwoo and Lee, Sangwook and Jeon, Moongu",
    booktitle = "ACCV",
    title = "Representation learning, scene understanding, and feature fusion for drowsiness detection",
    year = "2016"
}

Shih et al., MSTN: Multistage Spatial-Temporal Network for Driver Drowsiness Detection, ACCVW, 2017 | paper

Dataset(s): DDD

@inproceedings{2017_ACCVW_Shih,
    author = "Shih, Tun-Huai and Hsu, Chiou-Ting",
    booktitle = "ACCV",
    title = "MSTN: Multistage spatial-temporal network for driver drowsiness detection",
    year = "2016"
}

Huynh et al., Detection of Driver Drowsiness Using 3D Deep Neural Network and Semi-Supervised Gradient Boosting Machine, ACCVW, 2017 | paper

Dataset(s): DDD

@inproceedings{2017_ACCVW_Huynh,
    author = "Huynh, Xuan-Phung and Park, Sang-Min and Kim, Yong-Guk",
    booktitle = "ACCV",
    title = "Detection of driver drowsiness using 3D deep neural network and semi-supervised gradient boosting machine",
    year = "2016"
}

Weng et al., Driver Drowsiness Detection via a Hierarchical Temporal Deep Belief Network, ACCV, 2017 | paper

Dataset(s): DDD

@inproceedings{2017_ACCV_Weng,
    author = "Weng, Ching-Hua and Lai, Ying-Hsiu and Lai, Shang-Hong",
    booktitle = "ACCV",
    title = "Driver drowsiness detection via a hierarchical temporal deep belief network",
    year = "2016"
}

Park et al., Driver drowsiness detection system based on feature representation learning using various deep networks, ACCV, 2016 | paper

Dataset(s): DDD

@inproceedings{2016_ACCV_Park,
    author = "Park, Sanghyuk and Pan, Fei and Kang, Sunghun and Yoo, Chang D",
    booktitle = "ACCV",
    title = "Driver drowsiness detection system based on feature representation learning using various deep networks",
    year = "2016"
}

Dashcam dataset | link

Description: Driving videos with steering information recorded on road

Data: scene video

Used in papers:

Mund et al., Visualizing the Learning Progress of Self-Driving Cars, ITSC, 2018 | paper

Dataset(s): Dashcam dataset

@inproceedings{2018_ITSC_Mund,
    author = {Mund, Sandro and Frank, Rapha{\"e}l and Varisteas, Georgios and State, Radu},
    booktitle = "ITSC",
    title = "{Visualizing the Learning Progress of Self-Driving Cars}",
    year = "2018"
}

DriveAHead | paper | link

Description: Videos of drivers with frame-level head pose annotations obtained from a motion-capture system

Data: driver video

Annotations: occlusion, head pose, depth

@inproceedings{2017_CVPRW_Schwarz,
    author = "Schwarz, Anke and Haurilet, Monica and Martinez, Manuel and Stiefelhagen, Rainer",
    booktitle = "Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops",
    pages = "1--10",
    title = "Driveahead-a large-scale driver head pose dataset",
    year = "2017"
}

2016

Brain4Cars | paper | link

Description: Synchronized videos from scene and driver-facing cameras of drivers performing various maneuvers in traffic

Data: driver video, scene video, vehicle data

Annotations: action labels

@inproceedings{2015_ICCV_Jain,
    author = "Jain, Ashesh and Koppula, Hema S and Raghavan, Bharad and Soh, Shane and Saxena, Ashutosh",
    booktitle = "ICCV",
    title = "Car that knows before you do: Anticipating maneuvers via learning temporal driving models",
    year = "2015"
}

Used in papers:

Guo et al., Temporal Information Fusion Network for Driving Behavior Prediction, Trans. ITS, 2023 | paper

Dataset(s): Brain4Cars, private

@article{2023_T-ITS_Guo,
    author = "Guo, Chenghao and Liu, Haizhuang and Chen, Jiansheng and Ma, Huimin",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Temporal Information Fusion Network for Driving Behavior Prediction",
    year = "2023"
}

Jain et al., Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture, ICRA, 2016 | paper | code

Dataset(s): Brain4Cars

@inproceedings{2016_ICRA_Jain,
    author = "Jain, Ashesh and Singh, Avi and Koppula, Hema S and Soh, Shane and Saxena, Ashutosh",
    booktitle = "ICRA",
    title = "Recurrent neural networks for driver activity anticipation via sensory-fusion architecture",
    year = "2016"
}

Jain et al., Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models, ICCV, 2015 | paper

Dataset(s): Brain4Cars

@inproceedings{2015_ICCV_Jain,
    author = "Jain, Ashesh and Koppula, Hema S and Raghavan, Bharad and Soh, Shane and Saxena, Ashutosh",
    booktitle = "ICCV",
    title = "Car that knows before you do: Anticipating maneuvers via learning temporal driving models",
    year = "2015"
}

DAD | paper | link

Description: Videos of accidents recorded with dashboard cameras sourced from video hosting sites with annotations for accidents and road users involved in them

Data: scene video

Annotations: bounding boxes, accident category labels

@inproceedings{2016_ACCV_Chan,
    author = "Chan, Fu-Hsiang and Chen, Yu-Ting and Xiang, Yu and Sun, Min",
    booktitle = "ACCV",
    title = "Anticipating accidents in dashcam videos",
    year = "2016"
}

Used in papers:

Chen et al., A Driving Risk Assessment Framework Considering Driver’s Fatigue State and Distraction Behavior, Trans. ITS, 2024 | paper

Dataset(s): SFDDD, DAD, private

@article{2024_T-ITS_Chen,
    author = "Chen, Jiansong and Zhang, Qixiang and Chen, Jinxin and Wang, Jinxiang and Fang, Zhenwu and Liu, Yahui and Yin, Guodong",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "A Driving Risk Assessment Framework Considering Driver’s Fatigue State and Distraction Behavior",
    year = "2024"
}

Kopuklu et al., Driver Anomaly Detection: A Dataset and Contrastive Learning Approach, WACV, 2021 | paper

Dataset(s): DAD

@inproceedings{2021_WACV_Kopuklu,
    author = "Kopuklu, Okan and Zheng, Jiapeng and Xu, Hang and Rigoll, Gerhard",
    booktitle = "Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision",
    pages = "91--100",
    title = "Driver anomaly detection: A dataset and contrastive learning approach",
    year = "2021"
}

DROZY | paper | link

Description: Videos and physiological data from subjects in different drowsiness states after prolonged waking

Data: driver video, physiological signal

Annotations: drowsiness labels

@inproceedings{2016_WACV_Massoz,
    author = "Massoz, Quentin and Langohr, Thomas and Fran{\c{c}}ois, Cl{\'e}mentine and Verly, Jacques G",
    booktitle = "WACV",
    title = "The ULg multimodality drowsiness database (called DROZY) and examples of use",
    year = "2016"
}

SFDDD | link

Full name: State Farm Distracted Driver Detection

Description: Videos of drivers performing secondary tasks

Data: driver video

Annotations: action labels

Used in papers:

Chen et al., A Driving Risk Assessment Framework Considering Driver’s Fatigue State and Distraction Behavior, Trans. ITS, 2024 | paper

Dataset(s): SFDDD, DAD, private

@article{2024_T-ITS_Chen,
    author = "Chen, Jiansong and Zhang, Qixiang and Chen, Jinxin and Wang, Jinxiang and Fang, Zhenwu and Liu, Yahui and Yin, Guodong",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "A Driving Risk Assessment Framework Considering Driver’s Fatigue State and Distraction Behavior",
    year = "2024"
}

Li et al., Domain Adaptive Driver Distraction Detection Based on Partial Feature Alignment and Confusion-Minimized Classification, Trans. ITS, 2024 | paper

Dataset(s): SFDDD, AUCD2

@article{2024_T-ITS_Li_2,
    author = "Li, Guofa and Wang, Guanglei and Guo, Zizheng and Liu, Qing and Luo, Xiyuan and Yuan, Bangwei and Li, Mingrui and Yang, Lu",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Domain Adaptive Driver Distraction Detection Based on Partial Feature Alignment and Confusion-Minimized Classification",
    year = "2024"
}

Li et al., A Lightweight and Efficient Distracted Driver Detection Model Fusing Convolutional Neural Network and Vision Transformer, Trans. ITS, 2024 | paper | code

Dataset(s): SFDDD, 100-Driver

@article{2024_T-ITS_Li_1,
    author = "Li, Zhao and Zhao, Xia and Wu, Fuwei and Chen, Dan and Wang, Chang",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "A Lightweight and Efficient Distracted Driver Detection Model Fusing Convolutional Neural Network and Vision Transformer",
    year = "2024"
}

Li et al., Domain Adaptive Driver Distraction Detection Based on Partial Feature Alignment and Confusion-Minimized Classification, Trans. ITS, 2024 | paper

Dataset(s): SFDDD, AUCD2

@article{2024_T-ITS_Li,
    author = "Li, Guofa and Wang, Guanglei and Guo, Zizheng and Liu, Qing and Luo, Xiyuan and Yuan, Bangwei and Li, Mingrui and Yang, Lu",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Domain Adaptive Driver Distraction Detection Based on Partial Feature Alignment and Confusion-Minimized Classification",
    year = "2024"
}

Hasan et al., Vision-Language Models Can Identify Distracted Driver Behavior From Naturalistic Videos, Trans. ITS, 2024 | paper | code

Dataset(s): DMD, SAM-DD, SFDDD, SynDD1

@article{2024_T-ITS_Hasan,
    author = "Hasan, Md Zahid and Chen, Jiajing and Wang, Jiyang and Rahman, Mohammed Shaiqur and Joshi, Ameya and Velipasalar, Senem and Hegde, Chinmay and Sharma, Anuj and Sarkar, Soumik",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Vision-language models can identify distracted driver behavior from naturalistic videos",
    year = "2024"
}

Chai et al., Rethinking the Evaluation of Driver Behavior Analysis Approaches, Trans. ITS, 2024 | paper

Dataset(s): SFDDD, AI CITY NDAR

@article{2024_T-ITS_Chai,
    author = "Chai, Weiheng and Wang, Jiyang and Chen, Jiajing and Velipasalar, Senem and Sharma, Anuj",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Rethinking the Evaluation of Driver Behavior Analysis Approaches",
    year = "2024"
}

Ma et al., ViT-DD: Multi-Task Vision Transformer for Semi-Supervised Driver Distraction Detection, IV, 2024 | paper | code

Dataset(s): SFDDD, AUCD2

@inproceedings{2024_IV_Ma,
    author = "Ma, Yunsheng and Wang, Ziran",
    booktitle = "2024 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "417--423",
    title = "Vit-dd: Multi-task vision transformer for semi-supervised driver distraction detection",
    year = "2024"
}

Mittal et al., CAT-CapsNet: A Convolutional and Attention Based Capsule Network to Detect the Driver’s Distraction, Trans. ITS, 2023 | paper

Dataset(s): SFDDD, AUCD2

@article{2023_T-ITS_Mittal,
    author = "Mittal, Himanshu and Verma, Bindu",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "CAT-CapsNet: A Convolutional and Attention Based Capsule Network to Detect the Driver’s Distraction",
    year = "2023"
}

TETD | paper | link

Full name: Traffic Eye Tracking Dataset

Description: A set of 100 images of traffic scenes with corresponding eye-tracking data from 20 subjects

Data: eye-tracking, scene images

@article{2016_T-ITS_Deng,
    author = "Deng, Tao and Yang, Kaifu and Li, Yongjie and Yan, Hongmei",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "7",
    pages = "2051--2062",
    publisher = "IEEE",
    title = "Where does the driver look? Top-down-based saliency detection in a traffic driving environment",
    volume = "17",
    year = "2016"
}

Used in papers:

Deng et al., Learning to Boost Bottom-Up Fixation Prediction in Driving Environments via Random Forest, Trans. ITS, 2018 | paper

Dataset(s): TETD

@article{2018_T-ITS_Deng,
    author = "Deng, Tao and Yan, Hongmei and Li, Yong-Jie",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "9",
    pages = "3059--3067",
    title = "Learning to boost bottom-up fixation prediction in driving environments via random forest",
    volume = "19",
    year = "2017"
}

2015

DIPLECS Surrey | paper | link

Description: Driving videos with steering information recorded in different cars and environments

Data: scene video, vehicle data

@article{2015_TranVehTech_Pugeault,
    author = "Pugeault, Nicolas and Bowden, Richard",
    journal = "IEEE Transactions on Vehicular Technology",
    number = "12",
    pages = "5424--5438",
    publisher = "IEEE",
    title = "How much of driving is preattentive?",
    volume = "64",
    year = "2015"
}

2014

YawDD | paper | link

Full name: Yawning Detection Dataset

Description: Recordings of human subjects in parked vehicles simulating normal driving, singing and taslking, and yawning

Data: driver video

Annotations: bounding boxes, action labels

@inproceedings{2014_ACM_Abtahi,
    author = "Abtahi, Shabnam and Omidyeganeh, Mona and Shirmohammadi, Shervin and Hariri, Behnoosh",
    booktitle = "Proceedings of the ACM Multimedia Systems Conference",
    title = "{YawDD: A yawning detection dataset}",
    year = "2014"
}

Used in papers:

Chiou et al., Driver Monitoring Using Sparse Representation With Part-Based Temporal Face Descriptors, Trans. ITS, 2019 | paper

Dataset(s): YawDD, DDD

@article{2019_T-ITS_Chiou,
    author = "Chiou, Chien-Yu and Wang, Wei-Cheng and Lu, Shueh-Chou and Huang, Chun-Rong and Chung, Pau-Choo and Lai, Yun-Yang",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "1",
    pages = "346--361",
    publisher = "IEEE",
    title = "Driver monitoring using sparse representation with part-based temporal face descriptors",
    volume = "21",
    year = "2019"
}

Yang et al., Video-Based Driver Drowsiness Detection With Optimised Utilization of Key Facial Features, Trans. ITS, 2024 | paper

Dataset(s): YawDD, DDD, private

@article{2023_T-ITS_Yang,
    author = "Yang, Haohan and Liu, Haochen and Hu, Zhongxu and Nguyen, Anh-Tu and Guerra, Thierry-Marie and Lv, Chen",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Quantitative Identification of Driver Distraction: A Weakly Supervised Contrastive Learning Approach",
    year = "2023"
}

Lu et al., JHPFA-Net: Joint Head Pose and Facial Action Network for Driver Yawning Detection Across Arbitrary Poses in Videos, Trans. ITS, 2023 | paper

Dataset(s): YawDD

@article{2023_T-ITS_Lu,
    author = "Lu, Yansha and Liu, Chunsheng and Chang, Faliang and Liu, Hui and Huan, Hengqiang",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "JHPFA-Net: Joint Head Pose and Facial Action Network for Driver Yawning Detection Across Arbitrary Poses in Videos",
    year = "2023"
}

2011

3DDS | paper | link

Full name: 3D Driving School Dataset

Description: Videos and eye-tracking data of people playing 3D driving simulator game

Data: eye-tracking, scene video

@inproceedings{2011_BMVC_Borji,
    author = "Borji, Ali and Sihite, Dicky N and Itti, Laurent",
    booktitle = "BMVC",
    title = "Computational Modeling of Top-down Visual Attention in Interactive Environments.",
    year = "2011"
}

Used in papers:

Tavakoli et al., Digging Deeper into Egocentric Gaze Prediction, WACV, 2019 | paper

Dataset(s): 3DDS

@inproceedings{2019_WACV_Tavakoli,
    author = "Tavakoli, Hamed Rezazadegan and Rahtu, Esa and Kannala, Juho and Borji, Ali",
    booktitle = "WACV",
    title = "Digging deeper into egocentric gaze prediction",
    year = "2019"
}

Borji et al., What/Where to Look Next? Modeling Top-Down Visual Attention in Complex Interactive Environments, Transactions on Systems, Man, and Cybernetics Systems, 2014 | paper

Dataset(s): 3DDS

@article{2014_TransSysManCybernetics_Borji,
    author = "Borji, Ali and Sihite, Dicky N and Itti, Laurent",
    journal = "IEEE Transactions on Systems, Man, and Cybernetics: Systems",
    number = "5",
    pages = "523--538",
    title = "{What/where to look next? Modeling top-down visual attention in complex interactive environments}",
    volume = "44",
    year = "2013"
}

Borji et al., Probabilistic Learning of Task-Specific Visual Attention, CVPR, 2012 | paper | code

Dataset(s): 3DDS

@inproceedings{2012_CVPR_Borji,
    author = "Borji, Ali and Sihite, Dicky N and Itti, Laurent",
    booktitle = "CVPR",
    title = "Probabilistic learning of task-specific visual attention",
    year = "2012"
}

Borji et al., Computational Modeling of Top-down Visual Attention in Interactive Environments, BMVC, 2011 | paper

Dataset(s): 3DDS

@inproceedings{2011_BMVC_Borji,
    author = "Borji, Ali and Sihite, Dicky N and Itti, Laurent",
    booktitle = "BMVC",
    title = "Computational Modeling of Top-down Visual Attention in Interactive Environments.",
    year = "2011"
}

2010

DIPLECS Sweden | paper | link

Description: Driving videos with steering information recorded in different cars and environments

Data: scene video, vehicle data

@inproceedings{2010_ACCV_Pugeault,
    author = "Pugeault, Nicolas and Bowden, Richard",
    booktitle = "ECCV",
    title = "Learning pre-attentive driving behaviour from holistic visual features",
    year = "2010"
}

2000

BU HeadTracking | paper | link

Full name: Boston University Head Tracking Dataset

Description: Videos and head tracking information for multiple human subjects recorded in diverse conditions

Data: driver video

Annotations: head pose

@article{2000_PAMI_LaCascia,
    author = "La Cascia, Marco and Sclaroff, Stan and Athitsos, Vassilis",
    journal = "IEEE Transactions on pattern analysis and machine intelligence",
    number = "4",
    pages = "322--336",
    publisher = "IEEE",
    title = "Fast, reliable head tracking under varying illumination: An approach based on registration of texture-mapped 3D models",
    volume = "22",
    year = "2000"
}

Used in papers:

Mbouna et al., Visual Analysis of Eye State and Head Pose for Driver Alertness Monitoring, Trans. ITS, 2013 | paper

Dataset(s): BU HeadTracking, private

@article{2013_T-ITS_Mbouna,
    author = "Mbouna, Ralph Oyini and Kong, Seong G and Chun, Myung-Geun",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "3",
    pages = "1462--1469",
    title = "Visual analysis of eye state and head pose for driver alertness monitoring",
    volume = "14",
    year = "2013"
}